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Perdomo-González DI, Id-Lahoucine S, Molina A, Cánovas A, Laseca N, Azor PJ, Valera M. Transmission ratio distortion detection by neutral genetic markers in the Pura Raza Española horse breed. Animal 2023; 17:101012. [PMID: 37950978 DOI: 10.1016/j.animal.2023.101012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 10/05/2023] [Accepted: 10/06/2023] [Indexed: 11/13/2023] Open
Abstract
Transmission Ratio Distortion (TRD) is a genetic phenomenon widely demonstrated in several livestock species, but barely in equine species. The TRD occurs when certain genotypes are over- or under-represented in the offspring of a particular mating and can be caused by a variety of factors during gamete formation or during embryonic development. For this study, 126 394 trios consisting of a stallion, mare, and offspring were genotyped using a panel of 17 neutral microsatellite markers recommended by the International Society for Animal Genetics for paternity tests and individual identification. The number of alleles available for each marker ranges from 13 to 18, been 268 the total number of alleles investigated. The TRDscan v.2.0 software was used with the biallelic procedure to identify regions with distorted segregation ratios. After completing the analysis, a total of 12 alleles (out of 11 microsatellites) were identified with decisive evidence for genotypic TRD; 3 and 9 with additive and heterosis patterns, respectively. In addition, 19 alleles (out of 10 microsatellites) were identified displaying allelic TRD. Among them, 14 and 5 were parent-unspecific and stallion-mare-specific TRD. Out of the TRD regions, 24 genes were identified and annotated, predominantly associated with cholesterol metabolism and homeostasis. These genes are often linked to non-specific symptoms like impaired fertility, stunted growth, and compromised overall health. The results suggest a significant impact on the inheritance of certain genetic traits in horses. Further analysis and validation are needed to better understand the TRD impact before the potential implementation in the horse breeding programme strategies.
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Affiliation(s)
| | - S Id-Lahoucine
- Department of Animal and Veterinary Science, Scotland's Rural College, Easter Bush, Edinburgh EH25 9RG, United Kingdom
| | - A Molina
- Departamento de Genética, Universidad de Córdoba, Córdoba 14014, Spain
| | - A Cánovas
- Center of Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - N Laseca
- Departamento de Genética, Universidad de Córdoba, Córdoba 14014, Spain
| | - P J Azor
- Real Asociación Nacional de Criadores de Caballos de Pura Raza Española (ANCCE), Sevilla 41014, Spain
| | - M Valera
- Departamento de Agronomía, ETSIA, Universidad de Sevilla, Sevilla 41005, Spain
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2
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Xiang R, Fang L, Liu S, Macleod IM, Liu Z, Breen EJ, Gao Y, Liu GE, Tenesa A, Mason BA, Chamberlain AJ, Wray NR, Goddard ME. Gene expression and RNA splicing explain large proportions of the heritability for complex traits in cattle. CELL GENOMICS 2023; 3:100385. [PMID: 37868035 PMCID: PMC10589627 DOI: 10.1016/j.xgen.2023.100385] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 08/10/2022] [Accepted: 07/26/2023] [Indexed: 10/24/2023]
Abstract
Many quantitative trait loci (QTLs) are in non-coding regions. Therefore, QTLs are assumed to affect gene regulation. Gene expression and RNA splicing are primary steps of transcription, so DNA variants changing gene expression (eVariants) or RNA splicing (sVariants) are expected to significantly affect phenotypes. We quantify the contribution of eVariants and sVariants detected from 16 tissues (n = 4,725) to 37 traits of ∼120,000 cattle (average magnitude of genetic correlation between traits = 0.13). Analyzed in Bayesian mixture models, averaged across 37 traits, cis and trans eVariants and sVariants detected from 16 tissues jointly explain 69.2% (SE = 0.5%) of heritability, 44% more than expected from the same number of random variants. This 69.2% includes an average of 24% from trans e-/sVariants (14% more than expected). Averaged across 56 lipidomic traits, multi-tissue cis and trans e-/sVariants also explain 71.5% (SE = 0.3%) of heritability, demonstrating the essential role of proximal and distal regulatory variants in shaping mammalian phenotypes.
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Affiliation(s)
- Ruidong Xiang
- Faculty of Veterinary & Agricultural Science, the University of Melbourne, Parkville, VIC 3052, Australia
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC 3083, Australia
- Cambridge-Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
| | - Lingzhao Fang
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, the University of Edinburgh, Edinburgh, UK
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Shuli Liu
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
| | - Iona M. Macleod
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC 3083, Australia
| | - Zhiqian Liu
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC 3083, Australia
| | - Edmond J. Breen
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC 3083, Australia
| | - Yahui Gao
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, MD 20705, USA
| | - George E. Liu
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, MD 20705, USA
| | - Albert Tenesa
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, the University of Edinburgh, Edinburgh, UK
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, the University of Edinburgh, Midlothian EH25 9RG, UK
| | - CattleGTEx Consortium
- Faculty of Veterinary & Agricultural Science, the University of Melbourne, Parkville, VIC 3052, Australia
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC 3083, Australia
- Cambridge-Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, the University of Edinburgh, Edinburgh, UK
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, MD 20705, USA
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, the University of Edinburgh, Midlothian EH25 9RG, UK
- Institute for Molecular Bioscience, the University of Queensland, Brisbane, QLD 4072, Australia
- Queensland Brain Institute, the University of Queensland, Brisbane, QLD 4072, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia
| | - Brett A. Mason
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC 3083, Australia
| | - Amanda J. Chamberlain
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC 3083, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia
| | - Naomi R. Wray
- Institute for Molecular Bioscience, the University of Queensland, Brisbane, QLD 4072, Australia
- Queensland Brain Institute, the University of Queensland, Brisbane, QLD 4072, Australia
| | - Michael E. Goddard
- Faculty of Veterinary & Agricultural Science, the University of Melbourne, Parkville, VIC 3052, Australia
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC 3083, Australia
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3
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Liu X, Mi S, Li W, Zhang J, Augustino SMA, Zhang Z, Zhang R, Xiao W, Yu Y. Molecular regulatory mechanism of key LncRNAs in subclinical mastitic cows with folic acid supplementation. BMC Genomics 2023; 24:464. [PMID: 37592228 PMCID: PMC10436419 DOI: 10.1186/s12864-023-09466-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 06/20/2023] [Indexed: 08/19/2023] Open
Abstract
BACKGROUND Folic acid is a water-soluble B vitamin (B9), which is closely related to the body's immune and other metabolic pathways. The folic acid synthesized by rumen microbes has been unable to meet the needs of high-yielding dairy cows. The incidence rate of subclinical mastitis in dairy herds worldwide ranged between 25%~65% with no obvious symptoms, but it significantly causes a decrease in lactation and milk quality. Therefore, this study aims at exploring the effects of folic acid supplementation on the expression profile of lncRNAs, exploring the molecular mechanism by which lncRNAs regulate immunity in subclinical mastitic dairy cows. RESULTS The analysis identified a total of 4384 lncRNA transcripts. Subsequently, differentially expressed lncRNAs in the comparison of two groups (SF vs. SC, HF vs. HC) were identified to be 84 and 55 respectively. Furthermore, the weighted gene co-expression network analysis (WGCNA) and the KEGG enrichment analysis result showed that folic acid supplementation affects inflammation and immune response-related pathways. The two groups have few pathways in common. One important lncRNA MSTRG.11108.1 and its target genes (ICAM1, CCL3, CCL4, etc.) were involved in immune-related pathways. Finally, through integrated analysis of lncRNAs with GWAS data and animal QTL database, we found that differential lncRNA and its target genes could be significantly enriched in SNPs and QTLs related to somatic cell count (SCC) and mastitis, such as MSTRG.11108.1 and its target gene ICAM1, CXCL3, GRO1. CONCLUSIONS For subclinical mastitic cows, folic acid supplementation can significantly affect the expression of immune-related pathway genes such as ICAM1 by regulating lncRNAs MSTRG.11108.1, thereby affecting related immune phenotypes. Our findings laid a ground foundation for theoretical and practical application for feeding folic acid supplementation in subclinical mastitic cows.
