Effect of population structure corrections on the results of association mapping tests in complex maize diversity panels

Abstract

Association mapping of sequence polymorphisms underlying the phenotypic variability of quantitative agronomical traits is now a widely used method in plant genetics. However, due to the common presence of a complex genetic structure within the plant diversity panels, spurious associations are expected to be highly frequent. Several methods have thus been suggested to control for panel structure. They mainly rely on ad hoc criteria for selecting the number of ancestral groups; which is often not evident for the complex panels that are commonly used in maize. It was thus necessary to evaluate the effect of the selected structure models on the association mapping results. A real maize data set (342 maize inbred lines and 12,000 SNPs) was used for this study. The panel structure was estimated using both Bayesian and dimensional reduction methods, considering an increasing number of ancestral groups. Effect on association tests depends in particular on the number of ancestral groups and on the trait analyzed. The results also show that using a high number of ancestral groups leads to an over-corrected model in which all causal loci vanish. Finally the results of all models tested were combined in a meta-analysis approach. In this way, robust associations were highlighted for each analyzed trait.Electronic supplementary materialThe online version of this article (doi:10.1007/s00122-010-1519-y) contains supplementary material, which is available to authorized users.

Population structure and linkage disequilibrium in elite barley breeding germplasm from the United States

“…2). Similar conclusions were also reported by others (Patterson et al, 2006;Song et al, 2009;Mezmouk et al, 2011). Thus, it appears that there are no serious shortcomings of using PCA for population structure analysis.…”

Construction of Core Collections Suitable for Association Mapping to Optimize Use of Mediterranean Olive (Olea europaea L.) Genetic Resources

“…As the sub-structure within subsets and the relatedness between genotypes (known also as the kinship coefficient) are the major components to take into consideration in association mapping analyses [80]–[82], an assessment of both factors in proposed core collections was performed. Two approaches were used to assess the genetic structure; (i) principal coordinate analysis (PCoA) implemented in the DAR win v.5.0.137 program using a simple matching coefficient to describe the spatial distribution of genotypes; and (ii) model-based Bayesian clustering implemented in S tructure v.2.2 [83] according to the parameters described in Haouane et al [52].…”

Novel measures of linkage disequilibrium that correct the bias due to population structure and relatedness

“…Consequently, such a structured sample can lead to a biased estimate of linkage disequilibrium, which may increase the rate of false positives statistically associated to the trait without actually being involved in its variation. Altshuler et al (2008) and references therein mentioned this problem and recently, Mezmouk et al (2011) illustrated the strong effect of structure corrections on the association mapping results using a real maize data set. Moreover, samples for linkage disequilibrium estimation are usually composed of unrelated individuals or individuals related through a pedigree so complex that it is ignored.…”