Large-scale genome-wide association (GWA) studies have become an important tool in human genomics, mostly focused on disease but also on adaptive variations such as skin colour. The technique is now shown to be similarly useful in plants. Atwell et al. report a GWA study of over a hundred phenotypes in naturally occurring inbred lines of Arabidopsis thaliana. The results range from significant associations, usually for single genes, to more difficult-to-interpret findings that indicate confounding by complex genetics and population structure. The accompanying paper by Todesco et al. demonstrates the ability of this technique to detect major-effect gene loci. Using forward genetics and GWA analyses, they show that variation at a single locus (ACD6) in Arabidopsis underlies phenotypic variation in vegetative growth as well as resistance to infection. The strong enhancement of resistance mediated by one of the alleles at this locus explains its persistence in natural populations throughout the world, despite it drastically reducing new leaf production. Here, large-scale genome-wide association studies were carried out with the naturally occurring inbred lines of Arabidopsis thaliana, which can be genotyped once and phenotyped repeatedly. The results range from significant associations, usually corresponding to single genes, to findings that are more difficult to interpret, because confounding by complex genetics and population structure makes it hard to distinguish true associations from false. Although pioneered by human geneticists as a potential solution to the challenging problem of finding the genetic basis of common human diseases1,2, genome-wide association (GWA) studies have, owing to advances in genotyping and sequencing technology, become an obvious general approach for studying the genetics of natural variation and traits of agricultural importance. They are particularly useful when inbred lines are available, because once these lines have been genotyped they can be phenotyped multiple times, making it possible (as well as extremely cost effective) to study many different traits in many different environments, while replicating the phenotypic measurements to reduce environmental noise. Here we demonstrate the power of this approach by carrying out a GWA study of 107 phenotypes in Arabidopsis thaliana, a widely distributed, predominantly self-fertilizing model plant known to harbour considerable genetic variation for many adaptively important traits3. Our results are dramatically different from those of human GWA studies, in that we identify many common alleles of major effect, but they are also, in many cases, harder to interpret because confounding by complex genetics and population structure make it difficult to distinguish true associations from false. However, a-priori candidates are significantly over-represented among these associations as well, making many of them excellent candidates for follow-up experiments. Our study demonstrates the feasibility of GWA studies in A. thaliana and suggests that the approach will be appropriate for many other organisms.

Arabidopsis thaliana serves as a model organism for the study of fundamental physiological, cellular, and molecular processes. It has also greatly advanced our understanding of intraspecific genome variation. We present a detailed map of variation in 1,135 high-quality re-sequenced natural inbred lines representing the native Eurasian and North African range and recently colonized North America. We identify relict populations that continue to inhabit ancestral habitats, primarily in the Iberian Peninsula. They have mixed with a lineage that has spread to northern latitudes from an unknown glacial refugium and is now found in a much broader spectrum of habitats. Insights into the history of the species and the fine-scale distribution of genetic diversity provide the basis for full exploitation of A. thaliana natural variation through integration of genomes and epigenomes with molecular and non-molecular phenotypes.

Magnus Nordborg and colleagues report a multi-locus mixed-model method (MLMM) for genome-wide association studies in structured populations. Their simulations show that MLMM offers increased power and a reduced false discovery rate, and applications to both human and Arabidopsis thaliana data sets identify new associations and allelic heterogeneity. Population structure causes genome-wide linkage disequilibrium between unlinked loci, leading to statistical confounding in genome-wide association studies. Mixed models have been shown to handle the confounding effects of a diffuse background of large numbers of loci of small effect well, but they do not always account for loci of larger effect. Here we propose a multi-locus mixed model as a general method for mapping complex traits in structured populations. Simulations suggest that our method outperforms existing methods in terms of power as well as false discovery rate. We apply our method to human and Arabidopsis thaliana data, identifying new associations and evidence for allelic heterogeneity. We also show how a priori knowledge from an A. thaliana linkage mapping study can be integrated into our method using a Bayesian approach. Our implementation is computationally efficient, making the analysis of large data sets (n > 10,000) practicable.

We resequenced 876 short fragments in a sample of 96 individuals of Arabidopsis thaliana that included stock center accessions as well as a hierarchical sample from natural populations. Although A. thaliana is a selfing weed, the pattern of polymorphism in general agrees with what is expected for a widely distributed, sexually reproducing species. Linkage disequilibrium decays rapidly, within 50 kb. Variation is shared worldwide, although population structure and isolation by distance are evident. The data fail to fit standard neutral models in several ways. There is a genome-wide excess of rare alleles, at least partially due to selection. There is too much variation between genomic regions in the level of polymorphism. The local level of polymorphism is negatively correlated with gene density and positively correlated with segmental duplications. Because the data do not fit theoretical null distributions, attempts to infer natural selection from polymorphism data will require genome-wide surveys of polymorphism in order to identify anomalous regions. Despite this, our data support the utility of A. thaliana as a model for evolutionary functional genomics.

Detlef Weigel and colleagues report the genome sequence of Arabidopsis lyrata. In comparison with the much smaller genome of A. thaliana, from which A. lyrata diverged about 10 million years ago, they find that the reduction in genome size is attributed to a large number of deletions across the genome. We report the 207-Mb genome sequence of the North American Arabidopsis lyrata strain MN47 based on 8.3× dideoxy sequence coverage. We predict 32,670 genes in this outcrossing species compared to the 27,025 genes in the selfing species Arabidopsis thaliana. The much smaller 125-Mb genome of A. thaliana, which diverged from A. lyrata 10 million years ago, likely constitutes the derived state for the family. We found evidence for DNA loss from large-scale rearrangements, but most of the difference in genome size can be attributed to hundreds of thousands of small deletions, mostly in noncoding DNA and transposons. Analysis of deletions and insertions still segregating in A. thaliana indicates that the process of DNA loss is ongoing, suggesting pervasive selection for a smaller genome. The high-quality reference genome sequence for A. lyrata will be an important resource for functional, evolutionary and ecological studies in the genus Arabidopsis.