In the first chapter of this dissertation, I summarize previously published findings from genome-wide association studies (GWAS) of 172 specific chronic human diseases. Using this data, I estimate the percentage of additional heritability that could be explained by future GWAS using microarrays with improved coverage of germline single nucleotide polymorphisms versus larger sample sizes (more individuals genotyped). In the second chapter, I describe a method for identifying and visualizing genetic pathways that have a significant number of small somatic mutations in human tumor genomes. This method uses random forests to identify the significant pathways and interactions between them, and hive plots to visualize these results. In the third chapter, I describe the results of my analysis of whole genome sequence data from 24 patients with aggressive prostate cancer. I apply the pathway analysis method described above to the small somatic mutations, and look for patterns of copy number aberrations, structural rearrangements, and differential methylation in these tumor genomes compared to patient-matched normal genomes.

Motivation: Prostate cancer remains the most commonly diagnosed neoplasm in American men, with existing biomarkers (i.e. PSA, nomograms, MRI) having varying levels of sensitivity and specificity in identifying more advanced and potentially aggressive disease. Tumor tissue biopsies remain the gold standard for confirming the presence of prostate cancer, as well as evaluating the genomic heterogeneity and clonal architecture that may be predictive of poor outcomes (i.e. recurrence and metastasis). However, tissue biopsies are limited in their ability to comprehensively assess tumors, and may lead to underestimation of disease grade and stage. These hurdles may be overcome with cell-free DNA (cfDNA), which allows for minimally invasive, repeated sampling through blood draws. This is particularly important when tumor tissue is unavailable during active surveillance or disease monitoring for the detection of residual disease or progression. Additionally, genomic interrogation via cfDNA sequencing typically requires prior knowledge of existing mutations from a patient’s tumor. The work presented here leverages a number of methods to ensure broad, yet sensitive detection of cfDNA variants for patients with localized prostate cancer, including sequencing with a machine-learning guided 2.5Mb targeted panel. In this dissertation, I investigate the use of cfDNA concentration, fragment size, and sequencing to identify advanced prostate cancer, as well as detect somatic mutations present in patient-matched tumors.

Methods: The patient cohort included in these studies are composed of 268 individuals: 34 healthy individuals, 112 men with localized prostate cancer who underwent radical prostatectomy (RP), and 122 men with metastatic castration-resistant prostate cancer (mCRPC). Plasma cfDNA concentration and fragment size were quantified with a Qubit fluorometer or Bioanalyzer utilizing a chip-based capillary electrophoresis method for nucleic acid analysis. Low-pass whole-genome and targeted sequencing were used to identify single nucleotide variants (SNVs), small insertions and deletions (indels), and copy number alterations (CNAs) for a subset of patients. Plasma cfDNA was barcoded with duplex Unique Molecular Identifiers (UMIs) to construct consensus reads and improve variant detection by leveraging duplicate reads and sequence complementarity of the two DNA strands. Extensive tissue sampling was used to capture tumor heterogeneity and provide a patient-specific gold standard for comparison of matched cfDNA.

Results and Conclusions: Patients with advanced mCRPC had higher cfDNA concentration than men with localized disease or healthy controls, and those with localized disease had shorter average fragment sizes than controls. Importantly, cfDNA concentration and fragment size remained independent predictors after adjusting for age and PSA. We found that targeted sequencing of cfDNA—without a priori patient-specific tumor mutation information—identified somatic alterations found in matched tumor tissue from multiple regions, potentially allowing for dynamic monitoring of emerging resistant subclones throughout the course of disease. Detection of these concordant variants was associated with seminal vesicle invasion and the number of somatic variants found in the tumor tissue samples, predicating its use for patients with poor prognostic factors in a localized setting. Similar to cfDNA concentration, plasma cfDNA mutational burden was also found to increase with disease severity. The results from our studies demonstrate the ability of cfDNA to identify somatic variants in patients with heterogeneous, localized prostate cancer.

