Methods for Human Genetic Mapping

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Project Summary/Abstract Common, complex diseases together account for a large portion of the health care burden in the United States, and genetic analysis of these traits remains one of the major challenges facing biomedical researchers. Advances in high- throughput technologies have led to increasing availability of large-scale genetic sequence information and other related biological data sets. If robust, powerful statistical and computational methods and tools are developed to analyze these data, then additional progress can be made on identifying and characterizing the genetic components of complex disorders. This, in turn, has the potential to (1) lead to better understanding of the biology of such disorders, (2) clarify the role of environmental risk factors, which could be targets of cost-effective treatment and prevention strategies, and (3) lead to improvements in personalized medical care. The goal of the project is development of robust, powerful trait-association data analysis methods that will be useful for a wide variety of complex traits in a full spectrum of study designs, including unrelated samples with mild population structure, samples of related individuals, and individuals from admixed or founder populations. Specific aims of the project are development of (1) more powerful association methods for binary traits, including joint analysis of multiple phenotypes and multiple genetic variants; (2) fast, robust methods for assessing significance in a wide variety of association studies, including methods to detect sparse and weak association signals; and (3) methods to analyze genetic interaction in an association analysis, for one genome or a pair of interacting genomes. The proposed methods incorporate relevant covariates, allow ascertainment, and account for population structure and relatedness of individuals in the sample. Together, the insights attained from the proposed methods and their application to current genetic questions will drive further discoveries into and create greater understanding of the genetics of complex traits. 1