Recovering Power in Association Mapping Panels with Variable Levels of Linkage Disequilibrium. Issue 1 (1st May 2014)

• Record Type: Journal Article
• Authors: Rincent, Renaud; Moreau, Laurence; Monod, HervÉ; Kuhn, Estelle; Melchinger, Albrecht E; Malvar, Rosa A; Moreno-Gonzalez, Jesus; Nicolas, StÉphane; Madur, Delphine; Combes, ValÉrie; Dumas, Fabrice; Altmann, Thomas; Brunel, Dominique; Ouzunova, Milena; Flament, Pascal; Dubreuil, Pierre; Charcosset, Alain; Mary-Huard, Tristan
• Is Part Of: Genetics. Volume 197:Issue 1(2014)
• Page Start: 375
• Page End: 387
• Publication Date: 2014-05-01
• Subjects: association mapping -- power -- kinship -- linkage disequilibrium -- Zea mays L; Genetics -- Periodicals; 576.5
• DOI: 10.1534/genetics.113.159731
• Languages: English
• ISSNs: 0016-6731

Abstract

Abstract: Association mapping has permitted the discovery of major QTL in many species. It can be applied to existing populations and, as a consequence, it is generally necessary to take into account structure and relatedness among individuals in the statistical model to control false positives. We analytically studied power in association studies by computing noncentrality parameter of the tests and its relationship with parameters characterizing diversity (genetic differentiation between groups and allele frequencies) and kinship between individuals. Investigation of three different maize diversity panels genotyped with the 50k SNPs array highlighted contrasted average power among panels and revealed gaps of power of classical mixed models in regions with high linkage disequilibrium (LD). These gaps could be related to the fact that markers are used for both testing association and estimating relatedness. We thus considered two alternative approaches to estimating the kinship matrix to recover power in regions of high LD. In the first one, we estimated the kinship with all the markers that are not located on the same chromosome than the tested SNP. In the second one, correlation between markers was taken into account to weight the contribution of each marker to the kinship. Simulations revealed that these two approaches were efficient to control false positives and were more powerful than classical models.