Genome-wide association studies in plants: the missing heritability is in the field

Abstract

Genome-wide association studies (GWAS) have been even more successful in plants than in humans. Mapping approaches can be extended to dissect adaptive genetic variation from structured background variation in an ecological context.

Figure 1

Influence of sampling strategy on GWAS confounding effects. (a) Relationship between an adaptive trait and the position along a transect across the species distribution. The phenotype could, for example, be flowering time in A. thaliana, and accession lines could have been sampled along a transect from the south to the north of the species' distribution. The relationship is positive because the phenotype is adaptive to an environmental variable varying along this transect. (b) Some traits show a gradual change along the transect. In the example of flowering time in A. thaliana, environmental factors such as temperature and photoperiod would show continuous change along the latitudinal clines. But (c) the phenotypes also show extensive variation at a given position along the transect, suggesting that other ecological factors, acting at smaller scales, might also be acting as selective pressures on the phenotype. These local environmental variations could be related to soil quality, exposition, competition or predation. They can differ between sites that are close to one another without following a trend across the entire species' distribution range. (d) The genetic structure of a species can be represented as the proportion of individuals assigned to each of three structure groups along the species-wide transect. (e) A global sample covers the entire species repartition range; alternatively, local sub-samples can be taken at locations chosen with reference to the pattern of the population structure and to small-scale environmental variations that have the potential to act as selective pressures. (f-i) Effect of the sampling scale (from local to species-wide sampling) on LD and confounding factors. Sampling at a local scale should reduce the effect of most confounding factors but might decrease mapping precision.

Figure 2

Strategies for GWAS include population re-structuring and regional re-sampling. (a) A schematic phylogenetic tree illustrating genetic diversity and population structure in a hypothetical sample of a species whose adaptive traits are to be investigated genetically. (b) To map the loci underlying adaptation at the broadest scale, a balanced core set of accessions is made by pruning closely related individuals from the global set. GWAS can be performed at this stage, but for traits whose variation that is confounded by population structure (Figure 1), crosses are needed. (c) To map loci underlying local adaption, the focus should be on less structured regional sub-samples that are identified in the initial sample (for example, RegMap lines in A. thaliana). GWAS can be performed on these regional samples, which have reduced allelic heterogeneity and confounding by population structure, but LD blocks are likely to be longer in the regional subsets and this will decrease mapping resolution. Regional re-sampling along an environmental cline in the field can increase the power of the association mapping.