WO2010048497A1

WO2010048497A1 - Genetic profile of the markers associated with alzheimer's disease

Info

Publication number: WO2010048497A1
Application number: PCT/US2009/061826
Authority: WO
Inventors: Abdelmajid Belouchi; John Verner Raelson; Judes Poirier; Bruno Paquin; Sem Kebache; Sophie Debrus; Paul Van Eerdewegh; Pascal Croteau; Jonathan Segal; Randall David Little; Tim Keith
Original assignee: Genizon Biosciences Inc.; Technosynapse Inc.
Priority date: 2008-10-24
Filing date: 2009-10-23
Publication date: 2010-04-29

Abstract

The present invention relates to a genetic profile of markers linked to Alzheimer's disease and identified by genome wide association studies based on linkage disequilibrium mapping. In particular, the invention relates to the fields of pharmacogenomics, diagnostics, therapeutics and the use of genetic information to predict an individuals susceptibility to Alzheimer's disease and/or their response to a particular agent.

Description

GENETIC PROFILE OF THE MARKERS ASSOCIATED WITH ALZHEIMER'S

DISEASE

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is related to U.S. provisional application 61/108,170 filed on October 24, 2009 the content of which is incorporated in its entirety.

This application contains a sequence listing submitted herewith electronically. The content of this electronic submission is incorporated by reference in this application.

FIELD OF THE INVENTION

The invention relates to the field of genomics and genetics, including genome analysis and the study of DNA variations associated with a particular condition. In particular, the invention relates to the fields of pharmacogenomics, diagnostics, therapeutics and the use of genetic information to predict an individual's susceptibility to Alzheimer's disease and/or their response to a particular drug or drugs, so that drugs tailored to genetic differences of population groups may be developed and/or administered to the appropriate population. The invention also relates to the use of the genetic information to stratify groups of afflicted individuals with respect to the risk of developing the disease or their response to a specific drug.

The invention also relates to a genetic profile indicative of Alzheimer's disease, which links DNA variations (in genie and/or non-genic regions) to an individual's susceptibility to Alzheimer's disease and/or response to a particular treatment regimen. The invention further relates to the genes disclosed in the profiles (see Table 3), which are related to methods and reagents for detection of an individual's increased or decreased risk for Alzheimer's disease and related sub-phenotypes, by identifying at least one polymorphism in one or a combination of the genes from the profile. Also related are the Candidate Regions identified in Table 2, which are associated with Alzheimer's disease. In addition, the invention further relates to nucleotide sequences of those genes including genomic DNA sequences, DNA sequences, single nucleotide polymorphisms (SNPs), other types of polymorphisms as well as alleles and haplotypes. BACKGROUND

Current treatments for Alzheimer's disease are primarily aimed at reducing symptoms and do not address the root cause of the disease. Despite a preponderance of evidence showing inheritance of a risk for Alzheimer's disease through epidemiological studies and genome wide linkage analyses, the genes affecting Alzheimer's disease have not all yet been discovered and/or characterized. There is a need in the art for identifying specific genes and/or genetic markers related to Alzheimer's disease to enable the development of therapeutics that address the causes of the disease rather than relieving its symptoms.

The failure in past studies to identify causative genes in complex diseases, such as Alzheimer's disease, has been due to the lack of appropriate methods to detect a sufficient number of variations in genomic DNA samples (markers), the insufficient quantity of necessary markers available, and the number of needed individuals to enable such a study.

Unfortunately, new therapies for Alzheimer's are few, and both diagnosis and treatment have been hampered by a lack of detailed knowledge of the etiology. There remains a need in the art for new and improved methods for treating this debilitating group of diseases.

As such, it would be highly desirable to be provided with a more complete group of genetic markers (linked or not to genes) associated with Alzheimer's disease in order to better diagnose, prevent and/or treat Alzheimer's disease.

SUMMARY

The present invention relates to the identification of genetic variations associated with Alzheimer's disease as well as to their use in diagnostics methods, therapeutics and/or for stratification purposes. The present invention also relates to the various uses of these genetic variations for diagnostic, prognostic, theranostic and therapeutic purposes.

According to a first aspect, the present application provides a method of diagnosing Alzheimer's disease, the predisposition to Alzheimer's disease, or the progression of Alzheimer's disease in an individual. Broadly, the method comprises determining, in a sample of the individual, a genetic profile comprising at least one marker in a Candidate Region listed in Table 2, and correlating the genetic profile with a reference profile in order to asses the presence of Alzheimer's disease, the predisposition to Alzheimer's disease, or the progression of Alzheimer's disease in the individual. In an embodiment, the at least one marker is a single nucleotide polymorphism (SNPs), an allele, a haplotype or combinations thereof. In another embodiment, the sample is at least one of blood and a brain biopsy. In a further embodiment, the at least one marker has a skewed genotype distribution towards individuals diagnosed, predisposed or afflicted with the Alzheimer's disease when compared to control individuals. In still a further embodiment, the at least one marker is associated with a risk event in at least one of the following loci: PDCD1 LG2, THBS1/FSIP1 , HIVEP3, ACTN2 and ITGB8. In yet a further embodiment, the at least one marker has a skewed genotype distribution towards control individuals when compared to individuals diagnosed, predisposed or afflicted with the Alzheimer's disease. In another embodiment, the at least one marker is associated with a protective event in at least one of the following loci: PDCD1 LG2, APOE, HIVEP3, ACTN2 and ITGB8. In an embodiment, the determination comprises assessing the genomic nucleic acid sequence of the at least one marker. In another embodiment, the determination comprises assessing the amount, concentration, splicing pattern and/or a nucleic acid sequence of a transcript expressed by a gene comprising the at least one marker. In still a further embodiment, the determination comprises assessing the amount, concentration, amino acid sequence and/or biological activity of a polypeptide encoded by a transcript expressed by a gene comprising the at least one marker, and in still a further embodiment, the amount, concentration, amino acid sequence and/or biological activity of the polypeptide is modulated by the presence of a splicing variant of the transcript. In another embodiment, the individual presents at least one of the following subphenotype: definite diagnosis, age of onset between 65 and 74 years, probable diagnosis, male subject and female subject.

According to a second aspect, the present application provides a method of predicting the response to an agent useful in the treatment of Alzheimer's disease in an individual predisposed to Alzheimer's disease or diagnosed with Alzheimer's disease. Broadly the method comprisese (i) determining, in a sample of the individual, a genetic profile comprising at least one a marker in a Candidate Region listed in Table 2; and (ii) correlating the genetic profile with a reference genetic profile to assess the response to the agent in the individual. In an embodiment, the method further comprises administering an effective amount of the agent to the individual if the profile is correlated with a positive response to the agent or with the absence of a negative response to the agent. In another embodiment, the method further comprises including the individual in a pre-clinical or clinical trial for the agent if the profile is correlated with a positive response to the agent or a lack of a negative response to the agent. In an embodiment, the at least one marker is a single nucleotide polymorphism (SNPs), an allele, a haplotype and combinations thereof. In another embodiment, the sample is at least one of blood and a brain biopsy. In another embodiment, the determination comprises assessing the genomic nucleic acid sequence of the at least one marker. In a further embodiment, the determination comprises assessing the amount, concentration, splicing and/or nucleic acid sequence of a transcript expressed by a gene comprising the at least one marker. In another embodiment, the determination comprises assessing the amount, concentration, amino acid sequence and/or biological activity of a polypeptide encoded by a transcript expressed by a gene comprising the at least one marker, and, in yet a further embodiment, the amount, concentration, amino acid sequence and/or biological activity of the polypeptide is modulated by the expression of a splicing variant of the transcript.

According to a third aspect, the present application provides a method of screening for an agent for the treatment of Alzheimer's disease. Broadly the method comprises in (i) contacting the agent with a polypeptide encoded by a gene located in a Candidate Region listed in Table 2, a transcript encoding said polypeptide and/or the gene expressing said transcript, and (ii) determining if the agent modulates the activity of the polypeptide, the expression of the gene, the stability of the transcript and/or the splicing of the transcript. The modulation of the activity of the polypeptide, the expression of the gene, the stability of the transcript and/or the splicing of the transcript is indicative that the agent is useful in the treatment of Alzheimer's disease. In an embodiment, the contacting step occurs in a cell. In another embodiment, the cell is in a non-human animal. In yet a further embodiment, the gene is listed in Table 3. According to a fourth aspect, the present application provides a method of treating Alzheimer's disease in an individual in need thereof. Broadly, the method comprises administering an agent capable of modulating the expression of a gene located in a Candidate Region listed in Table 2, the stability of a transcript of the gene, the splicing of a transcript of the gene and/or the activity of a polypeptide encoded by the transcript, thereby treating Alzheimer's disease in the individual. In an embodiment, the agent has been identified by the screening method described herein. In another embodiment, the individual has a genetic profile comprising at least one marker in a Candidate Region listed in Table 2, wherein said genetic profile is associated with a predisposition to or a diagnosis of Alzheimer's disease, and, in yet a further embodiment, the at least one marker is associated with a risk event in at least one of the following loci: PDCD1 LG2, THBS1/FSIP1 , HIVEP3, ACTN2 and ITGB8. In another embodiment, the individual has a genetic profile comprising at least one marker in a Candidate Region listed in Table 2, wherein said genetic profile is associated with a positive response to the agent or a lack of negative response to the agent.

According to a fifth aspect, the present application provides a method of treating Alzheimer's disease in an individual in need thereof. Broadly, the method comprises in (i) determining, in a sample from the individual, a genetic profile comprising at least one marker located in a Candidate Region listed in Table 2; (ii) correlating the genetic profile with a reference genetic profile to assess if the individual is associated with a positive response to an agent or a negative response to the agent, wherein the agent is useful in the treatment of Alzheimer's disease; (iii) administering the agent to the individual having the genetic profile associated with the positive response to the agent or lacking the genetic profile associated with the negative response to the agent. In an embodiment, the method further comprises including the individual in a pre-clinical or clinical trial for the agent if the profile is correlated with the positive response to the agent or with the absence of negative response to the agent. In an embodiment, the at least one marker is a single nucleotide polymorphisms (SNPs), an allele, an haplotype or combinations thereof. In still another embodiment, the sample is at least one of blood and a brain biopsy. In a further embodiment, the determination comprises assessing the genomic nucleic acid sequence of the marker. In another embodiment, the determination comprises assessing the amount, concentration, splicing and/or nucleic acid sequence of a transcript expressed by a gene comprising the at least one marker. In yet another embodiment, the determination comprises assessing the amount, concentration, amino acid sequence and/or biological activity of a polypeptide encoded by a transcript expressed by a gene comprising the at least one marker, and, in yet another embodiment, the amount, concentration, amino acid sequence and/or biological activity of the polypeptide is modulated by the expression of a splicing variant of the transcript.

According to a seventh aspect, the present application provides a method of stratifying a group of individuals. Broadly, the method comprises in (i) for each individual, determining, in a sample of the individual, a genetic profile comprising at least one marker located in a Candidate Region listed in Table 2; and (ii) dividing the group of individuals into subgroups of individuals having the genetic profile comprising the at least one marker or having the genetic profile lacking the at least one marker. In an embodiment, the subgroup of individuals have the genetic profile comprising at least one marker having a skewed genotype distribution towards individuals diagnosed, predisposed or afflicted with the Alzheimer's disease when compared to control individuals, and, in yet a further embodiment, the at least one marker is associated with a risk event in at least one of the following loci: PDCD1 LG2, THBS1/FSIP1 , HIVEP3, ACTN2 and ITGB8. In an embodiment, the subgroup of individuals have the genetic profile comprising at least one marker having a skewed genotype distribution towards control individuals when compared to individuals diagnosed, predisposed or afflicted with the Alzheimer's disease, and, in yet another embodiment, the at least one marker is associated with a protective event in at least one of the following loci: PDCD1 LG2, APOE, HIVEP3, ACTN2 and ITGB8. In another embodiment, the subgroup of individuals have the genetic profile comprising at least one marker having a skewed genotype distribution towards individuals responding positively to an agent useful for the treatment Alzheimer's disease when compared to individuals not responding or responding negatively to the agent. In yet a further embodiment, the subgroup of individuals have the genetic profile comprising at least one marker having a skewed genotype distribution towards to individuals not responding or responding negatively an agent useful for the treatment Alzheimer's disease when compared to individuals responding positively to the agent. In still a further embodiment, one subgroup of individuals is included or excluded from a pre-clinical or a clinical trial for an agent useful in the treatment of Alzheimer's disease. In yet another embodiment, within a subgroup, the individuals have similar phenotypic or subphenotypic traits associated with Alzheimer's disease. In an embodiment, the at least one marker is a single nucleotide polymorphisms (SNPs), an allele, a haplotype or combinations thereof. In a further embodiment, the sample is at least one of blood or a brain biopsy. In an embodiment, the determination comprises assessing the genomic nucleic acid sequence of the at least one marker. In another embodiment, the determination comprises assessing the amount, concentration, splicing and/or nucleic acid sequence of a transcript expressed by a gene comprising the at least one marker. In a further embodiment, the determination comprises assessing the amount, concentration, amino acid sequence and/or biological activity of a polypeptide encoded by a transcript expressed by a gene comprising the at least one marker. In yet another embodiment, the amount, concentration, amino acid sequence and/or biological activity of the polypeptide is modulated by the expression of a splicing variant of the nucleic acid.

According to a seventh aspect, there is provided the use of an agent capable of modulating the expression of a gene located in a Candidate Region listed in Table 2, the stability of a transcript of the gene, the splicing of the transcript of the gene and/or the activity of a polypeptide encoded by the transcript of the gene, for the treatment of Alzheimer's disease in an individual as well as the use of an agent capable of modulating the expression of a gene located in a Candidate Region listed Table 2, the stability of a transcript of the gene, the splicing of the transcript of the gene and/or the activity of a polypeptide encoded by the transcript of the gene, for the manufacture of a medicament for the treatment of Alzheimer's disease in an individual. In an embodiment, the agent used therein has been identified by the screening method described herein.

According to an eigth aspect, there is provided the use of a genetic profile from an individual for the treatment of Alzheimer's disease. In an embodiment, the genetic profile of the individual comprises at least one marker located in a Candidate Region listed in Table 2 and is associated with a predisposition to or a diagnosis of Alzheimer's disease. In that embodiment, an agent useful in the treatment of Alzheimer's disease is provided to the individual. In another embodiment, the genetic profile of the individual comprises at least one marker located in a Candidate Region listed in Table 2 and is associated with a positive response to the agent or a lack of negative response to the agent. In that embodiment, an agent useful in the treatment of Alzheimer's disease is provided to the individual. In an embodiment, the use further comprises including the individual in a pre-clinical or clinical trial for the agent. In another embodiment, the at least one marker is a single nucleotide polymorphisms (SNPs), an allele, an haplotype or a combination thereof. In a further embodiment, the genetic profile is determined by assessing the genomic nucleic acid sequence of the at least one marker. In still another embodiment, the genetic profile is determined by assessing the amount, concentration, splicing and/or nucleic acid sequence of a transcript expressed by a gene comprising the at least one marker. In yet another embodiment, the genetic profile is determined by assessing the amount, concentration, amino acid sequence and/or biological activity of a polypeptide encoded by a transcript expressed by a gene comprising the at least one marker, and, in an embodiment, the amount, concentration, amino acid sequence and/or biological activity of the polypeptide is modulated by the expression of a splicing variant of the transcript.

DETAILED DESCRIPTION OF THE VARIOUS EMBODIMENTS

The present invention relates specifically to a genetic profile of markers associated with Alzheimer's disease and their use in the diagnosis, prognosis and treatment of Alzheimer's disease. In view of the foregoing, identifying susceptibility genes associated with Alzheimer's and their respective biochemical pathways facilitates the identification of diagnostic markers as well as novel targets for improved therapeutics. It also helps improve the quality of life for those afflicted by this disease and reduces the economic costs of these afflictions at the individual and societal level. The identification of those genetic markers provides the basis for novel genetic tests and eliminates or reduces the therapeutic methods currently used. The identification of those genetic markers also provides the development of effective therapeutic intervention for the battery of laboratory, psychological and clinical evaluations typically required to diagnose Alzheimer's disease. Throughout the description of the present invention, several terms are used that are specific to the science of this field. For the sake of clarity and to avoid any misunderstanding, these definitions are provided to aid in the understanding of the specification and claims.

The term "allele" refers to one of a pair, or series, of forms of a genetic region that occur at a given locus in a chromosome. An "associated allele" refers to a specific allele at a polymorphic locus that is associated with a particular phenotype of interest, e.g., a predisposition to a disorder or a particular response to an agent. Within a population, given multiple loci, there may be more than one combination of alleles associated with a phenotype of interest.

Alzheimer's disease (also referred herein as the disease or AD) is used specifically to define the formation of extracellular protein deposits in the brain that consist predominantly of aggregates of β amyloid protein (senile plaques), neurofibrilary tangles (hyperphosphorylated tau protein) in the intracellular compartments, disturbances in calcium homeostasis, and degeneration/loss of synapses and neurons. An inflammatory process in the central nervous system is believed to play an important role in the pathway leading to neuronal cell death. The inflammatory response is mediated by activated microglia, resident immune cells of the central nervous system. Chronic activation of the microglia and astrocytes may cause damage of the brain-blood barrier and neuronal damage through the release of potentially cytotoxic molecules such as proinflammatory cytokines, reactive oxygen species, NO, and complement proteins. These alterations cause influx of immunocompetent cells from the periphery and their active participation in the local inflammatory reaction. Disturbances in the control mechanism of the inflammatory processes lead to perturbations in function and extensive brain degeneration. A characteristic symptom of AD dementia is associated with dysfunctions of cognitive memory such as calculation, space orientation, and speech impairment.

Candidate Regions or CR refers to the portions of the human chromosomes displayed in Table 2 bounded by the markers from Table 5 to Table 23 and associated with Alzheimer's disease. The nucleic acid or polypeptide sequences associated with the Candidate Region refer to a nucleic acid sequence that maps to at least one Candidate Regions listed in Table 2 or the polypeptide encoded therein. For nucleic acids, this encompasses sequences that are identical or complementary to the sequences from set forth in Table 2, as well as sequence-conservative, function-conservative, and non- conservative variants thereof. For polypeptides, this encompasses sequences that are identical to the polypeptide, as well as function-conservative and non- conservative variants thereof. Included are the alleles of naturally-occurring polymorphisms causative of Alzheimer's such as, but not limited to, alleles that cause altered expression of genes of Table 3 and alleles that cause altered protein levels, activity or stability (e.g., decreased levels, increased levels, increased activity, decreased activity, expression in an inappropriate tissue type, increased stability, and decreased stability).

Function-conservative variants are those in which a change in one or more nucleotides in a given codon position results in a polypeptide sequence in which a given amino acid residue in the polypeptide has been replaced by a conservative amino acid substitution. Function-conservative variants also include analogs of a given polypeptide and any polypeptides that have the ability to elicit antibodies specific for a designated polypeptide.

The term "founder population", also referred to as a "population isolate", designates a large number of people who have mostly descended, in genetic isolation from other populations, from a much smaller number of people who lived many generations ago.