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Affiliation(s)
- Xueqin Liu
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Siyuan Mi
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Wenlong Li
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Jinning Zhang
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Serafino M A Augustino
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
- School of Natural Resources and Environmental Studies, University of Juba, P. O. Box 82, Juba, South Sudan
| | - Zhichao Zhang
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Ruiqiang Zhang
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Wei Xiao
- Beijing Animal Husbandry Station, Beijing, 100029, China
| | - Ying Yu
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China.
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4
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Wang X, Li W, Feng X, Li J, Liu GE, Fang L, Yu Y. Harnessing male germline epigenomics for the genetic improvement in cattle. J Anim Sci Biotechnol 2023; 14:76. [PMID: 37277852 PMCID: PMC10242889 DOI: 10.1186/s40104-023-00874-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 04/02/2023] [Indexed: 06/07/2023] Open
Abstract
Sperm is essential for successful artificial insemination in dairy cattle, and its quality can be influenced by both epigenetic modification and epigenetic inheritance. The bovine germline differentiation is characterized by epigenetic reprogramming, while intergenerational and transgenerational epigenetic inheritance can influence the offspring's development through the transmission of epigenetic features to the offspring via the germline. Therefore, the selection of bulls with superior sperm quality for the production and fertility traits requires a better understanding of the epigenetic mechanism and more accurate identifications of epigenetic biomarkers. We have comprehensively reviewed the current progress in the studies of bovine sperm epigenome in terms of both resources and biological discovery in order to provide perspectives on how to harness this valuable information for genetic improvement in the cattle breeding industry.
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Affiliation(s)
- Xiao Wang
- Laboratory of Animal Genetics and Breeding, Ministry of Agriculture and Rural Affairs of China, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
- Konge Larsen ApS, Kongens Lyngby, 2800, Denmark
- Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences, Jinan, 250100, China
| | - Wenlong Li
- Laboratory of Animal Genetics and Breeding, Ministry of Agriculture and Rural Affairs of China, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Xia Feng
- Laboratory of Animal Genetics and Breeding, Ministry of Agriculture and Rural Affairs of China, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Jianbing Li
- Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences, Jinan, 250100, China
| | - George E Liu
- Animal Genomics and Improvement Laboratory, Agricultural Research Service, Henry A. Wallace Beltsville Agricultural Research Center, USDA, Beltsville, MD, 20705, USA
| | - Lingzhao Fang
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, 8000, Denmark.
| | - Ying Yu
- Laboratory of Animal Genetics and Breeding, Ministry of Agriculture and Rural Affairs of China, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China.
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5
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Hu Y, Yuan S, Du X, Liu J, Zhou W, Wei F. Comparative analysis reveals epigenomic evolution related to species traits and genomic imprinting in mammals. Innovation (N Y) 2023; 4:100434. [PMID: 37215528 PMCID: PMC10196708 DOI: 10.1016/j.xinn.2023.100434] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Accepted: 04/25/2023] [Indexed: 05/24/2023] Open
Abstract
DNA methylation is an epigenetic modification that plays a crucial role in various regulatory processes, including gene expression regulation, transposable element repression, and genomic imprinting. However, most studies on DNA methylation have been conducted in humans and other model species, whereas the dynamics of DNA methylation across mammals remain poorly explored, limiting our understanding of epigenomic evolution in mammals and the evolutionary impacts of conserved and lineage-specific DNA methylation. Here, we generated and gathered comparative epigenomic data from 13 mammalian species, including two marsupial species, to demonstrate that DNA methylation plays critical roles in several aspects of gene evolution and species trait evolution. We found that the species-specific DNA methylation of promoters and noncoding elements correlates with species-specific traits such as body patterning, indicating that DNA methylation might help establish or maintain interspecies differences in gene regulation that shape phenotypes. For a broader view, we investigated the evolutionary histories of 88 known imprinting control regions across mammals to identify their evolutionary origins. By analyzing the features of known and newly identified potential imprints in all studied mammals, we found that genomic imprinting may function in embryonic development through the binding of specific transcription factors. Our findings show that DNA methylation and the complex interaction between the genome and epigenome have a significant impact on mammalian evolution, suggesting that evolutionary epigenomics should be incorporated to develop a unified evolutionary theory.
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Affiliation(s)
- Yisi Hu
- CAS Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Center for Evolution and Conservation Biology, Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China
| | - Shenli Yuan
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Xin Du
- CAS Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jiang Liu
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Wenliang Zhou
- Center for Evolution and Conservation Biology, Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China
| | - Fuwen Wei
- CAS Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Center for Evolution and Conservation Biology, Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China
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6
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Pan Z, Wang Y, Wang M, Wang Y, Zhu X, Gu S, Zhong C, An L, Shan M, Damas J, Halstead MM, Guan D, Trakooljul N, Wimmers K, Bi Y, Wu S, Delany ME, Bai X, Cheng HH, Sun C, Yang N, Hu X, Lewin HA, Fang L, Zhou H. An atlas of regulatory elements in chicken: A resource for chicken genetics and genomics. SCIENCE ADVANCES 2023; 9:eade1204. [PMID: 37134160 PMCID: PMC10156120 DOI: 10.1126/sciadv.ade1204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
A comprehensive characterization of regulatory elements in the chicken genome across tissues will have substantial impacts on both fundamental and applied research. Here, we systematically identified and characterized regulatory elements in the chicken genome by integrating 377 genome-wide sequencing datasets from 23 adult tissues. In total, we annotated 1.57 million regulatory elements, representing 15 distinct chromatin states, and predicted about 1.2 million enhancer-gene pairs and 7662 super-enhancers. This functional annotation of the chicken genome should have wide utility on identifying regulatory elements accounting for gene regulation underlying domestication, selection, and complex trait regulation, which we explored. In short, this comprehensive atlas of regulatory elements provides the scientific community with a valuable resource for chicken genetics and genomics.
