A multi-tissue atlas of regulatory variants in cattle

被引:0
作者
Shuli Liu
Yahui Gao
Oriol Canela-Xandri
Sheng Wang
Ying Yu
Wentao Cai
Bingjie Li
Ruidong Xiang
Amanda J. Chamberlain
Erola Pairo-Castineira
Kenton D’Mellow
Konrad Rawlik
Charley Xia
Yuelin Yao
Pau Navarro
Dominique Rocha
Xiujin Li
Ze Yan
Congjun Li
Benjamin D. Rosen
Curtis P. Van Tassell
Paul M. Vanraden
Shengli Zhang
Li Ma
John B. Cole
George E. Liu
Albert Tenesa
Lingzhao Fang
机构
[1] Animal Genomics and Improvement Laboratory,National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology
[2] Henry A. Wallace Beltsville Agricultural Research Center,School of Life Sciences
[3] Agricultural Research Service,Department of Animal and Avian Sciences
[4] USDA,MRC Human Genetics Unit at the Institute of Genetics and Cancer
[5] China Agricultural University,Faculty of Veterinary & Agricultural Science
[6] Westlake University,The Roslin Institute, Royal (Dick) School of Veterinary Studies
[7] University of Maryland,INRAE, AgroParisTech, GABI
[8] The University of Edinburgh,Guangdong Provincial Key Laboratory of Waterfowl Healthy Breeding, College of Animal Science & Technology
[9] State Key Laboratory of Genetic Resources and Evolution,Center for Quantitative Genetics and Genomics
[10] Kunming Institute of Zoology,undefined
[11] Chinese Academy of Sciences,undefined
[12] Institute of Animal Science,undefined
[13] Chinese Academy of Agricultural Science,undefined
[14] Scotland’s Rural College (SRUC),undefined
[15] Roslin Institute Building,undefined
[16] The University of Melbourne,undefined
[17] Agriculture Victoria,undefined
[18] AgriBio,undefined
[19] Centre for AgriBiosciences,undefined
[20] The University of Edinburgh,undefined
[21] Université Paris-Saclay,undefined
[22] Zhongkai University of Agriculture and Engineering,undefined
[23] Aarhus University,undefined
来源
Nature Genetics | 2022年 / 54卷
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摘要
Characterization of genetic regulatory variants acting on livestock gene expression is essential for interpreting the molecular mechanisms underlying traits of economic value and for increasing the rate of genetic gain through artificial selection. Here we build a Cattle Genotype–Tissue Expression atlas (CattleGTEx) as part of the pilot phase of the Farm animal GTEx (FarmGTEx) project for the research community based on 7,180 publicly available RNA-sequencing (RNA-seq) samples. We describe the transcriptomic landscape of more than 100 tissues/cell types and report hundreds of thousands of genetic associations with gene expression and alternative splicing for 23 distinct tissues. We evaluate the tissue-sharing patterns of these genetic regulatory effects, and functionally annotate them using multiomics data. Finally, we link gene expression in different tissues to 43 economically important traits using both transcriptome-wide association and colocalization analyses to decipher the molecular regulatory mechanisms underpinning such agronomic traits in cattle.
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页码:1438 / 1447
页数:9
相关论文
共 110 条
[21]  
Freebern E(2021)Single-cell transcriptomic analyses of dairy cattle ruminal epithelial cells during weaning Genomics 113 151-94
[22]  
Fang L(2020)Cell type-specific genetic regulation of gene expression across human tissues Science 369 11.10.1-507
[23]  
Gao Y(2020)From FAANG to fork: application of highly annotated genomes to improve farmed animal production Genome Biol. 21 338-1485
[24]  
Kim-Hellmuth S(2021)Genome-wide fine-mapping identifies pleiotropic and functional variants that predict many traits across global cattle populations Nat. Commun. 12 e190-1884
[25]  
Clark EL(2014)Trimmomatic: a flexible trimmer for Illumina sequence data Bioinformatics 30 1571-1129
[26]  
Xiang RD(2016)Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown Nat. Protoc. 11 1754-2191
[27]  
Bolger AM(2014)featureCounts: an efficient general purpose program for assigning sequence reads to genomic features Bioinformatics 30 357-195
[28]  
Lohse M(2018)Annotation-free quantification of RNA splicing using LeafCutter Nat. Genet. 50 e81148-1358
[29]  
Usadel B(2013)From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline Curr. Protoc. Bioinformatics 43 75-106
[30]  
Pertea M(2018)A one-penny imputed genome from next-generation reference panels Am. J. Hum. Genet. 103 500-2048