Genome-wide association mapping studies(GWAS)based on Big Data are a potential approach to improve marker-assisted selection in plant breeding.The number of available phenotypic and genomic data sets in which medium-s...Genome-wide association mapping studies(GWAS)based on Big Data are a potential approach to improve marker-assisted selection in plant breeding.The number of available phenotypic and genomic data sets in which medium-sized populations of several hundred individuals have been studied is rapidly increasing.Combining these data and using them in GWAS could increase both the power of QTL discovery and the accuracy of estimation of underlying genetic effects,but is hindered by data heterogeneity and lack of interoperability.In this study,we used genomic and phenotypic data sets,focusing on Central European winter wheat populations evaluated for heading date.We explored strategies for integrating these data and subsequently the resulting potential for GWAS.Establishing interoperability between data sets was greatly aided by some overlapping genotypes and a linear relationship between the different phenotyping protocols,resulting in high quality integrated phenotypic data.In this context,genomic prediction proved to be a suitable tool to study relevance of interactions between genotypes and experimental series,which was low in our case.Contrary to expectations,fewer associations between markers and traits were found in the larger combined data than in the individual experimental series.However,the predictive power based on the marker-trait associations of the integrated data set was higher across data sets.Therefore,the results show that the integration of medium-sized to Big Data is an approach to increase the power to detect QTL in GWAS.The results encourage further efforts to standardize and share data in the plant breeding community.展开更多
Extensive exotic introgression could significantly enlarge the genetic distance of hybrid parental populations to promote strong heterosis.The goal of this study was to investigate whether genome-wide prediction can s...Extensive exotic introgression could significantly enlarge the genetic distance of hybrid parental populations to promote strong heterosis.The goal of this study was to investigate whether genome-wide prediction can support pre-breeding in populations with exotic introgressions.We evaluated seed yield,seed yield related traits and seed quality traits of 363 hybrids of Brassica napus (AACC) derived from two parental populations divergent on massive exotic introgression of related species in three environments.The hybrids presented strong heterosis on seed yield,which was much higher than other investigated traits.Five genomic best linear unbiased prediction models considering the exotic introgression and different marker effects (additive,dominance,and epistatic effects) were constructed to test the prediction ability for different traits of the hybrids.The analysis showed that the trait complexity,exotic introgression,genetic relationship between the training set and testing set,training set size,and environments affected the prediction ability.The models with best prediction ability for different traits varied.However,relatively high prediction ability (e.g.,0.728 for seed yield) was also observed when the simplest models were used,excluding the effects of the special exotic introgression and epistasis effect by5-fold cross validation,which would simplify the prediction for the trait with complex architecture for hybrids with exotic introgression.The results provide novel insights and strategies for genome-wide prediction of hybrids between genetically distinct parent groups with exotic introgressions.展开更多
Soybean cyst nematode(SCN)is a highly destructive pathogen.The soybean host genome harbors at least two major genes for resistance(rhg1 and Rhg4),as well as a minor locus(SCN3-11).In the present study,a splicing site ...Soybean cyst nematode(SCN)is a highly destructive pathogen.The soybean host genome harbors at least two major genes for resistance(rhg1 and Rhg4),as well as a minor locus(SCN3-11).In the present study,a splicing site in GmSNAP11,the potential causal gene of SCN3-11,was identified by comparison of the GmSNAP11 cDNA sequences generated from resistant and susceptible soybean accessions.The sequence information was used to design a codominant CAPS marker,GmSNAP11-2565,which was used to genotype a panel of 209 soybean accessions varying with respect to SCN resistance.Analyses of the effect of the haplotypes formed by GmSNAP11-2565 and another large-effect(nonsynonymous)locus,GmSNAP11-2307,previously identified in GmSNAP11,revealed linkage disequilibrium(P<0.0001)between the two loci,suggesting that GmSNAP11-2565 could be used as a marker for GmSNAP11.GmSNAP11-2565 was accordingly used,along with established markers for GmSNAP18(rhg1)and GmSHMT(Rhg4),to characterize the panel accessions.The mean SCN female index of accessions carrying only the GmSNAP11 allele associated with resistance(20.3%)was higher than that associated with accessions carrying alleles for resistance at both GmSNAP11 and GmSNAP18(12.4%),while the index for accessions carrying alleles for resistance at all of GmSNAP11,GmSNAP18,and GmSHMT was very low(1.9%).Selection on all three markers was effective for maintaining a high level of resistance to SCN race 3.展开更多
