Alexander Platt

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. thaliana 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.

The Scale of Population Structure in Arabidopsis thaliana

The population structure of an organism reflects its evolutionary history and influences its evolutionary trajectory. It constrains the combination of genetic diversity and reveals patterns of past gene flow. Understanding it is a prerequisite for detecting genomic regions under selection, predicting the effect of population disturbances, or modeling gene flow. This paper examines the detailed global population structure of Arabidopsis thaliana. Using a set of 5,707 plants collected from around the globe and genotyped at 149 SNPs, we show that while A. thaliana as a species self-fertilizes 97% of the time, there is considerable variation among local groups. This level of outcrossing greatly limits observed heterozygosity but is sufficient to generate considerable local haplotypic diversity. We also find that in its native Eurasian range A. thaliana exhibits continuous isolation by distance at every geographic scale without natural breaks corresponding to classical notions of populations. By contrast, in North America, where it exists as an exotic species, A. thaliana exhibits little or no population structure at a continental scale but local isolation by distance that extends hundreds of km. This suggests a pattern for the development of isolation by distance that can establish itself shortly after an organism fills a new habitat range. It also raises questions about the general applicability of many standard population genetics models. Any model based on discrete clusters of interchangeable individuals will be an uneasy fit to organisms like A. thaliana which exhibit continuous isolation by distance on many scales.

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.

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.

Coselected genes determine adaptive variation in herbivore resistance throughout the native range of Arabidopsis thaliana.

Significance How organisms adapt to the biotic and abiotic environment is a major question in evolutionary biology that addresses how natural selection shapes biodiversity. Using mass spectrometry, we characterized natural variation in major defense molecules, aliphatic glucosinolates, in hundreds of ecotypes of the model plant Arabidopsis thaliana, spanning the native range of the species. Using extensive genomic resources and field experiments, we provide strong evidence that populations are adapted to local herbivore communities along a striking longitudinal cline. In addition, we show that only a few genes of strong effect govern this natural variation and that alleles at these genes, located on different chromosomes, appear to have coevolved through epistatic selection. The “mustard oil bomb” is a major defense mechanism in the Brassicaceae, which includes crops such as canola and the model plant Arabidopsis thaliana. These plants produce and store blends of amino acid-derived secondary metabolites called glucosinolates. Upon tissue rupture by natural enemies, the myrosinase enzyme hydrolyses glucosinolates, releasing defense molecules. Brassicaceae display extensive variation in the mixture of glucosinolates that they produce. To investigate the genetics underlying natural variation in glucosinolate profiles, we conducted a large genome-wide association study of 22 methionine-derived glucosinolates using A. thaliana accessions from across Europe. We found that 36% of among accession variation in overall glucosinolate profile was explained by genetic differentiation at only three known loci from the glucosinolate pathway. Glucosinolate-related SNPs were up to 490-fold enriched in the extreme tail of the genome-wide FST scan, indicating strong selection on loci controlling this pathway. Glucosinolate profiles displayed a striking longitudinal gradient with alkenyl and hydroxyalkenyl glucosinolates enriched in the West. We detected a significant contribution of glucosinolate loci toward general herbivore resistance and lifetime fitness in common garden experiments conducted in France, where accessions are enriched in hydroxyalkenyls. In addition to demonstrating the adaptive value of glucosinolate profile variation, we also detected long-distance linkage disequilibrium at two underlying loci, GS-OH and GS-ELONG. Locally cooccurring alleles at these loci display epistatic effects on herbivore resistance and fitness in ecologically realistic conditions. Together, our results suggest that natural selection has favored a locally adaptive configuration of physically unlinked loci in Western Europe.

Association mapping through heuristic evolutionary history reconstruction-application to GAW15 Problem 3

This paper presents a novel method of identifying phenotypically important regions of the genome. It involves a form of association mapping that works by summarizing properties of the ancestral recombination graph (ARG) of a sample of unrelated phenotyped and genotyped individuals. By breaking the sample into many small sub-samples and averaging the results, it becomes computationally tractable to measure the degree to which the evolutionary history of any locus is consistent with the distribution of the phenotypes in the sample. Analysis of simulated rheumatoid arthritis data demonstrates the efficiency and effectiveness of this method in identifying loci of large phenotypic effect.

Protein evolution depends on multiple distinct population size parameters

That population size affects the fate of new mutations arising in genomes, modulating both how frequently they arise and how efficiently natural selection is able to filter them, is well established. It is therefore clear that these distinct roles for population size that characterize different processes should affect the evolution of proteins and need to be carefully defined. Empirical evidence is consistent with a role for demography in influencing protein evolution, supporting the idea that functional constraints alone do not determine the composition of coding sequences.Given that the relationship between population size, mutant fitness and fixation probability has been well characterized, estimating fitness from observed substitutions is well within reach with well-formulated models. Molecular evolution research has, therefore, increasingly begun to leverage concepts from population genetics to quantify the selective effects associated with different classes of mutation. However, in order for this type of analysis to provide meaningful information about the intra- and inter-specific evolution of coding sequences, a clear definition of concepts of population size, what they influence, and how they are best parameterized is essential.Here, we present an overview of the many distinct concepts that “population size” and “effective population size” may refer to, what they represent for studying proteins, and how this knowledge can be harnessed to produce better specified models of protein evolution.

Age distributions of rare lineages reveal recent demographic history and selection

The age of an allele of a given frequency offers insight into both its function and origin, and the distribution of ages of alleles in a population conveys significant information about its history. The rarer the allele the more likely it is to reveal functional biological insight and the more recent the historical revelation. By measuring the length of the haplotype shared between an individual carrying a rare variant and its closest relative not carrying the variant we are able to approximate the age of the variant and can apply this method even when only a single copy of a variant has been sampled in a population. Applying this technique to rare variants in a large population sample from the United Kingdom, we identify historical migration from West Africa approximately 400 years ago, evidence of direct selection against novel protein-altering rare variants in individual biological pathways, continued negative frequency dependent selection on protein-altering variants in olfactory transducers and the innate immune system, and map the impact of background selection on the most recent portions of the sample genealogy.