1会议背景和基本情况
发展中国家科学院（The Academy of Sciences for the Developing World, TWAS）成立于1983年11月，总部设在意大利的里雅斯特，是一个非政府、非政治和非营利性的国际科学组织，旨在促进发展中国家的科技发展，...1会议背景和基本情况
发展中国家科学院（The Academy of Sciences for the Developing World, TWAS）成立于1983年11月，总部设在意大利的里雅斯特，是一个非政府、非政治和非营利性的国际科学组织，旨在促进发展中国家的科技发展，支持发展中国家科技人员和科研机构之间的交流与合作。TWAS每年举行一次院士大会，每2—3年举行一次学术研讨会。TWAS历届会议均得到主办国政府的高度重视，中国在1987年和2003年成功地举办了第2届和第14届院士大会。展开更多
A genome-wide association study(GWAS)identifies trait-associated loci,but identifying the causal genes can be a bottleneck,due in part to slow decay of linkage disequilibrium(LD).A transcriptome-wide association study...A genome-wide association study(GWAS)identifies trait-associated loci,but identifying the causal genes can be a bottleneck,due in part to slow decay of linkage disequilibrium(LD).A transcriptome-wide association study(TWAS)addresses this issue by identifying gene expression-phenotype associations or integrating gene expression quantitative trait loci with GWAS results.Here,we used self-pollinated soybean(Glycine max[L.]Merr.)as a model to evaluate the application of TWAS to the genetic dissection of traits in plant species with slow LD decay.We generated RNA sequencing data for a soybean diversity panel and identified the genetic expression regulation of 29286 soybean genes.Different TWAS solutions were less affected by LD and were robust to the source of expression,identifing known genes related to traits from different tissues and developmental stages.The novel pod-color gene L2 was identified via TWAS and functionally validated by genome editing.By introducing a new exon proportion feature,we significantly improved the detection of expression variations that resulted from structural variations and alternative splicing.As a result,the genes identified through our TWAS approach exhibited a diverse range of causal variations,including SNPs,insertions or deletions,gene fusion,copy number variations,and alternative splicing.Using this approach,we identified genes associated with flowering time,including both previously known genes and novel genes that had not previously been linked to this trait,providing insights complementary to those from GWAS.In summary,this study supports the application of TWAS for candidate gene identification in species with low rates of LD decay.展开更多
Background Semen quality is one of the most important indicators of boar reproductive performance.In the past,boar breeding has mostly emphasized characteristics such as lean meat percentage,feed conversion efficiency...Background Semen quality is one of the most important indicators of boar reproductive performance.In the past,boar breeding has mostly emphasized characteristics such as lean meat percentage,feed conversion efficiency,and growth rate,while overlooking the genetic improvement of reproductive traits.This study employs advanced multi-omics approaches,such as transcriptome-wide association studies(TWAS)and colocalization between genome-wide association studies(GWAS)and expression quantitative trait loci(eQTLs),to provide a comprehensive understanding of the genetic mechanisms governing semen quality traits in boars.Results Here,we collected 190,000 ejaculate records across 11 semen quality traits from 3,604 Duroc boars.The heritability of semen quality traits ranged from 0.095 to 0.343.Genetic correlations between semen quality traits varied from−0.802 to 0.661,and phenotypic correlations ranged from−0.833 to 0.776.Single-trait GWAS identified 19 independent variants,corresponding to 13 quantitative trait loci(QTLs).By integrating PigGTEx and FAANG resources,we combined TWAS and colocalization analyses to reveal genetic regulation of semen quality traits.Notably,both GWAS and colocalization analyses pinpointed the DCAF12 as a crucial gene associated with multiple semen quality traits.Additionally,the ZSCAN9 gene and the variant rs322211455 were found to significantly affect sperm motility(SPMOT),possibly through hypothalamic-pituitary-gonadal axis.PheWAS further highlighted an association between rs322211455 and sperm abnormality rate,demonstrating the crucial role of ZSCAN9 in male fertility.Conclusion This study reveals the genetic basis and regulatory mechanisms underlying semen quality traits in Duroc boars,identifying key candidate genes such as DCAF12 and ZSCAN9.These findings provide important insight into the genetic regulation of semen quality in boars.展开更多
