IR64 is an elite Xian/indica variety developed by International Rice Research Institute(IRRl)in 1985,which has been the most widely grown variety and core breeding parent in South/Southeast Asia(Mackill and Khush,2018...IR64 is an elite Xian/indica variety developed by International Rice Research Institute(IRRl)in 1985,which has been the most widely grown variety and core breeding parent in South/Southeast Asia(Mackill and Khush,2018).IR64 has been utilized to develop stress-tolerant(such as drought-adapted and submergenceresistant)near-isogenic lines,underscoring its great potential in agricultural genomics(Tanaka et al.,2020).展开更多
Recent genomic research on plant populations has revealed abundant structural variations(SVs)of>50 bp within and among genotypes of major crop species,but the application of SVs as a unique type of molecular marker...Recent genomic research on plant populations has revealed abundant structural variations(SVs)of>50 bp within and among genotypes of major crop species,but the application of SVs as a unique type of molecular marker in genetic research and breeding remains limited.In this study,we used a panel of 305 rice accessions from the 3000 Rice Genome Project to compare 26,000+SVs(>90%deletions and translocations)and 180,000+high-quality SNPs in predicting the field performance of 20 traits using nine commonly used genomic selection models.We demonstrated that SVs had slightly lower prediction accuracies(PA)across 20 different traits than SNPs,but saved 53.8%–77.8%of computation time to achieve reasonably high PA.The major advantage of SVs as valuable markers in obtaining reliable genomic estimated breeding values(GEBVs)in GS experiments resulted primarily from their greater phenotypic effects on high heritability traits.Our results suggested that P0.01 was an appropriate threshold for marker filtering of both SVs and SNPs to achieve high PA in genomic selection experiments.A dataset containing all identified SVs from this study has been deposited at the China National Center for Bioinformation under BioProject:PRJCA038102,a publicly accessible molecular marker resource,enabling broad applications in rice research including gene/QTL discovery,allelic mining,and genomic selection experiments.展开更多
Genomic selection(GS)is a powerful tool for improving genetic gain in maize breeding.However,its routine application in large-scale breeding pipelines is limited by the high cost of genotyping platforms.Although seque...Genomic selection(GS)is a powerful tool for improving genetic gain in maize breeding.However,its routine application in large-scale breeding pipelines is limited by the high cost of genotyping platforms.Although sequencing-based and array-based genotyping platforms have been used for GS,few studies have compared prediction performance among platforms.In this study,we evaluated the predictabilities of four agronomic traits in 305 maize hybrids derived from 149 parental lines subjected to genotyping by sequencing(GBS),a 40K SNP array,and target sequence capture(TSC)using eight GS models.The GBS marker dataset yielded the highest predictabilities for all traits,followed by TSC and SNP array datasets.We investigated the effect of marker density and statistical models on predictability among genotyping platforms and found that 1K SNPs were sufficient to achieve comparable predictabilities to 10K and all SNPs,and BayesB,GBLUP,and RKHS performed well,while XGBoost performed poorly in most cases.We also selected significant SNP subsets using genome-wide association study(GWAS)analyses in three panels to predict hybrid performance.GWAS facilitated selecting effective SNP subsets for GS and thus reduced genotyping cost,but depended heavily on the GWAS panel.We conclude that there is still room for optimization of the existing SNP array,and using genotyping by target sequencing(GBTS)techniques to integrate a few functional markers identified by GWAS into the 1K SNP array holds great promise of being an effective strategy for developing desirable GS breeding arrays.展开更多
Precise mapping of quantitative trait loci(QTLs)is critical for assessing genetic effects and identifying candidate genes for quantitative traits.Interval and composite interval mappings have been the methods of choic...Precise mapping of quantitative trait loci(QTLs)is critical for assessing genetic effects and identifying candidate genes for quantitative traits.Interval and composite interval mappings have been the methods of choice for several decades,which have provided tools for identifying genomic regions harboring causal genes for quantitative traits.Historically,the concept was developed on the basis of sparse marker maps where genotypes of loci within intervals could not be observed.Currently,genomes of many organisms have been saturated with markers due to the new sequencing technologies.Genotyping by sequencing usually generates