Genome-wide prediction is a promising approach to boost selection gain in hybrid breeding.Our main objective was to evaluate the potential and limits of genome-wide prediction to identify superior hybrid combinations ...Genome-wide prediction is a promising approach to boost selection gain in hybrid breeding.Our main objective was to evaluate the potential and limits of genome-wide prediction to identify superior hybrid combinations adapted to Northwest China.A total of 490 hybrids derived from crosses among 119 inbred lines from the Shaan A and Shaan B heterotic pattern were used for genome-wide prediction of ten agronomic traits.We tested eight different statistical prediction models considering additive(A)effects and in addition evaluated the impact of dominance(D)and epistasis(E)on the prediction ability.Employing five-fold cross validation,we show that the average prediction ability ranged from 0.386 to 0.794 across traits and models.Six parametric methods,i.e.ridge regression,LASSO,Elastic Net,Bayes B,Bayes C and reproducing kernel Hilbert space(RKHS)approach,displayed a very similar prediction ability for each trait and two non-parametric methods(random forest and support vector machine)had a higher prediction performance for the trait rind penetrometer resistance of the third internode above ground(RPR_TIAG).The models of A+D RKHS and A+D+E RKHS were slightly better for predicting traits with a relatively high non-additive variance.Integrating trait-specific markers into the A+D RKHS model improved the prediction ability of grain yield by 3%,from 0.528 to 0.558.Of all 6328 potential hybrids,selection of the top 44 hybrids would lead to a 6%increase in grain yield compared with Zhengdan 958,a commercially successful hybrid variety.In conclusion,our results substantiate the value of genome-wide prediction for hybrid breeding and suggest dozens of promising single crosses for developing high-yielding hybrids for Northwest China.展开更多
Soybean cyst nematode(SCN,Heterodera glycines Ichinohe)is one of the most economically destructive pathogens.The soybean line Zhongpin03-5373(ZP),which combines resistance genes from several donors,is highly resistant...Soybean cyst nematode(SCN,Heterodera glycines Ichinohe)is one of the most economically destructive pathogens.The soybean line Zhongpin03-5373(ZP),which combines resistance genes from several donors,is highly resistant to SCN race 3(SCN3).In our previous study,two QTL(rhg1 and GmSNAP11)were identified in a population of recombinant inbred lines derived from a cross between ZP and the susceptible parent Zhonghuang 13.The two QTL explained around one-third of the resistance,suggesting the presence of further QTL contributing to SCN resistance.In the present study,we used an improved version of the geneticmap comprising the previously applied 1062 molecular markers and 47 newly developed InDel(insertion-deletion)markers.The improved map revealed a novel locus contributing to SCN3 resistance:qSCN3-1,flanked by InDelmarker InDel1-7 and SNPmarker Map-0047,explained 4.55%of the phenotypic variance for resistance to SCN3 and was not involved in digenic epistatic interaction with rhg1 and GmSNAP11.Haplotypes of Map-0047_CAPS(a CAPS marker developed for Map-0047)and InDel1-7 were significantly associated with SCN3 resistance in a panel of 209 resistant and susceptible accessions.Using further allele-combination analysis for three functional markers representing three cloned resistance genes(rhg1,Rhg4,andGmSNAP11)and twomarkers flanking qSCN3-1,we found that adding the resistance allele of qSCN3-1 greatly increased soybean resistance to SCN,even in diverse genetic backgrounds.The qSCN3-1 locus will be useful for marker-assisted polygene pyramid breeding and should be targeted for the future identification of candidate genes.展开更多
To extend the contemporary understanding into the grain yield heterosis of wheat, the current study investigated the contribution of deleterious alleles in shaping mid-parent heterosis(MPH). These alleles occur at low...To extend the contemporary understanding into the grain yield heterosis of wheat, the current study investigated the contribution of deleterious alleles in shaping mid-parent heterosis(MPH). These alleles occur at low frequency in the genome and are often missed by automated genotyping platforms like SNP arrays. The deleterious alleles herein were detected using a quantitative measurement of evolutionary conservation based on the phylogeny of wheat and investigations were made to:(1) assess the benefit of including deleterious alleles into MPH prediction models and(2) understand the genetic underpinnings of deleterious SNPs for grain yield MPH using contrasting crosses viz. elite × elite(Exp. 1) and elite × plant genetic resources(PGR;Exp. 2). In our study, we found a lower allele frequency of moderately deleterious alleles in elites compared to PGRs. This highlights the role of purifying selection for the development of elite wheat cultivars. It was shown that deleterious alleles are informative for MPH prediction models: modelling their additive-by-additive effects in Exp. 1 and dominance as well as associated digenic epistatic effects in Exp. 2 significantly boosts prediction accuracies of MPH. Furthermore,heterotic-quantitative trait loci's underlying MPH was investigated and their properties were contrasted in the two crosses. Conclusively, it was proposed that incomplete dominance of deleterious alleles contributes to grain yield heterosis in elite crosses(Exp. 1).展开更多
