Two genetic linkage maps, constructed by DH and RILs populations derived from the same parents, were carried out for the identification and comparison of QTLs controlling yield traits across different years in rice （...Two genetic linkage maps, constructed by DH and RILs populations derived from the same parents, were carried out for the identification and comparison of QTLs controlling yield traits across different years in rice （Oryza sativa L.）. A total of 194 SSR and STS markers were used in two maps, of which 114 markers were same. The distribution of Samgang allele was higher in RILs population than it in DH population. Comparing with DH population, RILs population has more lines with higher yield and wider phenotypic transgressive segression for yield traits. Although most of QTLs for the same trait were different in two populations across different years, 8 QTLs （including gwp 11.1, spp5.1, spp 10.1, spp 11.2, ssrl. 1, ssrl 1.1, tgw9.1 and tgwl 1.1） were detected over 2 yr. It is important to note that pppl0.1, sppl0.1 and tgw9.1 were identified in two populations, while sppl 0.1 and tgw9.1 were simultaneity observed across different years. Epistatic effects were more important than additive effects for PPP, SPP, yield in DH population and TGW, yield in R/Ls population. Epistatic effects of DH and RILs populations were different on the same genetic background in the present study, which illuminated the QE interaction played an important role on epistatic effect. Identification and comparison of QTLs for yield traits in DH and RILs populations should provide various and more precise information. The QTLs identified in present study would be valuable in marker-assisted selection program for improving rice yield.展开更多
Recombinant inbred lines(RILs) serve as powerful tools for genetic mapping.RILs are obtained by crossing two inbred lines followed by repeated selfing or sib-mating to create a set of new
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
基金supported by the Biogreen 21 R&D Program,Rural Development Administration,Republic of Korea(20100301-061-239-001-09-00)the National Agriculture Science Technology Achievement Transformation Fund of China(2011GB2B000006)
文摘Two genetic linkage maps, constructed by DH and RILs populations derived from the same parents, were carried out for the identification and comparison of QTLs controlling yield traits across different years in rice （Oryza sativa L.）. A total of 194 SSR and STS markers were used in two maps, of which 114 markers were same. The distribution of Samgang allele was higher in RILs population than it in DH population. Comparing with DH population, RILs population has more lines with higher yield and wider phenotypic transgressive segression for yield traits. Although most of QTLs for the same trait were different in two populations across different years, 8 QTLs （including gwp 11.1, spp5.1, spp 10.1, spp 11.2, ssrl. 1, ssrl 1.1, tgw9.1 and tgwl 1.1） were detected over 2 yr. It is important to note that pppl0.1, sppl0.1 and tgw9.1 were identified in two populations, while sppl 0.1 and tgw9.1 were simultaneity observed across different years. Epistatic effects were more important than additive effects for PPP, SPP, yield in DH population and TGW, yield in R/Ls population. Epistatic effects of DH and RILs populations were different on the same genetic background in the present study, which illuminated the QE interaction played an important role on epistatic effect. Identification and comparison of QTLs for yield traits in DH and RILs populations should provide various and more precise information. The QTLs identified in present study would be valuable in marker-assisted selection program for improving rice yield.
文摘Recombinant inbred lines(RILs) serve as powerful tools for genetic mapping.RILs are obtained by crossing two inbred lines followed by repeated selfing or sib-mating to create a set of new
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.