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Affiliation(s)
- Zhangyuan Pan
- Department of Animal Science, University of California, Davis, Davis 95616, CA, USA
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Ying Wang
- Department of Animal Science, University of California, Davis, Davis 95616, CA, USA
| | - Mingshan Wang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650000, China
| | - Yuzhe Wang
- State Key Laboratory for Agrobiotechnology, China Agricultural University, Beijing 100193, China
| | - Xiaoning Zhu
- State Key Laboratory for Agrobiotechnology, China Agricultural University, Beijing 100193, China
| | - Shenwen Gu
- Department of Animal Science, University of California, Davis, Davis 95616, CA, USA
| | - Conghao Zhong
- National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100193, China
| | - Liqi An
- Department of Animal Science, University of California, Davis, Davis 95616, CA, USA
| | - Mingzhu Shan
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Joana Damas
- The Genome Center, University of California, Davis, CA 95616, USA
| | - Michelle M Halstead
- Department of Animal Science, University of California, Davis, Davis 95616, CA, USA
| | - Dailu Guan
- Department of Animal Science, University of California, Davis, Davis 95616, CA, USA
| | - Nares Trakooljul
- Research Institute for Farm Animal Biology (FBN), Dummerstorf, Germany
| | - Klaus Wimmers
- Research Institute for Farm Animal Biology (FBN), Dummerstorf, Germany
- Faculty of Agricultural and Environmental Sciences, University Rostock, Rostock, Germany
| | - Ye Bi
- Department of Animal Science, University of California, Davis, Davis 95616, CA, USA
| | - Shang Wu
- Department of Animal Science, University of California, Davis, Davis 95616, CA, USA
| | - Mary E Delany
- Department of Animal Science, University of California, Davis, Davis 95616, CA, USA
| | - Xuechen Bai
- Department of Animal Science, University of California, Davis, Davis 95616, CA, USA
| | - Hans H Cheng
- USDA-ARS, Avian Disease and Oncology Laboratory, East Lansing, MI 48823, USA
| | - Congjiao Sun
- National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100193, China
| | - Ning Yang
- National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100193, China
| | - Xiaoxiang Hu
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650000, China
| | - Harris A Lewin
- The Genome Center, University of California, Davis, CA 95616, USA
- Department of Evolution and Ecology, University of California, Davis, CA 95616, USA
| | - Lingzhao Fang
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, 8000, DK
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Huaijun Zhou
- Department of Animal Science, University of California, Davis, Davis 95616, CA, USA
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7
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Chen S, Liu S, Shi S, Jiang Y, Cao M, Tang Y, Li W, Liu J, Fang L, Yu Y, Zhang S. Comparative epigenomics reveals the impact of ruminant-specific regulatory elements on complex traits. BMC Biol 2022; 20:273. [PMID: 36482458 PMCID: PMC9730597 DOI: 10.1186/s12915-022-01459-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 11/07/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Insights into the genetic basis of complex traits and disease in both human and livestock species have been achieved over the past decade through detection of genetic variants in genome-wide association studies (GWAS). A majority of such variants were found located in noncoding genomic regions, and though the involvement of numerous regulatory elements (REs) has been predicted across multiple tissues in domesticated animals, their evolutionary conservation and effects on complex traits have not been fully elucidated, particularly in ruminants. Here, we systematically analyzed 137 epigenomic and transcriptomic datasets of six mammals, including cattle, sheep, goats, pigs, mice, and humans, and then integrated them with large-scale GWAS of complex traits. RESULTS Using 40 ChIP-seq datasets of H3K4me3 and H3K27ac, we detected 68,479, 58,562, 63,273, 97,244, 111,881, and 87,049 REs in the liver of cattle, sheep, goats, pigs, humans and mice, respectively. We then systematically characterized the dynamic functional landscapes of these REs by integrating multi-omics datasets, including gene expression, chromatin accessibility, and DNA methylation. We identified a core set (n = 6359) of ruminant-specific REs that are involved in liver development, metabolism, and immune processes. Genes with more complex cis-REs exhibited higher gene expression levels and stronger conservation across species. Furthermore, we integrated expression quantitative trait loci (eQTLs) and GWAS from 44 and 52 complex traits/diseases in cattle and humans, respectively. These results demonstrated that REs with different degrees of evolutionary conservation across species exhibited distinct enrichments for GWAS signals of complex traits. CONCLUSIONS We systematically annotated genome-wide functional REs in liver across six mammals and demonstrated the evolution of REs and their associations with transcriptional output and conservation. Detecting lineage-specific REs allows us to decipher the evolutionary and genetic basis of complex phenotypes in livestock and humans, which may benefit the discovery of potential biomedical models for functional variants and genes of specific human diseases.
