An Arabidopsis Example of Association Mapping in Structured Samples

Abstract

A potentially serious disadvantage of association mapping is the fact that marker-trait associations may arise from confounding population structure as well as from linkage to causative polymorphisms. Using genome-wide marker data, we have previously demonstrated that the problem can be severe in a global sample of 95 Arabidopsis thaliana accessions, and that established methods for controlling for population structure are generally insufficient. Here, we use the same sample together with a number of flowering-related phenotypes and data-perturbation simulations to evaluate a wider range of methods for controlling for population structure. We find that, in terms of reducing the false-positive rate while maintaining statistical power, a recently introduced mixed-model approach that takes genome-wide differences in relatedness into account via estimated pairwise kinship coefficients generally performs best. By combining the association results with results from linkage mapping in F2 crosses, we identify one previously known true positive and several promising new associations, but also demonstrate the existence of both false positives and false negatives. Our results illustrate the potential of genome-wide association scans as a tool for dissecting the genetics of natural variation, while at the same time highlighting the pitfalls. The importance of study design is clear; our study is severely under-powered both in terms of sample size and marker density. Our results also provide a striking demonstration of confounding by population structure. While statistical methods can be used to ameliorate this problem, they cannot always be effective and are certainly not a substitute for independent evidence, such as that obtained via crosses or transgenic experiments. Ultimately, association mapping is a powerful tool for identifying a list of candidates that is short enough to permit further genetic study.

Mapping QTL for agronomic traits in breeding populations

“…This approach is also applicable to family mapping as it can be extended toward multiple segregating families (Blanc et al 2006). Association mapping, which has been developed by human geneticists, has recently been adopted by plant geneticists and applied to population mapping and to family mapping (Thornsberry et al 2001;Breseghello and Sorrells 2006;Zhao et al 2007;Harjes et al 2008). To detect QTL in populations with complex pedigree, mixed models (e.g.…”

Investigation of the domestication of common bean (Phaseolus vulgaris) using multilocus sequence data

“…As has been documented, population structure can result in spurious associations (false positives) between phenotypes and unlinked markers (Knowler et al 1988;Cardon and Palmer 2003). Even though mixed linear models, which account for population structure and relatedness can minimise the discovery of false positives, this is at the expense of reducing the power to detect true positives (Zhao et al 2007;Brachi et al 2010).…”

Interpreting principal component analyses of spatial population genetic variation

“…[23]). For the analogous 2-dimensional setting, because PC1 and PC2 are typically orthogonal gradients, a biplot of the first 2 PCs essentially reproduces the geographic arrangement of sampled individuals (explaining PCA results on genetic variation in Arabidopsis [24] for example), and biplots involving later PCs have intricate patterns analogous to Lissajous curves ( fig. S5).…”

“…For example, if PC1 and PC2 are orthogonal gradients in space, including them in a regression essentially controls for latitude and longitude, and allows for linear trends in phenotype across space; including higher-order PCs allows for more flexible spatial trends. While some practical issues remain, including how many PCs should be used (supplementary text) and how best to employ the PCs [24], this analogy between the PCAbased and spatial-statistics approaches gives an intuitively appealing justification for using PCA to control for spurious associations in spatially-structured populations. It also has two important practical advantages over simply using geographic information on each individual directly.…”