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Evaluation of Genetic Variants for Type 2 Diabetes Associated Kidney Disease in African Americans

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Evaluation of Genetic Variants for Type 2 Diabetes Associated Kidney Disease in African Americans Evaluation of genetic variants for Type 2 diabetes associated kidney disease in African Americans By Meijian Guan A Dissertation Submitted to the Graduate Faculty of WAKE FOREST UNIVERSITY GRADUATE SCHOOL OF ARTS AND SCIENCES in Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY In Integrative Physiology and Pharmacology August 2017 Winston-Salem, North Carolina Approved By: Maggie Ng, Ph.D. Advisor Barry Freedman, M.D., Chairman Donald Bowden, Ph.D. Fang-Chi Hsu, Ph.D. Timothy Howard, Ph.D. I Acknowledgements Foremost, I would like to thank my advisor Maggie Ng, for allowing me to have the opportunity to work in this lab, and providing extensive support, resources, and training. During my time in the lab, I appreciate your unique insights on many subjects, including genetic research, statistics, scientific writing, as well as problem solving. You taught me how to pay attention to the small details during problem-solving process. Because of you, I learned the value of examining all sides of a problem, instead of just scratching the surface, to make a conclusion that truly stands up to scrutiny. The lessons that I have learned from you during the past few years can be applied to so many situations in my life, not just limited to research. Dr. Bowden, thank you for creating such a wonderful environment that promotes open thinkings, collaboration, as well as encouragement of expanding our interests. And thank you for being a member of my advisory committee. I appreciate your invaluable advices on both of my research and career development. I would never be able to achieve what I achieve now without your generous support. Dr. Freedman, thank you for being my committee chair and for being so willing to share your expertise and experience with me. I cannot say how much I appreciate your support along the way, the advices on my research projects, the clinical interpretations of the genetic results, help with manuscript revisions, and the opportunities you offered to collaborate with your research group. To Fang-chi, I learned fundamentals of statistics and SAS programming skills from your class. You have been such a good listener and always willing to encourage me for further pursuing. Dr. Howard, thank you so much for filling in to help me out when Dr. Zhou left my committee. I enjoyed the classes you offered at genomics center, which furthered my understanding in human genetics. II To those in the Bowden Lab, I appreciate your training, guidance, advice, supports, as well as your great company. Nichole, thank you for sharing your experience in kidney disease research with me; the most updated APOL1 risk coding you provided was very helpful. Pam - thank you for your great help in providing the genotyping reports and phenotypic information. And thank you for your great patience; I don’t remember how many times questions I have asked you regarding the details of the data. Becky, I will miss the cakes you baked for me, as well as the interesting conversations between us. To the data administrators in Bowden Lab, JJ and Lucho – thank you for your help getting me started in the lab. Both of you helped to install softwares, set up systems and solve the IT problems. Lucho – you really gave a lot of invaluable advices on how to be a good programmer, and how to better manage my files. JJ, I appreciate your assistance in retrieving data from the database for me whenever I asked. Moreover, thank you for all your encouragements and advices on how to succeed. To Poorva, I still remember those complicated programs you help me to run when I started my project. You were always there when I have some programming issues. You already left the lab, wish you enjoy your new baby, as well as your new job. To Hayrettin, you are one of the best statisticians I ever worked with. You were the “go-to” person whenever I had difficulties to understand statistical concepts. To Jacob, Chuan, Jackie, Laura, Jeremy, Mary and Keri, we had so many good moments together. Thank you all for always sitting next me and inspiring me to be a better version of myself. I really enjoyed hanging out with each of you. Wish you all have the brightest future. To the faculty and staff of Integrative Physiology and Pharmacology, particularly Dr. Allyn Howlett, Dr. Paul Czoty and Denise Wolfe, I appreciate your advice, guidance, and endless support over the past years; I would not be as far along in my career if it was not for the support I have received from IPP. Lastly, I would like to thank my family for their endless support. They are my motivation to pursue a PhD. Thanks to my parents, Wanming Guan and Changfeng