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
Gene expression regulation plays an important role in controlling plant phenotypes and adaptation. Here, we report a comprehensive assessment of gene expression variation through the transcriptome analyses of a large ...Gene expression regulation plays an important role in controlling plant phenotypes and adaptation. Here, we report a comprehensive assessment of gene expression variation through the transcriptome analyses of a large maize-teosinte experimental population. Genome-wide mapping identified 25 660 expression quantitative trait loci （eQTL） for 17 311 genes, capturing an unprecedented range of expression variation. We found that local eQTL were more frequently mapped to adjacent genes, displaying a mode of expression piggybacking, which consequently created co-regulated gene clusters. Genes within the co-regulated gene clusters tend to have relevant functions and shared chromatin modifications. Distant eQTL formed 125 significant distant eQTL hotspots with their targets significantly enriched in specific functional cate- gories. By integrating different sources of information, we identified putative trans- regulators for a variety of metabolic pathways. We demonstrated that the bHLH transcription factor R1 and hexokinase HEX9 might act as crucial regulators for flavonoid biosynthesis and glycolysis, respectively. Moreover, we showed that domestication or improvement has significantly affected global gene expression, with many genes targeted by selection. Of particular interest, the Bx genes for benzoxazinoid biosynthesis may have undergone coordinated cis-regulatory divergence between maize and teosinte, and a transposon insertion that inactivates Bx12 was under strong selection as maize spread into temperate environments with a distinct herbivore community.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
目的位于染色体22q12.2区域的MTMR3(myotubularin related protein 3)基因是调控肌维管束蛋白表达的基因,MTMR3基因过度表达与肿瘤等疾病发生有关。本研究旨结合表达数量性状(expression Quati tatire Trait Loci,eQTL)信息探讨MTMR3基...目的位于染色体22q12.2区域的MTMR3(myotubularin related protein 3)基因是调控肌维管束蛋白表达的基因,MTMR3基因过度表达与肿瘤等疾病发生有关。本研究旨结合表达数量性状(expression Quati tatire Trait Loci,eQTL)信息探讨MTMR3基因多态性与非吸烟者肺癌易感性的关系,为探究肺癌的发病机制提供依据。方法对肺癌易感区域22q12.2进行连锁不平衡和eQTL分析,筛选具有调控基因表达的致病位点并预测其调控的基因。本研究采用病例对照研究方法,病例为2013-03-05-2014-12-30辽宁省肿瘤医院(96例)、中国医科大学附属第一医院(92例)、中国医科大学附属第四医院(90例)、沈阳军区总医院(95例)和人民解放军沈阳二0二医院(88例)5所三甲级医院的原发性肺癌患者461例(病例组),同期社区中健康对照472名(对照组)。应用TaqMan基因分型技术对rs36605位点进行基因分型。采用t检验比较年龄在病例组与对照组间分布的差异,采用χ~2检验比较性别、各基因型以及各环境暴露因素在病例组与对照组间分布的差异,应用Logistic回归计算OR值及其95%置信区间(CI)。结果经eQTL分析得到rs36605位点是MTMR3基因的一个顺式eQTL,rs36605位点可能与MTMR3基因的表达有关。以TT基因型为参照,AT基因型(OR=0.81,95%CI为0.60~1.10,P=0.178)和AA基因型(OR=1.85,95%CI为0.61~5.59,P=0.276)与肺癌的患病风险无统计学关联。也未发现在显性模型(OR=0.85,95%CI为0.63~1.15,P=0.291)以及隐形模型(OR=1.94,95%CI为0.64~5.86,P=0.238)中存在此关联。试验验证了烹饪油烟暴露增加了肺癌的患病风险,调整OR=1.61,95%CI为1.06~2.45,P=0.025,但未发现烹饪油烟暴露与rs36605位点多态性之间存在交互作用,P>0.05。结论试验未发现,22q12.2区域内的rs36605位点多态性与非吸烟者肺癌的易感性有关,尚不能得到MTMR3基因多态性与非吸烟者肺癌的易感性有关。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
MicroRNAs(miRNAs)are key regulators of myocyte development and traits,yet insight into the genetic basis of variation in miRNA expression is still limited.Here,we present a systematic analysis of expression quantitati...MicroRNAs(miRNAs)are key regulators of myocyte development and traits,yet insight into the genetic basis of variation in miRNA expression is still limited.Here,we present a systematic analysis of expression quantitative trait loci(eQTL)for miRNA profiling in longissimus muscle of pigs from an eight-breed crossed heterogeneous population.By integrating the whole-genome sequencing and miRNAomics data,we map 54 cis-and 292 trans-e QTLs at high resolution that are associated with the expression of 54 and 92miRNAs,respectively.Twenty-three trans-acting loci are identified to affect the expression of nine myomi Rs(known muscle-specific miRNAs).MiRNAs in mammalian conserved miRNA clusters are found to be subjected to regulation by shared cis-e QTLs,while the expression of mature miRNA-5p/-3p counterparts is more likely to be regulated by different cis-e QTLs.Fine mapping and bioinformatics analyses pinpoint the peak cis-e SNP of mi R-4331-5p,rs344650810,which is located in its seed region,as a causal variant for the changes in expression and function of this miRNA.Additionally,rs344650810 is significantly(P<0.01)correlated with the density and percentage of type I muscle fibers.Altogether,this study provides a comprehensive atlas of miRNA-e QTLs in porcine skeletal muscle and new insights into regulatory mechanisms of miRNA expression.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