The term "genetic profile" broadly refers to genetic information portraying the significant features of the Alzheimer's disease (the presence or absence of the disease, a positive or negative response to an agent) identified herein and presented in the various tables. These features include the markers described therein such as, for example, single nucleotide markers (suchs as SNPs) as well as haplotypes and their corresponding alleles. The features can be a single marker, a combination or selection of markers. The genetic profile of an individual can comprise one of the significant features presented herein or a combination of the significant features presented herein. The term "reference genetic profile" refers to the genetic profile of a control individual or to a compilation of genetic profiles of control individual. For diagnostic purposes, the control individual is an individual who is not experiencing the symptoms of the disease. For theranostic purposes, the control individual is an individual who positively or negatively reacts to the administration of an agent. The reference genetic profile is used, either alone or in combination with other reference genetic profiles, in the correlation of an individual's genetic profile with the presence/absence of the Alzheimer's disease and/or a positive or negative response to a specific agent.

"Genotype" represents a set of alleles at a specified locus or loci.

"Haplotype" refers to the allelic pattern of a group of (usually contiguous) DNA markers or other polymorphic loci along an individual chromosome or double helical DNA segment. Haplotypes identify individual chromosomes or chromosome segments. The presence of shared haplotype patterns among a group of individuals implies that the locus defined by the haplotype has been inherited, identical by descent (IBD), from a common ancestor. Detection of identical by descent haplotypes is the basis of linkage disequilibrium (LD) mapping. Haplotypes are broken down through the generations by recombination and mutation. In some instances, a specific allele or haplotype may be associated with susceptibility to a disorder or condition of interest, e.g. Alzheimer's disease, a risk sequence. In other instances, an allele or haplotype may be associated with a decrease in susceptibility to a disorder or condition of interest, e.g. Alzheimer's disease, a protective sequence.

"Identity by descent" or IBD is the identity among DNA sequences for different individuals that is due to the fact that they have all been inherited from a common ancestor. LD mapping identifies IBD haplotypes as the likely location of disorder genes shared by a group of patients.

"Identity", as known in the art, is a relationship between two or more polypeptide sequences or two or more polynucleotide sequences, as determined by comparing the sequences. In the art, identity also means the degree of sequence relatedness between polypeptide or polynucleotide sequences, as the case may be, as determined by the match between strings of such sequences. Identity and similarity can be readily calculated by known methods, including but not limited to those described in A.M. Lesk (ed), 1988, Computational Molecular Biology, Oxford University Press, NY; D. W. Smith (ed), 1993, Biocomputing. Informatics and Genome Projects, Academic Press, NY; A.M. Griffin and H. G. Griffin, H. G (eds), 1994, Computer Analysis of Sequence Data, Part 1 , Humana Press, NJ; G. von Heinje, 1987, Sequence Analysis in Molecular Biology, Academic Press; and M. Gribskov and J. Devereux (eds), 1991 , Sequence Analysis Primer, M Stockton Press, NY; H. Carillo and D. Lipman, 1988, SIAM J. Applied Math., 48:1073.

The term "linkage disequilibrium" or LD refers to the phenomenon where two or more alleles are correlated and not distributed randomly. Markers that are in high LD can be assumed to be located near each other and a marker or haplotype that is in high LD with a genetic trait can be assumed to be located near the gene that affects that trait. Linkage disequilibrium mapping refers to a population based gene mapping approach which locates disorder genes or disorder associated markers by identifying regions of the genome where haplotypes or marker variation patterns are shared statistically more frequently among subjects afflicted with a disease compared to healthy controls. This method is based upon the assumption that many of the patients will have inherited an allele associated with the disorder from a common ancestor (e.g. identity by descent), and that this allele will be in LD with the disorder gene. The term "identity by descent" or "IBD" refers to the identity among DNA sequences for different individuals that is due to the fact that they have all been inherited from a common ancestor. LD mapping identifies IBD haplotypes as the likely location of disorder genes shared by a group of subjects afflicted by a disease.

"Minor allele frequency" or MAF represents the population frequency of one of the alleles for a given polymorphism, which is equal or less than 50%. The sum of the MAF and the major allele frequency equals one.

"Markers" are defined herein as a sequence consisting of an identifiable DNA sequence that is variable (polymorphic) for different individuals within a population. These sequences facilitate the study of inheritance of a trait or a gene. Such markers are used in mapping the order of genes along chromosomes and in following the inheritance of particular genes; genes closely linked to the marker or in LD with the marker will generally be inherited with it. Two types of markers are commonly used in genetic analysis, microsatellites and SNPs.

"Non-conservative variants" are those in which a change in one or more nucleotides in a given codon position results in a polypeptide sequence in which a given amino acid residue in the polypeptide has been replaced by a non-conservative amino acid substitution. Non-conservative variants also include polypeptides comprising non- conservative amino acid substitutions.

"Regulatory sequence" refers to a nucleic acid sequence that controls or regulates expression of structural genes when operably linked to those genes. These include, for example, the lac systems, the trp system, major operator and promoter regions of the phage lambda, the control region of fd coat protein and other sequences known to control the expression of genes in prokaryotic or eukaryotic cells. Regulatory sequences will vary depending on whether the vector is designed to express the operably linked gene in a prokaryotic or eukaryotic host, and may contain transcriptional elements such as enhancer elements, termination sequences, tissue- specificity elements and/or translational initiation and termination sites.

"Single nucleotide polymorphism" or SNP consists of a variation of a single nucleotide at a specific position within a given population. This includes the replacement of one nucleotide by one or more nucleotide as well as the deletion or insertion of one or more nucleotide. Typically, SNPs are biallelic markers although tri- and tetra-allelic markers also exist. For a combination of SNPs, the term "haplotype" is used, e.g. the genotype of the SNPs in a single DNA strand that are linked to one another. In certain embodiments, the term "haplotype" is used to describe a combination of SNP alleles, e.g., the alleles of the SNPs found together on a single DNA molecule. In specific embodiments, the SNPs in a haplotype are in linkage disequilibrium with one another.

"Sequence-conservative" consists of variants in which a change of one or more nucleotides in a given codon position results in no alteration in the amino acid encoded at that position (e.g., silent mutation). A nucleic acid or fragment thereof is "substantially homologous" or "substantially identical" to another if, when optimally aligned (with appropriate nucleotide insertions and/or deletions) with the other nucleic acid (or its complementary strand), there is nucleotide sequence identity in at least 60% of the nucleotide bases, usually at least 70%, more usually at least 80%, preferably at least 90%, and more preferably at least 95-98% of the nucleotide bases. Alternatively, substantial homology or substantial identity exists when a nucleic acid or fragment thereof will hybridize, under selective hybridization conditions, to another nucleic acid (or a complementary strand thereof). Selectivity of hybridization exists when hybridization which is substantially more selective than total lack of specificity occurs. Typically, selective hybridization will occur when there is at least about 55% sequence identity over a stretch of at least about nine or more nucleotides, preferably at least about 65%, more preferably at least about 75%, and most preferably at least about 90% (M. Kanehisa, 1984, Nucl. Acids Res. 11 :203-213). The length of homology or identity comparison, as described, may be over longer stretches, and in certain embodiments will often be over a stretch of at least 5 nucleotides, at least 14 nucleotides, at least 20 nucleotides, more usually at least 24 nucleotides, typically at least 28 nucleotides, more typically at least 32 nucleotides, and preferably at least 36 or more nucleotides.

Genome wide association study

The present invention is based on the discovery of genes and genetic markers associated with Alzheimer's. In the preferred embodiment, disease-associated loci (Candidate Regions; Table 2) are identified by the statistically significant differences in allele or haplotype frequencies between the cases (subjects diagnosed with Alzheimer's) and the controls (healthy subjects). For the purpose of the present invention, 474 Candidate Regions (Table 2) have been identified.

By performing this type of analysis, it is possible to identify the susceptibility regions or regions that, in various combinations can cause disease. The genome-wide association studies enable the determination of the genomic regions involved with the disease. This is in clear contrast to expression studies which determine the expression of genes implicated in the cascade of events resulting from the onset of the disease.

The invention provides a method for the discovery of genes associated with Alzheimer's in a human population, comprising the following steps:

Step 1: Recruit patients (cases) and controls

For example, but not restricted to, the patients diagnosed with Alzheimer's disease along with two family members are recruited from the Alzheimer's population. The preferred trios recruited are parent-parent-child (PPC) trios. Trios can also be recruited as parent-child-child (PCC) trios.

In another embodiment, pairs of cases and controls are matched according to the region of origin and analyzed. The controls are optionally gender and/or age- matched to the cases.

In yet another embodiment, the present invention is performed as a whole or partially with DNA samples from individuals of another population resource.

Step 2: DNA extraction and quantification

Any sample comprising cells or nucleic acids from patients or controls may be used. Preferred samples are those easily obtained from the patient or control. Such samples include, but are not limited to blood, brain biopsies, peripheral lymphocytes, buccal swabs, epithelial cell swabs, vaginal swabs, nails, hair, bronchoalveolar lavage fluid, sputum, stool, urine, sweat or other body fluid or tissue obtained from an individual (including, without limitation, plasma, serum, cerebrospinal fluid, lymph, tears, saliva, milk, pus, stools, sperm, urine, sweat and tissue exudates and secretions). Also encompassed are samples from in vitro cell culture constituents or samples obtained from, for example, a laboratory procedure. DNA is extracted from such samples in the quantity and quality necessary to perform conventional DNA extraction and quantification techniques. Step 3: Genotype the recruited individuals

In one embodiment, the presence of SNP marker is determined. They can be determined, for example with assay-specific and/or locus-specific and/or allele- specific oligonucleotides for SNP markers that are organized onto one or more arrays. The genotype at each SNP locus can be determined by hybridizing short PCR fragments comprising each SNP locus onto these arrays. Preferably, the screening for the presence or absence of the SNP is conducted following known techniques in the art, such as an allele-specific hybridization assay, an oligonucleotide ligation assay, an allele-specific elongation/ligation assay, an allele- specific amplification assay, a single-base extension assay, a molecular inversion probe assay, an invasive cleavage assay, a selective termination assay, a restriction fragment length polymorphism (RFLP) assay, a sequencing assay, a single strand conformation polymorphism (SSCP) assay, a mismatch-cleaving assay, or a denaturing gradient gel electrophoresis assay.

Preferably, the arrays permit a high-throughput genome wide association study using DNA samples from individuals of the population. Such assay-specific and/or locus- specific and/or allele-specific oligonucleotides necessary for scoring each SNP of the present invention are preferably organized onto a solid support. Such supports can be arrayed on wafers, glass slides, beads or any other type of solid support. In another embodiment, the assay-specific and/or locus-specific and/or allele-specific oligonucleotides are not organized onto a solid support but are still used as a whole, in panels or one by one. In another embodiment, one or more portions of the SNP maps are used to screen the whole genome, a subset of chromosomes, a chromosome, a subset of genomic regions or a single genomic region.

In the preferred embodiment, the individuals composing the cases and controls or the trios are preferably individually genotyped with multiple markers, generating at least a few million genotypes; more preferably, at least a hundred million. In another embodiment, individuals are pooled in cases and control pools for genotyping and genetic analysis. Step 4: Exclusion of the markers that did not pass the quality control of the assay.

Preferably, the quality control assays comprise, but are not limited to, the following criteria: elimination of the SNPs that had a high rate of Mendelian errors (cut-off at 1 % Mendelian error rate), that deviate from the Hardy-Weinberg equilibrium, that are non-polymorphic in the population or have an excess of missing data (cut-off at 1% missing values or higher), or simply because they are non-polymorphic in the population (cut-off between 1% and 10% minor allele frequency (MAF).

Step 5: Perform the genetic analysis on the results obtained using haplotype information as well as single-marker association.

In the preferred embodiment, genetic analysis is performed on all the genotypes from Step 3, or alternatively, genetic analysis is performed on a subset of markers from Step 3 or from markers that passed the quality controls from Step 4.

In one embodiment, the genetic analysis consists of, but is not limited to, features corresponding to phase information and haplotype structures. Phase information and haplotype structures are preferably deduced from genotypes using Phasefinder™. Since chromosomal assignment (phase) cannot be estimated when all trio members are heterozygous, an Expectation-Maximization (EM) algorithm may be used to resolve chromosomal assignment ambiguities after Phasefinder™.

Furthermore, the PLEM algorithm (Partition-Ligation Expectation-Maximization, M; Niu et a/.., Am. J. Hum. Genet. 70: 157 (2002)) can be used to estimate haplotypes from the "genotype" data as a measured estimate of the reference allele frequency of a SNP in 11-marker windows that advance in increments of one marker across the data set. The results from such algorithms are converted into 11-marker haplotype files.

In another embodiment, the haplotype frequencies among patients are compared to those among the controls using LDSTATS™, a software tool that assesses the association of haplotypes with the disease. Such a program defines haplotypes using multi-marker windows that advance across the marker map in one-marker increments. Such windows can be for example 1 , 3, 5, 7 or 9 markers wide, and all these window sizes are tested concurrently. Larger multi-marker haplotype windows can also be used. At each position the frequency of haplotypes in cases is compared to the frequency of haplotypes in controls. Such allele frequency differences for single marker windows can be tested using Pearson's Chi-square test with any degree of freedom. Multi-allelic haplotype association can be tested using Smith's normalization of the square root of Pearson's Chi-square value. Such significance of association can be reported in two ways:

• The significance of association within any one haplotype window is plotted against the marker that is central to that window. P-values of association for each specific marker are calculated as a pooled P-value across all haplotype windows in which they occur. The pooled P-value is calculated using an expected value and variance and a permutation test that considers covariance between individual windows. Such pooled P-values can yield narrower regions of gene location than the window data.

• Conditional and subphenotype analyses can be performed on subsets of the original set of cases and controls using the program LDSTATS™. For conditional analyses, the selection of a subset of cases and their matched controls can be based on the carrier status of cases at a gene or locus of interest.

Step 6: SNP and DNA polymorphism discovery

In the preferred embodiment, all the candidate genes and regions identified in step 5 are sequenced for polymorphism identification. In another embodiment, the entire region, including all introns, is sequenced to identify all polymorphisms. Alternatively, the candidate genes are prioritized for sequencing, and only functional gene elements (promoters, conserved non-coding sequences, exons and splice sites for example) are sequenced.

In yet another embodiment, previously identified polymorphisms in the Candidate Regions can also be used. For example, SNPs from dbSNP, or others can also be used rather than resequencing the Candidate Regions to identify polymorphisms.

The discovery of SNPs and DNA polymorphisms generally comprises a step consisting of determining the major haplotypes in the region to be sequenced. The preferred samples are selected according to which haplotypes contribute to the association signal observed in the region to be sequenced. The purpose is to select a set of samples that covers all the major haplotypes in the given region. Each major haplotype is preferably analyzed in at least a few individuals.

Any analytical procedure may be used to detect the presence or absence of variant nucleotides at one or more polymorphic positions of the invention. In general, the detection of allelic variation requires a mutation discrimination technique, optionally an amplification reaction and optionally a signal generation system. For instance, DNA sequencing, scanning methods, hybridization, extension-based methods, incorporation-based methods, restriction enzyme-based methods and ligation-based methods may be used in the methods described herein.

Sequencing methods include, but are not limited to, direct sequencing, and sequencing by hybridization. Scanning methods include, but are not limited to, a protein truncation test (PTT), single-strand conformation polymorphism analysis (SSCP), denaturing gradient gel electrophoresis (DGGE), temperature gradient gel electrophoresis (TGGE), cleavage, heteroduplex analysis, chemical mismatch cleavage (CMC), and enzymatic mismatch cleavage. Hybridization-based methods of detection include, but are not limited to, solid phase hybridization such as dot blots, multiple allele specific diagnostic assay (MASDA), reverse dot blots, and oligonucleotide arrays (DNA Chips). Solution phase hybridization and amplification methods may also be used, such as Taqman™. Extension-based methods include, but are not limited to, amplification refractory mutation systems (ARMS), amplification refractory mutation system linear extension (ALEX), and competitive oligonucleotide priming systems (COPS). Incorporation based methods include, but are not limited to, mini-sequencing and arrayed primer extension (APEX). Restriction enzyme-based detection systems include, but are not limited to, restriction site generating PCR. Lastly, ligation based detection methods include, but are not limited to, oligonucleotide ligation assays (OLA). Signal generation or detection systems that may be used in the methods of the invention include, but are not limited to, fluorescence methods such as fluorescence resonance energy transfer (FRET), bioluminescence resonance energy transfer (BRET), protein fragment complementation assay (PCA), fluorescence quenching, fluorescence polarization as well as other chemiluminescence, electrochemiluminescence, Raman, radioactivity, colometric methods, hybridization protection assays and mass spectrometry methods. Further amplification methods include, but are not limited to self-sustained replication (SSR), nucleic acid sequence based amplification (NASBA), ligase chain reaction (LCR), strand displacement amplification (SDA) and branched DNA (B- DNA).

Step 7: Ultra fine Mapping

This step further maps the Candidate Regions and genes confirmed in the human population. The discovered SNPs and polymorphisms of step 6 are ultra fine mapped at a higher density of markers than the genome-wide scan (GWS) described herein using the same technology described in step 3.

Step 8: GeneMap construction

The confirmed variations in DNA (including both genie and non-genic regions) can optionally then be used to build a GeneMap for Alzheimer's disease. The gene content of this GeneMap is described in more detail below. Such GeneMaps can be used for example in other methods of the invention comprising the diagnostic methods described herein, the susceptibility to Alzheimer's disease, the response of a subject to a particular drug, the efficacy of a particular drug in a subject, the screening methods described herein and the treatment methods described herein.

A GeneMap consists of genes and genetic markers in a variety of combinations, identified from the Candidate Regions listed in Table 2. In another embodiment, all genes from Table 3 are present in the GeneMap. In another preferred embodiment, the GeneMap consists of a selection of genes from Table 3. The genes disclosed herein are arranged by Candidate Regions and by their chromosomal location for the purpose of clarity.

In one embodiment, genes identified in the GWAS and subsequent studies are evaluated using the Ingenuity Pathway Analysis™ application (IPA, Ingenuity systems) in order to identify direct biological interactions between these genes, and also to identify molecular regulators acting on those genes (indirect interactions) that could be also involved in Alzheimer's disease. The purpose of this effort is to decipher the molecules involved in contributing to Alzheimer's disease. These gene interaction networks are very valuable tools in the sense that they facilitate extension of the map of gene products that could represent potential drug targets for Alzheimer's disease.

Other means (such as functional biochemical assays and genetic assays) can be used to identify the biological interactions between genes to create a GeneMap.

As is evident to one of ordinary skill in the art, all of the above steps do not need to be performed, or performed in a given order to practice or use the SNPs, genomic regions, genes, proteins, etc. in the methods described herein.

Method to diagnose Alzheimer's disease, the predisposition to Alzheimer's disease or the progression of Alzheimer's disease

As indicated above, the markers identified herein are correlated to Alzheimer's disease. Therefore, they provide an interesting tool for the diagnosis of Alzheimer's disease. They are also very valuable in determining an individual's risk of developing the disease, evaluating the progression of the disease or determining the subclasses of the Alzheimer's disease.