Chen, for their III continuous love, support and encouragement. I am so appreciative that you taught me to be a good person and to work hard. To my parents in law, Zhe Wang and Sheying Ma, I appreciate your endless support. You offered your help when it is most needed. Thanks to my amazing wife Yan Wang for her unconditional love and support. We both know I would not have made it this far without you. Special thanks to my son Greyson who has brought so much happy and enjoyable moments to this family. I appreciate every minute I spend with you son. IV Table of Contents Page Number List of Figures and VI Tables List of Abbreviations IX Abstract XI Chapter 1 Introduction 1 Chapter 2 Genome-wide association study in African 16 Americans with T2D-attributed end-stage kidney disease Chapter 3 An exome-wide association study for type 2 61 diabetes-attributed end-stage kidney disease in African Americans Chapter 4 Association of kidney structure-related gene 102 variants with type 2 diabetes-attributed end-stage kidney disease in African Americans Chapter 5 Association analysis of the cubilin (CUBN) and 137 megalin (LRP2) genes with end-stage kidney disease in African Americans Chapter 6 Discussion and Conclusions 165 References 178 Curriculum Vitae 197 V List of Figures and Tables Page Number Chapter 2 Figure 1. Workflow of T2D-ESKD GWAS in AAs (Baseline model) 35 Figure 2.A. Locus plots of T2D-ESKD associations at P<5x10-8 in 36 baseline model Figure 2.B. Locus plots of all-cause ESKD associated variants at 37 P<5x10-8 in baseline model Figure 3. Locus plots of T2D-ESKD associations at P<5x10-8 in 38 APOL1-negative model Table 1. Clinical characteristics of Affy6.0 dataset (stage 1a) 39 Table 2. Clinical characteristics of Axiom and MEGA datasets 40 (stage 1b and 1c) Table 3. Independent T2D-ESKD associations (P<5x10-8) under 41 the baseline model Table 4. All-cause ESKD associated variants at P<5x10-6 in in 42 baseline model Table 5. Independent T2D-ESKD associations (P<5x10-8) under 43 APOL1-negative model Table 6. All-cause ESKD associated variants at P<5x10-6 in 44 APOL1-negative model Table 7. CKD and related associations from previous studies 45 replicated significance and consistent effect Sup. Figure 1. QQ plot of GWAS results of T2D-ESKD vs. non-diabetic 46 non-nephropathy controls under baseline model Sup. Table 1. Discrimination analysis for genome-wide significant T2D- 47 ESKD associated SNPs in baseline model Sup. Table 2. Discrimination analysis for genome-wide significant T2D- 48 ESKD associated SNPs in APOL1-negative model Sup. Table 3. Results of APOL1-negative model for top associations in 49 baseline model Sup. Table 4. Evaluation of previous loci associated with kidney disease 50 and related traits Sup. Table 5. Examination of top associations in previous T2D-ESKD 59 GWAS VI Chapter 3 Figure 1. Analysis workflow of single-variant association analysis for 81 T2D-ESKD Exome sequencing study (baseline model) Table 1. Clinical characteristics of study cohorts 82 Table 2. T2D-ESKD associated variants in meta-analysis from 83 discovery and replication cohorts (Baseline model) Table 3. Top T2D-ESKD associations in meta-analysis after 84 removing APOL1 renal-risk genotype carriers (APOL1- negative model) Table 4. Meta-analysis combining T2D-ESKD and all-cause ESKD 85 cohorts for rs41302867 Table 5. Top associations of gene-based analyses in baseline or 86 APOL1-negative models Sup. Table 1. Discrimination analysis for top associations of baseline 87 model and APOL1-negative model Sup. Table 2. GTEx results of top associations (P<1x10-4) 88 Sup. Table 3. Results of top associations from baseline model in 95 APOL1-negative model Sup. Table 4. Results of single-variant analysis detected loci in gene- 96 based analysis Chapter 4 Figure 1. Workflow of kidney structure-related gene analyses. 123 Table 1. Clinical characteristics of study cohorts 124 Table 2. T2D-ESKD associated SNPs in meta-analysis from 125 discovery and replication cohorts (Baseline model) Table 3. Additional associations in combined analysis after 126 removing APOL1 renal-risk genotype carriers (APOL1- negative model) Table 4. Top associations from the gene-based analysis in APOL1- 127 negative model Sup. Table 1. Kidney structure-related genes 130 Sup. Table 2. Differentiation steps for T2D-ESKD associated SNPs in 132 Baseline model Sup. Table 3. Associations approaching locus-wide significance in 133 Baseline model or APOL1-negative model Sup. Table 4. Differentiation steps for top associated SNPs in APOL1- 134 VII negative model Sup. Table 5. Results of the top three associations from Baseline model 135 in APOL1-negative model Sup. Table 6. eQTL analysis of T2D-ESKD associated SNPs and gene 136 expression Chapter 5 Figure 1. Cubilin gene (CUBN) and megalin gene (LRP2) SNP 154 selection and genetic association analysis workflow.
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