The theory and associated selection methods of classical quantitative genetics are based on the multifactorial or polygene hypothesis.Major genes or quantitative trait loci(QTL)in modern quantitative genetics based o...The theory and associated selection methods of classical quantitative genetics are based on the multifactorial or polygene hypothesis.Major genes or quantitative trait loci(QTL)in modern quantitative genetics based on a“major gene plus polygenes”genetic system have been paid much attention in genetic studies.However,it remains unclear how the numerous minor genes act,although the polygene theory has sustained genetic improvement in plants and animals for more than a hundred years.In the present study,we identified a novel minor gene,BnSOT-like1(BnaA09g53490D),which is a sulfotransferase(SOT)gene catalyzing the formation of the core glucosinolate(GSL)structure in Brassica napus.This gene has been occasionally found during investigations of plant height-related genes,but has not been identified by QTL mapping because of its small phenotypic effects on GSL content.The overexpression of BnSOT-like1 up-regulated the expression of aliphatic GSL-associated genes,leading to a high seed aliphatic GSL content,and the overexpression of the allelic gene Bnsot-like1 did not increase seed GSL content.These findings suggest that the SOT gene has a marked effect on a quantitative trait from a reverse genetics standpoint,but a minor effect on the quantitative trait in its natural biological state.Because of the redundancy of GSL biosynthetic genes in the allotetraploid species B.napus,mutations of a single functional gene in the pathway will not result in significant phenotypic changes,and that the genes in biosynthetic pathways such as BnSOT-like1 in our study have minor effects and may be called polygenes in contrast to the reported three regulatory genes(BnHAG1s)which strongly affect GSL content in B.napus.The present study has shed light on a minor gene for a quantitative trait.展开更多
Initial flowering date(IFD)is closely related to mature period of peanut pods.In present study,a population of recombinant inbred lines(RIL)derived from the cross between Silihong(female parent)and Jinonghei 3(male pa...Initial flowering date(IFD)is closely related to mature period of peanut pods.In present study,a population of recombinant inbred lines(RIL)derived from the cross between Silihong(female parent)and Jinonghei 3(male parent)was used to map QTLs associated with IFD.The RIL population and its two parental cultivars were planted in two locations of Hebei Province,China from 2015 to 2018(eight environments).Based on a high-density genetic linkage map(including 2996 SNP and 330 SSR markers)previously constructed in our laboratory,QTLs were analyzed using phenotypic data and the best linear unbiased prediction(BLUP)value of initial flowering date by inclusive composite interval mapping(ICIM)method.Interaction effects between every two QTLs and between individual QTL and environment were also analyzed.In cultivated peanut,IFD was affected by genotypic factor and environments simultaneously,and its broad sense heritability(h2)was estimated as 86.8%。Using the IFD phenotypic data from the eight environments,a total of 19 QTLs for IFD were detected,and the phenotypic variation explained(PVE)by each QTL ranged from 1.15 to 21.82%.Especially,five of them were also detected by the BLUP value of IFD.In addition,12 additive QTLs and 35 pairs of epistatic QTLs(62 loci involved)were identifed by the joint analysis of IFD across eight environments.Three QTLs(qIFDB04.1,qIFDB07.1 and qIFDB08.1)located on chromosome B04,B07 and B08 were identified as main-effect QTL for IFD,which had the most potential to be used in peanut breeding.This study would be helpful for the early-maturity and adaptability breeding in cultivated peanut.展开更多
Many QTL mapping methods have been developed in the past two decades.Statistically,the best method should have a high detection power but a low false discovery rate (FDR).Power and FDR cannot be derived theoretically ...Many QTL mapping methods have been developed in the past two decades.Statistically,the best method should have a high detection power but a low false discovery rate (FDR).Power and FDR cannot be derived theoretically for most QTL mapping methods,but they can be properly evaluated using computer simulations.In this paper,we used four genetic models (two for independent loci and two for linked loci) to illustrate power and FDR estimation for interval mapping (IM) and inclusive composite interval mapping (ICIM).For each model,we simulated 1000 populations each of 200 doubled haploids.A support interval (SI) was first defined to indicate to which predefined QTL the significant QTL belonged.Power was calculated by counting the number of simulation runs with significant peaks higher than the logarithm of odds (LOD) threshold in the SI.Quantitative trait loci not identified in any SIs were viewed as false positives.The FDR is the rate at which QTLs are identified as significant when they are actually non-significant.Simulation results allowed us to estimate power and FDR of IM and ICIM for two independent and two linkage genetic models.Our estimates allowed us to readily compare the efficiencies of different statistical methods for QTL mapping,including the ability to separate linkage,under a wide range of genetic models.We used IM and ICIM as examples of how to estimate power and FDR,but the principles shown in this paper can be used for power analysis and comparison of any other QTL mapping methods,especially those based on interval tests.展开更多
基金This work was supported by the National Key Research and Development Program of China(2018YFD0100600)the National Natural Science Foundation of China(31270386)the Cyrus Tang Seed Innovation Center at Nanjing Agricultural University.
