Fast Detection of IBD Segments Associated With Quantitative Traits in Genome-wide Association Studies. Z. Wang1, E. Kang1, B. Han2,3, S. Snir4, E. Eskin1 1) Computer Science Department, University of California, Los Angeles, CA 90095; 2) Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; 3) Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA; 4) Institute of Evolution, Department of Evolutionary and Environmental Biology, Faculty of Natural Sciences, University of Haifa, Israel.
Recently, many methods have been developed to detect the identity-by-descent (IBD) segments between a pair of individuals. These methods are able to detect very small shared IBD segments between a pair of individuals up to 2 centimorgans in length. This IBD information can be used to identify recent rare mutations associated with phenotype of interest. Previous approaches for IBD association were applicable to case/control phenotypes. In this work, we propose a novel and natural statistic for the IBD association testing, which can be applied to quantitative traits. A drawback of the statistic is that it requires a large number of permutations to assess the significance of the association, which can be a great computational challenge. We make a connection between the proposed statistic and linear models so that it does not require permutations to assess the significance of an association. In addition, our method can control population structure by utilizing linear mixed models.
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