A novel genome-wide association study method for detecting quantitative trait loci interacting with complex population structures in plant genetics.

Abstract

In plant genetics, most modern association analyses are performed on panels that bring together individuals from several populations, including admixed individuals whose genomes comprise chromosomal regions from different populations. These panels can identify quantitative trait loci (QTLs) with population-specific effects and epistatic interactions between QTLs and polygenic backgrounds. However, analyzing a diverse panel constitutes a challenge for statistical analysis. The statistical model must account for possible interactions between a QTL and the panel structure while strictly controlling the detection error rate. Although models to detect population-specific QTLs have already been developed, they rely on prior information about the population structure. In practice, this prior information may be missing as many genome-wide association study (GWAS) panels exhibit complex population structures. The present study introduces 2 new models for detecting QTLs interacting with complex population structures. Both incorporate an interaction term between single nucleotide polymorphism/haplotype block and genetic background into conventional GWAS models. The proposed models were compared with state-of-the-art models through simulation studies that considered QTLs with different levels of interaction with their genetic backgrounds. Results showed that models matching simulation settings were most effective for detecting corresponding QTLs while the proposed models outperformed classical models in detecting QTLs interacting with polygenes. Additionally, when applied to a soybean dataset, one of our models identified putative associated QTLs that conventional models failed to detect. The new models, implemented in the RAINBOWR package available on CRAN, are expected to help uncover complex trait genetic architectures.