Filters and Validation Testing

We used a new multinomial test to determine likelihood that a mutation was de novo. We also used a chi-square test to exclude loci that did not fit a simple germline model, and we excluded sites that were polymorphic or noisy over the population. We established thresholds for these tests and used additional microassembly criteria, comprising our filters for counting candidate events. We sampled calls for experimental validation testing to determine our false positive rate.

Because the vast majority of false positives originate from the chance undersampling of one parental allele, we made an empirical choice of likelihood thresholds that diminished the frequency with which known polymorphic loci in the population appeared as “de novo” mutations in the children (see Figure S1 available online). These thresholds define part of our “SNV filter.” For each indel call, we also used de Bruijn graph microassembly as a filter (Pevzner et al., 2001) of reads possibly covering candidate regions in each of the family members.

For validation testing, we designed barcoded primers from the reference genome for each mutation examined, individually PCR-amplified DNA from each family member for the locus, pooled by family relation to the proband, made libraries and sequenced pooled products (Experimental Procedures). Validation tests succeeded or failed, and if they succeeded, the results either confirmed or falsified the calls. A summary of results is found in Table 1, for SNVs and indels. The detailed results (including counts) are in Tables S1 and S3. We validated in three batches, each time blind to the gene or affected status. In the first batch, we selected from the SNV data available, picking random calls passing filter. In the other two batches, we focused on indels and nonsense mutations. In all three batches, we tested a few calls close to passing but excluded by our filters.

We sought to produce a list of autism candidates with as few false positives as possible and to be able to make the strongest statistical evaluation of the differential rates of de novo mutation between affecteds and siblings. We confirmed all 137 calls passing filters that we successfully tested. We subsequently loosened some and tightened other criteria for indels (the “indel filter”) to capture more events, as described below.

De Novo SNV Rates

We first investigated de novo SNVs. We counted 754 candidate de novo events passing our SNV filter (summarized in Table 2; complete list with details in Table S1). The distribution of events in families closely fit a Poisson model. Events were classified by affected status, gender, location (within exon, splice site, intron, 5′UTR, and 3′UTR) and type of coding mutation (synonymous, missense, or nonsense). The specific position of the mutation and the resulting coding change, if any, are also listed. In all cases examined, microassembly qualitatively validated the de novo SNV calls. Every de novo SNV candidate that passed filter and was successfully tested was confirmed present in the child and absent in the parents (89/89; Table 1 and Table S1).

Because variation in the number of mutations detected could be a function of variable sequence coverage in probands versus siblings, we also determined counts of mutation equalized by high coverage, assessing only regions where the joint coverage was at least 40×. At such high coverage, less than 5% of true de novo SNVs would be missed (as judged by simulations). We then determined the de novo SNV mutation rate by summing the total number of de novo SNVs in these 40× joint regions from all individual children, then dividing by the sum of base pairs within these regions in these children. The rate was 2.0 * 10⁻⁸ (±10⁻⁹) per base pair, or about 120 mutations per diploid genome per generation (6 * 10⁹ * 2 * 10⁻⁸), consistent with a range of estimates obtained by others (Awadalla et al., 2010; Conrad et al., 2011).

Table 2 contains a summary of our findings. The number of de novo SNVs only in probands versus the number only in their siblings is not significantly different than expected from the null hypothesis of equal rates between probands and siblings, whether counting all SNVs (380 versus 364), synonymous (79 versus 69), or missense (207 versus 207). Ten de novo variants occurred in both proband and sibling. The balance does not change if we examine only regions of joint coverage ≥40×. Applying additional filters for amino acid substitutions (conservative versus nonconservative) or genes expressed in brain also did not substantively change this conclusion (Table S1). However, this study lacks the statistical power to reject the hypothesis that missense or synonymous mutations make a major contribution (see Discussion).