文摘The theory and associated selection methods of classical quantitative genetics are based on the multifactorial or polygene hypothesis.Major genes or quantitative trait loci(QTL)in modern quantitative genetics based on a“major gene plus polygenes”genetic system have been paid much attention in genetic studies.However,it remains unclear how the numerous minor genes act,although the polygene theory has sustained genetic improvement in plants and animals for more than a hundred years.In the present study,we identified a novel minor gene,BnSOT-like1(BnaA09g53490D),which is a sulfotransferase(SOT)gene catalyzing the formation of the core glucosinolate(GSL)structure in Brassica napus.This gene has been occasionally found during investigations of plant height-related genes,but has not been identified by QTL mapping because of its small phenotypic effects on GSL content.The overexpression of BnSOT-like1 up-regulated the expression of aliphatic GSL-associated genes,leading to a high seed aliphatic GSL content,and the overexpression of the allelic gene Bnsot-like1 did not increase seed GSL content.These findings suggest that the SOT gene has a marked effect on a quantitative trait from a reverse genetics standpoint,but a minor effect on the quantitative trait in its natural biological state.Because of the redundancy of GSL biosynthetic genes in the allotetraploid species B.napus,mutations of a single functional gene in the pathway will not result in significant phenotypic changes,and that the genes in biosynthetic pathways such as BnSOT-like1 in our study have minor effects and may be called polygenes in contrast to the reported three regulatory genes(BnHAG1s)which strongly affect GSL content in B.napus.The present study has shed light on a minor gene for a quantitative trait.
基金Supported by the earmarked fund for China Agriculture Research System(CARS-13)the National Natural Science Foundatlon of China(31771833)+1 种基金the Science and Technology Supporting Plan Project of Hebei Province,China(16226301D)the Key Projects of Science and Technology Research in Higher Education Institution of Hebei Province,China(ZD2015056).
文摘Initial flowering date(IFD)is closely related to mature period of peanut pods.In present study,a population of recombinant inbred lines(RIL)derived from the cross between Silihong(female parent)and Jinonghei 3(male parent)was used to map QTLs associated with IFD.The RIL population and its two parental cultivars were planted in two locations of Hebei Province,China from 2015 to 2018(eight environments).Based on a high-density genetic linkage map(including 2996 SNP and 330 SSR markers)previously constructed in our laboratory,QTLs were analyzed using phenotypic data and the best linear unbiased prediction(BLUP)value of initial flowering date by inclusive composite interval mapping(ICIM)method.Interaction effects between every two QTLs and between individual QTL and environment were also analyzed.In cultivated peanut,IFD was affected by genotypic factor and environments simultaneously,and its broad sense heritability(h2)was estimated as 86.8%。Using the IFD phenotypic data from the eight environments,a total of 19 QTLs for IFD were detected,and the phenotypic variation explained(PVE)by each QTL ranged from 1.15 to 21.82%.Especially,five of them were also detected by the BLUP value of IFD.In addition,12 additive QTLs and 35 pairs of epistatic QTLs(62 loci involved)were identifed by the joint analysis of IFD across eight environments.Three QTLs(qIFDB04.1,qIFDB07.1 and qIFDB08.1)located on chromosome B04,B07 and B08 were identified as main-effect QTL for IFD,which had the most potential to be used in peanut breeding.This study would be helpful for the early-maturity and adaptability breeding in cultivated peanut.
基金supported by the NationalBasic Research Program of China(2011CB100100)the National Natural Science Foundation of China(31000540)
文摘Many QTL mapping methods have been developed in the past two decades.Statistically,the best method should have a high detection power but a low false discovery rate (FDR).Power and FDR cannot be derived theoretically for most QTL mapping methods,but they can be properly evaluated using computer simulations.In this paper,we used four genetic models (two for independent loci and two for linked loci) to illustrate power and FDR estimation for interval mapping (IM) and inclusive composite interval mapping (ICIM).For each model,we simulated 1000 populations each of 200 doubled haploids.A support interval (SI) was first defined to indicate to which predefined QTL the significant QTL belonged.Power was calculated by counting the number of simulation runs with significant peaks higher than the logarithm of odds (LOD) threshold in the SI.Quantitative trait loci not identified in any SIs were viewed as false positives.The FDR is the rate at which QTLs are identified as significant when they are actually non-significant.Simulation results allowed us to estimate power and FDR of IM and ICIM for two independent and two linkage genetic models.Our estimates allowed us to readily compare the efficiencies of different statistical methods for QTL mapping,including the ability to separate linkage,under a wide range of genetic models.We used IM and ICIM as examples of how to estimate power and FDR,but the principles shown in this paper can be used for power analysis and comparison of any other QTL mapping methods,especially those based on interval tests.
