A compressed variance component mixed model for detecting QTNs and QTN-by-environment and QTN-by-QTN interactions in genome-wide association studies
作者机构：Crop Information CenterCollege of Plant Science and TechnologyHuazhong Agricultural UniversityWuhan 430070China State Key Laboratory of Cotton BiologyAnyang 455000China
出 版 物：《Molecular Plant》 (分子植物（英文版）)
年 卷 期：2022年第15卷第4期
页 面：630-650页
核心收录：
学科分类：0710[理学-生物学] 07[理学] 09[农学] 071007[理学-遗传学] 0901[农学-作物学] 0902[农学-园艺学] 090102[农学-作物遗传育种]
基 金：supported by the National Natural Science Foundation of China(32070557 and 31871242) the Fundamental Research Funds for the Central Universities(2662020ZKPY017) the Huazhong Agricultural University Scientific&Technological Self-Innovation Foundation(2014RC020) the State Key Laboratory of Cotton Biology Open Fund(CB2021B01)
主 题：genome-wide association study QTN QTN-by-environment interaction QTN-by-QTN interaction compressed variance component mixed model rice
摘 要：Although genome-wide association studies are widely used to mine genes for quantitative traits,the effects to be estimated are confounded,and the methodologies for detecting interactions are *** address these issues,the mixed model proposed here first estimates the genotypic effects for AA,Aa,and aa,and the genotypic polygenic background replaces additive and dominance polygenic ***,the estimated genotypic effects are partitioned into additive and dominance effects using a one-way analysis of variance *** strategy was further expanded to cover QTN-by-environment interactions(QEIs)and QTN-by-QTN interactions(QQIs)using the same mixed-model ***,a three-variance-component mixed model was integrated with our multi-locus random-SNP-effect mixed linear model(mrMLM)method to establish a new methodological framework,3VmrMLM,that detects all types of loci and estimates their *** Monte Carlo studies,3VmrMLM correctly detected all types of loci and almost unbiasedly estimated their effects,with high powers and accuracies and a low false positive *** re-analyses of 10 traits in 1439 rice hybrids,detection of 269 known genes,45 known gene-by-environment interactions,and 20 known gene-by-gene interactions strongly validated *** 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 *** addition,a heteroscedasticity mixed model in multiple environments and dimension reduction methods in quite a number of environments were developed to detect QEIs,and variable selection under a polygenic background was proposed for QQI *** study provides a new approach for revealing the genetic architecture of quantitative traits.
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