We did see a differential signal when comparing the numbers of nonsense mutations (19 versus 9) and point mutations that alter splice sites (6 versus 3). Such mutations could reasonably be expected to disrupt protein function, and in the following we refer to such mutations as ‘likely gene disruptions’ (LGD). The LGD targets and the specifics of the mutations in the affected population are listed in Table 3, and more details for all children are provided in Table S2. The sum of LGDs caused by point mutation was 25 in affecteds to 12 in siblings, with a p value of 0.047 by the two-sided binomial test. Every nonsense candidate that passed filter and was successfully tested was confirmed present in the child and absent in the parents (21/21; Tables 1 and S1).

De Novo Indel Rates

Further support for the hypothesis that LGDs contribute to ASD comes from counting small insertions or deletions (indels) within coding regions. Small indels were ascertained using a simple protocol. This protocol works best for indels less than six base pairs: we surveyed all reads that required a gap to align to the reference genome, and marked where the gap was placed. After eliminating all gap positions that are common in the population, we again used our SNV filter: multinomial sampling to estimate the likelihood that a gap in the child was not inherited from either parent, and used a chi-square test for a germline model (Experimental Procedures). We set the same thresholds as used for SNVs. Microassembly excluded ten presumptive indel loci as inconsistent, failing either because of low count for confirmatory reads, absence of an indel, or finding the nonreference allele in a parent. For two loci, the sizes of deletions were corrected by microassembly. We tested 49 candidate de novo indels, and all 39 that passed the SNV filter were confirmed. Incidence of indels in families again followed a Poisson model.

It was quite clear from validation testing that many candidate indels excluded by the SNV filter were true positives. There was clearly allele imbalance favoring the reference allele over the indel in the exome sequencing, but this bias was absent in the validation testing (Table S2). Because of the importance of indels, we wished to establish an “indel filter” that diminished false negatives, so we lowered our chi-square stringency (from 10⁻⁴ to 10⁻⁹) and multinomial threshold (from 60 to 30). To guard against false positives resulting from undersampling the parents, we excluded any locus at which the variant allele was seen fewer than six times in the child, or appeared even once in the parents, and insisted on certain lower limits of coverage, all of which was done without respect to affected status (Experimental Procedures). Of the 49 tested loci, 47 passed this new filter and confirmed (Table 1).

With the indel filter, we detected 53 indels in probands and 32 in siblings (p value=0.03). Of these, 32 in probands and 15 in siblings caused frame shifts (p value = 0.02; see Table 4 for summary and Table S3 for complete list). Frame shift mutations, like nonsense and splice mutations, can cause severe disruption of coding capacity and hence we classify them as LGDs. Three more indels (2 in probands and 1 in siblings) are likely to be LGDs, as they either introduce stop codons or disrupt a splice site. It is likely that even in-frame insertions or deletions are more disruptive to a peptide than a mere substitution, and we refer later to an interesting example, but we do not count them as LGDs.

All LGD targets are listed in Table 3, with further details in Table S2. In summary, using our filters for SNVs and indels, we observe 59 LGDs in probands versus 28 in siblings (p value of 0.001).

Gender and Phenotype

The de novo LGD incidence by gender and status can be summarized from Tables 2 and ​and4.4. We observe 9 de novo LGD events in 29 females on the spectrum, and 50 in 314 males. Although only marginally statistically significant (p value = 0.07), the higher incidence in females matches the higher incidence of de novo CNVs seen in females on the spectrum (Levy et al., 2011), and does not reflect a higher rate of de novo mutations in females overall: we detected 12 in 182 female siblings and 16 in 161 male siblings. We observed no significant difference with respect to verbal or nonverbal IQ, or overall severity in children with or without detectable de novo LGDs.

Origin of Mutations

Our data are consistent with a paternal origin for variation of the type we detect. From the original sequencing and validation of our data, we were able to ascertain the parental haplotype for some de novo mutations, i.e., those that were linked to a polymorphism found in only one of the two parents. We found that the father is more frequently the parent of origin than the mother: 50/17 for SNVs and 6/1 for indels (Table S1), with a combined p value of 10⁻⁵. Although this was previously known for SNVs, or at least suspected (Conrad et al., 2011), our results suggest it is true for small indels as well. Because it is implausible that the origin of a parental haplotype should influence its global mutation rate in the child, we conclude that most of the de novo variants passing our filters originated in the parent.

