title: Analytical correction for multiple testing in admixture mapping.
abstract: Admixture mapping, using unrelated individuals from the admixture populations that result from recent mating between members of each parental population, is an efficient approach to localize disease-causing variants that differ in frequency between two or more historically separated populations. Recently, several methods have been proposed to test linkage between a susceptibility gene and a disease locus by using admixture-generated linkage disequilibrium (LD) for each of the genotyped markers. In a genome scan, admixture mapping usually tests 2,000 to 3,000 markers across the genome. Currently, either a very conservative Sidak (or Bonferroni) correction or a very time consuming simulation-based method is used to correct for the multiple tests and evaluate the overall p value. In this report, we propose a computationally efficient analytical approach for correction of the multiple tests and for calculating the overall p value for an admixture genome scan. Except for the Sidak (or Bonferroni) correction, our proposed method is the first analytical approach for correction of the multiple tests and for calculating the overall p value for a genome scan. Our simulation studies show that the proposed method gives correct overall type I error rates for genome scans in all cases, and is much more computationally efficient than simulation-based methods.
authors.affiliation: Department of Mathematical Sciences, Michigan Technological University, Houghton, 49931, USA.
authors.forename: Qiuying
authors.initials: Q
authors.lastname: Sha
authors.forename: Xihuan
authors.initials: X
authors.lastname: Zhang
authors.forename: Xiaofeng
authors.initials: X
authors.lastname: Zhu
authors.forename: Shuanglin
authors.initials: S
authors.lastname: Zhang
mesh_terms: D019295:Computational Biology / Q000379:methods* / Q000706:statistics & numerical data*; D008040:Genetic Linkage; D005828:Genetics, Population / Q000379:methods* / Q000706:statistics & numerical data*; D006801:Humans; D008957:Models, Genetic