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Affiliation(s)
- Siqian Chen
- grid.22935.3f0000 0004 0530 8290Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Shuli Liu
- grid.22935.3f0000 0004 0530 8290Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China ,grid.494629.40000 0004 8008 9315 School of Life Sciences, Westlake University, Hangzhou, China
| | - Shaolei Shi
- grid.22935.3f0000 0004 0530 8290Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Yifan Jiang
- grid.22935.3f0000 0004 0530 8290Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Mingyue Cao
- grid.22935.3f0000 0004 0530 8290Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Yongjie Tang
- grid.22935.3f0000 0004 0530 8290Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Wenlong Li
- grid.22935.3f0000 0004 0530 8290Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Jianfeng Liu
- grid.22935.3f0000 0004 0530 8290Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Lingzhao Fang
- grid.4305.20000 0004 1936 7988MRC Human Genetics Unit at the Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK ,grid.7048.b0000 0001 1956 2722Center for Quantitative Genetics and Genomics (QGG), Aarhus University, Aarhus, Denmark
| | - Ying Yu
- grid.22935.3f0000 0004 0530 8290Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Shengli Zhang
- grid.22935.3f0000 0004 0530 8290Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
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8
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Olasege BS, Porto-Neto LR, Tahir MS, Gouveia GC, Cánovas A, Hayes BJ, Fortes MRS. Correlation scan: identifying genomic regions that affect genetic correlations applied to fertility traits. BMC Genomics 2022; 23:684. [PMID: 36195838 PMCID: PMC9533527 DOI: 10.1186/s12864-022-08898-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 09/19/2022] [Indexed: 11/10/2022] Open
Abstract
Although the genetic correlations between complex traits have been estimated for more than a century, only recently we have started to map and understand the precise localization of the genomic region(s) that underpin these correlations. Reproductive traits are often genetically correlated. Yet, we don't fully understand the complexities, synergism, or trade-offs between male and female fertility. In this study, we used reproductive traits in two cattle populations (Brahman; BB, Tropical Composite; TC) to develop a novel framework termed correlation scan (CS). This framework was used to identify local regions associated with the genetic correlations between male and female fertility traits. Animals were genotyped with bovine high-density single nucleotide polymorphisms (SNPs) chip assay. The data used consisted of ~1000 individual records measured through frequent ovarian scanning for age at first corpus luteum (AGECL) and a laboratory assay for serum levels of insulin growth hormone (IGF1 measured in bulls, IGF1b, or cows, IGF1c). The methodology developed herein used correlations of 500-SNP effects in a 100-SNPs sliding window in each chromosome to identify local genomic regions that either drive or antagonize the genetic correlations between traits. We used Fisher's Z-statistics through a permutation method to confirm which regions of the genome harboured significant correlations. About 30% of the total genomic regions were identified as driving and antagonizing genetic correlations between male and female fertility traits in the two populations. These regions confirmed the polygenic nature of the traits being studied and pointed to genes of interest. For BB, the most important chromosome in terms of local regions is often located on bovine chromosome (BTA) 14. However, the important regions are spread across few different BTA's in TC. Quantitative trait loci (QTLs) and functional enrichment analysis revealed many significant windows co-localized with known QTLs related to milk production and fertility traits, especially puberty. In general, the enriched reproductive QTLs driving the genetic correlations between male and female fertility are the same for both cattle populations, while the antagonizing regions were population specific. Moreover, most of the antagonizing regions were mapped to chromosome X. These results suggest regions of chromosome X for further investigation into the trade-offs between male and female fertility. We compared the CS with two other recently proposed methods that map local genomic correlations. Some genomic regions were significant across methods. Yet, many significant regions identified with the CS were overlooked by other methods.
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Affiliation(s)
- Babatunde S Olasege
- The University of Queensland, School of Chemistry and Molecular Biosciences, Saint Lucia Campus, Brisbane, QLD, 4072, Australia.,CSIRO Agriculture and Food, Saint Lucia, QLD, 4067, Australia
| | | | - Muhammad S Tahir
- The University of Queensland, School of Chemistry and Molecular Biosciences, Saint Lucia Campus, Brisbane, QLD, 4072, Australia.,CSIRO Agriculture and Food, Saint Lucia, QLD, 4067, Australia
| | - Gabriela C Gouveia
- Animal Science Department, Veterinary School, Federal University of Minas Gerais, Belo Horizonte, 31270-901, Brazil
| | - Angela Cánovas
- Department of Animal Biosciences, Centre for Genetic Improvement of Livestock, University of Guelph, 50 Stone Rd E, Guelph, ON, N1G 2W1, Canada
| | - Ben J Hayes
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Saint Lucia Campus, Brisbane, QLD, 4072, Australia
| | - Marina R S Fortes
- The University of Queensland, School of Chemistry and Molecular Biosciences, Saint Lucia Campus, Brisbane, QLD, 4072, Australia. .,The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Saint Lucia Campus, Brisbane, QLD, 4072, Australia.
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9
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Zhang Y, Zhu F, Teng J, Zheng B, Lou Z, Feng H, Xue L, Qian Y. Effects of salinity stress on methylation of the liver genome and complement gene in large yellow croaker (Larimichthys crocea). FISH & SHELLFISH IMMUNOLOGY 2022; 129:207-220. [PMID: 36058436 DOI: 10.1016/j.fsi.2022.08.055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 08/06/2022] [Accepted: 08/21/2022] [Indexed: 06/15/2023]
Abstract
Salinity is an important environmental factor that affects the yield and quality of large yellow croaker (Larimichthys crocea) during aquaculture. Here, whole-genome bisulfite sequencing (WGBS), RNA-seq, bisulfite sequencing PCR (BSP), quantitative real-time PCR (qPCR), and dual luciferase reporter gene detection technologies were used to analyze the DNA methylation characteristics and patterns of the liver genome, the expression and methylation levels of important immune genes in large yellow croaker in response to salinity stress. The results of WGBS showed that the cytosine methylation of CG type was dominant, CpGIsland and repeat regions were important regions where DNA methylation occurred, and the DNA methylation in upstream 2k (2000bp upstream of the promoter) and repeat regions had different changes in the liver tissue of large yellow croaker in the response to the 12‰, 24‰, 36‰ salinity stress of 4 w (weeks). In the combined analysis of WGBS and transcriptome, the complement and coagulation cascade pathways were significantly enriched, in which the complement-related genes C7, C3, C5, C4, C1R, MASP1, and CD59 were mainly changed in response to salinity stress. In the studied area of MASP1 gene promoter, the methylation levels of many CpG sites as well as total cytosine were strongly negatively correlated with mRNA expression level. Methylation function analysis of MASP1 promoter further proved that DNA methylation could inhibit the activity of MASP1 promoter, indicating that salinity may affect the expressions of complement-related genes by DNA methylation of gene promoter region.
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Affiliation(s)
- Yu Zhang
- College of Marine Sciences, Ningbo University, Ningbo, Zhejiang, 315832, China; Fisheries College, Zhejiang Ocean University, Zhoushan, 316022, China
| | - Fei Zhu
- College of Marine Sciences, Ningbo University, Ningbo, Zhejiang, 315832, China; Jiangsu Marine Fisheries Research Institute, Nantong, Jiangsu, 226007, China
| | - Jian Teng
- College of Marine Sciences, Ningbo University, Ningbo, Zhejiang, 315832, China
| | - Baoxiao Zheng
- College of Marine Sciences, Ningbo University, Ningbo, Zhejiang, 315832, China
| | - Zhengjia Lou
- College of Marine Sciences, Ningbo University, Ningbo, Zhejiang, 315832, China
| | - Huijie Feng
- College of Marine Sciences, Ningbo University, Ningbo, Zhejiang, 315832, China
| | - Liangyi Xue
- College of Marine Sciences, Ningbo University, Ningbo, Zhejiang, 315832, China.
