Genome-wide association studies (GWAS) have mapped thousands of common trait-influencing variants yet the overwhelming majority of trait loci have yet to be discovered. The goal of this proposal is to develop and apply statistical approaches that move beyond the standard GWAS paradigm to map additional trait-influencing variation within the human genome. Most of our proposed tools are based on a flexible high-dimensional framework called kernel machine regression, which we have had past success employing for powerful gene mapping of complex traits in GWAS and next-generation sequencing (NGS) studies. We believe the inherent flexibility of the kernel framework makes it ideal for exploring new paradigms in gene mapping of complex human traits.
Aim 1 proposes novel kernel methods for integrated analysis of both single-nucleotide variation data (derived from GWAS and/or NGS) and genomic data (such as gene-expression and methylation patterns) that we believe will provide improved power for trait mapping.
Aim 2 proposes novel kernel methods for large scale gene-gene interaction analysis across the genome, as well as a computational approach that enables efficient adjustment for multiple testing when applying such exhaustive testing procedures.
Aim 3 establishes novel kernel methods for association mapping of SNVs on the X chromosome. The flexible nature of kernel machines makes it ideal for modeling potential sex-specific effects on this chromosome and the methods further can accommodate random X inactivation.
Aim 4 proposes novel kernel approach for robust analysis of rare trait-influencing variation within families;such family-based designs are generally not considered in current rare-variant procedures. We will evaluate these methods on large-scale datasets that we are actively involved in and will implement the methods in user-friendly software for public distribution (Aim 5).
The goal of this project is to develop novel and powerful statistical tools for identifying genetic loci acting independently or in conjunction with other genetic/environmental factors to influence complex human diseases or disease-related quantitative traits. Application of the proposed methods to applied datasets should improve our understanding of the genetic origins of complex traits and enhance existing risk-prediction models of complex disease.
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