The application of advanced omics technologies in plant science has generated an enormous dataset of sequences,expression profiles,and phenotypic traits,collectively termed“big data”for their significant volume,dive...The application of advanced omics technologies in plant science has generated an enormous dataset of sequences,expression profiles,and phenotypic traits,collectively termed“big data”for their significant volume,diversity,and rapid pace of accumulation.Despite extensive data generation,the process of analyzing and interpreting big data remains complex and challenging.Big data analyses will help identify genes and uncover different mechanisms controlling various agronomic traits in crop plants.The insights gained from big data will assist scientists in developing strategies for crop improvement.Although the big data generated from crop plants opens a world of possibilities,realizing its full potential requires enhancement in computational capacity and advances in machine learning(ML)or deep learning(DL)approaches.The present review discuss the applications of genomics,transcriptomics,proteomics,metabolomics,epigenetics,and phenomics“big data”in crop improvement.Furthermore,we discuss the potential application of artificial intelligence to genomic selection.Additionally,the article outlines the crucial role of big data in precise genetic engineering and understanding plant stress tolerance.Also we highlight the challenges associated with big data storage,analyses,visualization and sharing,and emphasize the need for robust solutions to harness these invaluable resources for crop improvement.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
Thermal sprayed Ni-20wt. % Al coating is fabricated on 6061-T6 aluminum alloy substrates by twin-wire arc spraying （TWAS）. Experimental results present that the average bonding strength is around 53 MPa and the aver...Thermal sprayed Ni-20wt. % Al coating is fabricated on 6061-T6 aluminum alloy substrates by twin-wire arc spraying （TWAS）. Experimental results present that the average bonding strength is around 53 MPa and the average hardness reaches 325 HV. The Vickers microhardness of NiAl and Ni3Al intermetaUic compounds is larger than that of the substrate, which is beneficial for improving the wear property. Wear mechanism exhibits features of adhesive wear. Friction and wear test results indicate that the wet friction coefficient is higher than the coefficient of dry friction after 200 cycles, and variations of the wet friction coefficient are relatively smaller.展开更多
Rice eating and cooking quality(ECQ)is significantly influenced by the physicochemical properties of rice starch.This study integrates whole-genome resequencing,transcriptomic data,and phenotypic analysis to identify t...Rice eating and cooking quality(ECQ)is significantly influenced by the physicochemical properties of rice starch.This study integrates whole-genome resequencing,transcriptomic data,and phenotypic analysis to identify the genetic factors that regulate transcript expression levels and contribute to phenotypic variation in rice ECQ traits.A TWAS(transcriptome-wide association study)identified 285 transcripts linked to 6 ECQ traits.Genome-wide mapping of these transcripts revealed 21747 local eQTLs(expression quantitative trait loci)and 45158 distal eQTLs.TWAS and eQTL analysis detected several known and novel genes,including starch synthesis-related genes,heat shock proteins,transcription factors,genes related to ATP accumulation,and UDP-glucosyltransferases,showcasing the complex genetic regulation of rice ECQ.WGCNA(weighted gene co-expression network analysis)uncovered key co-expression networks,including a module that links alpha-globulin1(GLB1)tostarchandsucrosemetabolism.GeneticdiversityanalysisoftheGLB1geneacrossaKorean rice collection identified 26 haplotypes,with indica and aus forming 7 and 3 haplotypes,respectively,which showedsignificantphenotypiceffectsonECQtraits.CRISPR-Cas9-createdknockoutlinesvalidatedthesefind-ings,demonstrating that loss of GLB1 function caused significant changes in seed storage proteins,reduced amylose content,altered starch granules,and modified pasting properties without affecting plant phenotypes.By integrating TWAS,eQTL mapping,haplotype analysis,gene expression networks,and CRISPR validation,this study establishes GLB1 as a regulator of ECQ,linking starch biosynthesis and protein accumulation path-ways.This transcriptogenomic convergence approach provides novel insights into the genetic regulation of ECQ in rice,demonstrating its effectiveness for characterizing complex traits and enabling precision breeding.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
