Washington University in St. Louis Washington University Open Scholarship All Theses and Dissertations (ETDs) Summer 9-3-2013 SNP Interactions in the Genetic Architecture of Blood Pressure Jacob John Basson Washington University in St. Louis Follow this and additional works at: https://openscholarship.wustl.edu/etd Recommended Citation Basson, Jacob John, "SNP Interactions in the Genetic Architecture of Blood Pressure" (2013). All Theses and Dissertations (ETDs). 1119. https://openscholarship.wustl.edu/etd/1119 This Dissertation is brought to you for free and open access by Washington University Open Scholarship. It has been accepted for inclusion in All Theses and Dissertations (ETDs) by an authorized administrator of Washington University Open Scholarship. For more information, please contact [email protected]. WASHINGTON UNIVERSITY IN ST. LOUIS Division of Biological and Biomedical Sciences Human and Statistical Genetics Dissertation Examination Committee: DC Rao, Chair Jim Cheverud Lisa de las Fuentes Mike Province Nancy Saccone Alan Templeton SNP Interactions in the Genetic Architecture of Blood Pressure by Jacob John Basson A dissertation presented to the Graduate School of Arts and Sciences of Washington University in partial fulfillment of the requirements for the degree of Doctor of Philosophy August 2013 St. Louis, Missouri © 2013 Jacob Basson Table of Contents List of Figures………………………………………………………………………….……………iv List of Tables…………………………………………………………………………….…………..v Acknowledgments……………………………………………………………………….………….vi Abstract……………………………………………………………………………………………..vii Chapter 1: Between Candidate Genes and Whole Genomes: Time for New Approaches in Blood Pressure Genetics? Introduction……………………………………………………………………..……………1 Candidate Gene Studies…………………………………………………………...…………2 Genome Wide Studies……………………………………………………………..………10 Discussion…………………………………………………………………………………..21 Chapter 2: Genome-Wide Association with BP in FHS Introduction…………………………………………………………………..……………..25 Framingham Heart Study……………………………………………………………….......25 Methods………………………………………………………………………….………….29 Results…………………………………………………………………………….…...……30 Discussion………………………………………………………………………………..…31 Chapter 3: SNP-SNP Interactions with BP in FHS Introduction……………………………………………………………………..…………..32 Methods……………………………………………………………………….…………….36 Results………………………………………………………………………….…………...40 Discussion…………………………………………………………………………………..40 ii Chapter 4: SNP-Environment Interactions with BP in FHS Introduction………………………………………………………………………..………..45 Methods………………………………………………………………………….………….46 Results…………………………………………………………………………….………...48 Discussion ………………………………………………………………………………48 Chapter 5: Multi-Marker Analysis of SNP Interactions with BP in FHS Introduction…………………………………………………………………….….………..51 Methods……………………………………………………………………….……………52 Results……………………………………………………………………….……………...53 Discussion…………………………………………………………………………………..53 Chapter 6: Conclusions Discussion…………………………………………………………………………………..56 Figures……………………………………………………………………….……………...61 Tables……………………………………………………………………….………………99 References………………………………………………………………….……………...131 iii List of Figures Figure 1: Distribution of SBP, DBP, BMI, and Age in FHS visit 7……………….……….………61 Figure 2: SBP and DBP GWAS Manhattan plots………………………………………………….63 Figure 3: Pathway-only Manhattan plot for SNP main effects………………………...…………...64 Figure 4: Mean SBP by genotype for selected SNP-SNP interactions……………….…….………66 Figure 5: Mean DBP by genotype for selected SNP-SNP interactions………………………..…...73 Figure 6: Pathway-only Manhattan plot for SNP-BMI interactions………………………………..74 Figure 7: Pathway –only Manhattan plot for SNP-Sex interactions………….…..………………...76 Figure 8: SNP-BMI (2 df) interactions vs. SNP main effects…………………….………………...77 Figure 9: SNP-Sex (2 df) interactions vs. SNP main effects……………………………………….81 Figure 10: Pathway-only Manhattan plot for SNP-Hypertension interactions and stratified effects……………………………………………………………………………………………….85 Figure 11: QQ plot for SNP-Hypertension interactions…………………………….……………...88 Figure 12: QQ plot for SNP-Hypertension stratified effects………………………………..……...92 Figure 13: Mean SBP for top SNP-Hypertension interaction…………………………………...….96 iv List of Tables Table 1: Mendelian hypertension genes……………….…………………………...………………97 Table 2: Selected BP candidate genes…………………………………………………...…………99 Table 3: FHS visit 7 sample summary…………………………………………………………….101 Table 4: FHS visit 7 normality check……………………………………………...……………...102 Table 5: Pathway genes…………………………………………………………..……………….102 Table 6: Top SNP-SNP interactions by p-value…………………………………………………..104 Table 7: Top SNP-SNP interactions by q-value…………………………………………………..107 Table 8: Top SNP-BMI and SNP-Sex interactions by p-value……………..