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
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
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:Genome-wide association studies(GWAS)have succeeded in identifying tens of thousands of genetic variants associated with complex human traits during the past decade,however,they are still hampered by limite...Background:Genome-wide association studies(GWAS)have succeeded in identifying tens of thousands of genetic variants associated with complex human traits during the past decade,however,they are still hampered by limited statistical power and difficulties in biological interpretation.With the recent progress in expression quantitative trait loci(eQTL)studies,transcriptome-wide association studies(TWAS)provide a framework to test for gene-trait associations by integrating information from GWAS and eQTL studies.Results:In this review,we will introduce the general framework of TWAS,the relevant resources,and the computational tools.Extensions of the original TWAS methods will also be discussed.Furthermore,we will briefly introduce methods that are closely related to TWAS,including MR-based methods and colocalization approaches.Connection and difference between these approaches will be discussed.Conclusion:Finally,we will summarize strengths,limitations,and potential directions for TWAS.展开更多
Background:Genome-wide association studies(GWAS)have been widely adopted in studies of human complex traits and diseases.Results:This review surveys areas of active research:quantifying and partitioning trait heritabi...Background:Genome-wide association studies(GWAS)have been widely adopted in studies of human complex traits and diseases.Results:This review surveys areas of active research:quantifying and partitioning trait heritability,fine mapping functional variants and integrative analysis,genetic risk prediction of phenotypes,and the analysis of sequencing studies that have identified millions of rare variants.Current challenges and opportunities are highlighted.Conclusion:GWAS have fundamentally transformed the field of human complex trait genetics.Novel statistical and computational methods have expanded the scope of GWAS and have provided valuable insights on the genetic architecture underlying complex phenotypes.展开更多
Background:Genome-wide association studies(GWAS)have identified thousands of genomic non-coding variants statistically associated with many human traits and diseases,including cancer.However,the functional interpretat...Background:Genome-wide association studies(GWAS)have identified thousands of genomic non-coding variants statistically associated with many human traits and diseases,including cancer.However,the functional interpretation of these non-coding variants remains a significant challenge in the post-GWAS era.Alternative polyadenylation(APA)plays an essential role in post-transcriptional regulation for most human genes.By employing different poly(A)sites,genes can either shorten or extend the 3'-UTRs that contain cu-regulatory elements such as miRNAs or RNA-binding protein binding sites.Therefore,APA can affect the mRNA stability,translation,and cellular localization of proteins.Population-scale studies have revealed many inherited genetic variants that potentially impact APA to further influence disease susceptibility and phenotypic diversity,but systematic computational investigations to delineate the connections are in their earliest states.Results:Here,we discuss the evolving definitions of the genetic basis of APA and the modern genomics tools to identify,characterize,and validate the genetic influences of APA events in human populations.We also explore the emerging and surprisingly complex molecular mechanisms that regulate APA and summarize the genetic control of APA that is associated with complex human diseases and traits.Conclusion:APA is an intermediate molecular phenotype that can translate human common non-coding variants to individual phenotypic variability and disease susceptibility.展开更多
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.
