Robust Methods for the Efficient Analysis and Integration of DNA Sequence Data

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Human genetics research is on the cusp of a major transformation in how genetic variation is captured-from a marker-based approach to one based on a complete characterization of an individual's genome by sequencing. This is an exciting prospect but not without its challenges. The imminent production of large amounts of sequence data raises several issues on how best to use these data. For example, because of the sheer scale of the data, statistical approaches for associating sequence variants with human disease need to be efficient, both statistically and computationally. In addition, most genetic association experiments in the near term will not rely solely on sequence data but instead will have sub-samples of individuals with sequence data while the rest of the sample will remain unsequenced but will contain genotype information. Alternatively, sequence data may be available on a separate, external sample. Thus it will be important to develop statistical methods that can appropriately integrate these various types of data into a unified inferential framework. This research project will address these issues by proposing to develop a novel class of sequence- based haplotype sharing statistics that exploit the implications of DNA sequence evolution in testing for variant/disease association (specific aim 1). Further, we propose to develop a statistical framework that allows for the unified analysis of DNA sequence and genotype data (specific aim 2). Throughout we will leverage our previous work developing robust methods for haplotype inference to develop computationally and statistically efficient procedures that remain robust to population genetic assumptions. A stratified analytic approach will be emphasized to allow for adjustment for confounding due to population stratification. Efficient Monte Carlo procedures will be proposed to account for the large number of sequence variants investigated. We will develop a suite of software tools that fully implement the methodology developed and make them freely available to the general research community (specific aim 3). Finally, using these tools, we will analyze a publicly available DNA sequence dataset with the goal of better localizing disease- associated sequence variants (specific aim 4). The methods developed through this proposal represent a unified and statistically rigorous framework for developing powerful tests that exploit evolutionary relationships between DNA sequences while allowing for disparate data types to be incorporated into a unified analysis. These procedures will give researchers the tools to more finely localize disease-associated sequence variants, allowing variants to be better prioritized for subsequent investigation via functional studies. Human genetics research is on the cusp of a major transformation in how genetic variation is captured-from a marker based approach to one based on a complete characterization of an individual's genome by sequencing. The imminent production of large amounts of sequencing data, however, leads to questions concerning their statistical analysis and incorporation into the larger experiment. We address these questions by proposing a unified and statistically rigorous framework for developing powerful tests that exploit evolutionary relationships between DNA sequences and that allow for disparate data types to be incorporated into a unified analysis.