The quantitative real-time reverse transcription PCR(qRT-PCR)is a widely used technique to analyze gene expression levels.Selecting a suitable reference gene is a crucial step to obtain an accurate result in qRT-PCR.H...The quantitative real-time reverse transcription PCR(qRT-PCR)is a widely used technique to analyze gene expression levels.Selecting a suitable reference gene is a crucial step to obtain an accurate result in qRT-PCR.However,most previous studies on fishes adopted reference genes that were commonly used in mammals without validation.In this study,we utilized 89 transcrip-tome datasets covering early developmental stages and different adult tissues,and carried out transcriptome-wide identification and validation of reference genes in Sebastes schlegelii.Finally,121 candidate reference genes were identified based on four criteria.Eight candidates(METAP2,BTF3L4,EIF5A1,TCTP,UBC,PAIRB,RAB10,and DLD)and four commonly used reference genes in mam-mals(TUBA,ACTB,GAPDH,RPL17)were selected for validation via qRT-PCR and four statistical analysis methods(delta-Ct,Best-Keeper,geNorm,and NormFinder).The results indicated that when the black rockfish are cultured in a general condition,the eight candidate reference genes are more stable than traditional reference genes in mammals,and RAB10,EIF5A1,PAIRB and BTF3L4 are the best reference genes in rockfish.This is the first study to conduct transcriptome-wide identification and validation of reference genes for quantitative RT-PCR in the black rockfish,and lay an important foundation for gene expression analysis in teleost.展开更多
Background:Hepatocellular carcinoma(HCC)is one of the most common cancers worldwide and is prevalent in East Asia.Although genome-wide association studies(GWASs)of HCC have identified 23 risk regions,the susceptibilit...Background:Hepatocellular carcinoma(HCC)is one of the most common cancers worldwide and is prevalent in East Asia.Although genome-wide association studies(GWASs)of HCC have identified 23 risk regions,the susceptibility genes underlying these associa-tions largely remain unclear.To identify novel candidate genes for HCC,we conducted liver single-tissue and cross-tissue transcrip-tomewide association studies(TWASs)in two populations of East Asia.Methods:GWAS summary statistics of 2,514 subjects(1,161 HCC cases and 1,353 controls)from the Chinese Qidong cohort and 161,323 subjects(2,122 HCC cases and 159,201 controls)from the BioBank Japan project were used to conduct TWAS analysis.The single-tissue and cross-tissue TWAS approaches were both used to detect the association between susceptible genes and the risk of HCC.TWAS identified genes were further annotated by Metascape,UALCAN,GEPIA2,and DepMap.Results:We identified 22 novel genes at 16 independent loci significantly associated with HCC risk after Bonferroni correction.Of these,13 genes were located in novel regions.Besides,we found 83 genes overlapped in the Chinese and Japanese cohorts with P<0.05,of which,three genes(NUAK2,HLA-DQA1,and ATP6V1G2)were discerned by both single-tissue and cross-tissue TWAS approaches.Among the genes identified through TWAS,a significant proportion of them exhibit a credible role in HCC biology,such as FAM96B,HSPA5,POLRMT,MPHOSPH10,and RABL2A.HLA-DQA1,NUAK2,and HSPA5 associated with the process of carcinogenesis in HCC as previously reported.Conclusions:Our findings highlight the value of leveraging the gene expression data to identify new candidate genes beyond the GWAS associations and could further provide a genetic insight for the biology of HCC.展开更多
Background:Genome-wide association studies(GWASs)have identified thousands of genetic variants that are associated with many complex traits.However,their biological mechanisms remain largely unknown.Transcriptome-wide...Background:Genome-wide association studies(GWASs)have identified thousands of genetic variants that are associated with many complex traits.However,their biological mechanisms remain largely unknown.Transcriptome-wide association studies(TWAS)have been recently proposed as an invaluable tool for investigating the potential gene regulatory mechanisms underlying variant-trait associations.Specifically,TWAS integrate GWAS with expression mapping studies based on a common set of variants and aim to identify genes whose GReX is associated with the phenotype.Various methods have been developed for performing TWAS and/or similar integrative analysis.Each such method has a different modeling assumption and many were initially