According to an aspect, the present application provides a method of diagnosing Alzheimer's disease, the predisposition to Alzheimer's disease, or the progression of Alzheimer's disease in an individual. In this particular method, a genetic profile is first determined in a sample of the individual. As indicated above, a genetic profile comprises genetic information portraying the significant features of Alzheimer's disease wherein such features are located within the Candidate Regions listed in Table 2. The genetic profile comprises at least one marker located in a Candidate Region from Table 2. The genetic profile can also comprise a combination or a selection of markers. The various markers of the genetic profile can be located in a single Candidate Region or different Candidate Region(s). Once the profile is determined, a correlation of the individual's genetic profile with the presence of Alzheimer's disease, the predisposition to Alzheimer's disease, or the progression of Alzheimer's disease can then be made. This correlation is usually done by comparing the genetic profile obtained with a plurality of reference profiles. The reference profiles contain the genetic information of control individuals for the marker(s).

The presentation of at least one marker that is being included in the genetic profile is not limited to a particular type of genetic polymorphism. For example, the marker can be single nucleotide polymorphisms (SNPs) as set forth any one of Tables 5 to 23, an allele as set forth in any one of Tables 5 to 42 and/or a haplotype as set forth in any one of Tables 24 to 42 as well as combinations thereof. In an embodiment, the genetic profile comprises at least one marker that is associated with Alzheimer's disease ("associated marker"), at least 5 associated markers, at least 10 associated markers, at least 50 associated markers, at least 100 associated markers, or at least 200 associated markers. For comparison purposes, the reference genetic profiles should optionally contain at least the same markers that those of the individual's genetic profile.

Two types of markers are usually found in the profile: those associated with an increased risk towards the disease (e.g. those having a skewed genotype distribution towards individuals diagnosed, predisposed or afflicted with the Alzheimer's disease when compared to control individuals, also refer to as "risk event") as well as those associated with a protection against the disease (e.g. those having a skewed genotype distribution towards control individuals when compared to individuals diagnosed, predisposed or afflicted with the Alzheimer's disease, also referred to as protective event). Profiles containing exclusively risk-associated markers are strong indicators of a risk of developing the disease and/or disease severity. On the other hand, profiles containing exclusively protection-associated markers are indicative of the absence of the disease. However, some profiles can comprise both risk- associated and protection associated markers. In these specific profiles, an analysis must be undertaken to weight the importance of each marker (or group of markers) with respect to risk and protection and to determine if the profile is more likely associated with risk (therefore onset of the disease and/or disease severity) or protection.

This diagnostic method can be embodied in a diagnostic system designed to perform the required steps. This diagnostic system comprises at least two modules: a first module for performing the determination of the genetic profile and a second module for correlating the genetic profile to a risk/protection towards the disease (e.g. a reference genetic profile). The first module comprises a detection module for determining the presence or absence of at least one marker in at least one of the Candidate Region(s). As indicated above, this detection can be made either at the DNA level, the RNA level and/or the polypeptide level. The detection module relies on the addition of a label to the sample and the quantification of the signal from the label for determining the presence or absence of the marker. The signal of the label is quantified by the detection module and is linked to the presence or absence of the marker. This label can directly or indirectly be linked to a quantifier specific for the marker. The information gathered by the detection module is then processed by the second module for determining the correlation. This second module can use a processor for comparing the genetic profile generated with the first module to a reference genetic profile (or a plurality of genetic profiles). The correlation module can then determine if the profile obtained from the determination module is more likely associated with risk or protection toward the disease and as such, the individual's susceptibility of having or developing the disease.

As indicated above, the determination of the profile can include the addition of a quantifier to the sample from the individual. The quantifier is a physical entity that enables the sample to be quantified. The sample can be purified or isolated prior to the addition of the quantifier. The quantifier can be, for example, an oligonucleotide specific for the nucleic acid to be quantified, an antibody specific for the polypeptide to be quantified or a ligand specific for the enzyme to be quantified. The addition of the quantifier generates a quantifiable sample that can then be submitted to an assay for the determination of the quantity of nucleic acid and/or polypeptide. The quantifier is either directly linked to a label or adapted to be indirectly linked to a label for its processing in the detection module.

The profile can be determined in any biological sample from the individual. These samples include, but are not limited to blood, brain biopsy, plasma, serum, cerebrospinal fluid, lymph, secretion, exudate, saliva, milk, stools, urine, epithelial cell swab and sweat. The markers are either located in genie or non-genic regions. Markers of the profiles located in genie regions can be detected by ascertaining the existence of at least one of: (1 ) a deletion of one or more nucleotides from a gene from Table 3; (2) an insertion of one or more nucleotides to a gene from Table 3; (3) a substitution of one or more nucleotides of a gene from Table 3; (4) a chromosomal rearrangement of a gene from Table 3; (5) an alteration in the level of a messenger RNA transcript of a gene from Table 3; (6) aberrant modification of a gene from Table 3, such as of the methylation pattern of the genomic DNA, (7) the presence of an alternative splicing pattern of a messenger RNA transcript of a gene from Table 3; (8) inappropriate post-translational modification of a polypeptide encoded by a gene from Table 3; and (9) alternative promoter use of a gene from Table 3.

The genetic profile can be determined at the genomic DNA level, at the messenger RNA level or at the protein level. Determination at the genomic DNA level is advantageous for determining the presence or absence of specific markers in any region, including non-genic regions. When the determination is done at the genomic level, various assays can be used to determine the sequence of the marker. Such assays include, but are not limited to an allele-specific hybridization assay, an oligonucleotide ligation assay, an allele-specific elongation/ligation assay, an allele- specific amplification assay, a single-base extension assay, a molecular inversion probe assay, an invasive cleavage assay, a selective termination assay, restriction fragment length polymorphism (RFLP), a sequencing assay, single strand conformation polymorphism (SSCP), a mismatch-cleaving assay and denaturing gradient gel electrophoresis. It is worth indicating that it is not necessary to determine the sequence of the entire Candidate Region to determine the presence or absence of a particular marker. A fragment (as small as one nucleotide long and as long as the complete Candidate Region minus one nucleotide) can also be sequenced to determine the presence or absence of the marker. If a fragment is sequenced, then it may be convenient to determine the position of the fragment that is being sequenced with respect to the Candidate Region.

When the marker is associated with a genie region and its polymorphism can be detected in the transcript(s) of a gene comprising the marker, then the determination can be done at the messenger RNA level. At this level, it is first assessed whether the amount, concentration and/or nucleic acid sequence of a transcript in an individual is different from those of a control. In order to do so, the skilled artisan can choose from many assays such as, for example, PCR, RT-PCR, microarray analysis and a sequencing assay. When determination is done at the messenger RNA level, it may be interesting to perform it in a sample of a suspected/afflicted tissue, such as a brain biopsy.

When the marker is associated with a genie region and its polymorphism can be detected in a polypeptide encoded by a particular gene comprising the marker, then the determination of the profile can be done at the polypeptide level. Some markers will cause a differential splicing of transcript(s) of the polypeptide and as such will likely cause mutation(s) in the expressed polypeptide (truncation, localization, glycosylation pattern for example). When the determination is done at the polypeptide level and the marker induces a modification in the presentation of epitopes of the polypeptide, it may be advantageous to use an antibody or fragment thereof specific for the polypeptide. The determination at the polypeptide level can be done with various assays, such as, for example, ELISA, FACS analysis, Western blot, immunological staining assay, mass spectrometry, protein degradation and/or protein sequencing.

Other types of markers can also be used for diagnostic purposes. For example, microsatellites can also be useful to detect the genetic predisposition of an individual to a given disorder. Microsatellites consist of short sequence motifs of one or a few nucleotides repeated in tandem. The most common motifs are polynucleotide runs, dinucleotide repeats (particularly the CA repeats) and trinucleotide repeats. However, other types of repeats can also be used. The microsatellites are very useful for genetic mapping because they are highly polymorphic in their length. Microsatellite markers can be typed by various means, including but not limited to DNA fragment sizing, oligonucleotide ligation assay and mass spectrometry.

The methods described herein may be performed, for example, by utilizing prepackaged diagnostic kits comprising at least one oligonucleotide specific for a marker or for amplifying a fragment containing the marker, an antibody or fragment thereof specific for a polypeptide containing a marker, which may be conveniently used, for example, in a clinical setting to diagnose individuals exhibiting symptoms of Alzheimer's disease or a family history of Alzheimer's disorder or disorder involving abnormal activity of one or a combination of genes from Table 3.

Method of predicting response to an agent useful in the treatment of Alzheimer's disease

It is believed that, since the markers identified herein are tied to disease-causing polymorphisms, they can also be correlated to a response to an agent useful in the treatment of Alzheimer's disease. As such, they are very valuable in determining an individual's response to a particular agent in order to limit the side-effects associated with the agent and optimize the treatment of the individual.

According to an aspect, the present application provides a method of predicting the response to an agent useful in the treatment of Alzheimer's disease in an individual predisposed to Alzheimer's disease or diagnosed with Alzheimer's disease. In this particular method, it is first determined, in a sample of the individual, a genetic profile of at least one marker. Once the genetic profile is determined, a correlation of the genetic profile with a reference genetic profile of a positive response to the agent and/or a negative response to the agent can then be made. This correlation can be done by comparing the genetic profile obtained with a reference genetic profile or a plurality of reference profiles. Depending on the context, the reference genetic profile can be derived from individuals either responding positively or negatively to the agent.

As used herein, the term "agent" refers to an agonist, an antagonist, a peptidomimetic, a polypeptide, a peptide, a nucleic acid (such as antisense DNA, a ribozyme and/or interfering RNA (RNAi)), a small molecule or a combination thereof that is useful in the treatment of Alzheimer's disease.

As used herein, the expression "a positive response to the agent" refers to the response of an individual who, upon (or thereafter) the administration of the agent, experiences the alleviation of at least one symptom associated with Alzheimer's disease and/or the absence of an adverse event in response to such agent. On the other hand, the expression "a negative response to the agent" refers to the response of an individual who, upon (or thereafter) the administration of the agent, does not experience an alleviation of at least one symptom associated with Alzheimer's disease and/or experiences adverse events in response to such agent.

In an embodiment, the agent that is being administered modulates the expression of at least one gene (or its encoded product) located in a Candidate Region as described herein. Thus, one embodiment of the present invention provides methods for determining whether an individual can be effectively treated with an agent for a disease associated with aberrant expression or activity of a gene (or its encoded gene product). In this particular embodiment, a test sample is obtained from the individual and the nucleic acids and/or polypeptides associated with a gene comprising a marker are detected/quantified. In yet a further embodiment, after analysis of the expression/activity values, one skilled in the art can determine whether such agent can effectively treat such individual. In another embodiment, the method includes obtaining a sample from an individual having or susceptible to developing Alzheimer's disease and determining his profile of markers associated with a particular response to an agent. After analysis of the profile, one skilled in the art can determine whether such agent can effectively treat such subject.

Because the information obtained by this method is very valuable in predicting response to a particular agent, it can further be used for the treatment of the individual or the inclusion (or exclusion) of an individual in a pre-clinical or clinical trial. For example, this method can also comprise administering an effective amount of the agent to the individual if the profile is correlated with a positive response to the agent or with the absence of a negative response to the agent. Similarly, the method can also comprise including the individual in a pre-clinical or clinical trial for the agent if the profile is correlated with a positive response to the agent or with the absence of a negative response to the agent.

Various embodiments of the markers and methods of determining profiles and markers have been presented above and could be used herein for the theranostic method.

For theranostic purposes, two types of markers are usually found in the profile: those associated with a positive response to the agent useful for the treatment of the disease (e.g. those having a skewed genotype distribution towards individuals having a positive response to the agent) as well as those associated with a negative response to the agent (e.g. those having a skewed genotype distribution towards individuals having a negative response to the agent). Profiles containing exclusively positive response-associated markers are strong indicators of individuals that will likely respond well to the agent and experience an alleviation of their symptoms upon the administration of the agent. On the other hand, profiles containing exclusively negative response-associated markers are indicative of individuals that will likely not respond to the agent, experience important side-effects related to the administration of the agent or will not notice an alleviation of their symptoms upon the administration of the agent. However, some profiles can comprise both positive response-associated and negative response-associated markers. In these specific profiles, an analysis must be undertaken to weight the importance of each marker (or group of markers) with respect to the response of the marker to determine if the profile is more likely associated with a positive or negative response.

This theranostic method can be embodied in a theranostic system designed to perform the required steps. This theranostic system comprises at least two modules: a first module for performing the determination of the genetic profile and a second module for correlating the genetic profile to a a reference genetic profile response to the agent. The first module comprises a detection module for determining the presence or absence of at least one marker in at least one of the Candidate Region(s). As indicated above, this detection can be made either at the DNA level, the RNA level and/or the polypeptide level. The detection module relies on the addition of label to the sample and the quantification of the signal of the label for determining the presence or absence of the marker. The signal of the label is quantified by the detection module and is linked to the presence or absence of the marker. This label can be directly or indirectly linked to a quantifier specific for the marker. The information gathered by the detection module is then processed by the second module for determining the correlation. This second module can use a processor for comparing the profile generated with the first module to a reference genetic profile (or a plurality of profiles) associated with a positive response to the agent and/or to a profile (or a plurality of profiles) associated with a negative response to the agent. The correlation module can then determine if the profile obtained from the determination module is more likely associated with a positive or negative response to the agent and as such, if the individuals will benefit from a therapy based on this agent.

As indicated above, the determination of the profile can include the addition of a quantifier to the sample from the individual. The quantifier is a physical entity that enables the sample to be quantified. The sample can be purified or isolated prior to the addition of the quantifier. The quantifier can be, for example, an oligonucleotide specific for the nucleic acid to be quantified, an antibody specific for the polypeptide to be quantified or a ligand specific for the enzyme to be quantified. The addition of the quantifier generates a quantifiable sample that can then be submitted to an assay for the determination of the quantity of nucleic acid and/or polypeptide. The quantifier can be directly or indirectly linked to the label that is quantified in the detection module.

The profile can be determined in any biological sample from the individual. These samples include, but are not limited to blood, brain biopsy, plasma, serum, cerebrospinal fluid, lymph, secretion, exudate, saliva, milk, stools, urine, epithelial cell swab and sweat.

The methods described herein may be performed, for example, by utilizing prepackaged theranostic kits comprising at least one oligonucleotide specific for a marker or for amplifying a fragment containing the marker, an antibody or fragment thereof specific for a polypeptide containing a marker, which may be conveniently used, for example, in a clinical setting to predict the individual's response to an agent and/or to include or exclude the individual from the clinical trial.

Method of screening for agents useful in the treatment of Alzheimer's disease

The Candidate Regions identified herein are associated with Alzheimer's disease. As such, the genes located in these Candidate Regions and gene products associated thereto can be used as therapeutic targets for the identification of agents useful in the treatment of Alzheimer's disease. Accordingly, the present application also relates to a method of screening for an agent for the treatment of Alzheimer's disease. The method comprises at least two steps: contacting the agent to be screened with a gene located in a Candidate Region or a gene product thereof and determining if the agent modulates the expression of the gene, the stability, activity, localization and/or transduction of the associated gene product. If a modulation occurs, and that modulation is associated with the alleviation of symptoms and/or treatment of Alzheimer's then it is indicative that the agent is useful in the treatment of Alzheimer's disease. An agent is said to modulate the expression of a gene or gene product if it is capable of up- or down- regulating expression of the gene in a cell, up- or down- regulating the stability, splicing or transcription of a transcript encoded by the gene and/or up- or down- regulating the amount, activity, localization of the polypeptide encoded by the gene product.

This method can be performed in vitro or in vivo. In an embodiment, the contacting step occurs in a cell, such as in an in vitro system. Some non-limiting examples of cells that can be used are: adipocytes, digestive system cells, muscle cells, neuronal cells, blood and vessels cells, T cells, mast cells, lymphocytes, monocytes, macrophages, and epithelial cells. Cells can also be host cells wherein a nucleic acid capable of expressing or limiting the expression of the gene of interest has been introduced. Cells can also be host cells recombinantly engineered to express a detectable identifier (e.g. a green fluorescent protein) when the expression of the gene or transcript of interest is up-regulated or down-regulated. In yet another embodiment, the contacting step occurs in a non-human animal, such as in an in vivo system. A sample of the animal is then submitted to a quantifying step to determination if modulation has occurred. Samples can be obtained from any parts of the body of the animal such as, for example, the hair, mouth, rectum, scalp, blood, brain, dermis, epidermis, skin cells, cutaneous surfaces, intertrigious areas, genitalia and fluids, vessels and endothelium. The results obtained in the various models are indicative of the in vivo situation in a human.

For screening purposes, it is advisable to select genes (or encoded gene products) whose expression or sequence is modulated prior to the onset of Alzheimer's disease or during Alzheimer's disease. In order to do so, a comparison of gene (or gene product) expression or sequence can be performed between individuals afflicted by Alzheimer's disease, predisposed to Alzheimer's disease or diagnosed with Alzheimer's disease and healthy individuals. This screening method can be embodied in a screening system designed to perform the required steps. This screening system comprises at least two modules: a first module for enabling the contact between the gene and/or the gene product and a second module for determining if the agent modulates the expression, activity, stability and/or sequence of the gene or its encoded product. The first module comprises an environment favorable for contacting the agent and the gene or the gene product. Then a sample from this environment is placed in the second module for the determination of modulation. As indicated above, this determination can be made either at the DNA level, the RNA level and/or the polypeptide level. The determination module relies on the addition of label to the sample and the quantification of the signal of the label for determining the modulation of the gene or its encoded product. The signal of the label is quantified by the determination module. This label can be directly or indirectly linked to a quantifier specific for the marker. The information gathered by the determination module is then used to determine the presence or absence of modulation with respect to a control. This second module can use a processor for comparing the effect of the agent on the gene or its encoded product.

As indicated above, the determination of the modulation can include the addition of a quantifier to the sample from the individual. The quantifier is a physical entity that enables the sample to be quantified. The sample can be purified or isolated prior to the addition of the quantifier. The quantifier can be, for example, an oligonucleotide specific for the nucleic acid to be quantified, an antibody specific for the polypeptide to be quantified or a ligand specific for the enzyme to be quantified. The addition of the quantifier generates a quantifiable sample that can then be submitted to an assay for the determination of the quantity of nucleic acid and/or polypeptide. The quantifier is either directly or indirectly linked to the quantifiable label.

In one assay format, the expression of a nucleic acid encoding a gene of interest (see Table 3) in a cell or tissue sample is monitored directly by hybridization to the nucleic acids specific for this gene or its transcript. Cell lines or tissues can be exposed to the agent to be tested under appropriate conditions and time, and total RNA or mRNA isolated, optionally amplified, and quantified. In another assay format, the specific activity of a polypeptide encoded by a gene, normalized to a standard unit, may be assayed in a cell line or a cell population that has been exposed to the agent to be tested and compared to an unexposed control cell line or cell population. Cell lines or populations are exposed to the agent to be tested under appropriate conditions and times. Cellular lysates may be prepared from the exposed cell line or population and a control, unexposed cell line or population. The cellular lysates can then be analyzed with a probe, such as an antibody probe or a fragment thereof.

