Alfalfa(M. sativa L.) is a highly valuable forage crop, providing >58 Mt of hay, silage, and pasture each year in the United States. As alfalfa is an outcrossing autotetraploid crop,however, breeding for enhanced a...Alfalfa(M. sativa L.) is a highly valuable forage crop, providing >58 Mt of hay, silage, and pasture each year in the United States. As alfalfa is an outcrossing autotetraploid crop,however, breeding for enhanced agronomic traits is challenging and progress has historically not been rapid. Methods that make use of genotypic information and statistical models to generate a genomic estimated breeding value(GEBV) for each plant at a young age hold a great deal of promise to accelerate breeding gains. An emerging genomic breeding pipeline employs SNP chips or genotyping-by-sequencing(GBS) to identify SNP markers in a training population, followed by the use of a statistical model to find associations between the discovered SNPs and traits of interest, followed by genomic selection(GS), a breeding program utilizing the trained model to predict breeding values and making selections based on the estimated breeding value(EBV). Much work has been done in recent years in all of these areas, to generate marker sets and discover SNPs associated with desirable traits, and the application of these technologies in alfalfa breeding programs is under way. However, GBS/GWAS/GS is still a new breeding paradigm,and work is ongoing to evaluate different models, software, and methods for use in such programs. In this review, we look at the progress of alfalfa genomics over the past halfdecade, and review work comparing models and methods relevant to this new type of breeding strategy.展开更多
Alfalfa(Medicago sativa L.)is the most important legume forage crop worldwide with high nutritional value and yield.For a long time,the breeding of alfalfa was hampered by lacking reliable information on the autotetra...Alfalfa(Medicago sativa L.)is the most important legume forage crop worldwide with high nutritional value and yield.For a long time,the breeding of alfalfa was hampered by lacking reliable information on the autotetraploid genome and molecular markers linked to important agronomic traits.We herein reported the de novo assembly of the allele-aware chromosome-level genome of Zhongmu-4,a cultivar widely cultivated in China,and a comprehensive database of genomic variations based on resequencing of 220 germplasms.Approximate 2.74 Gb contigs(N50 of 2.06 Mb),accounting for 88.39%of the estimated genome,were assembled,and 2.56 Gb contigs were anchored to 32 pseudo-chromosomes.A total of 34,922 allelic genes were identified from the allele-aware genome.We observed the expansion of gene families,especially those related to the nitrogen metabolism,and the increase of repetitive elements including transposable elements,which probably resulted in the increase of Zhongmu-4 genome compared with Medicago truncatula.Population structure analysis revealed that the accessions from Asia and South America had relatively lower genetic diversity than those from Europe,suggesting that geography may influence alfalfa genetic divergence during local adaption.Genome-wide association studies identified 101 single nucleotide polymorphisms(SNPs)associated with 27 agronomic traits.Two candidate genes were predicted to be correlated with fall dormancy and salt response.We believe that the alleleaware chromosome-level genome sequence of Zhongmu-4 combined with the resequencing data of the diverse alfalfa germplasms will facilitate genetic research and genomics-assisted breeding in variety improvement of alfalfa.展开更多
Given the escalating impact of climate change on agriculture and food security,gaining insights into the evolutionary dynamics of climatic adaptation and uncovering climate-adapted variation can empower the breeding o...Given the escalating impact of climate change on agriculture and food security,gaining insights into the evolutionary dynamics of climatic adaptation and uncovering climate-adapted variation can empower the breeding of climate-resilient crops to face future climate change.Alfalfa(Medicago sativa subsp.sativa),the queen of forages,shows remarkable adaptability across diverse global environments,making it an excellent model for investigating species responses to climate change.In this study,we performed population genomic analyses using genome resequencing data from 702 accessions of 24 Medicago species to unravel alfalfa’s climatic adaptation and genetic susceptibility to future climate change.We found that interspecific genetic exchange has contributed to the gene pool of alfalfa,particularly enriching defense and stress-response genes.Intersubspecific introgression between M.sativa subsp.falcata(subsp.falcata)and alfalfa not only aids alfalfa’s climatic adaptation but also introduces genetic burden.A total of 1671 genes were associated with climatic adaptation,and 5.7%of them were introgressions from subsp.falcata.By integrating climate-associated variants and climate data,we identified populations that are vulnerable to future climate change,particularly in higher latitudes of the Northern Hemisphere.These findings serve as a clarion call for targeted conservation initiatives and breeding efforts.We also identified preadaptive populations that demonstrate heightened resilience to climate fluctuations,illuminating a pathway for future breeding strategies.Collectively,this study enhances our understanding about the local adaptation mechanisms of alfalfa and facilitates the breeding of climate-resilient alfalfa cultivars,contributing to effective agricultural strategies for facing future climate change.展开更多
基金supported by the United States Department of Agriculture NIFA_AFRP(2015-70005-24071)the Agricultural Research Service base fund
文摘Alfalfa(M. sativa L.) is a highly valuable forage crop, providing >58 Mt of hay, silage, and pasture each year in the United States. As alfalfa is an outcrossing autotetraploid crop,however, breeding for enhanced agronomic traits is challenging and progress has historically not been rapid. Methods that make use of genotypic information and statistical models to generate a genomic estimated breeding value(GEBV) for each plant at a young age hold a great deal of promise to accelerate breeding gains. An emerging genomic breeding pipeline employs SNP chips or genotyping-by-sequencing(GBS) to identify SNP markers in a training population, followed by the use of a statistical model to find associations between the discovered SNPs and traits of interest, followed by genomic selection(GS), a breeding program utilizing the trained model to predict breeding values and making selections based on the estimated breeding value(EBV). Much work has been done in recent years in all of these areas, to generate marker sets and discover SNPs associated with desirable traits, and the application of these technologies in alfalfa breeding programs is under way. However, GBS/GWAS/GS is still a new breeding paradigm,and work is ongoing to evaluate different models, software, and methods for use in such programs. In this review, we look at the progress of alfalfa genomics over the past halfdecade, and review work comparing models and methods relevant to this new type of breeding strategy.
