Background A detailed understanding of genetic variants that affect beef merit helps maximize the efficiency of breeding for improved production merit in beef cattle.To prioritize the putative variants and genes,we ra...Background A detailed understanding of genetic variants that affect beef merit helps maximize the efficiency of breeding for improved production merit in beef cattle.To prioritize the putative variants and genes,we ran a com-prehensive genome-wide association studies(GWAS)analysis for 21 agronomic traits using imputed whole-genome variants in Simmental beef cattle.Then,we applied expression quantitative trait loci(eQTL)mapping between the genotype variants and transcriptome of three tissues(longissimus dorsi muscle,backfat,and liver)in 120 cattle.Results We identified 1,580 association signals for 21 beef agronomic traits using GWAS.We then illuminated 854,498 cis-eQTLs for 6,017 genes and 46,970 trans-eQTLs for 1,903 genes in three tissues and built a synergistic network by integrating transcriptomics with agronomic traits.These cis-eQTLs were preferentially close to the transcription start site and enriched in functional regulatory regions.We observed an average of 43.5%improvement in cis-eQTL discovery using multi-tissue eQTL mapping.Fine-mapping analysis revealed that 111,192,and 194 variants were most likely to be causative to regulate gene expression in backfat,liver,and muscle,respectively.The transcriptome-wide association studies identified 722 genes significantly associated with 11 agronomic traits.Via the colocalization and Mendelian randomization analyses,we found that eQTLs of several genes were associated with the GWAS signals of agronomic traits in three tissues,which included genes,such as NADSYN1,NDUFS3,LTF and KIFC2 in liver,GRAMD1C,TMTC2 and ZNF613 in backfat,as well as TIGAR,NDUFS3 and L3HYPDH in muscle that could serve as the candidate genes for economic traits.Conclusions The extensive atlas of GWAS,eQTL,fine-mapping,and transcriptome-wide association studies aid in the suggestion of potentially functional variants and genes in cattle agronomic traits and will be an invaluable source for genomics and breeding in beef cattle.展开更多
Background A gap currently exists between genetic variants and the underlying cell and tissue biology of a trait,and expression quantitative trait loci(eQTL)studies provide important information to help close that gap...Background A gap currently exists between genetic variants and the underlying cell and tissue biology of a trait,and expression quantitative trait loci(eQTL)studies provide important information to help close that gap.However,two concerns that arise with eQTL analyses using RNA-sequencing data are normalization of data across samples and the data not following a normal distribution.Multiple pipelines have been suggested to address this.For instance,the most recent analysis of the human and farm Genotype-Tissue Expression(GTEx)project proposes using trimmed means of M-values(TMM)to normalize the data followed by an inverse normal transformation.Results In this study,we reasoned that eQTL analysis could be carried out using the same framework used for dif-ferential gene expression(DGE),which uses a negative binomial model,a statistical test feasible for count data.Using the GTEx framework,we identified 35 significant eQTLs(P<5×10^(–8))following the ANOVA model and 39 significant eQTLs(P<5×10^(–8))following the additive model.Using a differential gene expression framework,we identified 930 and six significant eQTLs(P<5×10^(–8))following an analytical framework equivalent to the ANOVA and additive model,respectively.When we compared the two approaches,there was no overlap of significant eQTLs between the two frameworks.Because we defined specific contrasts,we identified trans eQTLs that more closely resembled what we expect from genetic variants showing complete dominance between alleles.Yet,these were not identified by the GTEx framework.Conclusions Our results show that transforming RNA-sequencing data to fit a normal distribution prior to eQTL analysis is not required when the DGE framework is employed.Our proposed approach detected biologically relevant variants that otherwise would not have been identified due to data transformation to fit a normal distribution.展开更多