Seed oil content(SOC)is a highly important and complex trait in oil crops.Here,we decipher the genetic basis of natural variation in SOC of Brassica napus by genome-and transcriptome-wide association studies using 505...Seed oil content(SOC)is a highly important and complex trait in oil crops.Here,we decipher the genetic basis of natural variation in SOC of Brassica napus by genome-and transcriptome-wide association studies using 505 inbred lines.We mapped reliable quantitative trait loci(QTLs)that control SOC in eight environments,evaluated the effect of each QTL on SOC,and analyzed selection in QTL regions during breeding.Six-hundred and ninety-two genes and four gene modules significantly associated with SOC were identified by analyzing population transcriptomes from seeds.A gene prioritization framework,POCKET(prioritizing the candidate genes by incorporating information on knowledge-based gene sets,effects of variants,genome-wide association studies,and transcriptome-wide association studies),was implemented to determine the causal genes in the QTL regions based on multi-omic datasets.A pair of homologous genes,BnPMT6s,in two QTLs were identified and experimentally demonstrated to negatively regulate SOC.This study provides rich genetic resources for improving SOC and valuable insights toward understanding the complex machinery that directs oil accumulation in the seeds of B.napus and other oil crops.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 identified many genetic variants associated with increased risk of Alzheimer^disease(AD).These susceptibility loci may effect AD indirectly through a combination of...Background:Genome wide association studies(GWAS)have identified many genetic variants associated with increased risk of Alzheimer^disease(AD).These susceptibility loci may effect AD indirectly through a combination of physiological brain changes.Many of these neuropathologic features are detectable via magnetic resonance imaging(MRI).Methods:In this study,we examine the effects of such brain imaging derived phenotypes(IDPs)with genetic etiology on AD,using and comparing the following methods:two-sample Mendelian randomization(2SMR),generalized summary statistics based Mendelian randomization(GSMR),transcriptome wide association studies(TWAS)and the adaptive sum of powered score(aSPU)test.These methods do not require individual-level genotypic and phenotypic data but instead can rely only on an external reference panel and GWAS summary statistics.Results:Using publicly available GWAS datasets from the International Genomics of Alzheimer's Project(IGAP)and UK Biobank's(UKBB)brain imaging initiatives,we identify 35 IDPs possibly associated with AD,many of which have well established or biologically plausible links to the characteristic cognitive impairments of this neurodegenerative disease.Conclusions:Our results highlight the increased power for detecting genetic associations achieved by multiple correlated SNP-based methods,i.e.,aSPU,GSMR and TWAS,over MR methods based on independent SNPs(as instrumental variables).展开更多
文摘1会议背景和基本情况
发展中国家科学院（The Academy of Sciences for the Developing World, TWAS）成立于1983年11月，总部设在意大利的里雅斯特，是一个非政府、非政治和非营利性的国际科学组织，旨在促进发展中国家的科技发展，支持发展中国家科技人员和科研机构之间的交流与合作。TWAS每年举行一次院士大会，每2—3年举行一次学术研讨会。TWAS历届会议均得到主办国政府的高度重视，中国在1987年和2003年成功地举办了第2届和第14届院士大会。
基金supported by the National Key Research and Development Program of China(2021YFD1201600)the National Natural Science Foundation of China(32201759 and U22A20473)+3 种基金the China Scientific Innovation 2030 Project(2022ZD0401703)the Earmarked Fund for CARS(CARS-04-PS01)the Agricultural Science and Technology Innovation Program(ASTIPCAAS-ZDRW202109).
文摘A genome-wide association study(GWAS)identifies trait-associated loci,but identifying the causal genes can be a bottleneck,due in part to slow decay of linkage disequilibrium(LD).A transcriptome-wide association study(TWAS)addresses this issue by identifying gene expression-phenotype associations or integrating gene expression quantitative trait loci with GWAS results.Here,we used self-pollinated soybean(Glycine max[L.]Merr.)as a model to evaluate the application of TWAS to the genetic dissection of traits in plant species with slow LD decay.We generated RNA sequencing data for a soybean diversity panel and identified the genetic expression regulation of 29286 soybean genes.Different TWAS solutions were less affected by LD and were robust to the source of expression,identifing known genes related to traits from different tissues and developmental stages.The novel pod-color gene L2 was identified via TWAS and functionally validated by genome editing.By introducing a new exon proportion feature,we significantly improved the detection of expression variations that resulted from structural variations and alternative splicing.As a result,the genes identified through our TWAS approach exhibited a diverse range of causal variations,including SNPs,insertions or deletions,gene fusion,copy number variations,and alternative splicing.Using this approach,we identified genes associated with flowering time,including both previously known genes and novel genes that had not previously been linked to this trait,providing insights complementary to those from GWAS.In summary,this study supports the application of TWAS for candidate gene identification in species with low rates of LD decay.