hundreds of thousands of single nucleotide polymorphisms(SNPs),which often include the causal polymorphisms.The concept of interval no longer exists,prompting the necessity of a norm change in QTL mapping technology to make use of the high-volume genomic data.Here we developed a statistical method and a software package to map QTLs by binning markers into haplotype blocks,called bins.The new method detects associations of bins with quantitative traits.It borrows the mixed model methodology with a polygenic control from genome-wide association studies(GWAS)and can handle all kinds of experimental populations under the linear mixed model(LMM)framework.We tested the method using both simulated data and data from populations of rice.The results showed that this method has higher power than the current methods.An R package named binQTL is available from GitHub.展开更多
Anaerobic digestion is one of the effective ways to dispose of antibiotic pharmaceutical waste.However,the inhibition of antibiotics on anaerobic fermentation microorganisms seriously hinders the normal physiological ...Anaerobic digestion is one of the effective ways to dispose of antibiotic pharmaceutical waste.However,the inhibition of antibiotics on anaerobic fermentation microorganisms seriously hinders the normal physiological activities of anaerobic microorganisms and then affects the efficiency of anaerobic digestion.In order to solve this problem,related scholars have done a lot of research.It has been found that pretreatment of anaerobic microorganisms and antibiotic pharmaceutical waste can significantly improve the efficiency of anaerobic digestion.In this paper,the current feasible pretreatment methods were summarized,and the application of different pretreatment methods was analyzed to provide reference for improving pretreatment methods and improving anaerobic biological treatment ability of antibiotic waste.展开更多
Dear Editor,Although traditional breeding methods,such as phenotypebased and marker-assisted selection,remain valuable,genomic selection(GS)has emerged as a promising new approach.To address limitations of GS,particul...Dear Editor,Although traditional breeding methods,such as phenotypebased and marker-assisted selection,remain valuable,genomic selection(GS)has emerged as a promising new approach.To address limitations of GS,particularly the time and cost involved in developing training populations,we developed Rice3KGS,a web-based tool that provides access to diverse datasets and integrates analytical functions to support crop breeding efforts.展开更多
Polymorphisms within gene coding regions represent the most important part of the overall genetic diversity of rice.We characterized the gene-coding sequence-haplotype(gcHap)diversity of 45963 rice genes in 3010 rice ...Polymorphisms within gene coding regions represent the most important part of the overall genetic diversity of rice.We characterized the gene-coding sequence-haplotype(gcHap)diversity of 45963 rice genes in 3010 rice accessions.With an average of 226±390 gcHaps per gene in rice populations,rice genes could be classified into three main categories:12865 conserved genes,10254 subspecific differentiating genes,and 22844 remaining genes.We found that 39218 rice genes carry>255179 major gcHaps of potential functional importance.Most(87.5%)of the detected gcHaps were specific to subspecies or populations.The inferred proto-ancestors of local landrace populations reconstructed from conserved predominant(ancient)gcHaps correlated strongly with wild rice accessions from the same geographic regions,supporting a multiorigin(domestication)model of Oryza sativa.Past breeding efforts generally increased the gcHap diversity of modern varieties and'caused significant frequency shifts in predominant gcHaps of 14266 genes due to independent selection in the two subspecies.Low frequencies of“favorable”gcHaps at most known genes related to rice yield in modern varieties suggest huge potential for rice improvement by mining and pyramiding of favorable gcHaps.The gcHap data were demonstrated to have greater power than SNPs for the detection of causal genes that affect complex traits.The rice gcHap diversity dataset generated in this study would facilitate rice basic research and improvement in the future.展开更多
基金supported by the Natural Science Foundation of Anhui Province(2408085MC058 and 2308085QC91)National Natural Science Foundation of China(32301783and U21A20214)+5 种基金Innovation Program of the Chinese Academy of Agricultural Sciences(CAAS CSIAF-202303)Nanfan special project,CAAS(YYLH2309,YBXM2322,YYLH2401)Scientific Innovation 2030 Project(2022ZD0401703)CAAS Innovative Team Award,Science and Technology of Innovative research program of Anhui Province(202423m1005002)National Key Research and Development Program of China(2023YFD1200900)the Natural Science Foundation General Program of Hebei Province(C2024204242).