| | - Yunxia Qian
- College of Marine Sciences, Ningbo University, Ningbo, Zhejiang, 315832, China
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10
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Yao Y, Liu S, Xia C, Gao Y, Pan Z, Canela-Xandri O, Khamseh A, Rawlik K, Wang S, Li B, Zhang Y, Pairo-Castineira E, D’Mellow K, Li X, Yan Z, Li CJ, Yu Y, Zhang S, Ma L, Cole JB, Ross PJ, Zhou H, Haley C, Liu GE, Fang L, Tenesa A. Comparative transcriptome in large-scale human and cattle populations. Genome Biol 2022; 23:176. [PMID: 35996157 PMCID: PMC9394047 DOI: 10.1186/s13059-022-02745-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 08/09/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Cross-species comparison of transcriptomes is important for elucidating evolutionary molecular mechanisms underpinning phenotypic variation between and within species, yet to date it has been essentially limited to model organisms with relatively small sample sizes. RESULTS Here, we systematically analyze and compare 10,830 and 4866 publicly available RNA-seq samples in humans and cattle, respectively, representing 20 common tissues. Focusing on 17,315 orthologous genes, we demonstrate that mean/median gene expression, inter-individual variation of expression, expression quantitative trait loci, and gene co-expression networks are generally conserved between humans and cattle. By examining large-scale genome-wide association studies for 46 human traits (average n = 327,973) and 45 cattle traits (average n = 24,635), we reveal that the heritability of complex traits in both species is significantly more enriched in transcriptionally conserved than diverged genes across tissues. CONCLUSIONS In summary, our study provides a comprehensive comparison of transcriptomes between humans and cattle, which might help decipher the genetic and evolutionary basis of complex traits in both species.
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Affiliation(s)
- Yuelin Yao
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, EH4 2XU Edinburgh, UK
- School of Informatics, The University of Edinburgh, Edinburgh, EH8 9AB UK
| | - Shuli Liu
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, Maryland 20705 USA
- College of Animal Science and Technology, China Agricultural University, Beijing, 100193 China
| | - Charley Xia
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, EH25 9RG UK
- Department of Psychology, 7 George Square, The University of Edinburgh, Edinburgh, EH8 9JZ UK
| | - Yahui Gao
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, Maryland 20705 USA
- Department of Animal and Avian Sciences, University of Maryland, College Park, MA 20742 USA
| | - Zhangyuan Pan
- Department of Animal Science, University of California, Davis, CA 95616 USA
- Present address: Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Oriol Canela-Xandri
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, EH4 2XU Edinburgh, UK
| | - Ava Khamseh
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, EH4 2XU Edinburgh, UK
- School of Informatics, The University of Edinburgh, Edinburgh, EH8 9AB UK
| | - Konrad Rawlik
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, EH25 9RG UK
| | - Sheng Wang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223 Yunnan China
| | - Bingjie Li
- Scotland’s Rural College (SRUC), Roslin Institute Building, Midlothian, EH25 9RG UK
| | - Yi Zhang
- College of Animal Science and Technology, China Agricultural University, Beijing, 100193 China
| | - Erola Pairo-Castineira
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, EH4 2XU Edinburgh, UK
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, EH25 9RG UK
| | - Kenton D’Mellow
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, EH4 2XU Edinburgh, UK
| | - Xiujin Li
- Guangdong Provincial Key Laboratory of Waterfowl Healthy Breeding, College of Animal Science & Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, 510225 Guangdong China
| | - Ze Yan
- College of Animal Science and Technology, China Agricultural University, Beijing, 100193 China
| | - Cong-jun Li
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, Maryland 20705 USA
| | - Ying Yu
- College of Animal Science and Technology, China Agricultural University, Beijing, 100193 China
| | - Shengli Zhang
- College of Animal Science and Technology, China Agricultural University, Beijing, 100193 China
| | - Li Ma
- Department of Animal and Avian Sciences, University of Maryland, College Park, MA 20742 USA
| | - John B. Cole
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, Maryland 20705 USA
| | - Pablo J. Ross
- Department of Animal Science, University of California, Davis, CA 95616 USA
| | - Huaijun Zhou
- Department of Animal Science, University of California, Davis, CA 95616 USA
| | - Chris Haley
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, EH4 2XU Edinburgh, UK
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, EH25 9RG UK
| | - George E. Liu
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, Maryland 20705 USA
| | - Lingzhao Fang
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, EH4 2XU Edinburgh, UK
- Present address: Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Albert Tenesa
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, EH4 2XU Edinburgh, UK
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, EH25 9RG UK
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11
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Gao Y, Li J, Cai G, Wang Y, Yang W, Li Y, Zhao X, Li R, Gao Y, Tuo W, Baldwin RL, Li CJ, Fang L, Liu GE. Single-cell transcriptomic and chromatin accessibility analyses of dairy cattle peripheral blood mononuclear cells and their responses to lipopolysaccharide. BMC Genomics 2022; 23:338. [PMID: 35501711 PMCID: PMC9063233 DOI: 10.1186/s12864-022-08562-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 04/13/2022] [Indexed: 11/24/2022] Open
Abstract
Background Gram-negative bacteria are important pathogens in cattle, causing severe infectious diseases, including mastitis. Lipopolysaccharides (LPS) are components of the outer membrane of Gram-negative bacteria and crucial mediators of chronic inflammation in cattle. LPS modulations of bovine immune responses have been studied before. However, the single-cell transcriptomic and chromatin accessibility analyses of bovine peripheral blood mononuclear cells (PBMCs) and their responses to LPS stimulation were never reported. Results We performed single-cell RNA sequencing (scRNA-seq) and single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) in bovine PBMCs before and after LPS treatment and demonstrated that seven major cell types, which included CD4 T cells, CD8 T cells, and B cells, monocytes, natural killer cells, innate lymphoid cells, and dendritic cells. Bioinformatic analyses indicated that LPS could increase PBMC cell cycle progression, cellular differentiation, and chromatin accessibility. Gene analyses further showed significant changes in differential expression, transcription factor binding site, gene ontology, and regulatory interactions during the PBMC responses to LPS. Consistent with the findings of previous studies, LPS induced activation of monocytes and dendritic cells, likely through their upregulated TLR4 receptor. NF-κB was observed to be activated by LPS and an increased transcription of an array of pro-inflammatory cytokines, in agreement that NF-κB is an LPS-responsive regulator of innate immune responses. In addition, by integrating LPS-induced differentially expressed genes (DEGs) with large-scale GWAS of 45 complex traits in Holstein, we detected trait-relevant cell types. We found that selected DEGs were significantly associated with immune-relevant health, milk production, and body conformation traits. Conclusion This study provided the first scRNAseq and scATAC-seq data for cattle PBMCs and their responses to the LPS stimulation to the best of our knowledge. These results should also serve as valuable resources for the future study of the bovine immune system and open the door for discoveries about immune cell roles in complex traits like mastitis at single-cell resolution. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-022-08562-0.
