The groundbreaking CattleGTEx Atlas reveals regulatory mechanisms underlying complex traits
Researchers from QGG and fellow project researchers have generated a cattle genotype–tissue expression atlas (CattleGTEx) based on the analysis of 7,180 public RNA-sequencing samples. The study was selected for the cover of the September issue of Nature Genetics.
The analysis of the many samples revealed genetic variants that regulate the transcriptome across 23 distinct bovine tissues. Integrating these data with GWAS advances the understanding of the fundamental molecular mechanisms that underpin complex traits in cattle.
First study of its kind
In the past two decades, genome-wide association studies (GWAS) have identified hundreds of thousands of genomic variants associated with complex traits in livestock, many of which are of economic importance. The molecular mechanisms by which such variants affect complex traits remain largely unknown, in large part because the vast majority of variants are non-coding, presumably affecting gene expression.
Thus, systematic characterization of the regulatory landscape of the transcriptome of livestock is essential for interpreting the molecular mechanisms underlying complex traits. Such investigation would provide insight into adaptive evolution and domestication, and for optimizing genetic improvement programs in livestock. No study is known to have systematically identified and characterized the regulatory variants of the transcriptome across a wide range of tissues in any farm animal species, as it would be too expensive and time-consuming.
Building a comprehensive, low-cost public resource in the research field
Given a wealth of publically available RNA-sequencing (RNA-seq) data in livestock and inspired by the GTEx project in humans, the researchers set out to develop the farm animal genotype–tissue expression (FarmGTEx) project, aiming to build a comprehensive public resource for studying the genetic regulation of the transcriptome and other molecular phenotypes across tissues in farm animals.
By integrating this comprehensive analysis with GWAS summary statistics, the researchers provided direct evidence of its relevance to dissect the genetic architecture underlying 43 complex traits in cattle by detecting the candidate variants and genes.
In summary, the research provides a comprehensive catalog of genetic regulatory variants in cattle. Although it is computationally and labor intensive, the researchers show that by combining public RNA-seq data, it is possible to develop an in silico CattleGTEx resource within a very short time span and at a small fraction of the cost of de novo generation of new data for many tissues with sufficient sample sizes.
Further improvements are possible
One of the main researchers behind the study, QGG researcher Lingzhao Fang, elaborates on future improvements of the atlas:
‘-Our work represents a step-change not only in understanding the regulatory landscape of cattle but also in the current ecosystem of animal and plant genomics. Although we provided a comprehensive view of the genetic regulation of transcriptomes in cattle, we are mindful that this resource can be further improved in terms of sample size, tissue/cell types, and biological contexts. We (the FarmGTEx consortium) are also working on other farm species, including pig, chicken, goat, sheep and duck. We hope more colleagues worldwide could join the FarmGTEx project to develop this valuable resource together. I believe that the further development of FarmGTEx will contribute significantly to basic biological discovery, the animal breeding industry, and human biomedicine.’
Senior Editor at Nature Genetics, Michael Fletcher, says:
‘-This study is an impressive example of how compilation of a broad range of published data, followed by uniform reprocessing and integrative analysis, can provide a valuable resource for the cattle genomics and breeding communities, and will hopefully lead to improvements in agronomic traits for this important and broadly kept livestock.’
The CattleGTEx atlas will serve as a valuable resource for cattle genomic prediction and editing, adaptation, veterinary medicine and cross-species mapping studies.
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This news item is largely based on the research briefing by QGG researcher Lingzhao Fang.
