Neighbor GWAS: incorporating neighbor genotypic identity into genome-wide association studies of field herbivory on Arabidopsis thaliana

ABSTRACT

An increasing number of field studies show that the phenotype of an individual plant depends not only on its genotype but also on that of neighboring plants; however, this fact is not taken into consideration in genome-wide association studies (GWAS). Based on the Ising model of ferromagnetism, we incorporated neighbor genotypic identity into a regression model, named “Neighbor GWAS”. Our simulations showed that phenotypic variation explained by neighbor effects approached a plateau when an effective spatial scale became narrow. Thus, the effective range of neighbor effects could be estimated based on the scale at which an increase in the explained variation peaked. The power to detect causal variants of neighbor effects was moderate to strong when a trait was governed by tens of variants. In contrast, the power decreased when hundreds of variants underlay a single trait. We applied neighbor GWAS to field herbivory data from 199 accessions of Arabidopsis thaliana and found a significant contribution of the neighbor effects to the observed damage variation. Interestingly, a locus responsive to methyl jasmonate (AT2G34810 called BBE16) was located near the second top-scoring single nucleotide polymorphism (SNP) of the neighbor effects on the herbivory, while self-genotype effects had the third top-scoring SNP near a locus encoding flavin-monooxygenase glucosinolate S-oxygenase 2 (FMO GS-OX2). Overall, neighbor GWAS highlight the overlooked role of plant neighborhood effects in shaping phenotypic variation. The neighbor GWAS method is available as an R package at CRAN (https://cran.r-project.org/package=rNeighborGWAS), providing a novel tool to analyse complex traits in spatially structured environments.