Parental age also appears to play a role in mutation rate, further evidence of the parental origin of the mutations we observe. We divided all the data of de novo SNV mutations from the 40× joint family coverage into three bins nearly equal in base pairs covered, separated by the age of the father at child’s birth, and then counted de novo SNVs in all three bins. The bins spanned fathers from 16.1 to 30.9 (mean of 27.3), 30.9 to 35.9 (mean of 33.4), and 35.9 to 58.0 (mean of 39.6) years old. There was no significant difference in overall SNV rate between probands and siblings; hence, we utilized both children. We measured the counts of de novo mutation in the three bins as 136, 139, and 181, respectively. The hypothesis that the counts for de novo SNVs in children with the youngest fathers and in those with the oldest arose from equal mutation rates has a p value of 0.013. Performing the same computation for mothers, we compute a p value of 0.002.

Our de novo filters are biased against somatic mutation, as our likelihood models are based on germline mutation. However, there are a handful of loci where the evidence is consistent with a somatic origin: diminished ratios of variant to reference allele in the child both in the exome coverage and in the counts from the PCR validation tests. Moreover, in one case where it could be discerned, the maternal haplotype was seen in association with both the variant and reference allele. All these examples, however, are also consistent with multiple copies of the loci in question. Our de novo filters are also biased against mosaicism in the blood of the parent. Nevertheless, we see two examples where deep sequencing of the PCR test revealed the presence of the variant in the parent: one SNV in mother (1,308 counts of reference to 28 counts of the variant) and one indel from the father (15,399 to 79). Not surprisingly, neither variant was observed in the parent in the sparser exome data.

Altogether, with the filters we use, the de novo events we report are largely and perhaps almost entirely germline in origin and this affects our assessment of the contribution of new mutation to autism.

Recurrence and Overlaps with FMRP-Associated Genes

We searched for recurrences and overlaps between the 59 LGD target genes and other gene lists (Tables 3 and ​and5),5), including genes struck by de novo missense or present in de novo CNVs from previous studies. There are no recurrences among our LGD targets (but see Discussion). Given the large number of potential autism target genes, failure to observe overlap in this small list is not surprising. There are two overlaps with the 72 most likely candidate genes from our previous CNV study: NRXN1 and PHF2. The former is considered to be casual for ASD (Ching et al., 2010). A few overlaps of the LGD targets and targets of missense mutations were observed, two in siblings and one in probands, but this is well within random expectation.

By contrast, we saw unexpected overlap between the LGD targets, CNV-derived autism candidate genes and the set of 842 FMRP-associated genes. This last set of genes corresponds to mRNAs whose translation may be controlled by the fragile X mental retardation gene product FMRP (Darnell et al., 2011). Microsatellite expansion in the X-linked FMR1 gene is an established cause of autism spectrum disorders. Significant overlap of the 842 FMRP-associated genes with autism candidate genes has been previously suggested (Darnell et al., 2011). 14 of our 59 LGD targets and 13 of 72 CNV target genes, with one in common, overlap with the 842 FMRP-associated genes. We calculate the p values to be 0.006 and 0.0004, respectively. The first p value is calculated relative to the cumulative gene length of FMRP-associated genes, whereas the second is more related to gene number and is determined by simulation (Experimental Procedures). Altogether, the observation of 26 genes (14 plus 13 minus one in common) out of 129 (59 plus 72 with two in common) overlapping with the 842 FMRP-associated genes has a p value of <10⁻¹³ (calculated on a per-gene basis). In contrast, we see no significant overlap between FMRP-associated genes with LGD targets from siblings (2 of 28) or with de novo missense mutations in probands (22 of 207) or siblings (30 of 207), all squarely within expectation given the size of the FMRP-associated genes. All data on overlaps are summarized in Table 5.