Read the research briefing here
Additional information | |
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Study type | Data mining; Transcriptomics |
Funding
| This work was supported in part by Agriculture and Food Research Initiative (AFRI) grant numbers 2016-67015-24886, 2019-67015-29321 and 2021-67015-33409 from the United States Department of Agriculture (USDA) National Institute of Food and Agriculture (NIFA) Animal Genome and Reproduction Programs, and US–Israel Binational Agricultural Research and Development (BARD) grant number US-4997-17 from the BARD Fund. L.F. was partially funded through Health Data Research UK (HDRUK) award HDR-9004 and the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 801215. A.T. acknowledged funding from the Biotechnology and Biological Sciences Research Council through program grants BBS/E/D/10002070 and BBS/E/D/30002275, Medical Research Council research grant MR/P015514/1 and HDRUK award HDR-9004. O.C.-X. was supported by MR/R025851/1. R.X. was supported by Australian Research Council’s Discovery Projects (DP200100499). Y. Yu. was supported by the National Science Foundation of China-Pakistan Science Foundation Joint Project (31961143009) and National Key R&D Program of China (2021YFD1200900 and 2021YFD1200903). L.M. was supported in part by AFRI grant numbers 2020-67015-31398 and 2021-67015-33409 from the NIFA. G.E.L., B.D.R. and C.P.V.T. were supported by appropriated project 8042-31000-001-00-D, ‘Enhancing Genetic Merit of Ruminants Through Improved Genome Assembly, Annotation, and Selection’ of the Agricultural Research Service (ARS) of the USDA. C.-J.L. was supported by appropriated project 8042-31310-078-00-D, ‘Improving Feed Efficiency and Environmental Sustainability of Dairy Cattle through Genomics and Novel Technologies’ of ARS-USDA. J.B.C. was supported by appropriated project 8042-31000-002-00-D, ‘Improving Dairy Animals by Increasing Accuracy of Genomic Prediction, Evaluating New Traits, and Redefining Selection Goals’ of ARS-USDA. This research used resources provided by the SCINet project of the ARS-USDA project number 0500-00093-001-00-D. Mention of trade names or commercial products in this article is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the USDA. The USDA is an equal opportunity provider and employer. All the funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank US dairy producers for providing phenotypic, genomic and pedigree data through the Council on Dairy Cattle Breeding under ARS-USDA Material Transfer Research Agreement 58-8042-8-007. Access to 1000 Bull Genomes Project data was provided under ARS-USDA Data Transfer Agreement 15443. International genetic evaluations were calculated by the International Bull Evaluation Service (Interbull; Uppsala, Sweden). |
Collaborators
| Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, MD, USA: Shuli Liu, Yahui Gao, Congjun Li, Benjamin D. Rosen, Curtis P. Van Tassell, Paul M. Vanraden, John B. Cole, George E. Liu & Lingzhao Fang National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China: Shuli Liu, Ying Yu, Ze Yan & Shengli Zhang School of Life Sciences, Westlake University, Hangzhou, China: Shuli Liu Department of Animal and Avian Sciences, University of Maryland, College Park, MD, USA: Yahui Gao & Li Ma MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK: Oriol Canela-Xandri, Erola Pairo-Castineira, Kenton D’Mellow, Yuelin Yao, Pau Navarro, Albert Tenesa & Lingzhao Fang State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China: Sheng Wang Institute of Animal Science, Chinese Academy of Agricultural Science, Beijing, China: Wentao Cai Scotland’s Rural College (SRUC), Roslin Institute Building, Midlothian, UK: Bingjie Li Faculty of Veterinary & Agricultural Science, The University of Melbourne, Parkville, Victoria, Australia: Ruidong Xiang Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, Victoria, Australia: Ruidong Xiang & Amanda J. Chamberlain The Roslin Institute, Royal School of Veterinary Studies, The University of Edinburgh, Midlothian, UK: Erola Pairo-Castineira, Konrad Rawlik, Charley Xia & Albert Tenesa INRAE, AgroParisTech, GABI, Université Paris-Saclay, Jouy-en-Josas, France: Dominique Rocha Guangdong Provincial Key Laboratory of Waterfowl Healthy Breeding, College of Animal Science & Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, China: Xiujin Li |
External commenting | n/a |
Conflicts of interest | The authors declare no competing interests. |
Read more | A multi-tissue atlas of regulatory variants in cattle Nature Genetics, Sept 2022 |
Contact
| Assistant professor (tenure track) Lingzhao Fang, Center for Quantitative Genetics and Genomics (QGG), Aarhus University. |