文摘Gene expression regulation plays an important role in controlling plant phenotypes and adaptation. Here, we report a comprehensive assessment of gene expression variation through the transcriptome analyses of a large maize-teosinte experimental population. Genome-wide mapping identified 25 660 expression quantitative trait loci （eQTL） for 17 311 genes, capturing an unprecedented range of expression variation. We found that local eQTL were more frequently mapped to adjacent genes, displaying a mode of expression piggybacking, which consequently created co-regulated gene clusters. Genes within the co-regulated gene clusters tend to have relevant functions and shared chromatin modifications. Distant eQTL formed 125 significant distant eQTL hotspots with their targets significantly enriched in specific functional cate- gories. By integrating different sources of information, we identified putative trans- regulators for a variety of metabolic pathways. We demonstrated that the bHLH transcription factor R1 and hexokinase HEX9 might act as crucial regulators for flavonoid biosynthesis and glycolysis, respectively. Moreover, we showed that domestication or improvement has significantly affected global gene expression, with many genes targeted by selection. Of particular interest, the Bx genes for benzoxazinoid biosynthesis may have undergone coordinated cis-regulatory divergence between maize and teosinte, and a transposon insertion that inactivates Bx12 was under strong selection as maize spread into temperate environments with a distinct herbivore community.
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.
文摘目的位于染色体22q12.2区域的MTMR3(myotubularin related protein 3)基因是调控肌维管束蛋白表达的基因,MTMR3基因过度表达与肿瘤等疾病发生有关。本研究旨结合表达数量性状(expression Quati tatire Trait Loci,eQTL)信息探讨MTMR3基因多态性与非吸烟者肺癌易感性的关系,为探究肺癌的发病机制提供依据。方法对肺癌易感区域22q12.2进行连锁不平衡和eQTL分析,筛选具有调控基因表达的致病位点并预测其调控的基因。本研究采用病例对照研究方法,病例为2013-03-05-2014-12-30辽宁省肿瘤医院(96例)、中国医科大学附属第一医院(92例)、中国医科大学附属第四医院(90例)、沈阳军区总医院(95例)和人民解放军沈阳二0二医院(88例)5所三甲级医院的原发性肺癌患者461例(病例组),同期社区中健康对照472名(对照组)。应用TaqMan基因分型技术对rs36605位点进行基因分型。采用t检验比较年龄在病例组与对照组间分布的差异,采用χ~2检验比较性别、各基因型以及各环境暴露因素在病例组与对照组间分布的差异,应用Logistic回归计算OR值及其95%置信区间(CI)。结果经eQTL分析得到rs36605位点是MTMR3基因的一个顺式eQTL,rs36605位点可能与MTMR3基因的表达有关。以TT基因型为参照,AT基因型(OR=0.81,95%CI为0.60~1.10,P=0.178)和AA基因型(OR=1.85,95%CI为0.61~5.59,P=0.276)与肺癌的患病风险无统计学关联。也未发现在显性模型(OR=0.85,95%CI为0.63~1.15,P=0.291)以及隐形模型(OR=1.94,95%CI为0.64~5.86,P=0.238)中存在此关联。试验验证了烹饪油烟暴露增加了肺癌的患病风险,调整OR=1.61,95%CI为1.06~2.45,P=0.025,但未发现烹饪油烟暴露与rs36605位点多态性之间存在交互作用,P>0.05。结论试验未发现,22q12.2区域内的rs36605位点多态性与非吸烟者肺癌的易感性有关,尚不能得到MTMR3基因多态性与非吸烟者肺癌的易感性有关。
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.
