Leveraging Distribution Quantiles to Detect Gene Interactions in the Pursuit of Personalized Medicine

Anticipations of personalized medicine are primarily attributed to the recent advances in computational science and high-throughput technologies that enable the ever-more realistic modeling of complex diseases. These diseases result from the interplay between genes and environment that have limited our ability to predict, prevent, or treat them. While many envision the utility of integrated high-dimensional patient-specific information, basic research towards developing accurate and reliable frameworks for personalized medicine is relatively slow in progress. This thesis provides a state-of-the-art review of current challenges towards personalized medicine. There is a need for global investment in basic research that includes 1) cost-effective generation of high-quality high-throughput data, 2) hybrid education and multidisciplinary teams, 3) data storage and processing, 4) data integration and interpretation, and 5) individual and global economic relevance; to be followed by global investments into public health to adopt routine personalized medicine. This review also highlights that unknown or unadjusted interactions result in true heterogeneity in the effect and relevance of patient data. This limits our ability to integrate and reliably utilize high-dimensional patient-specific data. This thesis further investigates the true heterogeneity in marginal effects of known BMI genetic variants. This involved the development of the novel statistical method, meta-quantile regression (MQR), to identify variants with potential gene-gene / gene-environment interactions. Applying MQR on public and local data (75,230 European adults) showed that FTO, PCSK1, TCF7L2, MC4R, FANCL, GIPR, MAP2K5, and NT5C2 have potential interactions on BMI. In addition, a gene score of 37 BMI variants shows that the genetic architecture of BMI is shaped by gene-gene and gene-environment interactions. The computational cost of fitting MQR models was greatly reduced using unconditional quantile regression. The utility of MQR was further compared to variance heterogeneity tests in identifying variants with potential interactions. MQR tests were found to have a higher power of detecting synergetic and antagonistic interactions for skewed quantitative traits while maintaining nominal Type I error rates compared to variance heterogeneity tests. Overall, MQR is a valuable tool to detect potential interactions without imposing assumptions on the nature of interactions. iv Acknowledgements Firstly, I would like to express my sincere gratitude to my supervisor, Dr. David Meyre, for his unwavering support, constructive feedback, and warm encouragement over the years. Without his guidance, consistent help, and engaging teaching style, this thesis would not have been possible. I am grateful for him. I would also like to extend my appreciation to my Advisory Committee, Dr Ben Bolker, and Dr. Russell de Souza, for their constructive feedback and helpful support throughout this project. I am very thankful to have worked with such knowledgeable and insightful faculty throughout this project. I would also like to thank my colleagues, Arkan Abadi, Amel Lamri, Aihua Li, and Sébastien Robiou du Pont, for their constant support and encouragements for this research project and stimulating discussions. Lastly, I would like to thank my parents, my wife, Mada Mohammed, and all my close family and friends. I would like to apologize for all the time this ‘thesis’ has taken to complete, and I am grateful for all the support I’ve received. v Declaration of Academic Achievement All chapters are separate manuscripts except for the introduction and conclusion of this thesis. Chapter 2 and 3 have been published, while Chapter 4 is prepared for submission and publication post reviews from co-authors. All scripting, analytical development, statistical analysis, manuscript write-up of each of the chapters were primarily completed as an individual effort. Contributions to the data preparation, analysis, and edits for Chapter 3 and 4 have been made by Arkan Abadi, while David Meyre has provided contributions to edits in Chapter 2, 3, and 4. Ben Bolker has provided edits and critical reviews to Chapter 3 and 4. vi Notation and Abbreviations BF Brown-Forsythe BGI Beijing Genomics Institute BMI Body Mass Index CI Confidence Interval CPU Centeral Processing Units CQR Conditional Quantile Regression dbGaP Database of Genotypes and Phenotypes DNA Deoxyribonucleic acid EA European Ancestry GS Gene Score GPU Graphic Processing Units GWAS Genome Wide Association Study GWIS Genome Wide Interaction Study HIV Human Immunodeficiency Virus HWE Hardy Weinberg equilibrium IDB Identity by descent MAF Minor Allele Frequency MCQR Meta Conditional Quantile Regression MODMatcher Multi-Omics Data Matcher MONA multi-level ontology analysis MQR Meta-Quantile Regression MR Meta-Regression MUQR Meta Uconditional Quantile Regression NW Normal Weight OLS Ordinary least-squares OW Over Weight QC Quality Control QR Quantile Regression RNA Ribonucleic acid SN Skew-Normal SNP Single nucleotide polymorphism T2D Type 2 Diabetes UQR Unconditional Quantile Regression vii