Method of treating Alzheimer's disease

Since the genes located in the Candidate Regions described herein are known to be linked to Alzheimer's disease, it is believed that the administration of an agent capable of correcting the genetic defect associated with the Candidate Region and present in Alzheimer's disease will be useful in the treatment of Alzheimer's disease. For example, in the situation wherein a marker is associated with the down- regulation of the expression of a gene listed in Table 3, an agent that up-regulates the expression of that gene should be beneficial to the subject. As such, the present application provides a method of treating Alzheimer's disease in an individual in need thereof. In order to do so, an agent capable of modulating the expression of a gene located in a Candidate Region listed in Table 2, the stability of a transcript of the gene, the splicing of a transcript of the gene and/or the activity of a polypeptide encoded by the transcript is administered to the individual. This method likely treats Alzheimer's disease or alleviates symptoms associated with Alzheimer's disease in the individual. In an embodiment, the agent that is being administered has been identified by the screening method described herein or is described below. In order to optimize therapy, it is possible to administer the agent only to individuals who have a profile associated with a predisposition to or a diagnosis of Alzheimer's disease and/or associated with a positive response to the agent or a lack of negative response to the agent. Various embodiments of the profile of markers and how to determine the profile have been described above and could be used in this method.

In an embodiment, in order to optimize therapeutic regimen, the method can also comprise analyzing a biological sample that includes nucleic acids or polypeptide derived from a cell from an individual clinically diagnosed with Alzheimer's disease for the presence of modified levels of expression. This determination can be done in at least 1 gene, at least 10 genes, at least 50 genes, at least 100 genes, or at least 200 genes listed in Table 3. A treatment plan that is most effective for individuals clinically diagnosed as having a condition associated with Alzheimer's disease is then selected on the basis of the detected expression of such genes in a cell.

The application also presents the use of an agent capable of modulating the expression of a gene located in a Candidate Region listed in Table 2, the stability of a transcript of the said, the splicing of the transcript and/or the activity of a polypeptide encoded by the transcript, for the treatment of Alzheimer's disease in an individual as well as for the manufacture of a medicament for the treatment of Alzheimer's disease in an individual. The agent used therein can be identified by the screening method described above or is described below. In an embodiment, the treated individual has a profile comprising at least one marker located in a Candidate Region listed in Table 2, wherein the profile is associated with a predisposition to or a diagnosis of Alzheimer's disease. In another embodiment, the treated individual has a profile comprising at least one marker located in a Candidate Region listed in Table 2, wherein said profile is associated with a positive response to the agent or a lack of negative response to the agent. The treated individual can optionally be included in a pre-clinical or clinical trial for the agent if the profile is correlated with the positive response to the agent or the lack of negative response to the agent. Various embodiments of the markers, the sample, and the profile (and methods of determining it) presented above can be applied herein.

Agents that can be used for the treatment of Alzheimer's disease

The agents that can be administered for the treatment of disease include, but are not limited to, small molecules, peptides, antibodies, nucleic acids, analogs thereof, multimers thereof, fragments thereof, derivatives thereof and combinations thereof.

Nucleic Acids. The nucleic acids specific for any genes or encoding any gene described herein whose expression is modulated at the onset or during Alzheimer's disease can be used as an agent. These nucleic acids can be inserted into any of a number of well-known vectors for their introduction in target cells and subjects as described below. The nucleic acids are introduced into cells, ex vivo or in vivo, through the interaction of the vector and the target cell. The nucleic acids encoding a gene from Table 3, under the control of a promoter, are then express the encoded protein, thereby mitigating the effects of absent, partial inactivation, or abnormal expression of the gene.

Antisense. In a particular embodiment of the invention, an antisense nucleic acid or oligonucleotide is wholly or partially complementary to, and can hybridize with, a target nucleic acid (either DNA or RNA). For example, an antisense nucleic acid or oligonucleotide can be sufficient to inhibit expression of at least one gene listed in Table 3. Alternatively, an antisense nucleic acid or oligonucleotide can be complementary to 5' or 3' untranslated regions, or can overlap the translation initiation codon (5' untranslated and translated regions) of at least one gene from Table 3, or its functional equivalent. In another embodiment, the antisense nucleic acid is wholly or partially complementary to, and can hybridize with, a target nucleic acid that encodes a polypeptide encoded by a gene described in Table 3. As non- limiting examples, antisense oligonucleotides may be targeted to hybridize to the following regions: mRNA cap region; translation initiation site; translational termination site; transcription initiation site; transcription termination site; polyadenylation signal; 3' untranslated region; 5' untranslated region; 5' coding region; mid coding region; 3' coding region; DNA replication initiation and elongation sites. Preferably, the complementary oligonucleotide is designed to hybridize to the most unique 5' sequence of a gene described in Table 3, including any of about 15- 35 nucleotides spanning the 5' coding sequence. In accordance with the present invention, the antisense oligonucleotide can be synthesized, formulated as a pharmaceutical composition, and administered to a subject.

Triplex oligonucleotides. In addition, oligonucleotides can be constructed which will bind to duplex nucleic acid (Ae., DNA:DNA or DNA:RNA), to form a stable triple helix containing or triplex nucleic acid. Such triplex oligonucleotides can inhibit transcription and/or expression of a gene from Table 3, or its functional equivalent. Triplex oligonucleotides are constructed using the base-pairing rules of triple helix formation and the nucleotide sequence of the genes described in Table 3. Oligonucleotides. In the context of this application, the term "oligonucleotide" refers to naturally-occurring species or synthetic species formed from naturally-occurring subunits or their close homologs. The term may also refer to moieties that function similarly to oligonucleotides, but have non-naturally-occurring portions. Thus, oligonucleotides may have altered sugar moieties or inter-sugar linkages. Exemplary among these are phosphorothioate and other sulfur containing species which are known in the art. In preferred embodiments, at least one of the phosphodiester bonds of the oligonucleotide has been substituted with a structure that functions to enhance the ability of the compositions to penetrate into the region of cells where the RNA whose activity is to be modulated is located. It is preferred that such substitutions comprise phosphorothioate bonds, methyl phosphonate bonds, or short chain alkyl or cycloalkyl structures. In accordance with other preferred embodiments, the phosphodiester bonds are substituted with structures which are, at once, substantially non-ionic and non-chiral, or with structures which are chiral and enantiomerically specific. Persons of ordinary skill in the art will be able to select other linkages for use in the practice of the invention. Oligonucleotides may also include species that include at least some modified base forms. Thus, purines and pyrimidines other than those normally found in nature may be so employed. Similarly, modifications on the furanosyl portions of the nucleotide subunits may also be affected, as long as the essential tenets of this invention are adhered to. Examples of such modifications are 2'-O-alkyl- and 2'-halogen-substituted nucleotides. Some non-limiting examples of modifications at the 2' position of sugar moieties which are useful in the present invention include OH, SH, SCH₃, F, OCH₃, OCN, 0(CH₂), NH₂ and O(CH₂)_nCH3, where n is from 1 to about 10. Such oligonucleotides are functionally interchangeable with natural oligonucleotides or synthesized oligonucleotides, which have one or more differences from the natural structure. All such analogs are comprehended herein so long as they function effectively to hybridize with at least one gene from Table 3 DNA or RNA to inhibit the function thereof.

Expression vectors. Alternatively, expression vectors derived from retroviruses, adenovirus, herpes or vaccinia viruses or from various bacterial plasmids may be used for delivery of nucleotide sequences to the targeted organ, tissue or cell population. Methods which are well known to those skilled in the art can be used to construct recombinant vectors which will express nucleic acid sequence that is complementary to the nucleic acid sequence encoding a polypeptide from the genes described in Table 3.

RNAi. RNA interference (RNAi) is a post-transcriptional gene silencing process that is induced by a miRNA or a dsRNA (a small interfering RNA; siRNA), and has been used to modulate gene expression. Generally, RNAi is being performed by contacting cells with a double stranded siRNA ou a small hairpin RNA (shRNA). However, manipulation of RNA outside of cells is tedious due to the sensitivity of RNA to degradation. It is thus also encompassed herein a deoxyribonucleic acid (DNA) compositions encoding small interfering RNA (siRNA) molecules, or intermediate siRNA molecules (such as shRNA), comprising one strand of an siRNA. Accordingly, the present invention provides an isolated DNA molecule, which includes an expressible template nucleotide sequence of at least about 16 nucleotides encoding an intermediate siRNA, which, when a component of an siRNA, mediates RNA interference (RNAi) of a target RNA. The present invention further concerns the use of RNA interference (RNAi) to modulate the expression of genes described in Table 3 in target cells. In yet another embodiment, the mRNAi is presented in Table 4. While the invention is not limited to a particular mode of action, RNAi may involve degradation of messenger RNA (e.g., mRNA of genes described in Table 3) by an RNA induced silencing complex (RISC), preventing translation of the transcribed targeted mRNA. Alternatively, it may involve methylation of genomic DNA, which shuts down transcription of a targeted gene. The suppression of gene expression caused by RNAi may be transient or it may be more stable, even permanent.

siRNA. "Small interfering RNA" of the present invention refers to any nucleic acid molecule capable of mediating RNA interference "RNAi" or gene silencing. For example, siRNA of the present invention are double stranded RNA molecules from about ten to about 30 nucleotides long that are named for their ability to specifically interfere with protein expression. In one embodiment, siRNAs of the present invention are 12-28 nucleotides long, more preferably 15-25 nucleotides long, even more preferably 19-23 nucleotides long and most preferably 21-23 nucleotides long. Therefore preferred siRNA of the present invention are 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28 nucleotides in length. As used herein, siRNA molecules need not to be limited to those molecules containing only RNA, but further encompass chemically modified nucleotides and non-nucleotides. siRNA of the present invention are designed to decrease expression of at least one gene described in Table 3 in a target cell by RNA interference. siRNAs of the present invention comprise a sense region and an antisense region wherein the antisense region comprises a sequence complementary to an mRNA sequence for a gene described in Table 3 and the sense region comprises a sequence complementary to the antisense sequence of the gene's mRNA. An siRNA molecule can be assembled from two nucleic acid fragments wherein one fragment comprises the sense region and the second fragment comprises the antisense region of siRNA molecule. The sense region and antisense region can also be covalently connected via a linker molecule. The linker molecule can be a polynucleotide linker or a non-polynucleotide linker.

Ribozymes. A ribozyme (from ribonucleic acid enzyme, also called RNA enzyme or catalytic RNA) is an RNA molecule that catalyzes a chemical reaction. Some ribozymes may play an important role as therapeutic agents, as enzymes which target defined RNA sequences, as biosensors, and for applications in functional genomics and gene discovery. Ribozymes can be genetically engineered to specifically cleave a transcript of a gene from a Candidate Region that is being upregulated with the disease.

Gene therapy. Delivery of the gene or genetic material into the cell is the first critical step in gene therapy treatment of a disorder. A large number of delivery methods are well known to those of skill in the art. Preferably, the nucleic acids are administered for in vivo or ex vivo gene therapy uses. Non-viral vector delivery systems include DNA plasmids, naked nucleic acid, and nucleic acid complexed with a delivery vehicle such as a liposome. Viral vector delivery systems include DNA and RNA viruses, which have either episomal or integrated genomes after delivery to the cell.

The use of RNA or DNA based viral systems for the delivery of nucleic acids take advantage of highly evolved processes for targeting a virus to specific cells in the body and trafficking the viral payload to the nucleus. Viral vectors can be administered directly to patients (in vivo) or they can be used to treat cells in vitro and the modified cells then administered to patients (ex vivo). Conventional viral based systems for the delivery of nucleic acids could include retroviral, lentiviral, adenoviral, adeno-associated and herpes simplex virus vectors for gene transfer. Viral vectors are currently the most efficient and versatile method of gene transfer in target cells and tissues. Integration in the host genome is possible with the retrovirus, lentivirus, and adeno-associated virus gene transfer methods, often resulting in long term expression of the inserted transgene. Additionally, high transduction efficiencies have been observed in many different cell types and target tissues.

In applications where transient expression of the nucleic acid is preferred, adenoviral based systems are typically used. Adenoviral based vectors are capable of very high transduction efficiency in many cell types and do not require cell division. With such vectors, high titer and levels of expression have been obtained. This vector can be produced in large quantities in a relatively simple system. Adeno-associated virus ("AAV") vectors are also used to transduce cells with target nucleic acids, e.g., in the in vitro production of nucleic acids and peptides, and for in vivo and ex vivo gene therapy procedures.

In particular, numerous viral vector approaches are currently available for gene transfer in clinical trials, with retroviral vectors by far the most frequently used system. All of these viral vectors utilize approaches that involve complementation of defective vectors by genes inserted into helper cell lines to generate the transducing agent. pLASN and MFG-S are examples are retroviral vectors that have been used in clinical trials.

Recombinant adeno-associated virus vectors (rAAV) are a promising alternative gene delivery systems based on the defective and nonpathogenic parvovirus adeno- associated type 2 virus. All vectors are derived from a plasmid that retains only the AAV 145 bp inverted terminal repeats flanking the transgene expression cassette. Efficient gene transfer and stable transgene delivery due to integration into the genomes of the transduced cell are key features for this vector system. Replication-deficient recombinant adenoviral vectors (Ad) are predominantly used in transient expression gene therapy; because they can be produced at high titer and they readily infect a number of different cell types. Most adenovirus vectors are engineered such that a transgene replaces the Ad E1a, E1 b, and E3 genes; subsequently the replication defective vector is propagated in human 293 cells that supply the deleted gene function in trans. Ad vectors can transduce multiple types of tissues in vivo, including non-dividing, differentiated cells such as those found in the liver, kidney and muscle tissues. Conventional Ad vectors have a large carrying capacity.

In many gene therapy applications, it is desirable that the gene therapy vector be delivered with a high degree of specificity to a particular tissue type. A viral vector is typically modified to have specificity for a given cell type by expressing a ligand as a fusion protein with a viral coat protein on the viruses outer surface. The ligand is chosen to have affinity for a receptor known to be present on the cell type of interest.

Gene therapy vectors can be delivered in vivo by administration to an individual subject, typically by systemic administration (e.g., intravenous, intraperitoneal, intramuscular, subdermal, or intracranial infusion) or topical application. Alternatively, vectors can be delivered to cells ex vivo, such as cells explanted from an individual patient (e.g., lymphocytes, bone marrow aspirates, and tissue biopsy) or universal donor hematopoietic stem cells, followed by re-implantation of the cells into the subject, usually after selection for cells which have incorporated the vector.

Ex vivo cell transfection for diagnostics, research, or for gene therapy (e.g. via re- infusion of the transfected cells into the host organism) is well known to those of skill in the art. In a preferred embodiment, cells are isolated from the subject organism, a nucleic acid (gene or cDNA) of interest is introduced therein, and the cells are re- infused back into the subject organism (e.g., patient). Various cell types suitable for ex vivo treatment are well known to those of skill in the art.

In one embodiment, stem cells are used in ex vivo procedures for cell transfection and gene therapy. The advantage to using stem cells is that they can be differentiated into other cell types in vitro, or can be introduced into a mammal (such as the donor of the cells) where they will engraft at an appropriate location (such as in the bone marrow). Methods for differentiating CD34+ cells in vitro into clinically important immune cell types using cytokines such as for example GM-CSF, IFN-γ and TNF-α are known.

Stem cells are isolated for transduction and differentiation using known methods. For example, stem cells can be isolated from bone marrow cells by panning the bone marrow cells with antibodies which bind unwanted cells, such as CD4+ and CD8+ (T cells), CD45+ (panB cells), GR-1 (granulocytes), and lad (differentiated antigen presenting cells).

Peptide mimetics. Peptide mimetics mimic the three-dimensional structure of the polypeptide encoded by a gene from Table 3. Such peptide mimetics may have significant advantages over naturally occurring peptides, including, for example: more economical production, greater chemical stability, enhanced pharmacological properties (half-life, absorption, potency, efficacy, etc.), altered specificity (e.g., a broad-spectrum of biological activities), reduced antigenicity and others. In one form, mimetics are peptide-containing molecules that mimic elements of protein secondary structure. The underlying rationale behind the use of peptide mimetics is that the peptide backbone of proteins exists chiefly to orient amino acid side chains in such a way as to facilitate molecular interactions, such as those of antibody and antigen. A peptide mimetic is expected to permit molecular interactions similar to the natural molecule. In another form, peptide analogs are commonly used in the pharmaceutical industry as non-peptide drugs with properties analogous to those of the template peptide. Peptide mimetics that are structurally similar to therapeutically useful peptides may be used to produce an equivalent therapeutic or prophylactic effect.

Antibodies. Naturally occurring immunoglobulins have a common core structure in which two identical light chains (about 24 kD) and two identical heavy chains (about 55 or 70 kD) form a tetramer. The amino-terminal portion of each chain is known as the variable (V) region and can be distinguished from the more conserved constant (C) regions of the remainder of each chain. Within the variable region of the light chain is a C-terminal portion known as the J region. Within the variable region of the heavy chain, there is a D region in addition to the J region. Most of the amino acid sequence variation in immunoglobulins is confined to three separate locations in the V regions known as hypervariable regions or complementarity determining regions (CDRs) which are directly involved in antigen binding. Proceeding from the amino- terminus, these regions are designated CDR1 , CDR2 and CDR3, respectively. The CDRs are held in place by more conserved framework regions (FRs). Proceeding from the amino-terminus, these regions are designated FR1 , FR2, FR3, and FR4, respectively. The locations of CDR and FR regions and a numbering system have been defined by Kabat et al. (Kabat, E. A. et al., Sequences of Proteins of Immunological Interest, Fifth Edition, U.S. Department of Health and Human Services, U.S. Government Printing Office (1991 )).

Antibody derivatives include, but are not limited to, humanized antibodies. As used herein, the term "humanized antibody" refers to an immunoglobulin that comprises both a region derived from a human antibody or immunoglobulin and a region derived from a non-human antibody or immunoglobulin. The action of humanizing an antibody consists in substituting a portion of a non-human antibody with a corresponding portion of a human antibody. For example, a humanized antibody as used herein could comprise a non-human variable region (such as a region derived from a murine antibody) capable of specifically recognizing a polypeptide encoded by a gene as described herein and a human constant region derived from a human antibody. In another example, the humanized immunoglobulin can comprise a heavy chain and a light chain, wherein the light chain comprises a complementarity determining region derived from an antibody of non-human origin which binds to the popyleptide and a framework region derived from a light chain of human origin, and the heavy chain comprises a complementarity determining region derived from an antibody of non-human origin which binds to the polypeptide and a framework region derived from a heavy chain of human origin.