基金supported by the National Natural Science Foundation of China(31790413 and 31872339)。
文摘MicroRNAs(miRNAs)are key regulators of myocyte development and traits,yet insight into the genetic basis of variation in miRNA expression is still limited.Here,we present a systematic analysis of expression quantitative trait loci(eQTL)for miRNA profiling in longissimus muscle of pigs from an eight-breed crossed heterogeneous population.By integrating the whole-genome sequencing and miRNAomics data,we map 54 cis-and 292 trans-e QTLs at high resolution that are associated with the expression of 54 and 92miRNAs,respectively.Twenty-three trans-acting loci are identified to affect the expression of nine myomi Rs(known muscle-specific miRNAs).MiRNAs in mammalian conserved miRNA clusters are found to be subjected to regulation by shared cis-e QTLs,while the expression of mature miRNA-5p/-3p counterparts is more likely to be regulated by different cis-e QTLs.Fine mapping and bioinformatics analyses pinpoint the peak cis-e SNP of mi R-4331-5p,rs344650810,which is located in its seed region,as a causal variant for the changes in expression and function of this miRNA.Additionally,rs344650810 is significantly(P<0.01)correlated with the density and percentage of type I muscle fibers.Altogether,this study provides a comprehensive atlas of miRNA-e QTLs in porcine skeletal muscle and new insights into regulatory mechanisms of miRNA expression.
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.
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National Natural Science Foundation of China(No.11601259)Shanghai Municipal Science and Technology Major Project(No.2017SHZDZX01).Y.X.and N.S.were supported in part by the China Scholarship Council,and H.Z.was supported in part by NIH grant R01GM122078,NSF grants DMS 1713120 and DMS 1902903.
文摘Background:Genome-wide association studies(GWAS)have succeeded in identifying tens of thousands of genetic variants associated with complex human traits during the past decade,however,they are still hampered by limited statistical power and difficulties in biological interpretation.With the recent progress in expression quantitative trait loci(eQTL)studies,transcriptome-wide association studies(TWAS)provide a framework to test for gene-trait associations by integrating information from GWAS and eQTL studies.Results:In this review,we will introduce the general framework of TWAS,the relevant resources,and the computational tools.Extensions of the original TWAS methods will also be discussed.Furthermore,we will briefly introduce methods that are closely related to TWAS,including MR-based methods and colocalization approaches.Connection and difference between these approaches will be discussed.Conclusion:Finally,we will summarize strengths,limitations,and potential directions for TWAS.
基金This work is supported by NIH R35GM127063(HT)and NIH AG066206(ZH).
文摘Background:Genome-wide association studies(GWAS)have been widely adopted in studies of human complex traits and diseases.Results:This review surveys areas of active research:quantifying and partitioning trait heritability,fine mapping functional variants and integrative analysis,genetic risk prediction of phenotypes,and the analysis of sequencing studies that have identified millions of rare variants.Current challenges and opportunities are highlighted.Conclusion:GWAS have fundamentally transformed the field of human complex trait genetics.Novel statistical and computational methods have expanded the scope of GWAS and have provided valuable insights on the genetic architecture underlying complex phenotypes.
基金support from the National Institutes of Health grant R01-GM134539(EJ.W).
文摘Background:Genome-wide association studies(GWAS)have identified thousands of genomic non-coding variants statistically associated with many human traits and diseases,including cancer.However,the functional interpretation of these non-coding variants remains a significant challenge in the post-GWAS era.Alternative polyadenylation(APA)plays an essential role in post-transcriptional regulation for most human genes.By employing different poly(A)sites,genes can either shorten or extend the 3'-UTRs that contain cu-regulatory elements such as miRNAs or RNA-binding protein binding sites.Therefore,APA can affect the mRNA stability,translation,and cellular localization of proteins.Population-scale studies have revealed many inherited genetic variants that potentially impact APA to further influence disease susceptibility and phenotypic diversity,but systematic computational investigations to delineate the connections are in their earliest states.Results:Here,we discuss the evolving definitions of the genetic basis of APA and the modern genomics tools to identify,characterize,and validate the genetic influences of APA events in human populations.We also explore the emerging and surprisingly complex molecular mechanisms that regulate APA and summarize the genetic control of APA that is associated with complex human diseases and traits.Conclusion:APA is an intermediate molecular phenotype that can translate human common non-coding variants to individual phenotypic variability and disease susceptibility.