……………………..112 Table 9: Multi-marker model summary………………………………….………………………..112 Table 10: Multi-marker results……………………………………………………………………113 v Acknowledgements This research was supported by Grants T32HL083822, R21HL095054, R01HL111239, and R01HL107552 from the National Heart, Lung, and Blood Institute. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Heart, Lung, and Blood Institute or the National Institutes of Health. Mentors: DC Rao, Lisa de las Fuentes Committee: Jim Cheverud, Mike Province, Nancy Saccone, Alan Templeton Rao Lab: Yun Ju Sung, Jeannette Simino, Karen Schwander, Rezart Kume, Treva Rice HSG Directors: John Rice, Allison Goate, and Anne Bowcock Mom, Dad, Duncan, Alex vi ABSTRACT OF THE DISSERTATION SNP Interactions in the Genetic Architecture of Blood Pressure By Jacob John Basson Doctor of Philosophy in Human and Statistical Genetics Washington University in St. Louis, 2013 Professor DC Rao, Co-Chair and Assistant Professor Lisa de las Fuentes, Co-Chair Evidence suggests that there is a substantial genetic component to the regulation of blood pressure (BP) but most BP variance remains unattributed to specific genetic variants. Candidate gene studies show linkage to rare Mendelian disorders but inconsistent association with BP. Massive consortia of genome-wide association studies have identified several dozen loci explaining less than 3% of BP variance. Many explanations have been offered for these observations, of which the current study addresses two: the focus on SNP main effects to the exclusion of interactions, and the low statistical power resulting from the strong multiple-testing burden necessarily imposed when analyzing millions of variants. This study examined the role of interactions among a limited set of variants (818 SNPs in 70 genes) drawn from two broad signaling pathways: renal ion balance and inflammation. Exhaustive SNP-SNP interaction testing was performed, as was analysis of SNP-sex, SNP-BMI, and SNP-hypertension interactions. In hypertensive subjects only, rs12821401 in ADIPOR2 was significantly associated with SBP (p=6.1e-5). A set of three SNP-SNP interactions among 6 inflammation genes (including ADIPOR2) showed significant evidence of containing one or more true association signals (FDR=4.2%), though no single interaction reached experiment- wise significance. These results provide evidence of a role for SNP-SNP and other SNP vii interactions and implicate an inflammation gene, ADIPOR2, not previously associated with hypertension viii Ch1: Between candidate genes and whole genomes: time for new approaches in blood pressure genetics? I. Introduction The US Department of Health and Human Services estimates that almost one third of US adults over 20 years of age suffer from hypertension [1], a major risk factor for cerebrovascular disease, ischemic heart disease, and cardiac and renal failure [2]. The nature and genetic origins of hypertension have long been the subject of scientific inquiry, dating back to the 1940s and the famous debate between Dr. Robert Platt and Dr. George Pickering. Platt argued that hypertension was characterized by a bimodal distribution of blood pressure values, with a single (unknown) genetic defect, transmitted with Mendelian inheritance and leading to a discrete disease state. Pickering, on the other hand, believed that the spectrum of blood pressure values observed in the population was better described as a unimodal distribution with polygenic inheritance, and that subjects with hypertension were simply those falling at the extreme high end of the continuum [3]. Today it’s known that there’s some truth to both sides. For a variety of rare familial hypertensive diseases, traditional linkage and association has been very successful in identifying genes carrying disease-causing mutations with Mendelian inheritance. For the remaining cases of hypertension – the vast majority – the complex architecture of the genetic and environmental interactions that regulate blood pressure and become pathologic in disease has yet to be elucidated. However, decades of research, aided by an increasingly powerful and affordable technological arsenal, have yielded substantial insights into the mechanisms of blood pressure regulation, even as they have failed to identify the complete set of genetic polymorphisms, environmental risk factors, and their interactions that underlie these cases of essential hypertension. 1 Several opportunities for methodological progress have emerged from these insights, of which the first two listed are pursued in the work that follows: 1) test the role of gene-gene and other interactions; 2) reduce the multiple testing burden by focusing on variants concentrated in pathways; 3) test the role of rare variants;