基金supported by the National Natural Science Foundation of China(Grant Nos.31971758 to QY and 32071865 to RL)the Collaborative Research Key Project between China and EU(Grant No.2017YFE0111000)+2 种基金the China Agriculture Research System of MOF and MARA(Grant No.CARS-34)the Agricultural Science and Technology Innovation Program of Chinese Academy of Agricultural Sciences(Grant No.ASTIP-IAS14)the Key Projects in Science and Technology of Inner Mongolia,China(Grant No.2021ZD0031)。
文摘Alfalfa(Medicago sativa L.)is the most important legume forage crop worldwide with high nutritional value and yield.For a long time,the breeding of alfalfa was hampered by lacking reliable information on the autotetraploid genome and molecular markers linked to important agronomic traits.We herein reported the de novo assembly of the allele-aware chromosome-level genome of Zhongmu-4,a cultivar widely cultivated in China,and a comprehensive database of genomic variations based on resequencing of 220 germplasms.Approximate 2.74 Gb contigs(N50 of 2.06 Mb),accounting for 88.39%of the estimated genome,were assembled,and 2.56 Gb contigs were anchored to 32 pseudo-chromosomes.A total of 34,922 allelic genes were identified from the allele-aware genome.We observed the expansion of gene families,especially those related to the nitrogen metabolism,and the increase of repetitive elements including transposable elements,which probably resulted in the increase of Zhongmu-4 genome compared with Medicago truncatula.Population structure analysis revealed that the accessions from Asia and South America had relatively lower genetic diversity than those from Europe,suggesting that geography may influence alfalfa genetic divergence during local adaption.Genome-wide association studies identified 101 single nucleotide polymorphisms(SNPs)associated with 27 agronomic traits.Two candidate genes were predicted to be correlated with fall dormancy and salt response.We believe that the alleleaware chromosome-level genome sequence of Zhongmu-4 combined with the resequencing data of the diverse alfalfa germplasms will facilitate genetic research and genomics-assisted breeding in variety improvement of alfalfa.
基金supported by the earmarked fund for CARS(CARS-34)the Agricultural Science and Technology Innovation Program of Chinese Academy of Agricultural Sciences(ASTIP-IAS14)the Science Fund Program for Distinguished Young Scholars of the National Natural Science Foundation of China(Overseas)to Yongfeng Zhou.
文摘Given the escalating impact of climate change on agriculture and food security,gaining insights into the evolutionary dynamics of climatic adaptation and uncovering climate-adapted variation can empower the breeding of climate-resilient crops to face future climate change.Alfalfa(Medicago sativa subsp.sativa),the queen of forages,shows remarkable adaptability across diverse global environments,making it an excellent model for investigating species responses to climate change.In this study,we performed population genomic analyses using genome resequencing data from 702 accessions of 24 Medicago species to unravel alfalfa’s climatic adaptation and genetic susceptibility to future climate change.We found that interspecific genetic exchange has contributed to the gene pool of alfalfa,particularly enriching defense and stress-response genes.Intersubspecific introgression between M.sativa subsp.falcata(subsp.falcata)and alfalfa not only aids alfalfa’s climatic adaptation but also introduces genetic burden.A total of 1671 genes were associated with climatic adaptation,and 5.7%of them were introgressions from subsp.falcata.By integrating climate-associated variants and climate data,we identified populations that are vulnerable to future climate change,particularly in higher latitudes of the Northern Hemisphere.These findings serve as a clarion call for targeted conservation initiatives and breeding efforts.We also identified preadaptive populations that demonstrate heightened resilience to climate fluctuations,illuminating a pathway for future breeding strategies.Collectively,this study enhances our understanding about the local adaptation mechanisms of alfalfa and facilitates the breeding of climate-resilient alfalfa cultivars,contributing to effective agricultural strategies for facing future climate change.
