LAUSR

Although genome-wide association studies are widely used in mining genes for quantitative traits, effects to be estimated are confounded and methodologies of detecting interactions are imperfect. To address these issues, first, the mixed model proposed here estimates the genotypic effects for AA, Aa, and aa, while the genotypic polygenic background replaces additive and dominance polygenic backgrounds. Then, the estimated genotypic effects are partitioned into additive and dominance effects using one-way analysis-of-variance model. This strategy was further expanded to cover QTN-by-environment interaction (QEI) and QTN-by-QTN interaction (QQI) using the same mixed model framework. Thus, a three variance components mixed model was integrated with our mrMLM method to establish a new methodological framework that detects all types of loci and estimates their effects, namely 3VmrMLM. In Monte Carlo studies, 3VmrMLM correctly detected all types of loci and almost unbiasedly estimated their effects, with high powers and accuracies and low false positive rate. In the re-analyses of ten traits in 1439 rice hybrids, 269 known genes, 45 known gene-by-environment interactions and 20 known gene-by-gene interactions strongly validated 3VmrMLM. Further analyses of known genes showed more small (67.49%), minor allele frequency (35.52%), and pleiotropic (30.54%) genes, with higher repeatability across datasets (54.36%), and more dominance loci. In addition, heteroscedasticity mixed model in multiple environments and dimension reduction methods in quite a number of environments were developed to detect QEI, and variable selection under polygenic background was proposed in QQI detection. This study provides a new approach to reveal the genetic architecture of quantitative traits.