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

This article focuses on the development and evaluation of two novel genome-wide association study (GWAS) models, SNPxGB and HBxGB, designed to detect quantitative trait loci (QTLs) interacting with complex population structures in plant genetics without requiring prior knowledge of population stratification. SNPxGB incorporates interaction terms between single nucleotide polymorphisms (SNPs) and genetic backgrounds, while HBxGB extends haplotype block (HB)-based models by including interactions between HBs and polygenic backgrounds, effectively capturing both discrete and continuous population structures. Simulation studies using a soybean dataset demonstrated that these models control false positives effectively and outperform classical GWAS models, particularly in detecting QTLs interacting with polygenic backgrounds, with HBxGB showing superior power for continuous structure interactions. Application to real soybean phenotypes revealed that these models complement existing approaches by identifying QTL candidates missed by conventional methods, suggesting their utility in uncovering complex genetic architectures in diverse populations. The models are implemented in the R package RAINBOWR, facilitating their use in plant genetics and potentially in animal and human genetics involving complex population structures.