The technological and computational breakthroughs in the decade 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 thousands of diseased individuals. However, major statistical questions remain about optimal design and analysis of studies using next-generation sequencing data to study the contribution of rare variation to common diseases. At the foundation of many such questions is the lack of power for single marker, rare variant tests of association, motivating the development of many, potentially more powerful, variant-set based tests, which aggregate evidence from several individual variants into a single test statistic. Current and newly proposed variant-set based tests which attempt to address large variant situations vary in how they combine and weight variants, leading to poorly understood differences in performance under different genetic models. Much of the current focus is on developing an all-around best rare variant test, typically through assessment on simulated data. Regardless of which test--or, more likely, tests--emerge as optimal, several challenges will remain toward applying these methods to real, imperfect sequence data and then inferring underlying genetic architecture based on a statistically significant test result. Thus, rather than focus exclusively on novel test development, our research will center on gaining a deeper understanding of the behavior of rare variant set tests, the realistic application of these tests, and the development of methods to decompose significant test statistics to gain information that can guide future studies. We will pay specific attention to the interplay of various underlying disease models, test statistics, and study designs. This work will provide a critical step towards successfully identifying rare risk variants in future sequencing experiments and translating the results into public health practice. To achieve these goals, we propose the following specific aims: We will (1) develop a framework to understand the behavior of rare variant set tests, (2) evaluate rare variant set tests in the presence of imperfect data and (3) develop post-hoc analyses to identify causal variants and inform replication study design. We will conduct the research using a combination of analytic, computational and simulation approaches. 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.

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Academic Research Enhancement Awards (AREA) (R15)
Project #
2R15HG006915-02
Application #
9099474
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Ramos, Erin
Project Start
2012-09-20
Project End
2019-05-31
Budget Start
2016-06-10
Budget End
2019-05-31
Support Year
2
Fiscal Year
2016
Total Cost
Indirect Cost
Name
Dordt College
Department
Biostatistics & Other Math Sci
Type
Schools of Arts and Sciences
DUNS #
078009222
City
Sioux Center
State
IA
Country
United States
Zip Code
51250
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