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Affiliation(s)
- Yahui Gao
- Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences, No.202, Gongyebei Road, Jinan, 250100, China.,Animal Genomics and Improvement Laboratory, BARC, USDA-ARS, Beltsville, MD, 20705, USA
| | - Jianbin Li
- Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences, No.202, Gongyebei Road, Jinan, 250100, China.
| | - Gaozhan Cai
- Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences, No.202, Gongyebei Road, Jinan, 250100, China.,Shandong Ox Livestock Breeding Co., Ltd, Jinan, 250100, China
| | - Yujiao Wang
- Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences, No.202, Gongyebei Road, Jinan, 250100, China
| | - Wenjing Yang
- College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Yanqin Li
- Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences, No.202, Gongyebei Road, Jinan, 250100, China
| | - Xiuxin Zhao
- Shandong Ox Livestock Breeding Co., Ltd, Jinan, 250100, China
| | - Rongling Li
- Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences, No.202, Gongyebei Road, Jinan, 250100, China
| | - Yundong Gao
- Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences, No.202, Gongyebei Road, Jinan, 250100, China
| | - Wenbin Tuo
- Animal Parasitic Diseases Laboratory, BARC, USDA-ARS, Beltsville, MD, 20705, USA
| | - Ransom L Baldwin
- Animal Genomics and Improvement Laboratory, BARC, USDA-ARS, Beltsville, MD, 20705, USA
| | - Cong-Jun Li
- Animal Genomics and Improvement Laboratory, BARC, USDA-ARS, Beltsville, MD, 20705, USA.
| | - Lingzhao Fang
- MRC Human Genetics Unit at the Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK.
| | - George E Liu
- Animal Genomics and Improvement Laboratory, BARC, USDA-ARS, Beltsville, MD, 20705, USA.
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12
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Arneson A, Haghani A, Thompson MJ, Pellegrini M, Kwon SB, Vu H, Maciejewski E, Yao M, Li CZ, Lu AT, Morselli M, Rubbi L, Barnes B, Hansen KD, Zhou W, Breeze CE, Ernst J, Horvath S. A mammalian methylation array for profiling methylation levels at conserved sequences. Nat Commun 2022; 13:783. [PMID: 35145108 PMCID: PMC8831611 DOI: 10.1038/s41467-022-28355-z] [Citation(s) in RCA: 60] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 01/20/2022] [Indexed: 12/12/2022] Open
Abstract
Infinium methylation arrays are not available for the vast majority of non-human mammals. Moreover, even if species-specific arrays were available, probe differences between them would confound cross-species comparisons. To address these challenges, we developed the mammalian methylation array, a single custom array that measures up to 36k CpGs per species that are well conserved across many mammalian species. We designed a set of probes that can tolerate specific cross-species mutations. We annotate the array in over 200 species and report CpG island status and chromatin states in select species. Calibration experiments demonstrate the high fidelity in humans, rats, and mice. The mammalian methylation array has several strengths: it applies to all mammalian species even those that have not yet been sequenced, it provides deep coverage of conserved cytosines facilitating the development of epigenetic biomarkers, and it increases the probability that biological insights gained in one species will translate to others. Methods to probe DNA methylation in the majority of non-human mammals are lacking. Here the authors developed a Mammalian Methylation Array that includes 36k well-conserved CpGs in mammals which will facilitate cross-species comparisons. They annotate the conserved CpGs in > 200 species. The array allows one to measure methylation in all mammalian species including unsequenced ones.
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Affiliation(s)
- Adriana Arneson
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, CA, 90095, USA.,Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
| | - Amin Haghani
- Dept. of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Michael J Thompson
- Molecular, Cell and Developmental Biology, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Matteo Pellegrini
- Molecular, Cell and Developmental Biology, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Soo Bin Kwon
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, CA, 90095, USA.,Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
| | - Ha Vu
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, CA, 90095, USA.,Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
| | - Emily Maciejewski
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA.,Computer Science Department, University of California, Los Angeles, Los Angeles, CA, USA
| | - Mingjia Yao
- Dept. of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Caesar Z Li
- Dept. of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Ake T Lu
- Dept. of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Marco Morselli
- Molecular, Cell and Developmental Biology, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Liudmilla Rubbi
- Molecular, Cell and Developmental Biology, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Bret Barnes
- Illumina, Inc, 5200 Illumina Way, San Diego, CA, 92122, USA
| | - Kasper D Hansen
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.,Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Wanding Zhou
- Center for Computational and Genomic Medicine, Children's Hospital of Philadelphia, Philadelphia, USA
| | | | - Jason Ernst
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, CA, 90095, USA. .,Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA. .,Computer Science Department, University of California, Los Angeles, Los Angeles, CA, USA. .,Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research at University of California, Los Angeles, Los Angeles, CA, USA. .,Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, USA. .,Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA, USA. .,Molecular Biology Institute, University of California, Los Angeles, Los Angeles, CA, USA.
| | - Steve Horvath
- Dept. of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, 90095, USA. .,Dept. of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, 90095, USA. .,Altos Labs, San Diego, CA, USA.
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13
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Vogt G. Studying phenotypic variation and DNA methylation across development, ecology and evolution in the clonal marbled crayfish: a paradigm for investigating epigenotype-phenotype relationships in macro-invertebrates. Naturwissenschaften 2022; 109:16. [PMID: 35099618 DOI: 10.1007/s00114-021-01782-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 12/10/2021] [Accepted: 12/15/2021] [Indexed: 12/17/2022]
Abstract
Animals can produce different phenotypes from the same genome during development, environmental adaptation and evolution, which is mediated by epigenetic mechanisms including DNA methylation. The obligatory parthenogenetic marbled crayfish, Procambarus virginalis, whose genome and methylome are fully established, proved very suitable to study this issue in detail. Comparison between developmental stages and DNA methylation revealed low expression of Dnmt methylation and Tet demethylation enzymes from the spawned oocyte to the 256 cell embryo and considerably increased expression thereafter. The global 5-methylcytosine level was 2.78% at mid-embryonic development and decreased slightly to 2.41% in 2-year-old adults. Genetically identical clutch-mates raised in the same uniform laboratory setting showed broad variation in morphological, behavioural and life history traits and differences in DNA methylation. The invasion of diverse habitats in tropical to cold-temperate biomes in the last 20 years by the marbled crayfish was associated with the expression of significantly different phenotypic traits and DNA methylation patterns, despite extremely low genetic variation on the whole genome scale, suggesting the establishment of epigenetic ecotypes. The evolution of marbled crayfish from its parent species Procambarus fallax by autotriploidy a few decades ago was accompanied by a significant increase in body size, fertility and life span, a 20% reduction of global DNA methylation and alteration of methylation in hundreds of genes, suggesting that epigenetic mechanisms were involved in speciation and fitness enhancement. The combined analysis of phenotypic traits and DNA methylation across multiple biological contexts in the laboratory and field in marbled crayfish may serve as a blueprint for uncovering the role of epigenetic mechanisms in shaping of phenotypes in macro-invertebrates.
