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Addressing the Missing Heritability of Coronary Artery Disease

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Addressing the Missing Heritability of Coronary Artery Disease Addressing the Missing Heritability of Coronary Artery Disease DISSERTATION Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Katherine Louise Seal Hartmann Biomedical Sciences Graduate Program The Ohio State University 2016 Dissertation Committee: Wolfgang Sadee, Advisor Rebecca Jackson Joseph Kitzmiller Ray Hershberger Copyrighted by Katherine Louise Seal Hartmann 2016 Abstract There is often a strong genetic component to human health and disease – reflected in the importance of an individual’s family history in clinical practice. This is true for coronary artery disease (CAD), with heritability estimates calculated by comparing concordance between monozygotic and dizygotic twins ranging from 40 to 60%. Sequencing of the human genome in the early 2000s led to the advent of Genome Wide Association Studies (GWAS) that test millions of variants across the genome for an association with disease. These large-scale studies have identified 58 loci associated with risk of CAD; yet each variant contributes relatively little to disease risk (odds ratios range from 1.01 to 1.92) and in total they account for only about 10% of the genetic risk. The remainder of the genetic risk remains unexplained, a phenomenon termed ‘missing heritability’. This work aims to address this ‘missing heritability’ by considering the potential for non-linear interactions contributing to CAD risk. The following chapters are aimed at uncovering disease relevant interactions. The same technological advancements that allowed for sequencing of the human genome have facilitated large-scale tissue-specific RNAsequencing efforts (GTEx), genome-wide scans for methylation and other signatures of regulatory elements (ENCODE), population specific catalogs of genetic variants and linkage disequilibrium ii (1000 genomes), and characterization of clinical phenotypes in dozens of GWA and other clinical studies, each built from thousands of samples. With all of these being publicly available, the question addressed in part in the following chapters is how to integrate such diverse datasets to inform our understanding of the role of genetic variants in disease. Each chapter uses these resources to evaluate potential interactions, whether among variants, genes, or pathways. The first chapter serves as an introduction. The second is a survey of genes implicated in CAD by GWAS. Expression is used as a guide to prioritize the relevant gene locus for each GWAS variant, revealing loci with multiple protein-coding and non-coding transcripts and expression patterns that cannot be accounted for by a single functional haplotype. The third chapter considers differential expression of GWAS-based candidate genes in the context of disease. Gene expression, which incorporates both genetic and environmental factors, is used as a guide to search for interactions, revealing non-additive effects between gene expression patterns that are associated with risk of myocardial infarction. The next two chapters both focus on specific gene loci, allowing greater resolution to probe molecular mechanisms in detail. Chapter four considers the nicotinic receptor locus consisting of three genes: CHRNA5, CHRNA3, and CHRNB4 that each form a subunit of the nicotinic receptor. I identify three main signals (rs16969968, rs88053, and rs1948) associated with gene expression, which form diplotypes that better account for risk nicotine dependence than individual variants, thus demonstrating the importance of interactions. The final chapter reveals a potential role of Cholesteryl Ester Transfer Protein (CETP), a drug target for CAD, in immune-related functions based on iii expression and co-expression patterns. Three candidate variants are considered, two with opposing effects on expression (rs247616 and rs6498863) and the third associated with splicing (rs5883). Each of these studies supports the role for non-linear, non- additive interactions contributing to coronary artery disease. iv Dedicated to my Dad for believing that for me ‘not even the sky is the limit.’ Such sincere faith in my abilities has made me dream bigger and strive for more than I feel possible. v Acknowledgments Many thanks to Wolfgang for being what can only be described as an inspiration for how joyous life and science can be. I would also like to thank Michał Seweryn for seeing in me something no one else did, not even myself, and for helping me to find a rhythm in science and in life that I expect will lead to a great many adventures. To my brother Justin and his wife Emily, thank you for always being a source of love and for the inspiration you provide in how you both live your lives so fully and with such passion. To my Mom and her husband Don, thanks for your constant love, support and interest. A special thanks to Amanda & Andy Campbell and Steven, Allison, Evie, Eric, and Joey Scoville for the ‘three amigos’ family. To all other professors and students who have talked with and helped me along the way, my sincere gratitude. vi Vita May 2006 ...................................................... Brookwood High School May 2010 ...................................................... B.A. Biology, Cornell University 2010 to present ............................................. MSTP Trainee, The Ohio State University Publications J. P. Kitzmiller, P. F. Binkley, S. R. Pandey, A. M. Suhy, D. Baldassarre, and K. Hartmann, “Statin pharmacogenomics: pursuing biomarkers for predicting clinical outcomes.,” Discov. Med., vol. 16, no. 86, pp. 45–51, Aug. 2013. A. Suhy, K. Hartmann, L. Newman, A. Papp, T. Toneff, V. Hook, and W. Sadee, “Genetic variants affecting alternative splicing of human cholesteryl ester transfer protein.,” Biochem. Biophys. Res. Commun., vol. 443, no. 4, pp. 1270–4, Jan. 2014. W. Sadee, K. Hartmann, M. Seweryn, M. Pietrzak, S. K. Handelman, and G. A. Rempala, “Missing heritability of common diseases and treatments outside the protein- coding exome.,” Hum. Genet., vol. 133, no. 10, pp. 1199–215, Oct. 2014. vii R. Lu, R. M. Smith, M. Seweryn, D. Wang, K. Hartmann, A. Webb, W. Sadee, and G. A. Rempala, “Analyzing allele specific RNA expression using mixture models.,” BMC Genomics, vol. 16, no. 1, p. 566, Jan. 2015. S. K. Handelman, M. Seweryn, R. M. Smith, K. Hartmann, D. Wang, M. Pietrzak, A. D. Johnson, A. Kloczkowski, and W. Sadee, “Conditional entropy in variation-adjusted windows detects selection signatures associated with expression quantitative trait loci (eQTLs).,” BMC Genomics, vol. 16 Suppl 8, p. S8, Jan. 2015. A. Suhy, K. Hartmann, A. C. Papp, D. Wang, and W. Sadee, “Regulation of cholesteryl ester transfer protein expression by upstream polymorphisms: reduced expression associated with rs247616.,” Pharmacogenet. Genomics, Jun. 2015. F. Makadia, P. P. Mehta, C. E. Wisely, J. E. Santiago-Torres, K. Hartmann, M. J. Welker, and D. Habash, “Creating and Completing Service-Learning within Medical School Curricula: From the Learner’s Perspective,” International Journal of Medical Students, vol. 3, no. 1. 31-Aug-2015. P. P. Mehta, J. E. Santiago-Torres, C. E. Wisely, K. Hartmann, F. A. Makadia, M. J. Welker, and D. L. Habash, “Primary Care Continuity Improves Diabetic Health Outcomes: From Free Clinics to Federally Qualified Health Centers.,” J. Am. Board Fam. Med., vol. 29, no. 3, pp. 318–24, May-June 2016. viii Fields of Study Major Field: Biomedical Sciences Graduate Program IBGP ix Table of Contents Abstract ............................................................................................................................ ii Acknowledgments ........................................................................................................... vi Vita ................................................................................................................................. vii Publications .................................................................................................................... vii Fields of Study ................................................................................................................ ix Table of Contents ............................................................................................................. x List of Tables ................................................................................................................. xii List of Figures ............................................................................................................... xiv Chapter 1: Introduction to the Genetics of Common Disease ........................................ 1 Chapter 2: A Survey of Genes Implicated in Coronary Artery Disease ........................ 12 Chapter 3: Non-linear Interactions Between Candidate Genes of Myocardial Infarction Revealed in mRNA Expression Profiles ........................................................................ 39 Chapter 4: The CHRNA5/CHRNA3/CHRNB4 nicotinic receptor regulome: genomic architecture, regulatory variants, and clinical associations ............................................ 73 Chapter 5: Genetic variation in the cholesteryl ester transfer protein locus ............... 104 x Chapter 6: Concluding Remarks .................................................................................. 117 References .................................................................................................................... 124 Appendix A: Ethics Approval .....................................................................................
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