As used herein, the present application also relates to fragments of the antibodies. As used herein, a "fragment" of an antibody (e.g. a monoclonal antibody) is a portion of an antibody that is capable of specifically recognizing the same epitope as the full version of the antibody. In the present patent application, antibody fragments are capable of specifically recognizing the polypeptide. Antibody fragments include, but are not limited to, the antibody light chain, single chain antibodies, Fv, Fab, Fab' and F(ab')₂ fragments. Such fragments can be produced by enzymatic cleavage or by recombinant techniques. For instance, papain or pepsin cleavage can be used to generate Fab or F(ab')₂ fragments, respectively. Antibodies can also be produced in a variety of truncated forms using antibody genes in which one or more stop codons have been introduced upstream of the natural stop site. For example, a chimeric gene encoding the heavy chain of an F(ab')₂ fragment can be designed to include DNA sequences encoding the CHi domain and hinge region of the heavy chain. Antibody fragments can also be humanized. For example, a humanized light chain comprising a light chain CDR (i.e. one or more CDRs) of non-human origin and a human light chain framework region. In another example, a humanized immunoglobulin heavy chain can comprise a heavy chain CDR (i.e., one or more CDRs) of non-human origin and a human heavy chain framework region. The CDRs can be derived from a non-human immunoglobulin.

Small molecule. Any agent capable of alleviating at least one symptom associated with disease is considered as a putative agent.

Administration is by any of the routes normally used for introducing a molecule into ultimate contact with blood or tissue cells. The nucleic acids are administered in any suitable manner, preferably with the pharmaceutically acceptable carriers or excipients. The terms "pharmaceutically acceptable carrier", "excipients" and "adjuvant" and "physiologically acceptable vehicle" and the like are to be understood as referring to an acceptable carrier or adjuvant that may be administered to a patient, together with a compound of this invention, and which does not destroy the pharmacological activity thereof. Further, as used herein "pharmaceutically acceptable carrier" or "pharmaceutical carrier" are known in the art and include, but are not limited to, 0.01-0.1 M and preferably 0.05 M phosphate buffer or 0.8% saline. Additionally, such pharmaceutically acceptable carriers may be aqueous or nonaqueous solutions, suspensions, and emulsions. Examples of non-aqueous solvents are propylene glycol, polyethylene glycol, vegetable oils such as olive oil, and injectable organic esters such as ethyl oleate. Aqueous carriers include water, alcoholic/aqueous solutions, emulsions or suspensions, including saline and buffered media. Parenteral vehicles include sodium chloride solution, Ringer's dextrose, dextrose and sodium chloride, lactated Ringer's or fixed oils. Intravenous vehicles include fluid and nutrient replenishers, electrolyte replenishers such as those based on Ringer's dextrose, and the like. Preservatives and other additives may also be present, such as, for example, antimicrobials, antioxidants, collating agents, inert gases and the like.

As used herein, "pharmaceutical composition" means therapeutically effective amounts (dose) of the agent together with pharmaceutically acceptable diluents, preservatives, solubilizers, emulsifiers, adjuvants and/or carriers. A "therapeutically effective amount" as used herein refers to that amount which provides a therapeutic effect for a given condition and administration regimen. Such compositions are liquids or lyophilized or otherwise dried formulations and include diluents of various buffer content (e.g., Tris-HCI, acetate, phosphate), pH and ionic strength, additives such as albumin or gelatin to prevent absorption to surfaces, and detergents (e.g., Tween 20™, Tween 80™, Pluronic F68™, bile acid salts). The pharmaceutical composition of the present invention can comprise pharmaceutically acceptable solubilizing agents (e.g., glycerol, polyethylene glycerol), anti-oxidants (e.g., ascorbic acid, sodium metabisulfite), preservatives (e.g., thimerosal, benzyl alcohol, parabens), bulking substances or tonicity modifiers (e.g., lactose, mannitol), covalent attachment of polymers such as polyethylene glycol to the protein, complexation with metal ions, or incorporation of the material into or onto particulate preparations of polymeric compounds such as polylactic acid, polyglycolic acid, hydrogels, etc, or onto liposomes, microemulsions, micelles, unilamellar or multilamellar vesicles, erythrocyte ghosts, or spheroplasts. Such compositions will influence the physical state, solubility, stability, rate of in vivo release, and rate of in vivo clearance. Controlled or sustained release compositions include formulation in lipophilic depots (e.g., fatty acids, waxes, oils). Also comprehended by the invention are particulate compositions coated with polymers (e.g., poloxamers or poloxamines).

Suitable methods of administering such nucleic acids are available and well known to those of skill in the art, and, although more than one route can be used to administer a particular composition, a particular route can often provide a more immediate and more effective reaction than another route. The present invention further provides other methods of treating Alzheimer's disease such as administering to a subject having Alzheimer's disease an effective amount of an agent that regulates the expression, activity or physical state of at least one gene from Table 3. An "effective amount" of an agent is an amount that modulates a level of expression or activity of a gene from Table 3, in a cell in the individual at least about 10%, at least about 20%, at least about 30%, at least about 40%, at least about 50%, at least about 60%, at least about 70%, at least about 80% or more, compared to a level of the respective gene from Table 3 in a cell in the individual in the absence of the compound. The preventive or therapeutic agents of the present invention may be administered, either orally or parenterally, systemically or locally. For example, intravenous injection such as drip infusion, intramuscular injection, intraperitoneal injection, subcutaneous injection, suppositories, intestinal lavage, oral enteric coated tablets, and the like can be selected, and the method of administration may be chosen, as appropriate, depending on the age and the conditions of the patient. The effective dosage is chosen from the range of 0.01 mg to 100 mg per kg of body weight per administration. Alternatively, the dosage in the range of 1 to 1000 mg, preferably 5 to 50 mg per patient may be chosen.

Stratification method based on profile of markers associated with Alzheimer's disease

The profile of markers can be used to stratify a group of the individuals based either on their risk of developing or being diagnosed with a Alzheimer's disease or on their response to an agent. These groups of individuals can then be used for various purposes, including targeted treatment, selection for clinical trials and testing for the response to a drug.

In an embodiment, the method of stratifying a group of individuals comprises determining, in a sample from each individual, the genetic profile comprising at least one marker located in a Candidate Region listed in Table 2. Once the genetic profiles are determined, then the group of individuals is divided into subgroups of individuals having a common genetic marker (or combination of genetic markers) in their respective genetic profile or lacking a common genetic marker (or a combination of genetic markers) in their respective genetic profile. For example, one of the resulting subgroups will contain individuals having the profile comprising at least one marker having a skewed genotype distribution towards individuals diagnosed, predisposed or afflicted with the Alzheimer's disease when compared to control individuals. In another embodiment, one of the resulting subgroups of individuals can have a profile comprising at least one marker having a skewed genotype distribution towards control individuals when compared to individuals diagnosed, predisposed or afflicted with the Alzheimer's disease. In yet another embodiment, one of the resulting subgroups can have a profile comprising at least one marker having a skewed genotype distribution towards individuals responding positively to an agent useful for the treatment Alzheimer's disease when compared to individuals not responding or responding negatively to the agent. In still yet another embodiment, one of the resulting subgroups of individuals can have the profile comprising at least one marker having a skewed genotype distribution towards to individuals not responding or responding negatively an agent useful for the treatment Alzheimer's disease when compared to individuals responding positively to the agent. As a result of this method, one, some or all of the subgroups of individuals created can be included or excluded from a pre-clinical or a clinical trial for an agent useful in the treatment of Alzheimer's disease. In some instances, within a subgroup, the individuals have similar phenotypic or subphenotypic traits associated with Alzheimer's disease. Various embodiments of the marker, the sample and the profile (as well as how to determine it) have been described above and can be applied herein.

This stratification method can be embodied in a stratification system designed to perform the required steps. This stratification system comprises at least two modules: a first module for performing the determination of the profile and a second module for dividing the individuals into subgroups. The first module comprises a detection module for determining the presence or absence of at least one marker in at least one of the Candidate Regions identified herein. As indicated above, this detection can be made either at the DNA level, the RNA level and/or the polypeptide level. The detection module relies on the addition of label to the sample and the quantification of the signal of the label for determining the presence or absence of the marker. The signal of the label is quantified by the detection module and is linked to the presence or absence of the marker. This label can directly or indirectly be linked to a quantifier specific for the marker. The information gathered by the detection module is then processed by the second module for creating the subgroups. This second module can use a processor for comparing the profiles generated amongst each other and to divide individuals in subgroups having similar profiles.

As indicated above, the determination of the profile can include the addition of a quantifier to the sample from the individual. The quantifier is a physical entity that enables the sample to be quantified. The sample can be purified or isolated prior to the addition of the quantifier. The quantifier can be, for example, an oligonucleotide specific for the nucleic acid to be quantified, an antibody specific for the polypeptide to be quantified or a ligand specific for the enzyme to be quantified. The addition of the quantifier generates a quantifiable sample that can then be submitted to an assay for the determination of the quantity of nucleic acid and/or polypeptide. The quantifier is either directly or indirectly linked to the quantifiable label.

Nucleic acid sequences

The method described above identifies specific nucleic acid sequences associated with Alzheimer's disease. The nucleic acid sequences of the present invention may be derived from a variety of sources including DNA, cDNA, synthetic DNA, synthetic RNA, derivatives, mimetics or combinations thereof. Such sequences may comprise genomic DNA, which may or may not include naturally occurring introns, genie regions, nongenic regions, and regulatory regions. Moreover, such genomic DNA may be obtained in association with promoter regions or poly (A) sequences. The sequences, genomic DNA, or cDNA may be obtained in any of several ways. Genomic DNA can be extracted and purified from suitable cells by means well known in the art. Alternatively, mRNA can be isolated from a cell and used to produce cDNA by reverse transcription or other means. The nucleic acids described herein are used in certain embodiments of the methods of the present invention for production of RNA, proteins or polypeptides, through incorporation into host cells, tissues, or organisms. In one embodiment, DNA containing all or part of the coding sequence for the genes described in Table 3, the SNP markers described in any one of Tables 5 to 23, the alleles listed in any one of Tables 5 to 23 and the haplotype presented in any one of Tables 24 to 42 are incorporated into vectors for expression of the encoded polypeptide in suitable host cells.

Mapping technologies

The present invention includes various methods which employ mapping technologies to map SNPs and polymorphisms. For purpose of clarity, this section comprises, but is not limited to, the description of mapping technologies that can be utilized to achieve the embodiments described herein. Mapping technologies may be based on amplification methods, restriction enzyme cleavage methods, hybridization methods, sequencing methods, and cleavage methods using agents.

Amplification methods include self sustained sequence replication, transcriptional amplification system, Q-Beta Replicase, isothermal amplification, or any other nucleic acid amplification method, followed by the detection of the amplified molecules using techniques well known to those of ordinary skill in the art. These detection schemes are especially useful for the detection of nucleic acid molecules if such molecules are present in very low number.

SNPs and SNP maps of the invention can be identified or generated by hybridizing sample nucleic acids, e.g., DNA or RNA, to high density arrays or bead arrays containing oligonucleotide probes corresponding to the polymorphisms set forth in any one of Tables 5 to 42.

Methods of forming high density arrays of oligonucleotides with a minimal number of synthetic steps are known. The oligonucleotide analogue array can be synthesized on a single or on multiple solid substrates by a variety of methods, including, but not limited to, light-directed chemical coupling, and mechanically directed coupling.

In brief, the light-directed combinatorial synthesis of oligonucleotide arrays on a glass surface proceedes using automated phosphoramidite chemistry and chip masking techniques. In one specific implementation, a glass surface is derivatized with a silane reagent containing a functional group, e.g., a hydroxyl or amine group blocked by a photolabile protecting group. Photolysis through a photolithogaphic mask is used selectively to expose functional groups which are then ready to react with incoming 5' photoprotected nucleoside phosphoramidites. The phosphoramidites react only with those sites which are illuminated (and thus exposed by removal of the photolabile blocking group). Thus, the phosphoramidites only add to those areas selectively exposed from the preceding step. These steps are repeated until the desired array of sequences has been synthesized on the solid surface. Combinatorial synthesis of different oligonucleotide analogues at different locations on the array is determined by the pattern of illumination during synthesis and the order of addition of coupling reagents.

High density nucleic acid arrays can also be fabricated by depositing pre-made or natural nucleic acids in predetermined positions. Synthesized or natural nucleic acids are deposited on specific locations of a substrate by light directed targeting and oligonucleotide directed targeting. Another embodiment uses a dispenser that moves from region to region to deposit nucleic acids in specific spots.

Nucleic acid hybridization simply involves contacting a probe and target nucleic acid under conditions where the probe and its complementary target can form stable hybrid duplexes through complementary base pairing. It is generally recognized that nucleic acids are denatured by increasing the temperature or decreasing the salt concentration of the buffer containing the nucleic acids. Under low stringency conditions (e.g., low temperature and/or high salt) hybrid duplexes (e.g., DNA:DNA, RNA:RNA, or RNA:DNA) will form even where the annealed sequences are not perfectly complementary. Thus, specificity of hybridization is reduced at lower stringency. Conversely, at higher stringency (e.g., higher temperature or lower salt) successful hybridization tolerates fewer mismatches. One of skill in the art will appreciate that hybridization conditions may be selected to provide any degree of stringency as described in Sambrook ef a/. (1989, Molecular Cloning: A Laboratory Manual, 2d Ed., Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY).

As used herein, oligonucleotide sequences that are complementary to one or more of the genes or fragments described in Table 3 refer to oligonucleotides that are capable of hybridizing under stringent conditions to at least part of the nucleotide sequences of said genes. Such hybridizable oligonucleotides will typically exhibit at least about 75% sequence identity at the nucleotide level to said genes, preferably about 80% or 85% sequence identity or more preferably about 90% or 95% or more sequence identity to said genes (see GeneChip^® Expression Analysis Manual, Affymetrix, Rev. 3, which is herein incorporated by reference in its entirety).

The phrase "hybridizing specifically to" or "specifically hybridizes" refers to the binding, duplexing, or hybridizing of a molecule substantially to or only to a particular nucleotide sequence or sequences under stringent conditions when that sequence is present in a complex mixture (e.g., total cellular) of DNA or RNA.

Methods of detecting polymorphisms include methods in which protection from cleavage agents is used to detect mismatched bases in RNA/RNA, DNA/DNA or RNA/DNA heteroduplexes. In general, the technique of "mismatch cleavage" starts by providing heteroduplexes formed by hybridizing (labeled) RNA or DNA containing a control sequence with a RNA or DNA obtained from a sample. The double- stranded duplexes are treated with an agent that cleaves single-stranded regions of the duplex such as which will exist due to basepair mismatches between the control and sample strands. For instance, RNA/DNA duplexes can be treated with RNase and DNA/DNA hybrids treated with S1 nuclease to enzymatically digest the mismatched regions. In other embodiments, either DNA/DNA or RNA/DNA duplexes can be treated with hydroxylamine or osmium tetroxide and with piperidine in order to digest mismatched regions. After digestion of the mismatched regions, the resulting material is then separated by size on denaturing polyacrylamide gels to determine the site of a mutation or SNP.

In still another embodiment, the mismatch cleavage reaction employs one or more proteins that recognize mismatched base pairs in double-stranded DNA (so called "DNA mismatch repair" enzymes) in defined systems for detecting and mapping polymorphisms. For example, the mutY enzyme of E. coli cleaves A at G/A mismatches. Other examples include, but are not limited to, the MutHLS enzyme complex of E. coli and CeI 1 from the celery, both cleaving the DNA at various mismatches.

In other embodiments, alterations in electrophoretic mobility can be used to identify polymorphisms in a sample. For example, single strand conformation polymorphism (SSCP) analysis can be used to detect differences in electrophoretic mobility between mutant and wild type nucleic acids. Single-stranded DNA fragments of case and control nucleic acids will be denatured and allowed to renature. The secondary structure of single-stranded nucleic acids varies according to sequence. The resulting alteration in electrophoretic mobility enables the detection of even a single base change.

In yet another embodiment, the movement of mutant or wild-type fragments in a polyacrylamide gel containing a gradient of denaturant is assayed using denaturing gradient gel electrophoresis (DGGE). When DGGE is used as the method of analysis, DNA will be modified to insure that it does not completely denature, for example by adding a GC clamp of approximately 40 bp of high-melting GC-rich DNA by PCR.

Examples of other techniques for detecting polymorphisms include, but are not limited to, selective oligonucleotide hybridization, selective amplification, selective primer extension, selective ligation, single-base extension, selective termination of extension or invasive cleavage assay.

The present invention will be more readily understood by referring to the following examples which are given to illustrate the invention rather than to limit its scope.

EXAMPLE I - Identification of cases and controls

All individuals were sampled from the Quebec founder population (QFP). Membership in the QFP was defined as having four grandparents of the affected case or unaffected control having French Canadian family names and being born in the Province of Quebec, Canada or in adjacent areas of the Provinces of New Brunswick and Ontario or in New England or New York State. DNA samples for this study were selected from two sources: QFP blood samples and brain samples collected from the Douglas Hospital brain bank. The samples were collected as cases (665 from blood, 391 from brain) and controls (1515 from blood, 251 from brain).

The cases inclusion criteria for the study included the following criteria:

• Age of onset being equal or greater than 65; and • Presence of definite Alzheimer's disease as confirmed by neuropathology findings on autopsy (brain samples selected as definite subtype); or

• Presence of probable Alzheimer's disease based on DSM-IV criteria (brain samples selected as not definite subtype).

The controls were selected if one of the following criteria was met:

• Age of subject at time of recruitment / death equal to or greater than 75 with absence of AD as confirmed by neuropathology findings on autopsy; or

• Age of subject at time of recruitment equal to or greater than 75 with 3MS score equal to or greater than 80.

Subjects having close family relationship with another sample were excluded.

In the situation where blood was used as a sample, enrolled QFP subjects (cases and controls) provided a 20 ml. blood sample (2 bar-coded tubes of 10 ml_). Samples were processed immediately upon arrival at the laboratory. All samples were scanned and logged into a LabVantage™ Laboratory Information Management System (LIMS), which served as a hub between the clinical data management system and the genetic analysis system. Following centrifugation, the buffy coat containing the white blood cells was isolated from each tube. Genomic DNA was extracted from the buffy coat from one of the tubes, and stored at 4°C until required for genotyping. DNA extraction was performed with a commercial kit using a guanidine hydrochloride based method (FlexiGene™, Qiagen) according to the manufacturer's instructions. The extraction method yielded high molecular weight DNA, and the quality of every DNA sample was verified by agarose gel electrophoresis. Genomic DNA appeared on the gel as a large band of very high molecular weight. The remaining buffy coats were stored at -80°C as backups.

EXAMPLE Il - Genome-Wide Association Study

Genotyping was performed using the lllumina lnfinium HumanHap550v3_A BeadChip that contains 561 ,466 SNPs supplemented with two APOE functional SNPs rs429358 and rs7412 Taqman® pre-designed assays. The genotyping information was entered into a database from which it was accessed using custom- built programs for export to the genetic analysis pipeline. Analyses of these genotypes were performed with the statistical tools described in Example III.