Absent Statistical Signal from Inheritance

This study lacks the power to discover small effects due to inheritance (see Discussion). Nevertheless, we sought evidence for large effects. From 686 parents, we enumerated all rare synonymous, missense, nonsense, and splice site variants in the parents, over a set of well-annotated genes (the set of ~18,000 CCDS genes; Pruitt et al., 2009), and the intersection of that set with candidate genes from previous CNV studies (Gilman et al., 2011; Levy et al., 2011), candidate genes from the present study of de novo LGDs, and all FMRP-associated genes. We considered only rare variants (defined as occurring only once in the population), eliminating the polymorphic variants so that all variants were on an equal footing. We then examined transmission to children, by affected status. We observed no statistically significant transmission bias of either missense or LGDs (nonsense plus splice variants) in any gene set to either probands or siblings. There was, in fact, slightly lower transmission to the affected population than to the siblings (Tables 6A and 6B). None of these statements change if we look specifically at variants carried by the mother.

We examined as well the prevalence of compound heterozygotes of rare LGD variants, where an offspring receives one rare variant from each parent, and again we see no statistically significant difference between probands and unaffected siblings (Table 6C). In this case, however, there is a slight increase in the number of compound heterozygotes of well-annotated genes in probands compared to siblings (242 versus 224).

We specifically examined the possibility of compound heterozygosity in offspring at loci hit by de novo LGDs, caused by transmission of rare missense or LGD mutations. We observed nine such events in probands and twelve in siblings, all but one in each group a combination of the de novo LGD event and a rare missense variant. Thus, there is no differential signal for compound heterozygosity and no evidence that the de novo event in the affected created a homozygous null.

Differential Signal from De Novo Missense and Gene-Disrupting Mutations

Missense mutation should contribute to autism to some degree, as gene function can be severely altered by single-amino-acid substitutions. However, we see no statistical evidence in our work for the hypothesis that de novo missense mutations contribute to autism. The number of de novo missense events we observe is not greater in probands than in siblings. Moreover, the ratio of numbers of missense mutations in probands to siblings is not significantly different than the observed ratio of numbers of synonymous mutations. Even when we filter for genes expressed in brain, count missense mutations that cause nonconservative amino acid changes, or count missense mutations at positions conserved among vertebrates (Table S1, columns BA–BJ), we see no statistical evidence for contribution from this type of mutation. This is also true when we look for overlap of de novo missense mutations with FMRP-associated genes (Table 5). The lack of signal is not attributable to the type of population we study, as we observe de novo copy number imbalance of the expected magnitude in this very same population (Levy et al., 2011; Sanders et al., 2011). But given the size of the population and background mutation rate, we are unable to find signal in the present study. A simple power calculation indicates that we cannot rule out confidently even a 20% contribution to autism from de novo missense mutation. Despite these caveats, it is worth considering that de novo mutation causing merely amino acid substitution may only rarely create a dominant allele of strong effect.

We make a strikingly different observation for mutations that are likely to disrupt gene function. In contrast to de novo missense mutation, we do get signal from de novo mutations likely to severely disrupt coding: mutations at splice sites, nonsense mutations, and small indels, particularly indels that cause frame shifts. We observe 59 likely gene disruptions (LGD) in affected and 28 in siblings, a ratio of two to one. We note that girls on the autistic spectrum have a higher rate (9/29) than boys (50/314), a bias we have previously noted for de novo CNV events. The total contribution from LGD mutations can be estimated as 31 events in 343 families (59 events in probands minus 28 events in siblings), or roughly 10% of affected children.

Germline Origin: Rates, Parental Age, and Paternal Variation

We observed de novo point mutations in children at the rate expected from other studies (Awadalla et al., 2010; Conrad et al., 2011), about 120 point mutations per genome per generation. We observe that the frequency of de novo mutation is dependent on parental age, and know this with a high degree of statistical certainty. This observation is in keeping with, and potentially explains, other studies that have shown increased incidence of certain genetic disorders in the progeny of older parents, including ASD (Saha et al., 2009).