As genotyping has become more affordable, there has been a growing effort to leverage genetic data to improve our understanding of the determinants of disease at the population level. New challenges arise with more widespread use of genetic data in epidemiological studies, from developing statistical methods and study designs that leverage genetic data to answering new bioethical questions around sharing research data with genetic information. This dissertation was comprised of three projects that showcase important topics for epidemiologists to consider when using genetic data. These include developing genetic risk scores, determining the analysis method most suitable for the available data; and understanding the ethical, legal and social implications of widely sharing biobank data, particularly genetic data. The first chapter explores differential genetic susceptibility between aggressive and non-aggressive prostate cancer and whether these differences can be used to develop a polygenic risk score for disease aggressiveness. This study was conducted in a Kaiser Permanente patient population electronic medical record and genotype data. Results from this analysis identified a subset of the known prostate cancer variants that are able to differentiate between aggressive and non-aggressive disease. The second chapter contrasts different gene-environment interaction parameters from various study designs using birth defects as the case example. This review aimed to clarify the options for conducting gene-environment interaction studies in the National Birth Defects Prevention Study. Lastly, the third chapter utilizes qualitative methods to explore the perceived harms and benefits of sharing biobank data, which includes genetic information, from the perspective of several stakeholder groups involved in the development of a Kaiser Permanente biobank, the Research Program on Genes, Environment, and Health. Results from this investigation revealed that although the benefits of data sharing are clear, it is paramount that biobank participants are given the opportunity to be involved in the data governance and/or access process. The lessons learned from this dissertation provide evidence that genetic data allows for exploration of inherited susceptibilities that can influence risk of disease but care must be taken to understand which groups of individuals should be compared and what questions are being answered, both scientifically and ethically.

Preterm birth, defined as birth prior to 37 weeks of gestation, affects approximately 1 in 10 live births and is the leading cause of neonatal morbidity and mortality in the United States. One known risk factor associated with preterm delivery is perinatal exposure to air pollution, and more specifically, fine particulate matter (particulate matter <2.5μm in diameter (PM2.5)).

In this dissertation, I present three retrospective cohort studies investigating the relationship between PM2.5 and preterm birth. The full cohort consisted of all births in the state of California, from 2007 to 2020. Data included maternal sociodemographic characteristics, residential address at the time of delivery, hospital delivery records, and infant characteristics. I combined this cohort data with publicly available data sources, including census data, transportation data, and environmental data repositories.

In the first study (Chapter 2), I examined the role that exposure to major highways plays in preterm birth risk. Comparing the risk associated with residential distance to major roads to that of fine particulate matter exposure, I found that the latter was significantly associated with preterm birth, while the former was not.

In the second (Chapter 3), I considered the impacts of wildfire-driven high PM2.5 values. This study focused specifically on the San Francisco Bay Area during the 2018 California Camp Fire. Both exposure to the wildfire smoke and the degree of wildfire smoke experienced were significantly associated with preterm birth.

Finally, in the third study (Chapter 4), I investigated whether a positive test for SARS-CoV- 2 modified the association between PM2.5 and preterm birth. I found that such an effect modification was not present.

Genomics is expected to soon overtake astronomy, particle physics, and even YouTube as the biggest creator of digital information (1). Analysis of this information has already led to important and ground breaking discoveries relevant to our health, but ongoing work will require creative solutions to the multitude of challenges arising from this volume of data. Practically speaking, one such challenge comes from determining what data should be collected and how it is to be managed. As cohort sizes in population based studies grow into the hundreds of thousands, practical issues about collection, storage, and filtering have begun to come more into focus. Additionally, frameworks that seamlessly integrate disparate datasets and also allow for flexible analysis will be required. Finally, as technical challenges and limitations arise, new analytical approaches and designs will have to be considered.

This dissertation work was comprised of three projects relating to these questions as approached from the perspective of a bioinformatician. These projects describe the development of new software and methods for sample management, data integration and analysis, and design strategies to improve signal in noisy data.