Genes encoding early signaling events in pathogen defense often are identified only by their phenotype. Such genes involved in barley-powdery mildew interactions include Mla, specifying race-specific resistance; Rarl ...Genes encoding early signaling events in pathogen defense often are identified only by their phenotype. Such genes involved in barley-powdery mildew interactions include Mla, specifying race-specific resistance; Rarl （Required for Mla12-specified resistance1）, and Roml （Restoration of Mla-specified resistancel）. The HSP90-SGT1-RAR1 complex appears to function as chaperone in MLA-specified resistance, however, much remains to be discovered regarding the precise signaling underlying plant immunity. Genetic analyses of fast-neutron mutants derived from CI 16151 （Mla6） uncovered a novel locus, designated Rar3 （R_equired for Mla6-specified resitance3）. Rar3 segregates independent of Mla6 and Rarl, and rar3 mutants are susceptible to Blumeria graminis f. sp. hordei （Bgh） isolate 5874 （A VRar）, whereas, wild-type progenitor plants are resistant. Comparative expression analyses of the rar3 mutant vs. its wild-type progenitor were conducted via Barleyl GeneChip and GAIIx paired-end RNA-Seq. Whereas Rarl affects transcription of relatively few genes; Rar3 appears to influence thousands, notably in genes controlling ATP binding, catalytic activity, transcription, and phosphorylation; possibly membrane bound or in the nucleus, eQTL analysis of a segregating doubled haploid population identified over two-thousand genes as being regulated by Mla （q value/FDR=0.00001）, a subset of which are significant in Rar3 interactions. The intersection of datasets derived from mla-loss-of-function mutants, Mla-associated eQTL, and rar3-mediated transcriptome reprogramming are narrowing the focus on essential genes required for Mla-specified immunity.展开更多
Detecting genes that affect specific traits(such as human diseases and crop yields)is important for treating complex diseases and improving crop quality.A genome-wide association study(GWAS)provides new insights and d...Detecting genes that affect specific traits(such as human diseases and crop yields)is important for treating complex diseases and improving crop quality.A genome-wide association study(GWAS)provides new insights and directions for understanding complex traits by identifying important single nucleotide polymorphisms.Many GWAS summary statistics data related to various complex traits have been gathered recently.Studies have shown that GWAS risk loci and expression quantitative trait loci(e QTLs)often have a lot of overlaps,which makes gene expression gradually become an important intermediary to reveal the regulatory role of GWAS.In this review,we review three types of gene-trait association detection methods of integrating GWAS summary statistics and e QTLs data,namely colocalization methods,transcriptome-wide association study-oriented approaches,and Mendelian randomization-related methods.At the theoretical level,we discussed the differences,relationships,advantages,and disadvantages of various algorithms in the three kinds of gene-trait association detection methods.To further discuss the performance of various methods,we summarize the significant gene sets that influence highdensity lipoprotein,low-density lipoprotein,total cholesterol,and triglyceride reported in 16 studies.We discuss the performance of various algorithms using the datasets of the four lipid traits.The advantages and limitations of various algorithms are analyzed based on experimental results,and we suggest directions for follow-up studies on detecting gene-trait associations.展开更多
An expression quantitative trait locus (eQTL) is a chromosomal region where genetic variants are associated with the expression levels of specific genes that can be both nearby or distant. The identifications of eQTLs...An expression quantitative trait locus (eQTL) is a chromosomal region where genetic variants are associated with the expression levels of specific genes that can be both nearby or distant. The identifications of eQTLs for different tissues, cell types, and contexts have led to a better understanding of the dynamic regulations of gene expressions and implications of functional genes and variants for complex traits and diseases. Although most eQTL studies have been performed on data collected from bulk tissues, recent studies have demonstrated the importance of cell-type-specific and context-dependent gene regulations in biological processes and disease mechanisms. In this review, we discuss statistical methods that have been developed to enable the detection of cell-type-specific and context-dependent eQTLs from bulk tissues, purified cell types, and single cells. We also discuss the limitations of the current methods and future research opportunities.展开更多