文摘IR64 is an elite Xian/indica variety developed by International Rice Research Institute(IRRl)in 1985,which has been the most widely grown variety and core breeding parent in South/Southeast Asia(Mackill and Khush,2018).IR64 has been utilized to develop stress-tolerant(such as drought-adapted and submergenceresistant)near-isogenic lines,underscoring its great potential in agricultural genomics(Tanaka et al.,2020).
基金supported by the Natural Science Foundation of Anhui Province(2408085MC058 and 2308085QC91)National Natural Science Foundation of China(32301783 and U21A20214)+4 种基金the Biological Breeding-National Science and Technology Major Project(2022ZD04017)Innovation Program of the Chinese Academy of Agricultural Sciences(CAAS CSIAF-202303)Nanfan Special Project,CAAS(YYLH2401)the Scientific Innovation 2030 Project(2022ZD0401703)the CAAS Innovative Team Award.
文摘Recent genomic research on plant populations has revealed abundant structural variations(SVs)of>50 bp within and among genotypes of major crop species,but the application of SVs as a unique type of molecular marker in genetic research and breeding remains limited.In this study,we used a panel of 305 rice accessions from the 3000 Rice Genome Project to compare 26,000+SVs(>90%deletions and translocations)and 180,000+high-quality SNPs in predicting the field performance of 20 traits using nine commonly used genomic selection models.We demonstrated that SVs had slightly lower prediction accuracies(PA)across 20 different traits than SNPs,but saved 53.8%–77.8%of computation time to achieve reasonably high PA.The major advantage of SVs as valuable markers in obtaining reliable genomic estimated breeding values(GEBVs)in GS experiments resulted primarily from their greater phenotypic effects on high heritability traits.Our results suggested that P0.01 was an appropriate threshold for marker filtering of both SVs and SNPs to achieve high PA in genomic selection experiments.A dataset containing all identified SVs from this study has been deposited at the China National Center for Bioinformation under BioProject:PRJCA038102,a publicly accessible molecular marker resource,enabling broad applications in rice research including gene/QTL discovery,allelic mining,and genomic selection experiments.
基金supported by grants from the National Natural Science Foundation of China(32061143030,32170636,32100448)the Key Research and Development Program of Jiangsu Province(BE2022343)+6 种基金the Seed Industry Revitalization Project of Jiangsu Province(JBGS[2021]009)Project of Hainan Yazhou Bay Seed Lab(B21HJ0223)the State Key Laboratory of North China Crop Improvement and Regulation(NCCIR2021KF-5,NCCIR2021ZZ-4)Jiangsu Province Agricultural Science and Technology Independent Innovation(CX(21)1003)the Independent Scientific Research Project of the Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding(PLR202102)the Open Funds of the Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding(PL202005)Yangzhou University High-end Talent Support Program,and the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD).
文摘Genomic selection(GS)is a powerful tool for improving genetic gain in maize breeding.However,its routine application in large-scale breeding pipelines is limited by the high cost of genotyping platforms.Although sequencing-based and array-based genotyping platforms have been used for GS,few studies have compared prediction performance among platforms.In this study,we evaluated the predictabilities of four agronomic traits in 305 maize hybrids derived from 149 parental lines subjected to genotyping by sequencing(GBS),a 40K SNP array,and target sequence capture(TSC)using eight GS models.The GBS marker dataset yielded the highest predictabilities for all traits,followed by TSC and SNP array datasets.We investigated the effect of marker density and statistical models on predictability among genotyping platforms and found that 1K SNPs were sufficient to achieve comparable predictabilities to 10K and all SNPs,and BayesB,GBLUP,and RKHS performed well,while XGBoost performed poorly in most cases.We also selected significant SNP subsets using genome-wide association study(GWAS)analyses in three panels to predict hybrid performance.GWAS facilitated selecting effective SNP subsets for GS and thus reduced genotyping cost,but depended heavily on the GWAS panel.We conclude that there is still room for optimization of the existing SNP array,and using genotyping by target sequencing(GBTS)techniques to integrate a few functional markers identified by GWAS into the 1K SNP array holds great promise of being an effective strategy for developing desirable GS breeding arrays.