developed to answer different biological questions.Consequently,it is not straightforward to understand their modeling property from a theoretical perspective.Results:We present a technical review on thirteen TWAS methods.Importantly,we show that these methods can all be viewed as two-sample Mendelian randomization(MR)analysis,which has been widely applied in GWASs for examining the causal effects of exposure on outcome.Viewing different TWAS methods from an MR perspective provides us a unique angle for understanding their benefits and pitfalls.We systematically introduce the MR analysis framework,explain how features of the GWAS and expression data influence the adaptation of MR for TWAS,and re-interpret the modeling assumptions made in different TWAS methods from an MR angle.We finally describe future directions for TWAS methodology development.Conclusions:We hope that this review would serve as a useful reference for both methodologists who develop TWAS methods and practitioners who perform TWAS analysis.展开更多
Transcriptome-wide association study(TWAs)is a powerful approach for investigating the molecular mechanisms linking genetic loci to com-plex phenotypes.However,the complexity of the TWAS analytical pipeline,including ...Transcriptome-wide association study(TWAs)is a powerful approach for investigating the molecular mechanisms linking genetic loci to com-plex phenotypes.However,the complexity of the TWAS analytical pipeline,including the construction of gene expression reference panels,gene expression prediction,and association analysis using data from genome-wide association studies(GWASs),poses challenges for genetic studies in many species.In this study,we provide the Farm Animal Genotype-Tissue Expression(FarmGTEx)TWAS-server,an interactive and user-friendly multispecies platform designed to streamline the translation of genetic findings across tissues and species.The server incorpo-rates gene expression data from 49 human tissues(838 individuals),34 pig tissues(5457 individuals),and 23 cattle tissues(4889 individuals),providing prediction models for 38,180 human genes,21,037 pig genes,and 17,942 cattle genes.It supports genotype-based gene expression prediction,GWAS summary statistics imputation,customizable TWAS analysis,functional annotation,and result visualization.Additionally,we provide 479,203,1208,and 657 tissue-gene-trait associations for 1129 human traits,41 cattle traits,and 11 pig traits,respectively.Utilizing the TWAS-server,we validated the association of the ABCD4 gene with pig teat number.Furthermore,we identified that pig backfat thickness may share genetic similarities with human diastolic blood pressure,sarcoidosis(Lofgren syndrome),and body mass index.The FarmGTEx TWAS-server offers a comprehensive and accessible platform for researchers to perform TWAS analyses across tissues and species.It is freely avail-able at https:/twas.farmgtex.org,with regular updates planned as the FarmGTEx project expands to include more species.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
The rapid development of multiome(transcriptome,proteome,cistrome,imaging,and regulome)-wide association study methods have opened new avenues for biologists to understand the susceptibility genes underlying complex d...The rapid development of multiome(transcriptome,proteome,cistrome,imaging,and regulome)-wide association study methods have opened new avenues for biologists to understand the susceptibility genes underlying complex diseases.Thorough comparisons of these methods are essential for selecting the most appropriate tool for a given research objective.This review provides a detailed categorization and summary of the statistical models,use cases,and advantages of recent multiome-wide association studies.In addition,to illustrate gene-disease association studies based on transcriptome-wide association study(TWAS),we collected 478 disease entries across 22 categories from 235 manually reviewed publications.Our analysis reveals that mental disorders are the most frequently studied diseases by TWAS,indicating its potential to deepen our understanding of the genetic architecture of complex diseases.In summary,this review underscores the importance of multiome-wide association studies in elucidating complex diseases and highlights the significance of selecting the appropriate method for each study.展开更多