From sequencing adjacent linked polymorphisms in children and parents, we infer that on the order of 3/4 of new point mutations (50 of 67) derive from the father’s germline. Although we have less data, this conclusion holds as well for de novo small indels (6 of 7). These data confirm the paternal line is the main source for these types of new human variation. The data also indicate that the majority of the de novo calls in this study are not somatic in origin, but occur prior to conception. We infer this by assuming that after zygote formation, the mother’s and father’s genomes are equally vulnerable to subsequent somatic mutation. By contrast, a previous study indicated that for de novo copy number variation both parents contribute almost equally (Sanders et al., 2011).

We observe very few cases where two siblings share the same de novo mutation, about one for every fifty occurrences, suggesting that the parent is rarely a broad mosaic. However, this conclusion could be an ascertainment bias, because our operational identification of “de novo” precludes observing the mutation in the parent at levels higher than expected from sequencing error. As presented, we do observe some evidence of parental mosaicism, and this is a subject of ongoing scrutiny using enhanced statistical modeling and validation.

Total Contribution from De Novo Mutation

Finding the correct contribution from each genetic mechanism is critical for understanding the nature of the factors causing autistic spectrum disorders. Adding the 6% differential for large-scale de novo copy number mutation previously observed (Levy et al., 2011; Sanders et al., 2011) to the 10% differential for LGDs, we reach a total differential of 16% between affected children and siblings. This is far less than our predictions, based on modeling the AGRE population (Zhao et al., 2007), that causal de novo mutations would occur in about 50% of the SSC. This gap could be attributable to having modeled a more severely affected population. The SSC is skewed to higher functioning cases with a male to female ratio of 6:1 (Fischbach and Lord, 2010), so there may be more borderline cases in that collection than in the AGRE collection (male to female ratio of 3:1), from which we built our model (Zhao et al., 2007).

But our differential must underestimate the contribution from de novo events. First, we use extremely stringent criteria meant to eliminate false positives, and we fail to detect many true positives as a consequence. Second, even among the de novo events we do observe, we may be missing gene-disruptive events, for example, mutations outside the consensus that disrupt splicing and in-frame indels that disrupt the spacing of the peptide backbone. It would not be unlikely to miss even a 5% differential from de novo missense mutation in a study of this size, given the high background rate of neutral missense mutation. Third, our coverage of the genome is incomplete. Some of this arises by chance, and some is systematic due to the exome capture reagents or errors in the reference genome. Fourth, large classes of mutations are eliminated by our filters, such as those that originate in a parent who is a mosaic, and in children who suffer somatic mutation early after zygote formation. Fifth, there are biases in correctly mapping reads covering regions of the genome that are highly rearranged in the child. Sixth, we have not implemented tools that can reliably detect large indels and rearrangements. Our present tool is efficient only for small indels, less than seven base pairs. Seventh, an entire class of events involving repetitive elements is presently unexplored by us because we currently demand that reads have unique mappings. Eighth, we make calls from only coding regions and thus are not able to assess noncoding events that might affect RNA expression or processing. From all these presently hidden sources, the contribution of de novo mutation could easily double or more.

While there is still a gap between the incidence of de novo gene disrupting events and our expectations from population analysis—especially in males—this gap may yet be filled by deeper coverage, more refined genomic tools, and whole-genome sequencing. Interpretation of a richer data set will undoubtedly require a greater understanding of biology, such as the role for noncoding RNAs and how transcript expression and processing are controlled. By contrast, the differential incidence of de novo mutation in females is very strong, and from CNV and exome sequencing data, runs at nearly twice the differential as in males.

Transmission Genetics and Gene Dosage

We find almost no evidence of a role for transmission genetics. We do not think the present study of only 343 families would display statistical evidence for any of the plausible models of contribution from transmission. Such studies will require greater power, and previous larger copy number studies of the SSC have found such evidence (Levy et al., 2011). There is, however, a weak signal from the increased ratio of compound heterozygotes of rare coding variants in probands to siblings (242 versus 224). This would be consistent with a 5% contribution from this genetic mechanism, but is also consistent with virtually no contribution (p value = 0.4). We can virtually rule out that such events are contributory in more than 20% of children on the spectrum. Fortunately, even a modestly larger study will resolve the strength of contribution from this source.