Soybean is a leguminous crop that provides oil and protein. Exploring the genomic signatures of soybean evolution is crucial for breeding varieties with improved adaptability to environmental extremes. We analyzed the...Soybean is a leguminous crop that provides oil and protein. Exploring the genomic signatures of soybean evolution is crucial for breeding varieties with improved adaptability to environmental extremes. We analyzed the genome sequences of 2,214 soybeans and proposed a soybean evolutionary route, i.e., the expansion of annual wild soybean(Glycine soja Sieb. & Zucc.) from southern China and its domestication in central China, followed by the expansion and local breeding selection of its landraces(G. max(L.) Merr.). We observed that the genetic introgression in soybean landraces was mostly derived from sympatric rather than allopatric wild populations during the geographic expansion. Soybean expansion and breeding were accompanied by the positive selection of flowering time genes, including GmSPA3c. Our study sheds light on the evolutionary history of soybean and provides valuable genetic resources for its future breeding.展开更多
DOUBLED-HAPLOID TECHNOLOGY FACES A GREAT CHALLENGE FOR HYBRID BREEDING,Ensuring food security for the ever-growing population is a common mission and a great challenge for agricultural scientists worldwide.Historicall...DOUBLED-HAPLOID TECHNOLOGY FACES A GREAT CHALLENGE FOR HYBRID BREEDING,Ensuring food security for the ever-growing population is a common mission and a great challenge for agricultural scientists worldwide.Historically,advances in crop breeding and management practices have contributed substantially to crop productivity.Indeed,the substantial increase in global grain yields over the last eight decades is largely due to the adoption of hybrids.However,the rate of increase of hybrid yields began to slow down in the early 2000s,and since then,it has reached a plateau for many crops and regions(https://faostat.fao.org).Therefore,we must find solutions to accelerating genetic gain and boost hybrid development,for which developing new breeding technologies provides novel creative opportunities.展开更多
基金funding within the Wheat BigData Project(German Federal Ministry of Food and Agriculture,FKZ2818408B18)。
文摘Genome-wide association mapping studies(GWAS)based on Big Data are a potential approach to improve marker-assisted selection in plant breeding.The number of available phenotypic and genomic data sets in which medium-sized populations of several hundred individuals have been studied is rapidly increasing.Combining these data and using them in GWAS could increase both the power of QTL discovery and the accuracy of estimation of underlying genetic effects,but is hindered by data heterogeneity and lack of interoperability.In this study,we used genomic and phenotypic data sets,focusing on Central European winter wheat populations evaluated for heading date.We explored strategies for integrating these data and subsequently the resulting potential for GWAS.Establishing interoperability between data sets was greatly aided by some overlapping genotypes and a linear relationship between the different phenotyping protocols,resulting in high quality integrated phenotypic data.In this context,genomic prediction proved to be a suitable tool to study relevance of interactions between genotypes and experimental series,which was low in our case.Contrary to expectations,fewer associations between markers and traits were found in the larger combined data than in the individual experimental series.However,the predictive power based on the marker-trait associations of the integrated data set was higher across data sets.Therefore,the results show that the integration of medium-sized to Big Data is an approach to increase the power to detect QTL in GWAS.The results encourage further efforts to standardize and share data in the plant breeding community.
基金supported by the National Natural Science Foundation of China (NSFC-DFG, 31861133016NSFC, 31970564)。
文摘Extensive exotic introgression could significantly enlarge the genetic distance of hybrid parental populations to promote strong heterosis.The goal of this study was to investigate whether genome-wide prediction can support pre-breeding in populations with exotic introgressions.We evaluated seed yield,seed yield related traits and seed quality traits of 363 hybrids of Brassica napus (AACC) derived from two parental populations divergent on massive exotic introgression of related species in three environments.The hybrids presented strong heterosis on seed yield,which was much higher than other investigated traits.Five genomic best linear unbiased prediction models considering the exotic introgression and different marker effects (additive,dominance,and epistatic effects) were constructed to test the prediction ability for different traits of the hybrids.The analysis showed that the trait complexity,exotic introgression,genetic relationship between the training set and testing set,training set size,and environments affected the prediction ability.The models with best prediction ability for different traits varied.However,relatively high prediction ability (e.g.,0.728 for seed yield) was also observed when the simplest models were used,excluding the effects of the special exotic introgression and epistasis effect by5-fold cross validation,which would simplify the prediction for the trait with complex architecture for hybrids with exotic introgression.The results provide novel insights and strategies for genome-wide prediction of hybrids between genetically distinct parent groups with exotic introgressions.