RNA can interact with RNA-binding proteins(RBPs),mRNA,or other non-coding RNAs(ncRNAs)to form complex regulatory networks.High-throughput CLIP-seq,degradome-seq,and RNA-RNA interactome sequencing methods represent pow...RNA can interact with RNA-binding proteins(RBPs),mRNA,or other non-coding RNAs(ncRNAs)to form complex regulatory networks.High-throughput CLIP-seq,degradome-seq,and RNA-RNA interactome sequencing methods represent powerful approaches to identify biologically relevant ncRNA-target and protein-ncRNA interactions.However,assigning ncRNAs to their regulatory target genes or interacting RNA-binding proteins(RBPs)remains technically challenging.Chemical modifications to mRNA also play important roles in regulating gene expression.Investigation of the functional roles of these modifications relies highly on the detection methods used.RNA structure is also critical at nearly every step of the RNA life cycle.In this review,we summarize recent advances and limitations in CLIP technologies and discuss the computational challenges of and bioinformatics tools used for decoding the functions and regulatory networks of ncRNAs.We also summarize methods used to detect RNA modifications and to probe RNA structure.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:The Genotype-Tissue Expression(GTEx)Project has collected genetic and transcriptome profiles from a wide spectrum of tissues in nearly 1,000 ceased indiv iduals,providing an opportunity to study the regulat...Background:The Genotype-Tissue Expression(GTEx)Project has collected genetic and transcriptome profiles from a wide spectrum of tissues in nearly 1,000 ceased indiv iduals,providing an opportunity to study the regulatory roles of genetic variants in transcriptome activities from both cross-tissue and tissue-specific perspectives.Moreover,transeriptome activities(e.g.,transcript abundance and alternative splicing)can be treated as mediators between genotype and phenotype to achieve phenotypic alteration.Knowing the genotype associated transcriptome status,researchers can better understand the biological and molecular mechanisms of genetic risk variants in complex traits.Results:In this article,we first explore the genetic architecture of gene expression traits,and then review recent methods on quantitative trait locus(QTL)and co-expression network analysis.To further exemplify the usage of associations between genotype and transcriptome status,we briefly review methods that either directly or indirectly integrate expression/splicing QTL information in genome wide association studies(GWASs).Conclusions:The GTEx Project provides the largest and useful resouree to investigate the associations between genotype and transcriptome status.The integration of results from the GTEx Project and existing GWASs further advances our understanding of roles of gene expression changes in bridging both the genetic variants and complex traits.展开更多
基金This study was supported by the National Key R&D Program of China(No.2018YFD0900101).
文摘The quantitative real-time reverse transcription PCR(qRT-PCR)is a widely used technique to analyze gene expression levels.Selecting a suitable reference gene is a crucial step to obtain an accurate result in qRT-PCR.However,most previous studies on fishes adopted reference genes that were commonly used in mammals without validation.In this study,we utilized 89 transcrip-tome datasets covering early developmental stages and different adult tissues,and carried out transcriptome-wide identification and validation of reference genes in Sebastes schlegelii.Finally,121 candidate reference genes were identified based on four criteria.Eight candidates(METAP2,BTF3L4,EIF5A1,TCTP,UBC,PAIRB,RAB10,and DLD)and four commonly used reference genes in mam-mals(TUBA,ACTB,GAPDH,RPL17)were selected for validation via qRT-PCR and four statistical analysis methods(delta-Ct,Best-Keeper,geNorm,and NormFinder).The results indicated that when the black rockfish are cultured in a general condition,the eight candidate reference genes are more stable than traditional reference genes in mammals,and RAB10,EIF5A1,PAIRB and BTF3L4 are the best reference genes in rockfish.This is the first study to conduct transcriptome-wide identification and validation of reference genes for quantitative RT-PCR in the black rockfish,and lay an important foundation for gene expression analysis in teleost.
基金supported by the National Natural Science Foundation of China[grant number:82272312]the 100 Top Talent Programs of Sun Yat-sen University[grant number:58000-12230029]the Shenzhen-Hong Kong-Macao Science and Technology Project(Category C project)[grant number:SGDX20220530111403024].