The first chapter of this dissertation consists of background material relating to the projects, including a description about the state of prostate cancer genomics, the development of biomarkers for its detection, and an exploration of a promising new biomarker, cell-free DNA (cfDNA). It also includes a discussion about some of the overarching questions of my PhD.

The second chapter describes a web based sample management system, called Samasy. Born out of necessity, this tool addresses a very practical issue of sample subsetting that is often required of resequencing studies. Samasy was used to facilitate the selection of 16,600 samples from a much larger cohort of 54,000 while preserving ethnicity and age balance among cases and controls. This tool integrates with liquid handling systems and provides a visually intuitive interface for plate/sample management and batch sample transfer execution.

The third chapter details Orchid, a framework designed to make machine learning of cancer variant data easy and extendible. It does so by integrating a variety of biological annotations (or features) and simple somatic tumor data available from large repositories like the The Cancer Genome Atlas (TCGA) or the International Cancer Genome Consortium (ICGC). This tool supports an efficient data store, MemSQL, that allows for very fast retrieval and filtering, and extends the popular python pandas and scikit-learn packages to facilitate machine learning of this data.

Finally, the fourth chapter outlines the creation of a custom targeted sequencing panel for prostate cancer that was designed for screening tumor variants in cfDNA. Building upon the power of Orchid, we detail how machine learning on whole genome prostate tumor datasets can be used to rank mutations by likelihood of being found in a patient with few mutations, or in other words, involved in early state disease. This ranking was used to build a targeted sequencing panel for detection of tumor-derived cfDNA variants. This panel was then validated and applied to a cohort of nine UCSF prostate cancer patients with multiple tumor foci that were collected at time of Radical Prostatectomy (RP).

Taken together, the information described in this dissertation provides tools and methodologies for the analysis of germline and somatic variants in prostate and other cancers. It also attempts to further technological development of cfDNA as biomarker for the detection or monitoring of diseases like cancer.

Tremendous advances in genomic capabilities, including high-throughput genotyping technology and algorithms, have significantly advanced our understanding of the genetic etiology of complex human traits, including cancer of the prostate gland. Although over 150 genomic loci predictive of prostate cancer susceptibility have been previously reported in the scientific literature, particular questions remain outstanding in our understanding of prostate cancer genetics. These open questions include the extent to which low frequency variants not detectable by genome-wide association studies may appreciably modify risk of prostate cancer, which genes may modify risk of prostate cancer through up- or down-regulation by genetic polymorphisms including prostate cancer risk loci, and the degree to which the normal tissues collected from prostate cancer patients reflect a faithful representation of their baseline germline genetics. The latter is explored in Chapter 2 of this dissertation, using genome-wide genotypes from prostate cancer patients at the University of California, San Francisco medical center. This work reveals significant genetic heterogeneity among diverse sources of DNA deriving from a common patient, and challenges preconceived notions that seminal vesicle tissue DNA from a prostate cancer patient may serve as an accurate representation of germline genetics. In Chapter 3, many diverse biological datasets, including genotypes for over 230,000 UK Biobank and Kaiser Permanente subjects, are integrated to identify genes whose expression levels are associated with prostate cancer risk. Further explored are the mechanisms by which particular associated genes may influence prostate cancer etiology, as well as the mechanisms by which genetic variants may regulate gene expression levels for those genes associated. Finally, Chapter 4 reports the association of several novel prosate cancer risk variants, including two previously unreported rare variants in European-ancestry populations. By meta-analyzing custom microarray genotype data from Kaiser Permanente health plan members along with genetic data from the UK Biobank, this work seeks to clarify the genetic architecture of prostate cancer risk, as well as further characterize the aggregate risk of previously reported loci across multi-ethnic subjects. In conclusion, this dissertation reports novel findings pertaining to rare prostate cancer risk variants, prostatic gene expression in prostate cancer, and genetic mosaicism among urologic tissues.