Presently,integrating multi-omics information into a prediction model has become a ameliorate strategy for genomic selection to improve genomic prediction accuracy.Here,we set the genomic and transcriptomic data as th...Presently,integrating multi-omics information into a prediction model has become a ameliorate strategy for genomic selection to improve genomic prediction accuracy.Here,we set the genomic and transcriptomic data as the training population data,using BSLMM,TWAS,and eQTL mapping to prescreen features according to |β_(b)|>0,top 1%of phenotypic variation explained(PVE),expression-associated single nucleotide polymorphisms(eSNPs),and egenes(false discovery rate(FDR)<0.01),where these loci were set as extra fixed effects(named GBLUP-Fix)and random effects(GFBLUP)to improve the prediction accuracy in the validation population,respectively.The results suggested that both GBLUP-Fix and GFBLUP models could improve the accuracy of longissimus dorsi muscle(LDM),water holding capacity(WHC),shear force(SF),and pH in Huaxi cattle on average from 2.14 to 8.69%,especially the improvement of GFBLUP-TWAS over GBLUP was 13.66%for SF.These methods also captured more genetic variance than GBLUP.Our study confirmed that multi-omics-assisted large-effects loci prescreening could improve the accuracyofgenomic prediction.展开更多
A detailed understanding of genetic architecture of mRNA expression by millions of genetic variants is important for studying quantitative trait variation. In this study, we identified 1.25M SNPs with a minor allele f...A detailed understanding of genetic architecture of mRNA expression by millions of genetic variants is important for studying quantitative trait variation. In this study, we identified 1.25M SNPs with a minor allele frequency greater than 0.05 by combining reduced genome sequencing （GBS）, high- density array technologies （600K）, and previous deep RNA-sequencing data from 368 diverse inbred lines of maize. The balanced allelic frequencies and distributions in a relatively large and diverse natural panel helped to identify expression quantitative trait loci （eQTLs） associated with more than 18 000 genes （63.4% of tested genes）. We found that distant eQTLs were more frequent （~75% of all eQTLs） across the whole genome. Thirteen novel associated loci affecting maize kernel oil concentration were identified using the new dataset, among which one intergenic locus affected the kernel oil variation by controlling expression of three other known oil-related genes. Altogether, this study provides resources for expanding our understanding of cellular regulatory mechanisms of transcriptome variation and the landscape of functional variants within the maize genome, thereby enhancing the understanding of quantitative variations.展开更多
基金supported by grants from the Central Public-interest Scientific Institution Basal Research Fund(2020-YWF-YB-02)the Young Scientists Fund of the National Natural Science Foundation of China(32202652)+1 种基金China Agriculture Research System of MOF and MARA(CARS-37)the Science and Technology Project of Inner Mongolia Autonomous Region(2020GG0210).
文摘Background A detailed understanding of genetic variants that affect beef merit helps maximize the efficiency of breeding for improved production merit in beef cattle.To prioritize the putative variants and genes,we ran a com-prehensive genome-wide association studies(GWAS)analysis for 21 agronomic traits using imputed whole-genome variants in Simmental beef cattle.Then,we applied expression quantitative trait loci(eQTL)mapping between the genotype variants and transcriptome of three tissues(longissimus dorsi muscle,backfat,and liver)in 120 cattle.Results We identified 1,580 association signals for 21 beef agronomic traits using GWAS.We then illuminated 854,498 cis-eQTLs for 6,017 genes and 46,970 trans-eQTLs for 1,903 genes in three tissues and built a synergistic network by integrating transcriptomics with agronomic traits.These cis-eQTLs were preferentially close to the transcription start site and enriched in functional regulatory regions.We observed an average of 43.5%improvement in cis-eQTL discovery using multi-tissue eQTL mapping.Fine-mapping analysis revealed that 111,192,and 194 variants were most likely to be causative to regulate gene expression in backfat,liver,and muscle,respectively.The transcriptome-wide association studies identified 722 genes significantly associated with 11 agronomic traits.Via the colocalization and Mendelian randomization analyses,we found that eQTLs of several genes were associated with the GWAS signals of agronomic traits in three tissues,which included genes,such as NADSYN1,NDUFS3,LTF and KIFC2 in liver,GRAMD1C,TMTC2 and ZNF613 in backfat,as well as TIGAR,NDUFS3 and L3HYPDH in muscle that could serve as the candidate genes for economic traits.Conclusions The extensive atlas of GWAS,eQTL,fine-mapping,and transcriptome-wide association studies aid in the suggestion of potentially functional variants and genes in cattle agronomic traits and will be an invaluable source for genomics and breeding in beef cattle.