基金supported by grants from The Sci-Tech Innovation 2030 Agenda(2023ZD04045)Guangxi Key Research and Development Program(AB241484033).
文摘Background Semen quality is one of the most important indicators of boar reproductive performance.In the past,boar breeding has mostly emphasized characteristics such as lean meat percentage,feed conversion efficiency,and growth rate,while overlooking the genetic improvement of reproductive traits.This study employs advanced multi-omics approaches,such as transcriptome-wide association studies(TWAS)and colocalization between genome-wide association studies(GWAS)and expression quantitative trait loci(eQTLs),to provide a comprehensive understanding of the genetic mechanisms governing semen quality traits in boars.Results Here,we collected 190,000 ejaculate records across 11 semen quality traits from 3,604 Duroc boars.The heritability of semen quality traits ranged from 0.095 to 0.343.Genetic correlations between semen quality traits varied from−0.802 to 0.661,and phenotypic correlations ranged from−0.833 to 0.776.Single-trait GWAS identified 19 independent variants,corresponding to 13 quantitative trait loci(QTLs).By integrating PigGTEx and FAANG resources,we combined TWAS and colocalization analyses to reveal genetic regulation of semen quality traits.Notably,both GWAS and colocalization analyses pinpointed the DCAF12 as a crucial gene associated with multiple semen quality traits.Additionally,the ZSCAN9 gene and the variant rs322211455 were found to significantly affect sperm motility(SPMOT),possibly through hypothalamic-pituitary-gonadal axis.PheWAS further highlighted an association between rs322211455 and sperm abnormality rate,demonstrating the crucial role of ZSCAN9 in male fertility.Conclusion This study reveals the genetic basis and regulatory mechanisms underlying semen quality traits in Duroc boars,identifying key candidate genes such as DCAF12 and ZSCAN9.These findings provide important insight into the genetic regulation of semen quality in boars.
基金Fund for International Young Scientists by the National Natural Science Foundation of China (32150410354)to Naresh Vasupallithe Department of Biotechnology,Government of India,for the Ramalingaswami Fellowship Award (BT/PR38279/GET/119/351/2020)to Humira SonahHaryana State Council for Science Innovation and Technology (HSCSIT)for the research grant PI ID 1270,HSCSIT/R&D/2024/511 to Rupesh Deshmukh and Humira Sonah.
文摘The application of advanced omics technologies in plant science has generated an enormous dataset of sequences,expression profiles,and phenotypic traits,collectively termed“big data”for their significant volume,diversity,and rapid pace of accumulation.Despite extensive data generation,the process of analyzing and interpreting big data remains complex and challenging.Big data analyses will help identify genes and uncover different mechanisms controlling various agronomic traits in crop plants.The insights gained from big data will assist scientists in developing strategies for crop improvement.Although the big data generated from crop plants opens a world of possibilities,realizing its full potential requires enhancement in computational capacity and advances in machine learning(ML)or deep learning(DL)approaches.The present review discuss the applications of genomics,transcriptomics,proteomics,metabolomics,epigenetics,and phenomics“big data”in crop improvement.Furthermore,we discuss the potential application of artificial intelligence to genomic selection.Additionally,the article outlines the crucial role of big data in precise genetic engineering and understanding plant stress tolerance.Also we highlight the challenges associated with big data storage,analyses,visualization and sharing,and emphasize the need for robust solutions to harness these invaluable resources for crop improvement.
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.
文摘Thermal sprayed Ni-20wt. % Al coating is fabricated on 6061-T6 aluminum alloy substrates by twin-wire arc spraying （TWAS）. Experimental results present that the average bonding strength is around 53 MPa and the average hardness reaches 325 HV. The Vickers microhardness of NiAl and Ni3Al intermetaUic compounds is larger than that of the substrate, which is beneficial for improving the wear property. Wear mechanism exhibits features of adhesive wear. Friction and wear test results indicate that the wet friction coefficient is higher than the coefficient of dry friction after 200 cycles, and variations of the wet friction coefficient are relatively smaller.
基金supported by National Research Foundation of Korea(NRF)grants funded by the Korean government(MSIT)(NRF-2022R1A 4A1030348 and NRF-2023R1A2C1004432)the Development of Next-Generation Crop Breeding Technologies(RS-2024-00322378)Rural Development Administration,Republic of Korea.
