Integrating Multiple Omics Approaches and Human Biology for Understanding Kidney Function
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Chronic kidney disease (CKD) is a global public health problem that affects greater than 10% of adults and is associates with multiple complications. Prevention of CKD incidence and early progression informed by an accurate understanding of the risk factors and causes is important since later risks are high and hard to treat. Previous studies have examined several potential risk factors of CKD; however, few studies have evaluated longitudinal change in kidney function as an outcome. Furthermore, research addressing the underlying causal relationships and risk prediction has been limited. Advances in genomics and proteomics provide new opportunities for understanding CKD risk factors. We therefore investigated kidney function at three layers: association, causation, and prediction, integrating multiple omics approaches into our epidemiological studies. Chapter 1 provides an introduction. In Chapter 2 and 3, we rigorous examined risk factors for longitudinal kidney function decline using classical epidemiological approaches. We modeled 30-year kidney function trajectories and demonstrated the associations of hypertension and obesity with random effects models for kidney function decline over 30-year of follow-up. In Chapter 4, we used genome-wide association study (GWAS) results and Mendelian randomization methods to examine the causal directions between risk factors and kidney function. Not only did we demonstrate strong causal effects of lower kidney function on higher blood pressure, we devised a method using multiple markers to triangulate on the subset of genes that are likely to reflect kidney function susceptibility. In Chapter 5, we further examined the power of genetic susceptibility for kidney function to predict future risk and investigated the association between the genetic risk with an intermediate phenotype, plasma proteins. We demonstrated the link between genetic basis of kidney function measured as polygenic risk score (PRS) with incidence of CKD, end-stage kidney disease, kidney failure, and acute kidney injury, supporting the use of genetic information as risk factors for kidney diseases. We also found that protein associations were stronger with kidney function than its genetic risk. Overall, using multiple types of data and methods, this doctoral thesis advances our understanding of multiple non-genetic and genetic risk factors for CKD and its progression.