Bjarni J. Vilhjálmsson

Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines

Although pioneered by human geneticists as a potential solution to the challenging problem of finding the genetic basis of common human diseases, 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 traits. 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.u2009thaliana and suggests that the approach will be appropriate for many other organisms.

An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations

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.

Genome-wide patterns of genetic variation in worldwide Arabidopsis thaliana accessions from the RegMap panel

Arabidopsis thaliana is native to Eurasia and is naturalized across the world. Its ability to be easily propagated and its high phenotypic variability make it an ideal model system for functional, ecological and evolutionary genetics. To date, analyses of the natural genetic variation of A. thaliana have involved small numbers of individual plants or genetic markers. Here we genotype 1,307 worldwide accessions, including several regional samples, using a 250K SNP chip. This allowed us to produce a high-resolution description of the global pattern of genetic variation. We applied three complementary selection tests and identified new targets of selection. Further, we characterized the pattern of historical recombination in A. thaliana and observed an enrichment of hotspots in its intergenic regions and repetitive DNA, which is consistent with the pattern that is observed for humans but which is strikingly different from that observed in other plant species. We have made the seeds we used to produce this Regional Mapping (RegMap) panel publicly available. This panel comprises one of the largest genomic mapping resources currently available for global natural isolates of a non-human species.

A mixed-model approach for genome-wide association studies of correlated traits in structured populations

Genome-wide association studies (GWAS) are a standard approach for studying the genetics of natural variation. A major concern in GWAS is the need to account for the complicated dependence structure of the data, both between loci as well as between individuals. Mixed models have emerged as a general and flexible approach for correcting for population structure in GWAS. Here, we extend this linear mixed-model approach to carry out GWAS of correlated phenotypes, deriving a fully parameterized multi-trait mixed model (MTMM) that considers both the within-trait and between-trait variance components simultaneously for multiple traits. We apply this to data from a human cohort for correlated blood lipid traits from the Northern Finland Birth Cohort 1966 and show greatly increased power to detect pleiotropic loci that affect more than one blood lipid trait. We also apply this approach to an Arabidopsis thaliana data set for flowering measurements in two different locations, identifying loci whose effect depends on the environment.

Massive genomic variation and strong selection in Arabidopsis thaliana lines from Sweden

Despite advances in sequencing, the goal of obtaining a comprehensive view of genetic variation in populations is still far from reached. We sequenced 180 lines of A. thaliana from Sweden to obtain as complete a picture as possible of variation in a single region. Whereas simple polymorphisms in the unique portion of the genome are readily identified, other polymorphisms are not. The massive variation in genome size identified by flow cytometry seems largely to be due to 45S rDNA copy number variation, with lines from northern Sweden having particularly large numbers of copies. Strong selection is evident in the form of long-range linkage disequilibrium (LD), as well as in LD between nearby compensatory mutations. Many footprints of selective sweeps were found in lines from northern Sweden, and a massive global sweep was shown to have involved a 700-kb transposition.

Conditions under which genome-wide association studies will be positively misleading.

Genome-wide association mapping is a popular method for using natural variation within a species to generate a genotype–phenotype map. Statistical association between an allele at a locus and the trait in question is used as evidence that variation at the locus is responsible for variation of the trait. Indirect association, however, can give rise to statistically significant results at loci unrelated to the trait. We use a haploid, three-locus, binary genetic model to describe the conditions under which these indirect associations become stronger than any of the causative associations in the organism—even to the point of representing the only associations present in the data. These indirect associations are the result of disequilibrium between multiple factors affecting a single trait. Epistasis and population structure can exacerbate the problem but are not required to create it. From a statistical point of view, indirect associations are true associations rather than the result of stochastic noise: they will not be ameliorated by increasing sampling size or marker density and can be reproduced in independent studies.

The nature of confounding in genome-wide association studies

The authors argue that population structure per se is not a problem in genome-wide association studies — the true sources are the environment and the genetic background, and the latter is greatly underappreciated. They conclude that mixed models effectively address this issue.

Genome-wide association study of Arabidopsis thaliana leaf microbial community.

Identifying the factors that influence the outcome of host-microbial interactions is critical to protecting biodiversity, minimizing agricultural losses, and improving human health. A few genes that determine symbiosis or resistance to infectious disease have been identified in model species, but a comprehensive examination of how a hosts genotype influences the structure of its microbial community is lacking. Here we report the results of a field experiment with the model plant Arabidopsis thaliana to identify the fungi and bacteria that colonize its leaves and the host loci that influence the microbes’ numbers. The composition of this community differs among accessions of A. thaliana. Genome-wide association studies (GWAS) suggest that plant loci responsible for defense and cell wall integrity affect variation in this community. Furthermore, species richness in the bacterial community is shaped by host genetic variation, notably at loci that also influence the reproduction of viruses, trichome branching and morphogenesis.

GWAPP: A Web Application for Genome-Wide Association Mapping in Arabidopsis

A user-friendly, interactive Web-based application is presented for conducting genome-wide association studies in Arabidopsis. Genome-wide scans for association between phenotype and ∼206,000 single nucleotide polymorphisms in 1386 public accessions can be completed in minutes. The application combines a state-of-the-art mixed model with interactive Manhattan and linkage disequilibrium plots, making it easy to carry out exploratory analyses without programming skills. Arabidopsis thaliana is an important model organism for understanding the genetics and molecular biology of plants. Its highly selfing nature, small size, short generation time, small genome size, and wide geographic distribution make it an ideal model organism for understanding natural variation. Genome-wide association studies (GWAS) have proven a useful technique for identifying genetic loci responsible for natural variation in A. thaliana. Previously genotyped accessions (natural inbred lines) can be grown in replicate under different conditions and phenotyped for different traits. These important features greatly simplify association mapping of traits and allow for systematic dissection of the genetics of natural variation by the entire A. thaliana community. To facilitate this, we present GWAPP, an interactive Web-based application for conducting GWAS in A. thaliana. Using an efficient implementation of a linear mixed model, traits measured for a subset of 1386 publicly available ecotypes can be uploaded and mapped with a mixed model and other methods in just a couple of minutes. GWAPP features an extensive, interactive, and user-friendly interface that includes interactive Manhattan plots and linkage disequilibrium plots. It also facilitates exploratory data analysis by implementing features such as the inclusion of candidate polymorphisms in the model as cofactors.

Analysis and visualization of Arabidopsis thaliana GWAS using web 2.0 technologies

With large-scale genomic data becoming the norm in biological studies, the storing, integrating, viewing and searching of such data have become a major challenge. In this article, we describe the development of an Arabidopsis thaliana database that hosts the geographic information and genetic polymorphism data for over 6000 accessions and genome-wide association study (GWAS) results for 107 phenotypes representing the largest collection of Arabidopsis polymorphism data and GWAS results to date. Taking advantage of a series of the latest web 2.0 technologies, such as Ajax (Asynchronous JavaScript and XML), GWT (Google-Web-Toolkit), MVC (Model-View-Controller) web framework and Object Relationship Mapper, we have created a web-based application (web app) for the database, that offers an integrated and dynamic view of geographic information, genetic polymorphism and GWAS results. Essential search functionalities are incorporated into the web app to aid reverse genetics research. The database and its web app have proven to be a valuable resource to the Arabidopsis community. The whole framework serves as an example of how biological data, especially GWAS, can be presented and accessed through the web. In the end, we illustrate the potential to gain new insights through the web app by two examples, showcasing how it can be used to facilitate forward and reverse genetics research. Database URL: http://arabidopsis.usc.edu/