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Affiliation(s)
- Günter Vogt
- Faculty of Biosciences, University of Heidelberg, Im Neuenheimer Feld 234, 69120, Heidelberg, Germany.
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14
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Shi S, Zhang Z, Li B, Zhang S, Fang L. Incorporation of Trait-Specific Genetic Information into Genomic Prediction Models. Methods Mol Biol 2022; 2467:329-340. [PMID: 35451781 DOI: 10.1007/978-1-0716-2205-6_11] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Due to the rapid development of high-throughput sequencing technology, we can easily obtain not only the genetic variants at the whole-genome sequence level (e.g., from 1000 Genomes project and 1000 Bull Genomes project), but also a wide range of functional annotations (e.g., enhancers and promoters from ENCODE, FAANG, and FarmGTEx projects) across a wide range of tissues, cell types, developmental stages, and environmental conditions. This huge amount of information leads to a revolution in studying genetics and genomics of complex traits in humans, livestock, and plant species. In this chapter, we focused on and reviewed the genomic prediction methods that incorporate external biological information into genomic prediction, such as sequence ontology, linkage disequilibrium (LD) of SNPs, quantitative trait loci (QTL), and multi-layer omics data (e.g., transcriptome, epigenome, and microbiome).
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Affiliation(s)
- Shaolei Shi
- College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Zhe Zhang
- Department of Animal Breeding and genetics, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Bingjie Li
- The Roslin Institute Building, Scotland's Rural College, Edinburgh, UK
| | - Shengli Zhang
- College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Lingzhao Fang
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK.
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15
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Mi S, Chen S, Li W, Fang L, Yu Y. Effects of sperm DNA methylation on domesticated animal performance and perspectives on cross-species epigenetics in animal breeding. Anim Front 2021; 11:39-47. [PMID: 34934528 PMCID: PMC8683132 DOI: 10.1093/af/vfab053] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Affiliation(s)
- Siyuan Mi
- Laboratory of Animal Genetics and Breeding, Ministry of Agriculture of China, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Siqian Chen
- Laboratory of Animal Genetics and Breeding, Ministry of Agriculture of China, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Wenlong Li
- Laboratory of Animal Genetics and Breeding, Ministry of Agriculture of China, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Lingzhao Fang
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Ying Yu
- Laboratory of Animal Genetics and Breeding, Ministry of Agriculture of China, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
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16
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MacPhillamy C, Pitchford WS, Alinejad-Rokny H, Low WY. Opportunity to improve livestock traits using 3D genomics. Anim Genet 2021; 52:785-798. [PMID: 34494283 DOI: 10.1111/age.13135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/24/2021] [Indexed: 11/30/2022]
Abstract
The advent of high-throughput chromosome conformation capture and sequencing (Hi-C) has enabled researchers to probe the 3D architecture of the mammalian genome in a genome-wide manner. Simultaneously, advances in epigenomic assays, such as chromatin immunoprecipitation and sequencing (ChIP-seq) and DNase-seq, have enabled researchers to study cis-regulatory interactions and chromatin accessibility across the same genome-wide scale. The use of these data has revealed many unique insights into gene regulation and disease pathomechanisms in several model organisms. With the advent of these high-throughput sequencing technologies, there has been an ever-increasing number of datasets available for study; however, this is often limited to model organisms. Livestock species play critical roles in the economies of developing and developed nations alike. Despite this, they are greatly underrepresented in the 3D genomics space; Hi-C and related technologies have the potential to revolutionise livestock breeding by enabling a more comprehensive understanding of how production traits are controlled. The growth in human and model organism Hi-C data has seen a surge in the availability of computational tools for use in 3D genomics, with some tools using machine learning techniques to predict features and improve dataset quality. In this review, we provide an overview of the 3D genome and discuss the status of 3D genomics in livestock before delving into advancing the field by drawing inspiration from research in human and mouse. We end by offering future directions for livestock research in the field of 3D genomics.
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Affiliation(s)
- C MacPhillamy
- Davies Livestock Research Centre, The University of Adelaide, Roseworthy Campus, Mudla Wirra Rd, Roseworthy, SA, 5371, Australia
| | - W S Pitchford
- Davies Livestock Research Centre, The University of Adelaide, Roseworthy Campus, Mudla Wirra Rd, Roseworthy, SA, 5371, Australia
| | - H Alinejad-Rokny
- Biological & Medical Machine Learning Lab, The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, 2052, Australia.,School of Computer Science and Engineering, The University of New South Wales (UNSW Sydney), Sydney, NSW, 2052, Australia
| | - W Y Low
- Davies Livestock Research Centre, The University of Adelaide, Roseworthy Campus, Mudla Wirra Rd, Roseworthy, SA, 5371, Australia
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Zhao B, Luo H, Huang X, Wei C, Di J, Tian Y, Fu X, Li B, Liu GE, Fang L, Zhang S, Tian K. Integration of a single-step genome-wide association study with a multi-tissue transcriptome analysis provides novel insights into the genetic basis of wool and weight traits in sheep. Genet Sel Evol 2021; 53:56. [PMID: 34193030 PMCID: PMC8247193 DOI: 10.1186/s12711-021-00649-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 06/22/2021] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Genetic improvement of wool and growth traits is a major goal in the sheep industry, but their underlying genetic architecture remains elusive. To improve our understanding of these mechanisms, we conducted a weighted single-step genome-wide association study (WssGWAS) and then integrated the results with large-scale transcriptome data for five wool traits and one growth trait in Merino sheep: mean fibre diameter (MFD), coefficient of variation of the fibre diameter (CVFD), crimp number (CN), mean staple length (MSL), greasy fleece weight (GFW), and live weight (LW). RESULTS Our dataset comprised 7135 individuals with phenotype data, among which 1217 had high-density (HD) genotype data (n = 372,534). The genotypes of 707 of these animals were imputed from the Illumina Ovine single nucleotide polymorphism (SNP) 54 BeadChip to the HD Array. The heritability of these traits ranged from 0.05 (CVFD) to 0.36 (MFD), and between-trait genetic correlations ranged from - 0.44 (CN vs. LW) to 0.77 (GFW vs. LW). By integrating the GWAS signals with RNA-seq data from 500 samples (representing 87 tissue types from 16 animals), we detected tissues that were relevant to each of the six traits, e.g. liver, muscle and the gastrointestinal (GI) tract were the most relevant tissues for LW, and leukocytes and macrophages were the most relevant cells for CN. For the six traits, 54 quantitative trait loci (QTL) were identified covering 81 candidate genes on 21 ovine autosomes. Multiple candidate genes showed strong tissue-specific expression, e.g. BNC1 (associated with MFD) and CHRNB1 (LW) were specifically expressed in skin and muscle, respectively. By conducting phenome-wide association studies (PheWAS) in humans, we found that orthologues of several of these candidate genes were significantly (FDR < 0.05) associated with similar traits in humans, e.g. BNC1 was significantly associated with MFD in sheep and with hair colour in humans, and CHRNB1 was significantly associated with LW in sheep and with body mass index in humans. CONCLUSIONS Our findings provide novel insights into the biological and genetic mechanisms underlying wool and growth traits, and thus will contribute to the genetic improvement and gene mapping of complex traits in sheep.