We do not find evidence of compound heterozygosity at the vast majority of loci where one allele was hit by a disruptive mutation. These events are thus likely to have high impact by altering gene dosage, although we cannot rule out at present that the mutant allele acts by dominant interference.

Individual Vulnerability to New Mutation

Conceptually, any individual of a given genetic lineage has a “vulnerability” to a disorder caused by new mutation in that lineage. We can speak of the “naive genetic lineage” of the zygote as that which is inherited from the grandparents before the action of any mutation acquired during passage through the parental germline. We then define the number of individual vulnerability genes as the number of genes which if disrupted (either in the parental germline or by early somatic mutation after the zygote is formed) will result in the development of the disorder. The size of individual vulnerability is not the same as the target size of autism genes because the former depends on genetic background and future history. Children do not necessarily have the same set of vulnerability genes. The average individual vulnerability over a population can be measured from the ratio of number of de novo LGD events in probands and siblings, as follows.

We will solve for the general case. Assume the rate for a given mutation class in unaffecteds is R, and the rate in probands is AR. In a population of size P, roughly RP mutations of that class will occur, neglecting the small surplus coming from the small number of affected individuals. The number of affected individuals will be P / N, where 1 / N is the incidence in the population. Thus, ARP / N mutations of the class will be found in affecteds. RP / N of these will be present by chance and not contributory, whereas (A – 1)RP / N events are contributory. Thus the proportion of all de novo mutations in a population of size P that contribute to the condition is

S is the probability that a de novo mutation of the particular class will contribute to the condition, and S is a function only of AandN.

If each of G total genes had a uniform probability of being a target for a de novo mutation, and T was the mean number of vulnerability genes per affected, and mutations of the class were completely penetrant, we also have S = T / G, so

Now, for LGD in autism, taking N = 150, A = 2 and G = 25,000, we can compute the average individual vulnerability per child as 167 genes.

This of course is only a crude argument because genes do not have a uniform mutation rate, and not every LGD in a target gene will have complete penetrance. Nevertheless we make note that the size of individual vulnerability appears to be roughly half the target size of all autism genes (see last section of the Discussion).

Candidate Genes

Other than NRXN1, we did not see any genes among the detected de novo LGD targets that had been conclusively linked to ASD (independent of FMR1 association), although CTTNBP2 (encoding a cortactin-binding protein) was suggested as a potential candidate for the autism susceptibility locus (AUTS1) at 7q31 (Cheung et al., 2001). We now provide evidence, based on a de novo 2 bp frame shift deletion, that mutations in CTTNBP2 may cause ASD. In addition, a number of other candidates stood out as being potentially causal due to a combination of provocative expression patterns, known roles in human disease and suggestive mouse mutant phenotypes. Among these were RIMS1, a Ras superfamily member necessary for presynaptic long-term potentiation (Castillo et al., 2002). A targeted Rims1 mutation in the mouse leads to increased postsynaptic density and impaired associative learning as well as memory and cognition deficits (Powell et al., 2004; Schoch et al., 2002), and the frame shift allele we found may lead to a similarly severe condition. Another intriguing candidate was the serine/threonine-specific protein kinase DYRK1A, which is located within the Down syndrome critical region of chromosome 21 and believed to underlie at least some of the pathogenesis of Down syndrome as a consequence of increased dosage. Several reports of likely inactivating mutations in DYRK1A result in symptoms including developmental delay, behavioral problems, impaired speech and mental retardation (Møller et al., 2008; van Bon et al., 2011), and a heterozygous knockout in the mouse also led to developmental delay and increased neuronal densities (Fotaki et al., 2002). Truncating mutations in ZFYVE26 (encoding a zinc finger protein) are known to cause autosomal recessive spastic paraplegia-15, consisting of lower limb spasticity, cognitive deterioration, axonal neuropathy and white matter abnormalities (Hanein et al., 2008). It is possible that a heterozygous truncating mutation such as the de novo frame shift allele found in our study might cause a less severe version of this condition resulting in an ASD diagnosis. Other de novo mutations of interest were a 4 bp deletion in DST (encoding the basement membrane glycoprotein dystonin), which is associated with FMRP (Darnell et al., 2011) and produces a neurodegeneration phenotype when inactivated in the mouse, and a nonsense mutation in ANK2 (an ankyrin protein involved in synaptic stability [Koch et al., 2008]). A nonsense mutation in UNC80 has been linked to control of “slow” neuronal excitability (Lu et al., 2010).

