Recovering power in Association Mapping Panels with variable levels of linkage disequilibrium

Abstract : Association mapping has permitted the discovery of major QTLs 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 studied analytically power in association studies by computing non-centrality 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 estimate the kinship matrix to recover power in regions of high LD. In the first one, we estimated the kinship with all the markers located on other chromosomes 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 more powerful than classical models.