基金National Key R&D Program for Crop Breeding (2016YFD0100602, 2016YFD0100201)the Agricultural Science and Technology Innovation Program (ASTIP) of the Chinese Academy of Agricultural SciencesNational Science and Technology Platform
文摘Soybean cyst nematode(SCN)is a highly destructive pathogen.The soybean host genome harbors at least two major genes for resistance(rhg1 and Rhg4),as well as a minor locus(SCN3-11).In the present study,a splicing site in GmSNAP11,the potential causal gene of SCN3-11,was identified by comparison of the GmSNAP11 cDNA sequences generated from resistant and susceptible soybean accessions.The sequence information was used to design a codominant CAPS marker,GmSNAP11-2565,which was used to genotype a panel of 209 soybean accessions varying with respect to SCN resistance.Analyses of the effect of the haplotypes formed by GmSNAP11-2565 and another large-effect(nonsynonymous)locus,GmSNAP11-2307,previously identified in GmSNAP11,revealed linkage disequilibrium(P<0.0001)between the two loci,suggesting that GmSNAP11-2565 could be used as a marker for GmSNAP11.GmSNAP11-2565 was accordingly used,along with established markers for GmSNAP18(rhg1)and GmSHMT(Rhg4),to characterize the panel accessions.The mean SCN female index of accessions carrying only the GmSNAP11 allele associated with resistance(20.3%)was higher than that associated with accessions carrying alleles for resistance at both GmSNAP11 and GmSNAP18(12.4%),while the index for accessions carrying alleles for resistance at all of GmSNAP11,GmSNAP18,and GmSHMT was very low(1.9%).Selection on all three markers was effective for maintaining a high level of resistance to SCN race 3.
基金This work was supported by the National Key Research and Development Program of China(2016YFD0101200 and 2018YFD0100200)the Scientific Research Foundation for the Returned Overseas Chinese Scholars,Ministry of Education.
文摘Genome-wide prediction is a promising approach to boost selection gain in hybrid breeding.Our main objective was to evaluate the potential and limits of genome-wide prediction to identify superior hybrid combinations adapted to Northwest China.A total of 490 hybrids derived from crosses among 119 inbred lines from the Shaan A and Shaan B heterotic pattern were used for genome-wide prediction of ten agronomic traits.We tested eight different statistical prediction models considering additive(A)effects and in addition evaluated the impact of dominance(D)and epistasis(E)on the prediction ability.Employing five-fold cross validation,we show that the average prediction ability ranged from 0.386 to 0.794 across traits and models.Six parametric methods,i.e.ridge regression,LASSO,Elastic Net,Bayes B,Bayes C and reproducing kernel Hilbert space(RKHS)approach,displayed a very similar prediction ability for each trait and two non-parametric methods(random forest and support vector machine)had a higher prediction performance for the trait rind penetrometer resistance of the third internode above ground(RPR_TIAG).The models of A+D RKHS and A+D+E RKHS were slightly better for predicting traits with a relatively high non-additive variance.Integrating trait-specific markers into the A+D RKHS model improved the prediction ability of grain yield by 3%,from 0.528 to 0.558.Of all 6328 potential hybrids,selection of the top 44 hybrids would lead to a 6%increase in grain yield compared with Zhengdan 958,a commercially successful hybrid variety.In conclusion,our results substantiate the value of genome-wide prediction for hybrid breeding and suggest dozens of promising single crosses for developing high-yielding hybrids for Northwest China.
基金This research was financed by the National Key Research and Development Program of China(2016YFD0100201)the Agricultural Science and Technology Innovation Program(ASTIP)of the Chinese Academy of Agricultural Sciences.