We also note that thirteen of the 59 LGD candidates appear to be involved in either transcription regulation or chromatin remodeling. Among the latter are three proteins involved in epigenetic modification of histones: ASH1L, a histone H3/H4 methyltransferase that activates transcription (Gregory et al., 2007); KDM6B, a histone H3 demethylase implicated in multiple developmental processes (Swigut and Wysocka, 2007), and MLL5, a histone H3 methyltranserase involved in cell lineage determination (Fujiki et al., 2009). These three are also FMRP-associated genes.

Relation of Candidate Genes to FMRP-Associated Genes

Fragile X syndrome (FXS) is one of the most common genetic causes of intellectual disability, with up to 90% of affected children exhibiting autistic symptoms. This has suggested overlaying recent understanding of FXS biology onto candidate ASD genes (Darnell et al., 2011). The FMR1 gene is expressed in neurons and controls the translation of many products. A set of 842 FMRP-associated genes has been enumerated by cross-linking, immunoprecipitation, and high-throughput sequencing (HITS-CLIP), and this set was previously noted to overlap candidate genes from de novo CNVs (Darnell et al., 2011). Hence, we checked the list of FMRP-associated genes with our lists of 59 LGD targets and 72 most likely autism candidate genes from de novo CNVs, and found a remarkable overlap: 14 and 13 with one in common, thus 26/129, with a p value of 10⁻¹³ determined on a per gene basis (842 FMRP-associated genes out of 25,000 genes). This overlap is remarkable because half of the LGD targets should not be ASD related, and probably a similar number of the most likely CNV genes. We found no unusual overlap between the FMRP-associated genes and de novo LGD targets in unaffected siblings, or between FMRP-associated genes and de novo missense targets in either affected or unaffected children.

As a follow-up to this striking observation, we searched for de novo mutations in targets upstream of FMR1 and found an intriguing one: GRM5. It is hit by a deletion that is not a frame shift but removes a single amino acid and causes an additional substitution at the deletion site. GRM5 encodes mGluR5, a glutamate receptor coupled to a G protein (Bear et al., 2004). Defects in mGluR5 compensate for some of the fragile X symptoms in mice (Dölen et al., 2007), and mGluR5 antagonists are currently in clinical trial (Jacquemont et al., 2011).

Lack of LGD Variants in FMRP-Associated Genes in the Population

FMRP has been proposed to inhibit protein translation of certain critical transcripts involved in neuroplasticity, the coordinated sensitization or desensitization of neurons in response to activity. Hence, it is reasonable to suppose that the physiological mechanisms modulated by FMRP depend on protein concentration, which in turn might be sensitive to gene dosage.

Direct support for this idea comes from surveying the entire parental population for carriers of potentially disruptive gene variants. Using a well-annotated set of human genes as controls, FMRP-associated genes are strongly depleted for mutations that affect splicing or introduce stop codons. The statistical significance of the numbers is striking, whether computed as a rate relative to synonymous mutations or on a per gene basis. We see a similar depletion of LGDs in a set of human orthologs of mouse genes that are enriched for essential genes but we do not see this extreme depletion in a set of 250 genes linked to known disabling genetic disorders. This difference may reflect the strong purifying selection in humans against disruptions of even a single allele of genes in this set. The hypothesis that the majority of the FMRP-associated genes are dosage-sensitive requires a more thorough analysis.

Mediators of Neuroplasticity in Cognitive and Behavioral Disorders

FMRP may act as one component of a central regulator of synaptic plasticity, among others such as TSC2 (Darnell et al., 2011; Auerbach et al., 2011). Impairment of its function, or the components it regulates, or other regulators like it, might produce a deficit in human adaptive responses. This study shows these components may be dosage-sensitive targets in autism. By extension, neuroplasticity, the hallmark function of our nervous system that enables learning and adaption in responses to stimulation, might have a general vulnerability to mutation affecting gene dosage. Mediators of neuroplasticity could be searched profitably for involvement in other cognitive disorders.