基金partially funded by the Virginia Cattle Industry Board and the Virginia Agriculture CouncilVT Open Access Subvention Fund for the partial support of the publication fees
文摘Background A gap currently exists between genetic variants and the underlying cell and tissue biology of a trait,and expression quantitative trait loci(eQTL)studies provide important information to help close that gap.However,two concerns that arise with eQTL analyses using RNA-sequencing data are normalization of data across samples and the data not following a normal distribution.Multiple pipelines have been suggested to address this.For instance,the most recent analysis of the human and farm Genotype-Tissue Expression(GTEx)project proposes using trimmed means of M-values(TMM)to normalize the data followed by an inverse normal transformation.Results In this study,we reasoned that eQTL analysis could be carried out using the same framework used for dif-ferential gene expression(DGE),which uses a negative binomial model,a statistical test feasible for count data.Using the GTEx framework,we identified 35 significant eQTLs(P<5×10^(–8))following the ANOVA model and 39 significant eQTLs(P<5×10^(–8))following the additive model.Using a differential gene expression framework,we identified 930 and six significant eQTLs(P<5×10^(–8))following an analytical framework equivalent to the ANOVA and additive model,respectively.When we compared the two approaches,there was no overlap of significant eQTLs between the two frameworks.Because we defined specific contrasts,we identified trans eQTLs that more closely resembled what we expect from genetic variants showing complete dominance between alleles.Yet,these were not identified by the GTEx framework.Conclusions Our results show that transforming RNA-sequencing data to fit a normal distribution prior to eQTL analysis is not required when the DGE framework is employed.Our proposed approach detected biologically relevant variants that otherwise would not have been identified due to data transformation to fit a normal distribution.
基金Research supported in part by USA National Science Foundation-Plant Genome Program grant(0922746)
文摘Genes encoding early signaling events in pathogen defense often are identified only by their phenotype. Such genes involved in barley-powdery mildew interactions include Mla, specifying race-specific resistance; Rarl （Required for Mla12-specified resistance1）, and Roml （Restoration of Mla-specified resistancel）. The HSP90-SGT1-RAR1 complex appears to function as chaperone in MLA-specified resistance, however, much remains to be discovered regarding the precise signaling underlying plant immunity. Genetic analyses of fast-neutron mutants derived from CI 16151 （Mla6） uncovered a novel locus, designated Rar3 （R_equired for Mla6-specified resitance3）. Rar3 segregates independent of Mla6 and Rarl, and rar3 mutants are susceptible to Blumeria graminis f. sp. hordei （Bgh） isolate 5874 （A VRar）, whereas, wild-type progenitor plants are resistant. Comparative expression analyses of the rar3 mutant vs. its wild-type progenitor were conducted via Barleyl GeneChip and GAIIx paired-end RNA-Seq. Whereas Rarl affects transcription of relatively few genes; Rar3 appears to influence thousands, notably in genes controlling ATP binding, catalytic activity, transcription, and phosphorylation; possibly membrane bound or in the nucleus, eQTL analysis of a segregating doubled haploid population identified over two-thousand genes as being regulated by Mla （q value/FDR=0.00001）, a subset of which are significant in Rar3 interactions. The intersection of datasets derived from mla-loss-of-function mutants, Mla-associated eQTL, and rar3-mediated transcriptome reprogramming are narrowing the focus on essential genes required for Mla-specified immunity.
