A multi-locus association analysis method integrating phenotype and expression data reveals multiple novel associations to flowering time variation in wild-collected Arabidopsis thaliana

Abstract

AO_SCPLOWBSTRACTC_SCPLOWWhen a species adapts to a new habitat, selection for the fitness traits often result in a confounding between genome-wide genotype and adaptive alleles. It is a major statistical challenge to detect such adaptive polymorphisms if the confounding is strong, or the effects of the adaptive alleles are weak. Here, we describe a novel approach to dissect polygenic traits in natural populations. First, candidate adaptive loci are identified by screening for loci that are directly associated to the trait or control the expression of genes known to affect it. Then, the multi-locus genetic architecture is inferred using a backward elimination association analysis across all the candidate loci using an adaptive false-discovery rate based threshold. Effects of population stratification are controlled by corrections for population structure in the pre-screening step and by simultaneously testing all candidate loci in the multi-locus model. We illustrate the method by exploring the polygenic basis of an important adaptive trait, flowering time in Arabidopsis thaliana, using public data from the 1,001 genomes project. Our method revealed associations between 33 (29) loci and flowering time at 10 (16){degrees}C in this collection of natural accessions, where standard genome wide association analysis methods detected 5 (3) loci. The 33 (29) loci explained approximately 55 (48)% of the total phenotypic variance of the respective traits. Our work illustrates how the genetic basis of highly polygenic adaptive traits in natural populations can be explored in much greater detail by using new multi-locus mapping approaches taking advantage of prior biological information as well as genome and transcriptome data.