Existing quantitative trait locus(QTL)mapping had low efficiency in identifying small-effect and closely linked QTL-by-environment interactions(QEIs)in recombinant inbred lines(RILs),especially in the era of global cl...Existing quantitative trait locus(QTL)mapping had low efficiency in identifying small-effect and closely linked QTL-by-environment interactions(QEIs)in recombinant inbred lines(RILs),especially in the era of global climate change.To address this challenge,here we integrate the compressed variance component mixed model with our GCIM to propose 3vGCIM for identifying QEIs in RILs,and extend 3vGCIM-random to 3vGCIM-fixed.3vGCIM integrates genome-wide scanning with machine learning,significantly improving power.In the mixed full model,we consider all possible effects and control for all possible polygenic backgrounds.In simulation studies,3vGCIM exhibits higher power(∼92.00%),higher accuracy of the estimates for QTL position(∼1.900 cM2)and effect(∼0.050),and lower false positive rate(∼0.48‰)and false negative rate(<8.10%)in three environments of 300 RILs each than ICIM(47.57%;3.607 cM2,0.583;2.81‰;52.43%)and MCIM(60.30%;5.279 cM2,0.274;2.17‰;39.70%).In the real data analysis of rice yield-related traits in 240 RILs,3vGCIM mines more known genes(57–60)and known gene-by-environment interactions(GEIs)(14–19)and candidate GEIs(21–23)than ICIM(27,2,and 7),and MCIM(21,1,and 3),especially in small-effect and linked QTLs and QEIs.This makes 3vGCIM a powerful and sensitive tool for QTL mapping and molecular QTL mapping.展开更多
利用普通小麦品种藁城8901和PH85-16按单粒传方法构建的重组自交系群体F6(RIL-6)共112个家系,研究了影响小麦粉及面片色泽的主要因素。结果表明:硬度、吸水率、湿面筋、干面筋、蛋白质含量,与小麦粉白度、L*值及鲜面片0 h L*值呈负相关...利用普通小麦品种藁城8901和PH85-16按单粒传方法构建的重组自交系群体F6(RIL-6)共112个家系,研究了影响小麦粉及面片色泽的主要因素。结果表明:硬度、吸水率、湿面筋、干面筋、蛋白质含量,与小麦粉白度、L*值及鲜面片0 h L*值呈负相关,与小麦粉及鲜面片0 h的a*和b*值呈正相关;叶黄素和PPO活性,与小麦粉及鲜面片0 h L*值呈负相关,与b*值呈正相关;面筋指数和稳定时间,与小麦粉及面片的L*值呈正相关;淀粉糊化性状的几个参数,与小麦粉白度、L*值及鲜面片0 h L*值呈正相关,与小麦粉及鲜面片0 h的a*值呈负相关。由结果看出在小麦粉及面片色泽性状的选育过程中,对蛋白质和淀粉等组分含量进行选择的同时,也要注意内部特性的改良。选择面筋指数高、叶黄素和PPO活性低、淀粉糊化黏度高的株系,以满足人们对亮白色食品的需求。展开更多
基金supported by the National Natural Science Foundation of China(32270673 and 32470657).
文摘Existing quantitative trait locus(QTL)mapping had low efficiency in identifying small-effect and closely linked QTL-by-environment interactions(QEIs)in recombinant inbred lines(RILs),especially in the era of global climate change.To address this challenge,here we integrate the compressed variance component mixed model with our GCIM to propose 3vGCIM for identifying QEIs in RILs,and extend 3vGCIM-random to 3vGCIM-fixed.3vGCIM integrates genome-wide scanning with machine learning,significantly improving power.In the mixed full model,we consider all possible effects and control for all possible polygenic backgrounds.In simulation studies,3vGCIM exhibits higher power(∼92.00%),higher accuracy of the estimates for QTL position(∼1.900 cM2)and effect(∼0.050),and lower false positive rate(∼0.48‰)and false negative rate(<8.10%)in three environments of 300 RILs each than ICIM(47.57%;3.607 cM2,0.583;2.81‰;52.43%)and MCIM(60.30%;5.279 cM2,0.274;2.17‰;39.70%).In the real data analysis of rice yield-related traits in 240 RILs,3vGCIM mines more known genes(57–60)and known gene-by-environment interactions(GEIs)(14–19)and candidate GEIs(21–23)than ICIM(27,2,and 7),and MCIM(21,1,and 3),especially in small-effect and linked QTLs and QEIs.This makes 3vGCIM a powerful and sensitive tool for QTL mapping and molecular QTL mapping.
文摘利用普通小麦品种藁城8901和PH85-16按单粒传方法构建的重组自交系群体F6(RIL-6)共112个家系,研究了影响小麦粉及面片色泽的主要因素。结果表明:硬度、吸水率、湿面筋、干面筋、蛋白质含量,与小麦粉白度、L*值及鲜面片0 h L*值呈负相关,与小麦粉及鲜面片0 h的a*和b*值呈正相关;叶黄素和PPO活性,与小麦粉及鲜面片0 h L*值呈负相关,与b*值呈正相关;面筋指数和稳定时间,与小麦粉及面片的L*值呈正相关;淀粉糊化性状的几个参数,与小麦粉白度、L*值及鲜面片0 h L*值呈正相关,与小麦粉及鲜面片0 h的a*值呈负相关。由结果看出在小麦粉及面片色泽性状的选育过程中,对蛋白质和淀粉等组分含量进行选择的同时,也要注意内部特性的改良。选择面筋指数高、叶黄素和PPO活性低、淀粉糊化黏度高的株系,以满足人们对亮白色食品的需求。