文摘Background:Hepatocellular carcinoma(HCC)is one of the most common cancers worldwide and is prevalent in East Asia.Although genome-wide association studies(GWASs)of HCC have identified 23 risk regions,the susceptibility genes underlying these associa-tions largely remain unclear.To identify novel candidate genes for HCC,we conducted liver single-tissue and cross-tissue transcrip-tomewide association studies(TWASs)in two populations of East Asia.Methods:GWAS summary statistics of 2,514 subjects(1,161 HCC cases and 1,353 controls)from the Chinese Qidong cohort and 161,323 subjects(2,122 HCC cases and 159,201 controls)from the BioBank Japan project were used to conduct TWAS analysis.The single-tissue and cross-tissue TWAS approaches were both used to detect the association between susceptible genes and the risk of HCC.TWAS identified genes were further annotated by Metascape,UALCAN,GEPIA2,and DepMap.Results:We identified 22 novel genes at 16 independent loci significantly associated with HCC risk after Bonferroni correction.Of these,13 genes were located in novel regions.Besides,we found 83 genes overlapped in the Chinese and Japanese cohorts with P<0.05,of which,three genes(NUAK2,HLA-DQA1,and ATP6V1G2)were discerned by both single-tissue and cross-tissue TWAS approaches.Among the genes identified through TWAS,a significant proportion of them exhibit a credible role in HCC biology,such as FAM96B,HSPA5,POLRMT,MPHOSPH10,and RABL2A.HLA-DQA1,NUAK2,and HSPA5 associated with the process of carcinogenesis in HCC as previously reported.Conclusions:Our findings highlight the value of leveraging the gene expression data to identify new candidate genes beyond the GWAS associations and could further provide a genetic insight for the biology of HCC.
基金the National Institutes of Health(NIH)Grants RO1HG009124 and the National Science Foundation(NSF)Grant DMS1712933.
文摘Background:Genome-wide association studies(GWASs)have identified thousands of genetic variants that are associated with many complex traits.However,their biological mechanisms remain largely unknown.Transcriptome-wide association studies(TWAS)have been recently proposed as an invaluable tool for investigating the potential gene regulatory mechanisms underlying variant-trait associations.Specifically,TWAS integrate GWAS with expression mapping studies based on a common set of variants and aim to identify genes whose GReX is associated with the phenotype.Various methods have been developed for performing TWAS and/or similar integrative analysis.Each such method has a different modeling assumption and many were initially developed to answer different biological questions.Consequently,it is not straightforward to understand their modeling property from a theoretical perspective.Results:We present a technical review on thirteen TWAS methods.Importantly,we show that these methods can all be viewed as two-sample Mendelian randomization(MR)analysis,which has been widely applied in GWASs for examining the causal effects of exposure on outcome.Viewing different TWAS methods from an MR perspective provides us a unique angle for understanding their benefits and pitfalls.We systematically introduce the MR analysis framework,explain how features of the GWAS and expression data influence the adaptation of MR for TWAS,and re-interpret the modeling assumptions made in different TWAS methods from an MR angle.We finally describe future directions for TWAS methodology development.Conclusions:We hope that this review would serve as a useful reference for both methodologists who develop TWAS methods and practitioners who perform TWAS analysis.
基金financially supported by the National Key R&D Program of China(Grant No.2023YFD1300404)the Sanya Science and Technology Innovation Project,China(Grant No.2022KJCX49)+1 种基金the National Natural Science Foundation of China(Grant No.32272833)the Biotechnology and Biological Sciences Research Council(BBSRC),UK(Grant No.BB/X009505/1)to BL.
文摘Transcriptome-wide association study(TWAs)is a powerful approach for investigating the molecular mechanisms linking genetic loci to com-plex phenotypes.However,the complexity of the TWAS analytical pipeline,including the construction of gene expression reference panels,gene expression prediction,and association analysis using data from genome-wide association studies(GWASs),poses challenges for genetic studies in many species.In this study,we provide the Farm Animal Genotype-Tissue Expression(FarmGTEx)TWAS-server,an interactive and user-friendly multispecies platform designed to streamline the translation of genetic findings across tissues and species.The server incorpo-rates gene expression data from 49 human tissues(838 individuals),34 pig tissues(5457 individuals),and 23 cattle tissues(4889 individuals),providing prediction models for 38,180 human genes,21,037 pig genes,and 17,942 cattle genes.It supports genotype-based gene expression prediction,GWAS summary statistics imputation,customizable TWAS analysis,functional annotation,and result visualization.Additionally,we provide 479,203,1208,and 657 tissue-gene-trait associations for 1129 human traits,41 cattle traits,and 11 pig traits,respectively.Utilizing the TWAS-server,we validated the association of the ABCD4 gene with pig teat number.Furthermore,we identified that pig backfat thickness may share genetic similarities with human diastolic blood pressure,sarcoidosis(Lofgren syndrome),and body mass index.The FarmGTEx TWAS-server offers a comprehensive and accessible platform for researchers to perform TWAS analyses across tissues and species.It is freely avail-able at https:/twas.farmgtex.org,with regular updates planned as the FarmGTEx project expands to include more species.