Three separate GWAS's analyses were performed:

• GWS analysis A was performed on 753 cases and 753 controls that were matched on region of origin. Age of the controls ranged from 75 years old to 94 years old. The marker map represents lllumina lnfinium HumanHap550v3_A BeadChip, and includes a total of 561 ,466 SNPs, supplemented with two APOE functional SNPs rs429358 and rs7412 Taqman® pre-designed assays.

• GWS analysis B was performed on 691 cases and 691 controls that were matched on region of origin. Age of the controls was equal to or more than 75 years old. The marker map represents lllumina lnfinium HumanHap550v3_A BeadChip, and includes a total of 561 ,466 SNPs, supplemented with two APOE functional SNPs rs429358 and rs7412 Taqman® pre-designed assays.

• GWS analysis C was performed on 484 cases and 484 controls that were matched on region of origin. Age of the controls was equal to or more than 94 years old. The marker map represents lllumina lnfinium HumanHap550v3_A BeadChip, and includes a total of 561 ,466 SNPs, supplemented with two APOE functional SNPs rs429358 and rs7412 Taqman® pre-designed assays.

The GWAS, permitted the identification of highly significant Candidate Regions linked to Alzheimer's disease. The highest hits obtained are shown in Table 1. The Candidate Regions obtained are shown in Table 2.

EXAMPLE III - Genetic Analysis

Dataset pre-cleanup and clean-up.

The data was first subjected to a pre-cleaning step where individuals (cases and control) and markers containing > 1% missing data were excluded. In addition, PLINK™, a publicly available software package (Purcell et al., Am J Hum Genet 81 :559-575, 2007) was used to detect the following in the scope of cleaning:

• Relatedness. Relatedness between individuals was computed as the proportion of shared alleles identical by descent (IBD). For any pair of subjects related by four meiotic steps or fewer (IBD ≥ 12.5%), one of the two subjects must be removed from the dataset. In order to take the uncertainty of estimating IBD into account, a threshold of IBD ≥ 10% was applied.

• Outliers. An outlier is determined based on its identity by state (IBS) distance with its 10 closest neighbors. Standardized distances are defined between each individual and its 10 closest neighbors. If any of these standardized distances is less than or equal to -4, then this individual was considered an outlier and was removed.

In order for PLINK™ to estimate the expected proportion of IBD and the IBS-based Z scores in an unbiased way, filtering of both markers and individuals (cases and controls) was performed using the following criteria:

• Individuals with missing values >25% were not taken into consideration for the analysis; and

• Markers that failed any one of the following criteria were not taken into consideration for the analysis:

o Percentage of missing values > 25%;

o Minor allele frequency (MAF) < 4%; or

o Hardy-Weinberg equilibrium test's p-value < 10^-5.

The data was then subjected to a cleaning step, by calculating the following statistics per marker or per individual (cases and controls) on each chromosome:

• Minor allele frequency (MAF) for each marker;

• Departure from Hardy-Weinberg equilibrium within control individuals for each marker; • Percentage of missing values for each marker;

• Number of missing values for each individual; and

• Number and percentage of homozygote markers for each individual.

Markers and individuals (cases and controls) that failed to meet any one of the following criteria were removed:

• Non-Mendelian values per marker < 1 %;

• Missing values per marker < 1%;

• Minor allele frequency per marker (> 4 % for haplotype analysis and > 1 % for single marker analysis);

• Hardy-Weinberg Equilibrium value per marker does not exceed Bonferroni test value; or

• Missing value per individual on a genome-wide scale < 5%.

The data was further subjected to another cleaning step. In order to do so, the following statistics per individual (cases and controls) were then calculated on a genome-wide scale:

• % of missing values for each individual and

• % of homozygosity for each individual.

Individuals that did not meet any one of the following criteria were removed:

• Missing values per individual on a chromosome >5.5% dependent on the dataset; or

• Homozygosity per individual on a chromosome >80% dependent on the dataset.

Correction for population sub-structure. Following cleaning, in order to correct for the presence of population sub-structure, the dataset was matched via the region of origin of the subject's grandparents. In order to determine the presence of population sub-structure within the subject set, unrelated markers that are not associated with Alzheimer's must first be selected. Further, a 1 : 1 case to control matching was performed in order to have the best possible matching scores. In some instances, a 2 : 1 case to control matching was also performed to increase the power of the study.

Matching by region of origin was performed by matching subjects in pairs of one case to one control based on the region of origin information of the subjects' four grandparents. When possible, the cases and controls were matched by gender, where a female case was region-matched to a female control and a male case was region-matched to a male control.

Once the dataset was matched, dataset specific LD computation was performed to determine markers that are not in LD. Then, an evaluation of the stratification of the sample set was performed by calculating the mean chi-square and its confidence interval over the selection of markers that were in minimal linkage disequilibrium (LD) with each other. The median chi-square and Devlin's lambda genomic controls statistic, as well as other parameters of the distribution of chi-square values, including the variance, the skewness and the kurtosis were also calculated. In addition, Quantile-Quantile plots were also generated.

These statistics enable an evaluation of the extent of statistically significant differences in allele frequencies between case and control datasets that are due solely to population stratification and which confound the identification of differences due to disease status. Multiple subsets (matched pairs) of the full dataset were created based on various matching quality scores. The subset which displayed the best combination of absence of population substructure and maximum sample size was used for genetic analysis. In this Example, it was shown that the bias introduced by the population sub-structure was minimal.

Phase Determination. Haplogenotypes were estimated from the case/control genotype data using the PL- EM algorithm (Qin, ZS et al., Am J Hum Genet. 2002;71 :1242-1247). Haplotypes were estimated within 1 1-marker overlapping blocks, which advanced in one-marker increments across the chromosome. A threshold of 6 missing values was used for the analysis.

Haplotype association analysis.

Haplotype association analysis was performed using the software tool LDSTATS, a customized association analysis pipeline. LDSTATS tests for association of haplotypes with the disease phenotype. The algorithms LDSTATS (v2.0) and LDSTATS (v4.0) define haplotypes using multi-marker windows that advance across the marker map in one-marker increments. Windows of size 1 , 3, 5, 7 and 9 were analyzed. At each position the frequency of haplotypes in cases and controls was determined and a chi-square statistic was calculated from case control frequency tables.

For LDSTATS v2.0, the significance of the chi-square for single marker and 3-marker windows was calculated as Pearson's chi-square with appropriate degrees of freedom. Larger windows of multi-allelic haplotype association were tested using Smith's normalization of the square root of Pearson's Chi-square.

LDSTATS v4.0 calculates significance of chi-square values using a permutation test in which case-control status is randomly permuted until 350 permuted chi-square values are observed that are greater than or equal to chi-square value of the actual data. The p value is then calculated as 350/the number of permutations required.

Singletype analysis.

The software tool SINGLETYPE was used to calculate both allelic and genotypic association for each single marker individually using the genotype data. Allelic association was tested using a 2 X 2 contingency table comparing allele 1 in cases and controls and allele 2 in cases and controls. Genotypic association was tested using a 2 X 3 contingency table comparing genotype 11 in cases and controls, genotype 12 in cases and controls and genotype 22 in cases and controls. SINGLETYPE was also used to test dominant and recessive models (11 and 12 genotypes combined vs. 22; or 22 and 12 genotypes combined vs. 1 1 ). The software tool SINGLETYPE uses unphased data, whereas the single marker analysis component of the software tool LDSTATS uses phased data and only performs an allelic association test.

Peak determination.

To determine the SNPs to be reported, a region is defined around a significant SNP, which consists of a list of SNPs that may or may not be contiguous on the physical map, depending on the algorithm used to define the region.

For haplotype analyses, all -Iog10 p-values were first ranked and then boundaries were defined around the top SNP by advancing to the first markers left and right of the signal for which the -Iog10 p-value was below 1.5. After discarding all the SNPs that are part of Candidate Region 1 , this process is repeated until all SNPs with a - Iog10 p-value > 3 have been assigned to a region.

An asymmetric running average algorithm was applied to region identification for single marker analysis, both from LDSTATS and SINGLETYPE. It proceeds similarly as above except that the region boundaries are defined as the first marker on the left or the right (calculations are done separately) for which the average of the — Iog10 p- values of all SNPs between the signal and the boundary falls below 1.75.

An LD-based region identification approach was also applied to single marker analysis but differs from the method above in that it explicitly takes LD into account. Boundaries were defined as the leftmost and rightmost markers in a radius of 1 Mbp for which the r² with the signal was at least 0.1. Another difference is that a SNP can belong to more than one region, as long as its -log 10 p-value is below 3.

Full Cohort Analyses

Three different full cohort analyses were performed. Table A depicts the various analyses with a unique identifier for each. The phenotype of the cases and the controls and the sample size for the matched analyses are also provided. The loci identified via the GWAS performed in this subset of cases and their matched controls are described in the marker and allele tables listed in Table A. As an example, in the first analysis, the controls selection criteria was the absence of Alzheimer's disease and the age ≥ 75 years. Results of these full cohort analysis are presented in Tables 5, 22, 23, 24, 41 and 42.

Table A. Description of the full cohort analyses

Subphenotype Analyses

Based on the full cohort analyses described above, a subset of the population is analyzed as a subphenotype analysis where the goal is to determine whether the significant signals are due to a more homogeneous population within the full sample and/or if novel hits can be identified that are specific to the subphenotype analysis. Five subphenotype analyses were performed as depicted in Table B. As an example, subphenotype analysis #1 was performed by subsetting the 753-pairs full cohort to those pairs that have received a definite diagnostic of Alzheimer's (as indicated above, their matched controls from the full set are maintained), which resulted in a sample size of 147 pairs for the subanalysis. A test of heterogeneity is performed on the 147 pairs subset using the 753 pairs full cohort as reference. The loci identified via the GWAS performed in this subset of cases and their matched controls are described in the marker and allele tables listed in Table B.

Table B. Subphenotype analysis

Conditional Analyses

Based on the results of the GWAS with the full cohorts described above, several loci were selected to perform conditional analyses where the goal is to identify epistatic interactions among loci or the existence of independent risk factors. At a given locus, the genotypes or allele conferring an increased risk of developing Alzheimer's are identified and define the "risk group" or "risk set". Similarly, the genotypes or allele conferring a decreased risk of developing Alzheimer's are identified and define the "protective group" or "protective set". Using either the risk set or the protective set, a subset of cases and their matched controls based on the carrier status of cases is selected to perform a new GWAS. Table C describes eleven conditioning analyses performed. A unique identifier is provided as well as the locus selected and the chromosome it resides on. The event type describes if the risk or the protective set was selected and the resulting number of cases meeting the selection criteria. The sample size in the original or reference study in which the selection was performed is also provided. All conditional analyses were performed on genotypes. The actual genotypes defining the risk or the protective set are defined in Table D. As an example, for the first conditioning event, a SNP in the PDCD1 LG2 locus (chromosome 9) was selected. Cases carrying the 2/2 (or G/G) genotype at the PDCD1 LG2 locus were associated with protection against Alzheimer's disease. For the first conditional analysis, out of 753 cases, 49 cases which possessed the protection allele at the PDCD1 LG2 locus were selected in conjunction with their matched controls. Results obtained are shown in the Tables presented in Table C.

Table C. Genotype conditioning events information with respect to the various loci used. Chr.: chromosome, ET = even type, P = protective, R= risk, Sample size (conditional size/parent size).

Table D. Conditional event alleles in code.

Table 2. List of candidate regions identified from the Genome Wide Association Scan (GWAS) analyses. CR: Candidate Region; Chr.: Chromosome; B36 (or B37) Start Position: NCBI Build 36 (or 37) region start location in base pairs (bp); B36 (or B37) End Position: NCBI Build 36 (or 37) region end location in base pairs (bp).

Table 3. List of candidate genes from the Genome Wide Association Scan (GWAS) analyses. CR: Candidate Region; B37 Start Position and B37 End Position: chromosomal start and end coordinates respectively of the NCBI genome assembly derived from build 37 (B37) (the start and end position relate to the positive orientation of the NCBI assembly and do not necessarily correspond to the orientation of the gene); Gene Symbol: official gene symbol obtained from the NCBI Entrez Gene database; Entrez GenelD: NCBI Entrez Gene Identifier; Nucleotide (cDNA) SEQ. ID. NO. and Protein SEQ. ID. NO.

Table 4. List of micro RNA (miRNA) from the regions identified from the Genome Wide Association Scan (GWAS) analyses. CR: Candidate Region; Chr.: Chromosome; B37 Start Position and B37 End Position: chromosomal start and end coordinates respectively of the NCBI genome assembly derived from build 37 (B37) (the start and end position relate to the positive orientation of the NCBI assembly and don't necessarily correspond to the orientation of the miRNA); mirna_acc and mirna_id: miRNA accession and miRNA id, respectively, obtained from the miRBase Sequence Database; SEQ ID NO: unique numerical identifier for nucleotide (RNA) sequence.

Table 5: Genome Wide Association Scan (GWAS) results. CR: Candidate Region; B36 Position: NCBI Build 36 location in base pairs (bp); RS#: dbSNP data base (NCBI) reference number; SEQ. ID. NO.: unique numerical identifier for this patent application; - Iog10 p- values: - log 10 of the p-value for the association to the disease from the GWAS for single SNP markers and for the associated multi-marker haplotypes centered at the reference marker and defined by the sliding windows (W) of specified sizes (nominal p-values are displayed).

Table 6: Genome Wide Association Scan (GWAS) results for definitive Alzheimer's cases. CR: Candidate Region; B36 Position: NCBI Build 36 location in base pairs (bp); RS#: dbSNP data base (NCBI) reference number; SEQ. ID. NO.: unique numerical identifier for this patent application; - Iog10 p-values: - Iog10 of the p- value for the association to the disease from the GWAS for single SNP markers and for the associated multi-marker haplotypes centered at the reference marker and defined by the sliding windows (W) of specified sizes (nominal p-values are displayed).

Table 7: Genome Wide Association Scan (GWAS) results for cases where onset was determined in subjects having between 65 to 74 years of age. CR: Candidate Region; B36 Position: NCBI Build 36 location in base pairs (bp); RS#: dbSNP data base (NCBI) reference number; SEQ. ID. NO.: unique numerical identifier for this patent application; - Iog10 p-values: - Iog10 of the p-value for the association to the disease from the GWAS for single SNP markers and for the associated multi-marker haplotypes centered at the reference marker and defined by the sliding windows (W) of specified sizes (nominal p-values are displayed).

Table 8: Genome Wide Association Scan (GWAS) results for probable cases of Alzheimer's. CR: Candidate Region; B36 Position: NCBI Build 36 location in base pairs (bp); RS#: dbSNP data base (NCBI) reference number; SEQ. ID. NO.: unique numerical identifier for this patent application; - Iog10 p-values: - Iog10 of the p- value for the association to the disease from the GWAS for single SNP markers and for the associated multi-marker haplotypes centered at the reference marker and defined by the sliding windows of specified sizes (nominal p-values are displayed).

Table 9: Genome Wide Association Scan (GWAS) results for male cases. CR: Candidate Region; B36 Position: NCBI Build 36 location in base pairs (bp); RS#: dbSNP data base (NCBI) reference number; SEQ. ID. NO.: unique numerical identifier for this patent application; - Iog10 p-values: - Iog10 of the p-value for the association to the disease from the GWAS for single SNP markers and for the associated multi-marker haplotypes centered at the reference marker and defined by the sliding windows (W) of specified sizes (nominal p-values are displayed).

Table 10: Genome Wide Association Scan (GWAS) results for female cases. CR: Candidate Region; B36 Position: NCBI Build 36 location in base pairs (bp); RS#: dbSNP data base (NCBI) reference number; SEQ. ID. NO.: unique numerical identifier for this patent application; - Iog10 p-values: - Iog10 of the p-value for the association to the disease from the GWAS for single SNP markers and for the associated multi-marker haplotypes centered at the reference marker and defined by the sliding windows (W) of specified sizes (nominal p-values are displayed).

Table 1 1 : Genome Wide Association Scan (GWAS) results for cases having a protective event in the PDCD1 LG2 locus. CR: Candidate Region; B36 Position: NCBI Build 36 location in base pairs (bp); RS#: dbSNP data base (NCBI) reference number; SEQ. ID. NO.: unique numerical identifier for this patent application; - Iog10 p-values: - Iog10 of the p-value for the association to the disease from the GWAS for single SNP markers and for the associated multi-marker haplotypes centered at the reference marker and defined by the sliding windows (W) of specified sizes (nominal p-values are displayed).

Table 12: Genome Wide Association Scan (GWAS) results for cases having a risk event in the PDCD1 LG2 locus. CR: Candidate Region; B36 Position: NCBI Build 36 location in base pairs (bp); RS#: dbSNP data base (NCBI) reference number; SEQ. ID. NO.: unique numerical identifier for this patent application; - Iog10 p-values: - Iog10 of the p-value for the association to the disease from the GWAS for single SNP markers and for the associated multi-marker haplotypes centered at the reference marker and defined by the sliding windows (W) of specified sizes (nominal p-values are displayed).

Table 13: Genome Wide Association Scan (GWAS) results for cases having a risk event in the THBS1/FSIP1 locus. CR: Candidate Region; B36 Position: NCBI Build 36 location in base pairs (bp); RS#: dbSNP data base (NCBI) reference number; SEQ. ID. NO.: unique numerical identifier for this patent application; - Iog10 p- values: - Iog10 of the p-value for the association to the disease from the GWAS for single SNP markers and for the associated multi-marker haplotypes centered at the reference marker and defined by the sliding windows (W) of specified sizes (nominal p-values are displayed).

Table 14: Genome Wide Association Scan (GWAS) results for cases having a protective event in the APOE locus. CR: Candidate Region; B36 Position: NCBI Build 36 location in base pairs (bp); RS#: dbSNP data base (NCBI) reference number; SEQ. ID. NO.: unique numerical identifier for this patent application; - Iog10 p-values: - Iog10 of the p-value for the association to the disease from the GWAS for single SNP markers and for the associated multi-marker haplotypes centered at the reference marker and defined by the sliding windows (W) of specified sizes (nominal p-values are displayed).

Table 15: Genome Wide Association Scan (GWAS) results for cases having a protective event in the HIVEP3 locus. CR: Candidate Region; B36 Position: NCBI Build 36 location in base pairs (bp); RS#: dbSNP data base (NCBI) reference number; SEQ. ID. NO.: unique numerical identifier for this patent application; - Iog10 p-values: - Iog10 of the p-value for the association to the disease from the GWAS for single SNP markers and for the associated multi-marker haplotypes centered at the reference marker and defined by the sliding windows (W) of specified sizes (nominal p-values are displayed).

Table 16: Genome Wide Association Scan (GWAS) results in cases having a risk event in the HIVEP3 locus. CR: Candidate Region; B36 Position: NCBI Build 36 location in base pairs (bp); RS#: dbSNP data base (NCBI) reference number; SEQ. ID. NO.: unique numerical identifier for this patent application; - Iog10 p-values: - Iog10 of the p-value for the association to the disease from the GWAS for single SNP markers and for the associated multi-marker haplotypes centered at the reference marker and defined by the sliding windows (W) of specified sizes (nominal p-values are displayed).