基金supported by the National Key Research and Development Program (2016YFD0100802)the National Science Foundation Collaborative Research grant (DBI-1458515)
文摘Precise mapping of quantitative trait loci(QTLs)is critical for assessing genetic effects and identifying candidate genes for quantitative traits.Interval and composite interval mappings have been the methods of choice for several decades,which have provided tools for identifying genomic regions harboring causal genes for quantitative traits.Historically,the concept was developed on the basis of sparse marker maps where genotypes of loci within intervals could not be observed.Currently,genomes of many organisms have been saturated with markers due to the new sequencing technologies.Genotyping by sequencing usually generates hundreds of thousands of single nucleotide polymorphisms(SNPs),which often include the causal polymorphisms.The concept of interval no longer exists,prompting the necessity of a norm change in QTL mapping technology to make use of the high-volume genomic data.Here we developed a statistical method and a software package to map QTLs by binning markers into haplotype blocks,called bins.The new method detects associations of bins with quantitative traits.It borrows the mixed model methodology with a polygenic control from genome-wide association studies(GWAS)and can handle all kinds of experimental populations under the linear mixed model(LMM)framework.We tested the method using both simulated data and data from populations of rice.The results showed that this method has higher power than the current methods.An R package named binQTL is available from GitHub.
基金Supported by 2017 Innovation Project of Jilin Academy of Agricultural Sciences(c72083203)
文摘Anaerobic digestion is one of the effective ways to dispose of antibiotic pharmaceutical waste.However,the inhibition of antibiotics on anaerobic fermentation microorganisms seriously hinders the normal physiological activities of anaerobic microorganisms and then affects the efficiency of anaerobic digestion.In order to solve this problem,related scholars have done a lot of research.It has been found that pretreatment of anaerobic microorganisms and antibiotic pharmaceutical waste can significantly improve the efficiency of anaerobic digestion.In this paper,the current feasible pretreatment methods were summarized,and the application of different pretreatment methods was analyzed to provide reference for improving pretreatment methods and improving anaerobic biological treatment ability of antibiotic waste.
基金supported by the National Natural Science Foundation of China(32301783 and U21A20214)the Natural Science Foundation of Anhui Province(2308085QC91 and 2408085MC058)+3 种基金the Innovation Program of the Chinese Academy of Agricultural Sciences(CAAS-CSIAF202303)the Nanfan special project,CAAS(YYLH2309,YBXM2322,YYLH2401)the Scientific Innovation 2030 Project(2022ZD0401703)the CAAS Innovative Team Award.
文摘Dear Editor,Although traditional breeding methods,such as phenotypebased and marker-assisted selection,remain valuable,genomic selection(GS)has emerged as a promising new approach.To address limitations of GS,particularly the time and cost involved in developing training populations,we developed Rice3KGS,a web-based tool that provides access to diverse datasets and integrates analytical functions to support crop breeding efforts.
基金funded by the National Key Research and Development Program of China(2016YFD0100301)the National Natural Science Foundation of China(31771762)+1 种基金the Agricultural Science,and Technology Innovation Program and the Cooperation and Innovation Mission(CAAS-ZD>CT202001)the Talent Introduction Program(RC311901)of Anhui Agricultural University.
文摘Polymorphisms within gene coding regions represent the most important part of the overall genetic diversity of rice.We characterized the gene-coding sequence-haplotype(gcHap)diversity of 45963 rice genes in 3010 rice accessions.With an average of 226±390 gcHaps per gene in rice populations,rice genes could be classified into three main categories:12865 conserved genes,10254 subspecific differentiating genes,and 22844 remaining genes.We found that 39218 rice genes carry>255179 major gcHaps of potential functional importance.Most(87.5%)of the detected gcHaps were specific to subspecies or populations.The inferred proto-ancestors of local landrace populations reconstructed from conserved predominant(ancient)gcHaps correlated strongly with wild rice accessions from the same geographic regions,supporting a multiorigin(domestication)model of Oryza sativa.Past breeding efforts generally increased the gcHap diversity of modern varieties and'caused significant frequency shifts in predominant gcHaps of 14266 genes due to independent selection in the two subspecies.Low frequencies of“favorable”gcHaps at most known genes related to rice yield in modern varieties suggest huge potential for rice improvement by mining and pyramiding of favorable gcHaps.The gcHap data were demonstrated to have greater power than SNPs for the detection of causal genes that affect complex traits.The rice gcHap diversity dataset generated in this study would facilitate rice basic research and improvement in the future.