基金supported by the National Key Research and Development Program of China(2022YFD1201504)the Fundamental Research Funds for the Central Universities(2662022YLYJ010,2021ZKPY018,2662021JC008,SZYJY2021003)+2 种基金the Major Science and Technology Project of Hubei Province(2021AFB002)the Major Project of Hubei Hongshan Laboratory(2022HSZD031)the Yingzi Tech&Huazhong Agricultural University Intelligent Research Institute of Food Health(IRIFH202209)。
文摘Detecting genes that affect specific traits(such as human diseases and crop yields)is important for treating complex diseases and improving crop quality.A genome-wide association study(GWAS)provides new insights and directions for understanding complex traits by identifying important single nucleotide polymorphisms.Many GWAS summary statistics data related to various complex traits have been gathered recently.Studies have shown that GWAS risk loci and expression quantitative trait loci(e QTLs)often have a lot of overlaps,which makes gene expression gradually become an important intermediary to reveal the regulatory role of GWAS.In this review,we review three types of gene-trait association detection methods of integrating GWAS summary statistics and e QTLs data,namely colocalization methods,transcriptome-wide association study-oriented approaches,and Mendelian randomization-related methods.At the theoretical level,we discussed the differences,relationships,advantages,and disadvantages of various algorithms in the three kinds of gene-trait association detection methods.To further discuss the performance of various methods,we summarize the significant gene sets that influence highdensity lipoprotein,low-density lipoprotein,total cholesterol,and triglyceride reported in 16 studies.We discuss the performance of various algorithms using the datasets of the four lipid traits.The advantages and limitations of various algorithms are analyzed based on experimental results,and we suggest directions for follow-up studies on detecting gene-trait associations.
文摘An expression quantitative trait locus (eQTL) is a chromosomal region where genetic variants are associated with the expression levels of specific genes that can be both nearby or distant. The identifications of eQTLs for different tissues, cell types, and contexts have led to a better understanding of the dynamic regulations of gene expressions and implications of functional genes and variants for complex traits and diseases. Although most eQTL studies have been performed on data collected from bulk tissues, recent studies have demonstrated the importance of cell-type-specific and context-dependent gene regulations in biological processes and disease mechanisms. In this review, we discuss statistical methods that have been developed to enable the detection of cell-type-specific and context-dependent eQTLs from bulk tissues, purified cell types, and single cells. We also discuss the limitations of the current methods and future research opportunities.
基金This research was supported by the National Natural Science Foundations of China(31872975)the Science and Technology Project of Inner Mongolia Autonomous Region,China(2020GG0210)the Program of National Beef Cattle and Yak Industrial Technology System,China(CARS-37).
文摘Presently,integrating multi-omics information into a prediction model has become a ameliorate strategy for genomic selection to improve genomic prediction accuracy.Here,we set the genomic and transcriptomic data as the training population data,using BSLMM,TWAS,and eQTL mapping to prescreen features according to |β_(b)|>0,top 1%of phenotypic variation explained(PVE),expression-associated single nucleotide polymorphisms(eSNPs),and egenes(false discovery rate(FDR)<0.01),where these loci were set as extra fixed effects(named GBLUP-Fix)and random effects(GFBLUP)to improve the prediction accuracy in the validation population,respectively.The results suggested that both GBLUP-Fix and GFBLUP models could improve the accuracy of longissimus dorsi muscle(LDM),water holding capacity(WHC),shear force(SF),and pH in Huaxi cattle on average from 2.14 to 8.69%,especially the improvement of GFBLUP-TWAS over GBLUP was 13.66%for SF.These methods also captured more genetic variance than GBLUP.Our study confirmed that multi-omics-assisted large-effects loci prescreening could improve the accuracyofgenomic prediction.
文摘A detailed understanding of genetic architecture of mRNA expression by millions of genetic variants is important for studying quantitative trait variation. In this study, we identified 1.25M SNPs with a minor allele frequency greater than 0.05 by combining reduced genome sequencing （GBS）, high- density array technologies （600K）, and previous deep RNA-sequencing data from 368 diverse inbred lines of maize. The balanced allelic frequencies and distributions in a relatively large and diverse natural panel helped to identify expression quantitative trait loci （eQTLs） associated with more than 18 000 genes （63.4% of tested genes）. We found that distant eQTLs were more frequent （~75% of all eQTLs） across the whole genome. Thirteen novel associated loci affecting maize kernel oil concentration were identified using the new dataset, among which one intergenic locus affected the kernel oil variation by controlling expression of three other known oil-related genes. Altogether, this study provides resources for expanding our understanding of cellular regulatory mechanisms of transcriptome variation and the landscape of functional variants within the maize genome, thereby enhancing the understanding of quantitative variations.