Table 17: Genome Wide Association Scan (GWAS) results for cases having a protective event in the ACTN2 locus. CR: Candidate Region; B36 Position: NCBI Build 36 location in base pairs (bp); RS#: dbSNP data base (NCBI) reference number; SEQ. ID. NO.: unique numerical identifier for this patent application; - Iog10 p-values: - Iog10 of the p-value for the association to the disease from the GWAS for single SNP markers and for the associated multi-marker haplotypes centered at the reference marker and defined by the sliding windows (W) of specified sizes (nominal p-values are displayed).

Table 18: Genome Wide Association Scan (GWAS) results in cases having a protective event in the ACTN2 locus. CR: Candidate Region; B36 Position: NCBI Build 36 location in base pairs (bp); RS#: dbSNP data base (NCBI) reference number; SEQ. ID. NO.: unique numerical identifier for this patent application; - Iog10 p-values: - Iog10 of the p-value for the association to the disease from the GWAS for single SNP markers and for the associated multi-marker haplotypes centered at the reference marker and defined by the sliding windows (W) of specified sizes (nominal p-values are displayed).

Table 19: Genome Wide Association Scan (GWAS) results in cases having a protective event in the ITGB8 locus. CR: Candidate Region; B36 Position: NCBI Build 36 location in base pairs (bp); RS#: dbSNP data base (NCBI) reference number; SEQ. ID. NO.: unique numerical identifier for this patent application; - Iog10 p-values: - Iog10 of the p-value for the association to the disease from the GWAS for single SNP markers and for the associated multi-marker haplotypes centered at the reference marker and defined by the sliding windows (W) of specified sizes (nominal p-values are displayed).

Table 20: Genome Wide Association Scan (GWAS) results in cases having a risk event in the ITGB8 locus. CR: Candidate Region; B36 Position: NCBI Build 36 location in base pairs (bp); RS#: dbSNP data base (NCBI) reference number; SEQ. ID. NO.: unique numerical identifier for this patent application; - Iog10 p-values: - Iog10 of the p-value for the association to the disease from the GWAS for single SNP markers and for the associated multi-marker haplotypes centered at the reference marker and defined by the sliding windows (W) of specified sizes (nominal p-values are displayed).

Table 21 : Genome Wide Association Scan (GWAS) results in subjects having a risk event in the ITGB8 locus. CR: Candidate Region; B36 Position: NCBI Build 36 location in base pairs (bp); RS#: dbSNP data base (NCBI) reference number; SEQ. ID. NO.: unique numerical identifier for this patent application; - Iog10 p-values: - Iog10 of the p-value for the association to the disease from the GWAS for single SNP markers and for the associated multi-marker haplotypes centered at the reference marker and defined by the sliding windows (W) of specified sizes (nominal p-values are displayed).

Table 22: Genome Wide Association Scan (GWAS) results. CR: Candidate Region; B36 Position: NCBI Build 36 location in base pairs (bp); RS#: dbSNP data base (NCBI) reference number; SEQ. ID. NO.: unique numerical identifier for this patent application; - Iog10 p-values: - Iog10 of the p-value for the association to the disease from the GWAS for single SNP markers and for the associated multi-marker haplotypes centered at the reference marker and defined by the sliding windows (W) of specified sizes (nominal p-values are displayed).

Table 23: Genome Wide Association Scan (GWAS) results. CR: Candidate Region; B36 Position: NCBI Build 36 location in base pairs (bp); RS#: dbSNP data base (NCBI) reference number; SEQ. ID. NO.: unique numerical identifier for this patent application; - Iog10 p-values: - Iog10 of the p-value for the association to the disease from the GWAS for single SNP markers and for the associated multi-marker haplotypes centered at the reference marker and defined by the sliding windows (W) of specified sizes (nominal p-values are displayed).

Table 24a: List of significantly associated haplotypes from the Genome Wide Association Scan (GWAS) analyses. Individual haplotypes with associated relative risks are presented in each row of the table; these values were extracted from the associated marker haplotype window with the most significant p-value for each SNP in the corresponding p-values table results. CR: Candidate Region; Code: coded haplotype using 1 s and 2s for the different SNP alleles that compose the haplotype; Nucl.: specific nucleotides for the individual SNP alleles contributing to the haplotype; Case: number of case alleles for this haplotype; Control: number of control alleles for this haplotype; Total-Case: total number of case alleles for this haplotype; Total-Control: total number of control alleles with that haplotype; OR: odds ratio. The remainder of the columns lists the SEQ. ID. NOs of the SNPs contributing to the haplotype and their relative location compared with the central marker. Central marker (O): SEQ. ID. NO. for the central marker. Flanking markers are identified by minus (-) or plus (+) signs.

Table 24b: List of significantly associated haplotypes from the Genome Wide Association Scan (GWAS) analyses. Individual haplotypes with associated relative risks are presented in each row of the table; these values were extracted from the associated marker haplotype window with the most significant p-value for each SNP in the corresponding p-values table results. CR: Candidate Region; Code: coded haplotype using 1 s and 2s for the different SNP alleles that compose the haplotype; Nucl.: specific nucleotides for the individual SNP alleles contributing to the haplotype; Case: number of case alleles for this haplotype; Control: number of control alleles for this haplotype; Total-Case: total number of case alleles for this haplotype; Total-Control: total number of control alleles with that haplotype; OR: odds ratio. The remainder of the columns lists the SEQ. ID. NOs of the SNPs contributing to the haplotype and their relative location compared with the central marker. Central marker (O): SEQ. ID. NO. for the central marker. Flanking markers are identified by minus (-) or plus (+) signs.

Table 25: List of significantly associated haplotypes from the Genome Wide Association Scan (GWAS) analyses for definitive cases of Alzheimer's. Individual haplotypes with associated relative risks are presented in each row of the table; these values were extracted from the associated marker haplotype window with the most significant p-value for each SNP in the corresponding p-values table results. CR: Candidate Region; Code: coded haplotype using 1 s and 2s for the different SNP alleles that compose the haplotype; Nucl.: specific nucleotides for the individual SNP alleles contributing to the haplotype; Case: number of case alleles for this haplotype; Control: number of control alleles for this haplotype; TotalCase: total number of case alleles for this haplotype; TotalControl: total number of control alleles with that haplotype; OR: odds ratio. Central marker (0): SEQ. ID. NO. of the central marker of the haplotype.

Table 26: List of significantly associated haplotypes from the Genome Wide Association Scan (GWAS) analyses for cases where onset was observed in subjects aged between 65 and 74 years. Individual haplotypes with associated relative risks are presented in each row of the table; these values were extracted from the associated marker haplotype window with the most significant p- value for each SNP in the corresponding p-values table results. CR: Candidate Region; Code: coded haplotype using 1 s and 2s for the different SNP alleles that compose the haplotype; Nucl.: specific nucleotides for the individual SNP alleles contributing to the haplotype; Case: number of case alleles for this haplotype; Control: number of control alleles for this haplotype; Total-Case: total number of case alleles for this haplotype; Total-Control: total number of control alleles with that haplotype; OR: odds ratio. The remainder of the columns lists the SEQ. ID. NOs of the SNPs contributing to the haplotype and their relative location compared with the central marker. Central marker (O): SEQ. ID. NO. for the central marker. Flanking markers are identified by minus (-) or plus (+) signs.

Table 27: List of significantly associated haplotypes from the Genome Wide Association Scan (GWAS) analyses from cases being considered probable Alzheimer's. Individual haplotypes with associated relative risks are presented in each row of the table; these values were extracted from the associated marker haplotype window with the most significant p-value for each SNP in the corresponding p-values table results. CR: Candidate Region; Code: coded haplotype using 1 s and 2s for the different SNP alleles that compose the haplotype; Nucl.: specific nucleotides for the individual SNP alleles contributing to the haplotype; Case: number of case alleles for this haplotype; Control: number of control alleles for this haplotype; Total-Case: total number of case alleles for this haplotype; Total- Control: total number of control alleles with that haplotype; OR: odds ratio. Central marker (0): SEQ. ID. NO. of the central marker of the haplotype.

Table 28: List of significantly associated haplotypes from the Genome Wide Association Scan (GWAS) analyses for male cases. Individual haplotypes with associated relative risks are presented in each row of the table; these values were extracted from the associated marker haplotype window with the most significant p- value for each SNP in the corresponding p-values table results. CR: Candidate Region; Code: coded haplotype using 1 s and 2s for the different SNP alleles that compose the haplotype; Nucl.: specific nucleotides for the individual SNP alleles contributing to the haplotype; Case: number of case alleles for this haplotype; Control: number of control alleles for this haplotype; Total-Case: total number of case alleles for this haplotype; Total-Control: total number of control alleles with that haplotype; OR: odds ratio. Central marker (0): SEQ. ID. NO. of the central marker of the haplotype.

Table 29: List of significantly associated haplotypes from the Genome Wide Association Scan (GWAS) analyses from female cases. Individual haplotypes with associated relative risks are presented in each row of the table; these values were extracted from the associated marker haplotype window with the most significant p-value for each SNP in the corresponding p-values table results. CR: Candidate Region; Code: coded haplotype using 1 s and 2s for the different SNP alleles that compose the haplotype; Nucl.: specific nucleotides for the individual SNP alleles contributing to the haplotype; Case: number of case alleles for this haplotype; Control: number of control alleles for this haplotype; Total-Case: total number of case alleles for this haplotype; Total-Control: total number of control alleles with that haplotype; OR: odds ratio. The remainder of the columns lists the SEQ. ID. NOs of the SNPs contributing to the haplotype and their relative location compared with the central marker. Central marker (O): SEQ. ID. NO. for the central marker. Flanking markers are identified by minus (-) or plus (+) signs.

Table 30: List of significantly associated haplotypes from the Genome Wide Association Scan (GWAS) analyses for cases having a protective event in the PDCD1 LG2 locus. Individual haplotypes with associated relative risks are presented in each row of the table; these values were extracted from the associated marker haplotype window with the most significant p-value for each SNP in the corresponding p-values table results. CR: Candidate Region; Code: coded haplotype using 1 s and 2s for the different SNP alleles that compose the haplotype; Nucl.: specific nucleotides for the individual SNP alleles contributing to the haplotype; Case: number of case alleles for this haplotype; Control: number of control alleles for this haplotype; Total-Case: total number of case alleles for this haplotype; Total- Control: total number of control alleles with that haplotype; OR: odds ratio. Central marker (0): SEQ. ID. NO. of the central marker of the haplotype.

Table 31 : List of significantly associated haplotypes from the Genome Wide Association Scan (GWAS) analyses for cases having a risk event in the PDCD1 LG2 locus. Individual haplotypes with associated relative risks are presented in each row of the table; these values were extracted from the associated marker haplotype window with the most significant p-value for each SNP in the corresponding p-values table results. CR: Candidate Region; Code: coded haplotype using 1 s and 2s for the different SNP alleles that compose the haplotype; Nucl.: specific nucleotides for the individual SNP alleles contributing to the haplotype; Case: number of case alleles for this haplotype; Control: number of control alleles for this haplotype; Total-Case: total number of case alleles for this haplotype; Total-Control: total number of control alleles with that haplotype; OR: odds ratio. The remainder of the columns lists the SEQ. ID. NOs of the SNPs contributing to the haplotype and their relative location compared with the central marker. Central marker (O): SEQ. ID. NO. for the central marker. Flanking markers are identified by minus (-) or plus (+) signs.

Table 32: List of significantly associated haplotypes from the Genome Wide Association Scan (GWAS) analyses for cases having a risk event at the THBS1/FSIP1 locus. Individual haplotypes with associated relative risks are presented in each row of the table; these values were extracted from the associated marker haplotype window with the most significant p-value for each SNP in the corresponding p-values table results. CR: Candidate Region; Code: coded haplotype using 1 s and 2s for the different SNP alleles that compose the haplotype; Nucl.: specific nucleotides for the individual SNP alleles contributing to the haplotype; Case: number of case alleles for this haplotype; Control: number of control alleles for this haplotype; Total-Case: total number of case alleles for this haplotype; Total-Control: total number of control alleles with that haplotype; OR: odds ratio. The remainder of the columns lists the SEQ. ID. NOs of the SNPs contributing to the haplotype and their relative location compared with the central marker. Central marker (O): SEQ. ID. NO. for the central marker. Flanking markers are identified by minus (-) or plus (+) signs.

Table 33: List of significantly associated haplotypes from the Genome Wide Association Scan (GWAS) analyses for cases having a protective event at the APOE locus. Individual haplotypes with associated relative risks are presented in each row of the table; these values were extracted from the associated marker haplotype window with the most significant p-value for each SNP in the corresponding p-values table results. CR: Candidate Region; Code: coded haplotype using 1 s and 2s for the different SNP alleles that compose the haplotype; Nucl.: specific nucleotides for the individual SNP alleles contributing to the haplotype; Case: number of case alleles for this haplotype; Control: number of control alleles for this haplotype; Total-Case: total number of case alleles for this haplotype; Total-Control: total number of control alleles with that haplotype; OR: odds ratio. The remainder of the columns lists

th e SEQ. ID. NOs of the SNPs contributing to the haplotype and their relative location compared with the central marker. Central marker (O): SEQ. ID. NO. for the central marker. Flanking markers are identified by minus (-) or plus (+) signs.

-

360 1222122 TCCCTCC 14 664 664 1.77 8807 8808 8809 8810 8811 8812 8813

360 112121222 ATCTCTCGC 107 79 664 664 1.42 8808 8809 8810 8811 8812 8813 8814 8815 8816

360 221121112 CCATCTTAC 65 48 664 664 1.39 8808 8809 8810 8811 8812 8813 8814 8815 8816

360 212211222 CTCCTTCGC 664 664 3.53 8808 8809 8810 8811 8812 8813 8814 8815 8816

360 222122122 CCCTCCTGC 14 664 664 1.77 8808 8809 8810 8811 8812 8813 8814 8815 8816

360 221212122 CCACTCTGC 219 274 664 664 0.70 8808 8809 8810 8811 8812 8813 8814 8815 8816

360 222121122 CCCTCTTGC 17 30 664 664 0.56 8808 8809 8810 8811 8812 8813 8814 8815 8816

360 221121222 CCATCTCGC 24 10 664 664 2.45 8808 8809 8810 8811 8812 8813 8814 8815 8816

360 2121122 CTCTTGC 17 34 664 664 0.49 8810 8811 8812 8813 8814 8815 8816

360 2211222 CCTTCGC 664 664 3.53 8810 8811 8812 8813 8814 8815 8816

360 1212122 ACTCTGC 219 275 664 664 0.70 8810 8811 8812 8813 8814 8815 8816

360 1121112 ATCTTAC 66 47 664 664 1.45 8810 8811 8812 8813 8814 8815 8816

360 2121222 CTCTCGC 192 167 664 664 1.21 8810 8811 8812 8813 8814 8815 8816

360 1121222 ATCTCGC 24 10 664 664 2.45 8810 8811 8812 8813 8814 8815 8816

360 22211 GCGTA 23 36 664 664 0.63 8815 8816 8817 8818 8819

360 12211 ACGTA 16 664 664 2.71 8815 8816 8817 8818 8819

361 212 GTG 54 36 666 666 1.54 8820 8821 8822

361 222 GCG 36 61 666 666 0.57 8820 8821 8822

361 22211 GCGTT 36 61 666 666 0.57 8820 8821 8822 8823 8824

361 21211 GTGTT 47 22 666 666 2.22 8820 8821 8822 8823 8824

361 21212 GTGTG 14 666 666 0.49 8820 8821 8822 8823 8824

361 221211222 GCACTTGCC 666 666 4.02 8820 8821 8822 8823 8824 8825 8826 8827 8828

361 212111221 GTGTTTGCT 26 13 666 666 2.04 8820 8821 8822 8823 8824 8825 8826 8827 8828

361 122111221 ACGTTTGCT 666 666 0.33 8820 8821 8822 8823 8824 8825 8826 8827 8828

361 112112111 ATGTTCAAT 43 79 666 666 0.51 8820 8821 8822 8823 8824 8825 8826 8827 8828

361 212121222 GTGTGTGCC 666 666 0.33 8820 8821 8822 8823 8824 8825 8826 8827 8828

361 112111111 ATGTTTAAT 38 17 666 666 2.31 8820 8821 8822 8823 8824 8825 8826 8827 8828

361 222111121 GCGTTTACT 38 66 666 666 0.55 8820 8821 8822 8823 8824 8825 8826 8827 8828

361 112111122 ATGTTTACC 39 24 666 666 1.66 8820 8821 8822 8823 8824 8825 8826 8827 8828

361 212111111 GTGTTTAAT 19 666 666 3.23 8820 8821 8822 8823 8824 8825 8826 8827 8828

Table 34: List of significantly associated haplotypes from the Genome Wide Association Scan (GWAS) analysesfor cases having a protective event at the HIVEP3 locus. Individual haplotypes with associated relative risks are presented in each row of the table; these values were extracted from the associated marker haplotype window with the most significant p-value for each SNP in the corresponding p-values table results. CR: Candidate Region; Code: coded haplotype using 1 s and 2s for the different SNP alleles that compose the haplotype; Nucl.: specific nucleotides for the individual SNP alleles contributing to the haplotype; Case: number of case alleles for this haplotype; Control: number of control alleles for this haplotype; Total-Case: total number of case alleles for this haplotype; Total-Control: total number of control alleles with that haplotype; OR: odds ratio. The remainder of the columns lists the SEQ. ID. NOs of the SNPs contributing to the haplotype and their relative location compared with the central marker. Central marker (O): SEQ. ID. NO. for the central marker. Flanking markers are identified by minus (-) or plus (+) signs.

Table 35: List of significantly associated haplotypes from the Genome Wide Association Scan (GWAS) analyses for cases having a risk event at the HIVEP3 locus . Individual haplotypes with associated relative risks are presented in each row of the table; these values were extracted from the associated marker haplotype window with the most significant p-value for each SNP in the corresponding p-values table results. CR: Candidate Region; Code: coded haplotype using 1 s and 2s for the different SNP alleles that compose the haplotype; Nucl.: specific nucleotides for the individual SNP alleles contributing to the haplotype; Case: number of case alleles for this haplotype; Control: number of control alleles for this haplotype; Total-Case: total number of case alleles for this haplotype; Total-Control: total number of control alleles with that haplotype; OR: odds ratio. The remainder of the columns lists the SEQ. ID. NOs of the SNPs contributing to the haplotype and their relative location compared with the central marker. Central marker (0): SEQ. ID. NO. for the central marker. Flanking markers are identified by minus (-) or plus (+) signs.

Table 36: List of significantly associated haplotypes from the Genome Wide Association Scan (GWAS) analyses for cases having a protective event at the ACTN2 locus. Individual haplotypes with associated relative risks are presented in each row of the table; these values were extracted from the associated marker haplotype window with the most significant p-value for each SNP in the corresponding p-values table results. CR: Candidate Region; Code: coded haplotype using 1 s and 2s for the different SNP alleles that compose the haplotype; Nucl.: specific nucleotides for the individual SNP alleles contributing to the haplotype; Case: number of case alleles for this haplotype; Control: number of control alleles for this haplotype; Total-Case: total number of case alleles for this haplotype; Total- Control: total number of control alleles with that haplotype; OR: odds ratio. Central marker (0): SEQ. ID. NO. of the central marker of the haplotype.