文摘Rice eating and cooking quality(ECQ)is significantly influenced by the physicochemical properties of rice starch.This study integrates whole-genome resequencing,transcriptomic data,and phenotypic analysis to identify the genetic factors that regulate transcript expression levels and contribute to phenotypic variation in rice ECQ traits.A TWAS(transcriptome-wide association study)identified 285 transcripts linked to 6 ECQ traits.Genome-wide mapping of these transcripts revealed 21747 local eQTLs(expression quantitative trait loci)and 45158 distal eQTLs.TWAS and eQTL analysis detected several known and novel genes,including starch synthesis-related genes,heat shock proteins,transcription factors,genes related to ATP accumulation,and UDP-glucosyltransferases,showcasing the complex genetic regulation of rice ECQ.WGCNA(weighted gene co-expression network analysis)uncovered key co-expression networks,including a module that links alpha-globulin1(GLB1)tostarchandsucrosemetabolism.GeneticdiversityanalysisoftheGLB1geneacrossaKorean rice collection identified 26 haplotypes,with indica and aus forming 7 and 3 haplotypes,respectively,which showedsignificantphenotypiceffectsonECQtraits.CRISPR-Cas9-createdknockoutlinesvalidatedthesefind-ings,demonstrating that loss of GLB1 function caused significant changes in seed storage proteins,reduced amylose content,altered starch granules,and modified pasting properties without affecting plant phenotypes.By integrating TWAS,eQTL mapping,haplotype analysis,gene expression networks,and CRISPR validation,this study establishes GLB1 as a regulator of ECQ,linking starch biosynthesis and protein accumulation path-ways.This transcriptogenomic convergence approach provides novel insights into the genetic regulation of ECQ in rice,demonstrating its effectiveness for characterizing complex traits and enabling precision breeding.
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.
基金This study was supported by the National Key Research and Development Plan of China(2016YFD0101000,2017YFE0104800)the National Natural Science Foundation of China(32070559,31871658).
文摘Seed oil content(SOC)is a highly important and complex trait in oil crops.Here,we decipher the genetic basis of natural variation in SOC of Brassica napus by genome-and transcriptome-wide association studies using 505 inbred lines.We mapped reliable quantitative trait loci(QTLs)that control SOC in eight environments,evaluated the effect of each QTL on SOC,and analyzed selection in QTL regions during breeding.Six-hundred and ninety-two genes and four gene modules significantly associated with SOC were identified by analyzing population transcriptomes from seeds.A gene prioritization framework,POCKET(prioritizing the candidate genes by incorporating information on knowledge-based gene sets,effects of variants,genome-wide association studies,and transcriptome-wide association studies),was implemented to determine the causal genes in the QTL regions based on multi-omic datasets.A pair of homologous genes,BnPMT6s,in two QTLs were identified and experimentally demonstrated to negatively regulate SOC.This study provides rich genetic resources for improving SOC and valuable insights toward understanding the complex machinery that directs oil accumulation in the seeds of B.napus and other oil crops.
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NIH grants T32GM108557,R01AG065636,R01HL116720,R01GM113250 and R01GM126002by the Minnesota Supercomputing Institute at the University of Minnesota.
文摘Background:Genome wide association studies(GWAS)have identified many genetic variants associated with increased risk of Alzheimer^disease(AD).These susceptibility loci may effect AD indirectly through a combination of physiological brain changes.Many of these neuropathologic features are detectable via magnetic resonance imaging(MRI).Methods:In this study,we examine the effects of such brain imaging derived phenotypes(IDPs)with genetic etiology on AD,using and comparing the following methods:two-sample Mendelian randomization(2SMR),generalized summary statistics based Mendelian randomization(GSMR),transcriptome wide association studies(TWAS)and the adaptive sum of powered score(aSPU)test.These methods do not require individual-level genotypic and phenotypic data but instead can rely only on an external reference panel and GWAS summary statistics.Results:Using publicly available GWAS datasets from the International Genomics of Alzheimer's Project(IGAP)and UK Biobank's(UKBB)brain imaging initiatives,we identify 35 IDPs possibly associated with AD,many of which have well established or biologically plausible links to the characteristic cognitive impairments of this neurodegenerative disease.Conclusions:Our results highlight the increased power for detecting genetic associations achieved by multiple correlated SNP-based methods,i.e.,aSPU,GSMR and TWAS,over MR methods based on independent SNPs(as instrumental variables).