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supported by the National Natural Science Foundation of China(Grant Nos.62102068,62231013,61821002,81971750,81701833)the National Key R&D Program of China(Grant No.2017YFA0104300).
文摘The rapid development of multiome(transcriptome,proteome,cistrome,imaging,and regulome)-wide association study methods have opened new avenues for biologists to understand the susceptibility genes underlying complex diseases.Thorough comparisons of these methods are essential for selecting the most appropriate tool for a given research objective.This review provides a detailed categorization and summary of the statistical models,use cases,and advantages of recent multiome-wide association studies.In addition,to illustrate gene-disease association studies based on transcriptome-wide association study(TWAS),we collected 478 disease entries across 22 categories from 235 manually reviewed publications.Our analysis reveals that mental disorders are the most frequently studied diseases by TWAS,indicating its potential to deepen our understanding of the genetic architecture of complex diseases.In summary,this review underscores the importance of multiome-wide association studies in elucidating complex diseases and highlights the significance of selecting the appropriate method for each study.
文摘RNA can interact with RNA-binding proteins(RBPs),mRNA,or other non-coding RNAs(ncRNAs)to form complex regulatory networks.High-throughput CLIP-seq,degradome-seq,and RNA-RNA interactome sequencing methods represent powerful approaches to identify biologically relevant ncRNA-target and protein-ncRNA interactions.However,assigning ncRNAs to their regulatory target genes or interacting RNA-binding proteins(RBPs)remains technically challenging.Chemical modifications to mRNA also play important roles in regulating gene expression.Investigation of the functional roles of these modifications relies highly on the detection methods used.RNA structure is also critical at nearly every step of the RNA life cycle.In this review,we summarize recent advances and limitations in CLIP technologies and discuss the computational challenges of and bioinformatics tools used for decoding the functions and regulatory networks of ncRNAs.We also summarize methods used to detect RNA modifications and to probe RNA structure.
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This work was supported by the National Institutes of Health(NIH)grants R01 GM134005,and the National Science Foundation(NSF)grants DMS 1902903.Dr.Sheng Chih Jin's effort was supported by the Pathway to Independence Award(K99/R00)program,grants K99HL143036-01A1 and R00HL143036-02.
文摘Background:The Genotype-Tissue Expression(GTEx)Project has collected genetic and transcriptome profiles from a wide spectrum of tissues in nearly 1,000 ceased indiv iduals,providing an opportunity to study the regulatory roles of genetic variants in transcriptome activities from both cross-tissue and tissue-specific perspectives.Moreover,transeriptome activities(e.g.,transcript abundance and alternative splicing)can be treated as mediators between genotype and phenotype to achieve phenotypic alteration.Knowing the genotype associated transcriptome status,researchers can better understand the biological and molecular mechanisms of genetic risk variants in complex traits.Results:In this article,we first explore the genetic architecture of gene expression traits,and then review recent methods on quantitative trait locus(QTL)and co-expression network analysis.To further exemplify the usage of associations between genotype and transcriptome status,we briefly review methods that either directly or indirectly integrate expression/splicing QTL information in genome wide association studies(GWASs).Conclusions:The GTEx Project provides the largest and useful resouree to investigate the associations between genotype and transcriptome status.The integration of results from the GTEx Project and existing GWASs further advances our understanding of roles of gene expression changes in bridging both the genetic variants and complex traits.
