Empirical Bayes Method for Incorporating Data from Multiple Genome ScansBeasley T.M.a · Wiener H.b · Zhang K.a · Bartolucci A.A.a · Amos C.I.e · Allison D.a, c
Departments of aBiostatistics, Section of Statistical Genetics, bEpidemiology, and cNutrition Sciences and Clinical Nutrition Research Center, The University of Alabama at Birmingham, Birmingham, Ala.; eDepartment of Epidemiology, University of Texas, M.D. Anderson Cancer Center, Houston, Tex., USA
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Individual genome scans tend to have low power and can produce markedly biased estimates of QTL effects. Further, the confidence interval for their location is often prohibitively large for subsequent fine mapping and positional cloning. Given that a large number of genome scans have been conducted, not to mention the large number of variables and subsets tested, it is difficult to confidently rule out type 1 error as an explanation for significant effects even when there is apparent replication in a separate data set. We adapted Empirical Bayes (EB) methods [
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