The objective of this research proposal is to develop an entirely new approach to the analysis and summary of genome association data. In contrast to approaches that use asymptotic parametric results, or computationally intensive resampling, our approach uses exact permutation moments followed by a density approximation to the relevant statistics. The new approach will be far faster and provide more accurate values than current methods. We will develop these procedures into a new software package PANGEA. PANGEA will be especially useful for next-generation sequence data, and generally for even bigger-data future applications in genomics. The proposal is divided into three Aims: (i) To develop powerful and accurate testing procedures for genetic association studies of SNPs/variants, applicable both to SNP array and NGS platforms and with flexible handling of families and effective covariate control; (ii) To develop fast and accurate empirical pathway analysis approaches for genetic association; (iii) To provide an efficient and user-friendly software, further informed by comprehensive eQTL and ENCODE genomic annotation.
This proposed research will result in theory, methods, and software to use mathematical approximations to permutation testing. The work will greatly accelerate and enhance the conduct of genome association studies. This work will improve public health by enabling faster and more accurate identification of the genetic underpinnings of complex disease.
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