文摘Soybean cyst nematode(SCN,Heterodera glycines Ichinohe)is one of the most economically destructive pathogens.The soybean line Zhongpin03-5373(ZP),which combines resistance genes from several donors,is highly resistant to SCN race 3(SCN3).In our previous study,two QTL(rhg1 and GmSNAP11)were identified in a population of recombinant inbred lines derived from a cross between ZP and the susceptible parent Zhonghuang 13.The two QTL explained around one-third of the resistance,suggesting the presence of further QTL contributing to SCN resistance.In the present study,we used an improved version of the geneticmap comprising the previously applied 1062 molecular markers and 47 newly developed InDel(insertion-deletion)markers.The improved map revealed a novel locus contributing to SCN3 resistance:qSCN3-1,flanked by InDelmarker InDel1-7 and SNPmarker Map-0047,explained 4.55%of the phenotypic variance for resistance to SCN3 and was not involved in digenic epistatic interaction with rhg1 and GmSNAP11.Haplotypes of Map-0047_CAPS(a CAPS marker developed for Map-0047)and InDel1-7 were significantly associated with SCN3 resistance in a panel of 209 resistant and susceptible accessions.Using further allele-combination analysis for three functional markers representing three cloned resistance genes(rhg1,Rhg4,andGmSNAP11)and twomarkers flanking qSCN3-1,we found that adding the resistance allele of qSCN3-1 greatly increased soybean resistance to SCN,even in diverse genetic backgrounds.The qSCN3-1 locus will be useful for marker-assisted polygene pyramid breeding and should be targeted for the future identification of candidate genes.
基金supported by the German Federal Ministry of Food and Agriculture (FKZ2818408B18)the Federal Ministry of Education and Research of Germany (FKZ031B0184A, B)the China Scholarship Council (201906350045)。
文摘To extend the contemporary understanding into the grain yield heterosis of wheat, the current study investigated the contribution of deleterious alleles in shaping mid-parent heterosis(MPH). These alleles occur at low frequency in the genome and are often missed by automated genotyping platforms like SNP arrays. The deleterious alleles herein were detected using a quantitative measurement of evolutionary conservation based on the phylogeny of wheat and investigations were made to:(1) assess the benefit of including deleterious alleles into MPH prediction models and(2) understand the genetic underpinnings of deleterious SNPs for grain yield MPH using contrasting crosses viz. elite × elite(Exp. 1) and elite × plant genetic resources(PGR;Exp. 2). In our study, we found a lower allele frequency of moderately deleterious alleles in elites compared to PGRs. This highlights the role of purifying selection for the development of elite wheat cultivars. It was shown that deleterious alleles are informative for MPH prediction models: modelling their additive-by-additive effects in Exp. 1 and dominance as well as associated digenic epistatic effects in Exp. 2 significantly boosts prediction accuracies of MPH. Furthermore,heterotic-quantitative trait loci's underlying MPH was investigated and their properties were contrasted in the two crosses. Conclusively, it was proposed that incomplete dominance of deleterious alleles contributes to grain yield heterosis in elite crosses(Exp. 1).
基金supported by the National Key R&D Program of China(2021YFD1201601,2016YFD0100201,2020YFE0202300)the National Natural Science Foundation of China(32072091)+2 种基金the Platform of National Crop Germplasm Resources of China(2016-004,2017-004,2018-004,2019-04,2020-05)the Crop Germplasm Resources Protection(2016NWB036-05,2017NWB036-05,2018NWB03605,2019NWB036-05)the Agricultural Science and Technology Innovation Program(ASTIP)of Chinese Academy of Agricultural Sciences(CAASZDRW202109)。
文摘Soybean is a leguminous crop that provides oil and protein. Exploring the genomic signatures of soybean evolution is crucial for breeding varieties with improved adaptability to environmental extremes. We analyzed the genome sequences of 2,214 soybeans and proposed a soybean evolutionary route, i.e., the expansion of annual wild soybean(Glycine soja Sieb. & Zucc.) from southern China and its domestication in central China, followed by the expansion and local breeding selection of its landraces(G. max(L.) Merr.). We observed that the genetic introgression in soybean landraces was mostly derived from sympatric rather than allopatric wild populations during the geographic expansion. Soybean expansion and breeding were accompanied by the positive selection of flowering time genes, including GmSPA3c. Our study sheds light on the evolutionary history of soybean and provides valuable genetic resources for its future breeding.
基金supported by the National Key Research and Development Program of China(2020YFE0202300)Agricultural Science and Technology Innovation Program of CMS(ZDRW202004),and Project of Hainan Yazhou Bay Seed Lab(B21HJ0223).
文摘DOUBLED-HAPLOID TECHNOLOGY FACES A GREAT CHALLENGE FOR HYBRID BREEDING,Ensuring food security for the ever-growing population is a common mission and a great challenge for agricultural scientists worldwide.Historically,advances in crop breeding and management practices have contributed substantially to crop productivity.Indeed,the substantial increase in global grain yields over the last eight decades is largely due to the adoption of hybrids.However,the rate of increase of hybrid yields began to slow down in the early 2000s,and since then,it has reached a plateau for many crops and regions(https://faostat.fao.org).Therefore,we must find solutions to accelerating genetic gain and boost hybrid development,for which developing new breeding technologies provides novel creative opportunities.
