Recovering Power in Association Mapping Panels with Variable Levels of Linkage Disequilibrium

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.

Improvement of Predictive Ability by Uniform Coverage of the Target Genetic Space

“…$G_i$ represents a polygenic effect for genotype i , and $e_i$ is the nongenetic residual $(e_i\sim N\left(0,{\sigma }_e^2\right))$. The distribution of $G_i$ is $\sim N\left(0,A{\sigma }_g^2\right).$ A is the additive genetic relationship matrix calculated from the molecular marker information as in Rincent et al (2014a). In this method, a specific A is calculated for each linkage group by excluding the markers on that particular linkage group.…”

Genome-wide analysis of yield in Europe: allelic effects as functions of drought and heat scenarios

“…As in Rincent et al (2014), the variance-covariance matrix of G was determined by a genetic relatedness (or kinship) matrix, derived from all SNPs except those on the chromosome containing the SNP being tested (Supplemental Methods S2). The SNP effects b were estimated by generalized least squares, and their significance (H 0 : b ¼ 0) tested with an F-statistic.…”

Dissecting quantitative trait variation in the resequencing era: complementarity of bi-parental, multi-parental and association panels

“…The first one tends to predict a minimal genomic region around a genetic association signal within a LD bin with a high degree of accuracy by observing around an association signal LD between polymorphic markers that is known to be stronger in cases compared to controls [55]. The second approach tends to recover power in regions of high LD by whether estimating the kinship with all the markers that are not located on the same chromosome as the tested SNP or taking into account the correlation between markers to weight the contribution of each marker to the kinship [56]. Thus, as previously stated [57], the method chosen to define an associated chromosomal region influences GWAs reliability and this issue remains under investigated.…”