The technological and computational breakthroughs in the years since the sequencing of the human genome have provided an unprecedented opportunity to understand the etiology of complex human diseases. Notably, the diminishing cost of next-generation sequencing means that it is now possible for researchers to obtain complete genome sequence information on many thousands of individuals, with widespread access to that data via large repositories of electronic health records (EHRs; e.g,. biobanks). However, major statistical questions remain about biobank-era analysis strategies in order to study the contribution of genetic variation to common diseases. In particular, foundational statistical questions exist in the areas of: (a) a recognition of the need to minimize computational complexity and respect data privacy concerns, (b) random and non-random missing data, (c) data uncertainty and errors (both phenotypic and genotypic), and (d) the role of multi-marker (variant-set) tests, which aggregate evidence from many individual variants into a single test statistic. Research by our group as part of our existing award (R15-HG0006915 (2011-present)) began by developing a framework for evaluating the performance of existing variant-set tests. We then utilized this framework to provide a clear understanding of test performance in a variety of circumstances, developed novel robust and powerful tests, evaluated method performance in light of genotype uncertainty, developed methods to characterize underlying genetic architecture and demonstrated the utility of these methods to understand the genetics of fatty acids and high blood pressure. Recently, we proposed a novel method for utilizing summary statistics from simple one variant ? one phenotype linear models to draw inferences about complex phenotypes (in this case, the linear combination of phenotypes) as a first step to provide computationally efficient, biobank era-ready statistical methods for assessing genotype-phenotype association. Moving forward, our research will generalize this initial method to be applicable to any complex phenotype. Additionally, we will continue to build on a strong history of exploration of uncertainty, by considering the impact of random and non-random errors and uncertainty on genotype-phenotype association tests in the biobank era, and extension of these methods to multi-marker test settings. Methods we develop will be tested on both simulated and real data via the CHARGE consortium. Additionally, the work we will perform addresses the three main goals of NIH?s R15 program: (a) to conduct meritorious research that will (b) strengthen the research environment of the liberal arts college where the research will be conducted, while (c) exposing undergraduate students to statistical genetics research. With this last goal in mind, the fourth aim of our proposal is to provide research experiences to undergraduate students when conducting aims 1, 2 and 3.
The number of genetic association studies seeking to identify genetic variants that predispose to human diseases continues to grow. Furthermore, the environment for conducting these studies is rapidly changing due to declining sequencing and genotyping costs, new statistical technologies (e.g. imputation) and increasing understanding of the human genome. The proposed research will provide design and analysis strategies for genetic association studies in order to accelerate the pace of research towards the goal of a complete understanding of the genetic architecture of common human diseases.
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