Exome Capture and Sequencing

Sequence capture was performed with NimbleGen SeqCap EZ Exome v2.0, representing 36.0 Mb (approximately 300,000 exons) of the human genome (hg19 build). We used standard protocols from NimbleGen (http://www.nimblegen.com/products/lit/06403921001.pdf) with minor changes as per published procedures (Hodges et al., 2009). We made additional modifications as follows: 1 µg genomic DNA was sonicated from each individual on a Covaris E210 instrument (300 bp setting). Barcoded sequencing adapters were ligated prior to capture to allow multiplexing of samples. A total of 96 different barcodes were used; eight pools of twelve 8 nt barcodes each were created, and one pool applied to each individual. This allowed us to sequence two families (or 8 individuals) per sequencing lane. Following adaptor ligation, DNAs were purified using 0.4 volumes of AMPure XP beads (Agencourt). DNAs were then amplified for 8 cycles, and family sets (250 ng of DNA from each of 4 individuals) were pooled and captured in the same reaction. Post-capture DNAs were amplified for 15 cycles. Samples were quantitated after pre- and postcapture PCR on the Agilent 2100 Bioanalyzer and diluted to 10 nM concentration prior to loading on sequencing flow cells. All sequencing was performed on the Illumina HiSeq 2000 platform using paired-end 100 bp reads.

Validation

Candidate de novo variants were confirmed via a PCR and pooled highthroughput sequencing procedure. For each event, primers were designed using BatchPrimer3 (http://probes.pw.usda.gov/batchprimer/) according to the following conditions: primers were 18–27 nt in length; amplicons were 300 bp; and the optimal Tm of the primers was 62°C. The specificity of each primer pair was assessed by BLAST against the human genome reference sequence (hg19 build). Primers were synthesized by Integrated DNA Technologies (Coralville, IA, USA). For each of the target mutations (and for only those families of children with the candidate de novo event), we conducted four discrete PCR reactions (corresponding to the four family members). After PCR, we created four product pools, one each for fathers, mothers, probands and unaffected siblings, by combining the respective products from different families. A single adenosine nucleotide was added to the 3′ end of PCR amplicons, followed by ligation of barcoded adapters to each pool of 3′-adenylated products. All pools were then combined and enriched by 8 cycles of PCR. The resulting libraries of pooled validation products were quantitated (Agilent 2100) Bioanalyzer), loaded onto a MiSeq instrument (Illumina), and analyzed by 150 bp paired-end reads. After deconvolution of barcodes and mapping, reads were binned according to genomic location and family member.

Computational Pipeline

Additional detail for each of the following paragraphs is found in the Supplemental Information.

This work was supported by grants from the Simons Foundation (SF51 and SF235988) to M.W. and by a grant from the NIH (5RC2MH090028-02) to M.W. and W.R.M. We are grateful to all of the families at the participating SFARI Simplex Collection (SSC) sites, as well as the principal investigators (A. Beaudet, R. Bernier, J. Constantino, E. Cook, E. Fombonne, D. Geschwind, D. Grice, A. Klin, R. Kochel, D. Ledbetter, C. Lord, C. Martin, D. Martin, R. Maxim, J. Miles, O. Ousley, B. Pelphrey, B. Peterson, J. Piggot, C. Saulnier, M. State, W. Stone, J. Sutcliffe, C. Walsh, and E. Wijsman). The DNA samples used in this work are included within SSC release 13. Approved researchers can obtain the SSC population dataset described in this study by applying at https://base.sfari.org. We also thank Gerald Fischbach, Marian Carlson, Cori Bargmann, Richard Axel, Mark Bear, Catherine Lord, Matthew State, Stephan Sanders, Seungtai Yoon, David Donoho, and Jim Simons for helpful discussions.