Table 37: List of significantly associated haplotypes from the Genome Wide Association Scan (GWAS) analysesfrom cases having a risk event at the ACTN2 locus. Individual haplotypes with associated relative risks are presented in each row of the table; these values were extracted from the associated marker haplotype window with the most significant p-value for each SNP in the corresponding p-values table results. CR: Candidate Region; Code: coded haplotype using 1 s and 2s for the different SNP alleles that compose the haplotype; Nucl.: specific nucleotides for the individual SNP alleles contributing to the haplotype; Case: number of case alleles for this haplotype; Control: number of control alleles for this haplotype; Total-Case: total number of case alleles for this haplotype; Total-Control: total number of control alleles with that haplotype; OR: odds ratio. The remainder of the columns lists the SEQ. ID. NOs of the SNPs contributing to the haplotype and their relative location compared with the central marker. Central marker (O): SEQ. ID. NO. for the central marker. Flanking markers are identified by minus (-) or plus (+) signs.

-

286 2121211 GAGTGAT 91 133 740 744 0.64 6536 6537 6538 6539 6540 6541 6542

286 222111122 GCGATAACG 740 744 4.04 6538 6539 6540 6541 6542 6543 6544 6545 6546 ^■

286 112222122 ATGGCGACG 49 80 740 744 0.59 6538 6539 6540 6541 6542 6543 6544 6545 6546

286 212111122 GTGATAACG 94 131 740 744 0.68 6538 6539 6540 6541 6542 6543 6544 6545 6546

286 212112111 GTGATGATA 740 744 6.07 6538 6539 6540 6541 6542 6543 6544 6545 6546

286 212121122 GTGACAACG 10 20 740 744 0.50 6538 6539 6540 6541 6542 6543 6544 6545 6546

286 212112121 GTGATGACA 20 740 744 10.31 6538 6539 6540 6541 6542 6543 6544 6545 6546

286 112222121 ATGGCGACA 256 193 740 744 1.51 6538 6539 6540 6541 6542 6543 6544 6545 6546 286 212122222 GTGACGCCG 19 33 740 744 0.57 6538 6539 6540 6541 6542 6543 6544 6545 6546 286 22222 CGCCC 45 21 740 744 2.23 6553 6554 6555 6556 6557 286 21221 CTCCT 40 55 740 744 0.72 6553 6554 6555 6556 6557 286 11112 TTTTC 143 180 740 744 0.75 6553 6554 6555 6556 6557 286 22112 CGTTC 22 10 740 744 2.25 6553 6554 6555 6556 6557

Table 38: List of significantly associated haplotypes from the Genome Wide Association Scan (GWAS) analyses for cases having a protective event at the ITGB8 locus. Individual haplotypes with associated relative risks are presented in each row of the table; these values were extracted from the associated marker haplotype window with the most significant p-value for each SNP in the corresponding p-values table results. CR: Candidate Region; Code: coded haplotype using 1 s and 2s for the different SNP alleles that compose the haplotype; Nucl.: specific nucleotides for the individual SNP alleles contributing to the haplotype; Case: number of case alleles for this haplotype; Control: number of control alleles for this haplotype; Total-Case: total number of case alleles for this haplotype; Total-Control: total number of control alleles with that haplotype; OR: odds ratio. Central marker (0): SEQ. ID. NO. of the central marker of the haplotype.

Table 39: List of significantly associated haplotypes from the Genome Wide Association Scan (GWAS) analyses of cases having a risk event at the ITGB8 locus. Individual haplotypes with associated relative risks are presented in each row of the table; these values were extracted from the associated marker haplotype window with the most significant p-value for each SNP in the corresponding p-values table results. CR: Candidate Region; Code: coded haplotype using 1 s and 2s for the different SNP alleles that compose the haplotype; Nucl.: specific nucleotides for the individual SNP alleles contributing to the haplotype; Case: number of case alleles for this haplotype; Control: number of control alleles for this haplotype; Total-Case: total number of case alleles for this haplotype; Total-Control: total number of control alleles with that haplotype; OR: odds ratio. The remainder of the columns lists the SEQ. ID. NOs of the SNPs contributing to the haplotype and their relative location compared with the central marker. Central marker (O): SEQ. ID. NO. for the central marker. Flanking markers are identified by minus (-) or plus (+) signs.

-

Table 40: List of significantly associated haplotypes from the Genome Wide Association Scan (GWAS) analyses for cases having a risk event at the ITGB8 locus. Individual haplotypes with associated relative risks are presented in each row of the table; these values were extracted from the associated marker haplotype window with the most significant p-value for each SNP in the corresponding p-values table results. CR: Candidate Region; Code: coded haplotype using 1 s and 2s for the different SNP alleles that compose the haplotype; Nucl.: specific nucleotides for the individual SNP alleles contributing to the haplotype; Case: number of case alleles for this haplotype; Control: number of control alleles for this haplotype; Total-Case: total number of case alleles for this haplotype; Total-Control: total number of control alleles with that haplotype; OR: odds ratio. Central marker (0): SEQ. ID. NO. of the central marker of the haplotype.

-

-

Table 41 : List of significantly associated haplotypes from the Genome Wide Association Scan (GWAS) analyses. Individual haplotypes with associated relative risks are presented in each row of the table; these values were extracted from the associated marker haplotype window with the most significant p-value for each SNP in the corresponding p-values table results. CR: Candidate Region; Code: coded haplotype using 1 s and 2s for the different SNP alleles that compose the haplotype; Nucl.: specific nucleotides for the individual SNP alleles contributing to the haplotype; Case: number of case alleles for this haplotype; Control: number of control alleles for this haplotype; Total-Case: total number of case alleles for this haplotype; Total-Control: total number of control alleles with that haplotype; OR: odds ratio. The remainder of the columns lists the SEQ. ID. NOs of the SNPs contributing to the haplotype and their relative location compared with the central marker. Central marker (O): SEQ. ID. NO. for the central marker. Flanking markers are identified by minus (-) or plus (+) signs.

O

-

O CO

Table 42: List of significantly associated haplotypes from the Genome Wide Association Scan (GWAS) analyses. Individual haplotypes with associated relative risks are presented in each row of the table; these values were extracted from the associated marker haplotype window with the most significant p-value for each SNP in the corresponding p-values table results. CR: Candidate Region; Code: coded haplotype using 1 s and 2s for the different SNP alleles that compose the haplotype; Nucl.: specific nucleotides for the individual SNP alleles contributing to the haplotype; Case: number of case alleles for this haplotype; Control: number of control alleles for this haplotype; Total-Case: total number of case alleles for this haplotype; Total-Control: total number of control alleles with that haplotype; OR: odds ratio. The remainder of the columns lists the SEQ. ID. NOs of the SNPs contributing to the haplotype and their relative location compared with the central marker. Central marker (O): SEQ. ID. NO. for the central marker. Flanking markers are identified by minus (-) or plus (+) signs.

-

o o

While the invention has been described in connection with specific embodiments thereof, it will be understood that it is capable of further modifications and this application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains and as may be applied to the essential features hereinbefore set forth, and as follows in the scope of the appended claims.

Claims

WE CLAIM:

1. A method of diagnosing Alzheimer's disease, the predisposition to Alzheimer's disease, or the progression of Alzheimer's disease in an individual, said method comprising determining, in a sample of the individual, a genetic profile comprising at least one marker in a Candidate Region listed in Table 2, and correlating the genetic profile with a reference profile in order to asses the presence of Alzheimer's disease, the predisposition to Alzheimer's disease, or the progression of Alzheimer's disease in the individual.

2. The method of claim 1 , wherein the at least one marker is a single nucleotide polymorphism (SNPs), an allele, a haplotype or combinations thereof.

3. The method of claim 1 , wherein the sample is at least one of blood and a brain biopsy.

4. The method of claim 1 , wherein the at least one marker has a skewed genotype distribution towards individuals diagnosed, predisposed or afflicted with the Alzheimer's disease when compared to control individuals.

5. The method of claim 4, wherein the at least one marker is associated with a risk event in at least one of the following loci: PDCD1 LG2, THBS1/FSIP1 , HIVEP3, ACTN2 and ITGB8.

6. The method of claim 1 , wherein the at least one marker has a skewed genotype distribution towards control individuals when compared to individuals diagnosed, predisposed or afflicted with the Alzheimer's disease.

7. The method of claim 6, wherein the at least one marker is associated with a protective event in at least one of the following loci: PDCD1 LG2, APOE, HIVEP3, ACTN2 and ITGB8.

8. The method of claim 1 , wherein said determination comprises assessing the genomic nucleic acid sequence of the at least one marker.

9. The method of claim 1 , wherein said determination comprises assessing the amount, concentration, splicing pattern and/or a nucleic acid sequence of a transcript expressed by a gene comprising the at least one marker.

10. The method of claim 1 , wherein said determination comprises assessing the amount, concentration, amino acid sequence and/or biological activity of a polypeptide encoded by a transcript expressed by a gene comprising the at least one marker.

1 1. The method of claim 10, wherein the amount, concentration, amino acid sequence and/or biological activity of the polypeptide is modulated by the presence of a splicing variant of the transcript.

12. The method of claim 1 , wherein the individual presents at least one of the following subphenotype: definite diagnosis, age of onset between 65 and 74 years, probable diagnosis, male subject and female subject.

13. A method of predicting the response to an agent useful in the treatment of Alzheimer's disease in an individual predisposed to Alzheimer's disease or diagnosed with Alzheimer's disease, said method comprising:

i. determining, in a sample of the individual, a genetic profile comprising at least one a marker in a Candidate Region listed in Table 2; and

ii. correlating the genetic profile with a reference genetic profile to assess the response to the agent in the individual.

14. The method of claim 13, further comprising administering an effective amount of the agent to the individual if the profile is correlated with a positive response to the agent or with the absence of a negative response to the agent.

15. The method of claim 13, further comprising including the individual in a preclinical or clinical trial for the agent if the profile is correlated with a positive response to the agent or a lack of a negative response to the agent.

16. The method of claim 13, wherein the at least one marker is a single nucleotide polymorphism (SNPs), an allele, a haplotype or combinations thereof.

17. The method of claim 13, wherein the sample is at least one of blood and a brain biopsy.

18. The method of claim 13, wherein said determination comprises assessing the genomic nucleic acid sequence of the at least one marker.

19. The method of claim 13, wherein said determination comprises assessing the amount, concentration, splicing and/or nucleic acid sequence of a transcript expressed by a gene comprising the at least one marker.

20. The method of claim 13, wherein said determination comprises assessing the amount, concentration, amino acid sequence and/or biological activity of a polypeptide encoded by a transcript expressed by a gene comprising the at least one marker.

21. The method of claim 20, wherein the amount, concentration, amino acid sequence and/or biological activity of the polypeptide is modulated by the expression of a splicing variant of the transcript.

22. A method of screening for an agent for the treatment of Alzheimer's disease, said method comprising:

i. contacting the agent with a polypeptide encoded by a gene located in a Candidate Region listed in Table 2, a transcript encoding said polypeptide and/or the gene expressing said transcript, and

ii. determining if the agent modulates the activity of the polypeptide, the expression of the gene, the stability of the transcript and/or the splicing of the transcript; wherein the modulation of the activity of the polypeptide, the expression of the gene, the stability of the transcript and/or the splicing of the transcript is indicative that the agent is useful in the treatment of Alzheimer's disease.

23. The method of claim 22, wherein the contacting is in a cell.

24. The method of claim 22, wherein the cell is in a non-human animal.

25. The method of claim 22, wherein the gene is listed in Table 3.

26.A method of treating Alzheimer's disease in an individual in need thereof, said method comprising administering an agent capable of modulating the expression of a gene located in a Candidate Region listed in Table 2, the stability of a transcript of the gene, the splicing of a transcript of the gene and/or the activity of a polypeptide encoded by the transcript, thereby treating Alzheimer's disease in the individual.

27. The method of claim 26, wherein the agent has been identified by the method of claim 22.

28. The method of claim 26, wherein the individual has a genetic profile comprising at least one marker in a Candidate Region listed in Table 2, wherein said genetic profile is associated with a predisposition to or a diagnosis of Alzheimer's disease.

29. The method of claim 28, wherein the at least one marker is associated with a risk event in at least one of the following loci: PDCD1 LG2, THBS1/FSIP1 , HIVEP3, ACTN2 and ITGB8.

30. The method of claim 26, wherein the individual has a genetic profile comprising at least one marker in a Candidate Region listed in Table 2, wherein said genetic profile is associated with a positive response to the agent or a lack of negative response to the agent.

31. A method of treating Alzheimer's disease in an individual in need thereof, said method comprising: i. determining, in a sample from the individual, a genetic profile comprising at least one marker located in a Candidate Region listed in Table 2;

ii. correlating the genetic profile with a reference genetic profile to assess if the individual is associated with a positive response to an agent or a negative response to the agent, wherein the agent is useful in the treatment of Alzheimer's disease;

iii. administering the agent to the individual having the genetic profile associated with the positive response to the agent or lacking the genetic profile associated with the negative response to the agent.

32. The method of claim 31 , further comprising including the individual in a preclinical or clinical trial for the agent if the profile is correlated with the positive response to the agent or with the absence of negative response to the agent.

33. The method of claim 31 , wherein the at least one marker is a single nucleotide polymorphisms (SNPs), an allele, an haplotype or combinations thereof.

34. The method of claim 31 , wherein the sample is at least one of blood and a brain biopsy.

35. The method of claim 31 , wherein said determination comprises assessing the genomic nucleic acid sequence of the marker.

36. The method of claim 31 , wherein said determination comprises assessing the amount, concentration, splicing and/or nucleic acid sequence of a transcript expressed by a gene comprising the at least one marker.

37. The method of claim 31 , wherein said determination comprises assessing the amount, concentration, amino acid sequence and/or biological activity of a polypeptide encoded by a transcript expressed by a gene comprising the at least one marker.

38. The method of claim 37, wherein the amount, concentration, amino acid sequence and/or biological activity of the polypeptide is modulated by the expression of a splicing variant of the transcript.

39.A method of stratifying a group of individuals, said method comprising:

i. for each individual, determining, in a sample of the individual, a genetic profile comprising at least one marker located in a Candidate Region listed in Table 2; and

ii. dividing the group of individuals into subgroups of individuals having the genetic profile comprising the at least one marker or having the genetic profile lacking the at least one marker.

40. The method of claim 39, wherein the subgroup of individuals have the genetic profile comprising at least one marker having a skewed genotype distribution towards individuals diagnosed, predisposed or afflicted with the Alzheimer's disease when compared to control individuals.

41. The method of claim 40, wherein the at least one marker is associated with a risk event in at least one of the following loci: PDCD1 LG2, THBS1/FSIP1 , HIVEP3, ACTN2 and ITGB8.

42. The method of claim 39, wherein the subgroup of individuals have the genetic profile comprising at least one marker having a skewed genotype distribution towards control individuals when compared to individuals diagnosed, predisposed or afflicted with the Alzheimer's disease.

43. The method of claim 42, wherein the at least one marker is associated with a protective event in at least one of the following loci: PDCD1 LG2, APOE, HIVEP3, ACTN2 and ITGB8.

44. The method of claim 39, wherein the subgroup of individuals have the genetic profile comprising at least one marker having a skewed genotype distribution towards individuals responding positively to an agent useful for the treatment Alzheimer's disease when compared to individuals not responding or responding negatively to the agent.

45. The method of claim 39, wherein the subgroup of individuals have the genetic profile comprising at least one marker having a skewed genotype distribution towards to individuals not responding or responding negatively an agent useful for the treatment Alzheimer's disease when compared to individuals responding positively to the agent.

46. The method of claim 39, wherein one subgroup of individuals is included or excluded from a pre-clinical or a clinical trial for an agent useful in the treatment of Alzheimer's disease.

47. The method of claim 39, wherein, within a subgroup, the individuals have similar phenotypic or subphenotypic traits associated with Alzheimer's disease.

48. The method of claim 39, wherein the at least one marker is a single nucleotide polymorphisms (SNPs), an allele, a haplotype or combinations thereof.

49. The method of claim 39, wherein the sample is at least one of blood or a brain biopsy.

50. The method of claim 39, wherein said determination comprises assessing the genomic nucleic acid sequence of the at least one marker.

51. The method of claim 39, wherein said determination comprises assessing the amount, concentration, splicing and/or nucleic acid sequence of a transcript expressed by a gene comprising the at least one marker.

52. The method of claim 39, wherein said determination comprises assessing the amount, concentration, amino acid sequence and/or biological activity of a polypeptide encoded by a transcript expressed by a gene comprising the at least one marker.

53. The method of claim 52, wherein the amount, concentration, amino acid sequence and/or biological activity of the polypeptide is modulated by the expression of a splicing variant of the nucleic acid.

54. Use of an agent capable of modulating the expression of a gene located in a Candidate Region listed in Table 2, the stability of a transcript of the gene, the splicing of the transcript of the gene and/or the activity of a polypeptide encoded by the transcript of the gene, for the treatment of Alzheimer's disease in an individual.

55. Use of an agent capable of modulating the expression of a gene located in a Candidate Region listed Table 2, the stability of a transcript of the gene, the splicing of the transcript of the gene and/or the activity of a polypeptide encoded by the transcript of the gene, for the manufacture of a medicament for the treatment of Alzheimer's disease in an individual.

56. The use of claim 54 or 55, wherein the agent has been identified by the method of claim 22.

57. Use of a genetic profile from an individual for the treatment of Alzheimer's disease with an agent useful in the treatment of Alzheimer's disease, wherein said genetic profile comprises at least one marker located in a Candidate Region listed in Table 2 and is associated with a predisposition to or a diagnosis of Alzheimer's disease.

58. Use of a genetic profile from an individual for the treatment of Alzheimer's disease with an agent useful in the treatment of Alzheimer's disease, wherein said genetic profile comprises at least one marker located in a Candidate Region listed in Table 2 and is associated with a positive response to the agent or a lack of negative response to the agent.

59. The use of claim 58, further comprising including the individual in a pre-clinical or clinical trial for the agent.

60. The use of any one of claims 57 to 59, wherein the at least one marker is a single nucleotide polymorphisms (SNPs), an allele, an haplotype or a combination thereof.

61. The use of any one of claims 57 to 60, wherein the genetic profile is determined by assessing the genomic nucleic acid sequence of the at least one marker.

62. The use of any one of claims 57 to 60, wherein the genetic profile is determined by assessing the amount, concentration, splicing and/or nucleic acid sequence of a transcript expressed by a gene comprising the at least one marker.

63. The use of any one of claims 57 to 60, wherein the genetic profile is determined by assessing the amount, concentration, amino acid sequence and/or biological activity of a polypeptide encoded by a transcript expressed by a gene comprising the at least one marker.

64. The use of claim 63, wherein the amount, concentration, amino acid sequence and/or biological activity of the polypeptide is modulated by the expression of a splicing variant of the transcript.