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Affiliation(s)
- Bingru Zhao
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding, and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Hanpeng Luo
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding, and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Xixia Huang
- College of Animal Science, Xinjiang Agricultural University, Urumqi, China
| | - Chen Wei
- College of Animal Science, Xinjiang Agricultural University, Urumqi, China
| | - Jiang Di
- Key Laboratory of Genetics Breeding and Reproduction of the Fine Wool Sheep & Cashmere Goat in Xinjiang, Institute of Animal Science, Xinjiang Academy of Animal Sciences, Urumqi, China
| | - Yuezhen Tian
- Key Laboratory of Genetics Breeding and Reproduction of the Fine Wool Sheep & Cashmere Goat in Xinjiang, Institute of Animal Science, Xinjiang Academy of Animal Sciences, Urumqi, China
| | - Xuefeng Fu
- Key Laboratory of Genetics Breeding and Reproduction of the Fine Wool Sheep & Cashmere Goat in Xinjiang, Institute of Animal Science, Xinjiang Academy of Animal Sciences, Urumqi, China
| | - Bingjie Li
- Scotland's Rural College (SRUC), Roslin Institute Building, Midlothian, EH25 9RG, UK
| | - George E Liu
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, Agricultural Research Service, USDA, Beltsville, MD, USA
| | - Lingzhao Fang
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.
| | - Shengli Zhang
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding, and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China.
| | - Kechuan Tian
- Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences, Jinan, China.
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18
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Wang MS, Zhang JJ, Guo X, Li M, Meyer R, Ashari H, Zheng ZQ, Wang S, Peng MS, Jiang Y, Thakur M, Suwannapoom C, Esmailizadeh A, Hirimuthugoda NY, Zein MSA, Kusza S, Kharrati-Koopaee H, Zeng L, Wang YM, Yin TT, Yang MM, Li ML, Lu XM, Lasagna E, Ceccobelli S, Gunwardana HGTN, Senasig TM, Feng SH, Zhang H, Bhuiyan AKFH, Khan MS, Silva GLLP, Thuy LT, Mwai OA, Ibrahim MNM, Zhang G, Qu KX, Hanotte O, Shapiro B, Bosse M, Wu DD, Han JL, Zhang YP. Large-scale genomic analysis reveals the genetic cost of chicken domestication. BMC Biol 2021; 19:118. [PMID: 34130700 PMCID: PMC8207802 DOI: 10.1186/s12915-021-01052-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 05/19/2021] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Species domestication is generally characterized by the exploitation of high-impact mutations through processes that involve complex shifting demographics of domesticated species. These include not only inbreeding and artificial selection that may lead to the emergence of evolutionary bottlenecks, but also post-divergence gene flow and introgression. Although domestication potentially affects the occurrence of both desired and undesired mutations, the way wild relatives of domesticated species evolve and how expensive the genetic cost underlying domestication is remain poorly understood. Here, we investigated the demographic history and genetic load of chicken domestication. RESULTS We analyzed a dataset comprising over 800 whole genomes from both indigenous chickens and wild jungle fowls. We show that despite having a higher genetic diversity than their wild counterparts (average π, 0.00326 vs. 0.00316), the red jungle fowls, the present-day domestic chickens experienced a dramatic population size decline during their early domestication. Our analyses suggest that the concomitant bottleneck induced 2.95% more deleterious mutations across chicken genomes compared with red jungle fowls, supporting the "cost of domestication" hypothesis. Particularly, we find that 62.4% of deleterious SNPs in domestic chickens are maintained in heterozygous states and masked as recessive alleles, challenging the power of modern breeding programs to effectively eliminate these genetic loads. Finally, we suggest that positive selection decreases the incidence but increases the frequency of deleterious SNPs in domestic chicken genomes. CONCLUSION This study reveals a new landscape of demographic history and genomic changes associated with chicken domestication and provides insight into the evolutionary genomic profiles of domesticated animals managed under modern human selection.
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Affiliation(s)
- Ming-Shan Wang
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, China.,Howard Hughes Medical Institute, University of California Santa Cruz, Santa Cruz, CA, 95064, USA.,Department of Ecology and Evolutionary Biology, University of California Santa Cruz, Santa Cruz, CA, 95064, USA
| | - Jin-Jin Zhang
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, China
| | - Xing Guo
- College of Animal Science and Technology, Anhui Agricultural University, Hefei, 230036, China
| | - Ming Li
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, China
| | - Rachel Meyer
- Department of Ecology and Evolutionary Biology, University of California Santa Cruz, Santa Cruz, CA, 95064, USA
| | - Hidayat Ashari
- Museum Zoologicum Bogoriense, Research Center for Biology, Indonesian Institute of Science (LIPI), Cibinong, Bogor, 16911, Indonesia.,CAAS-ILRI Joint Laboratory on Livestock and Forage Genetic Resources, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100193, China
| | - Zhu-Qing Zheng
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, The Cooperative Innovation Center for Sustainable Pig Production, Ministry of Education, Huazhong Agricultural University, Wuhan, 430070, China
| | - Sheng Wang
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, China
| | - Min-Sheng Peng
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, China
| | - Yu Jiang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, China
| | - Mukesh Thakur
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.,Zoological Survey of India, New Alipore, Kolkata, West Bengal, 700053, India
| | - Chatmongkon Suwannapoom
- School of Agriculture and Natural Resources, University of Phayao, Phayao, 56000, Thailand.,Unit of Excellence on Biodiversity and Natural Resources Management, University of Phayao, Phayao, 56000, Thailand
| | - Ali Esmailizadeh
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.,Department of Animal Science, Shahid Bahonar University of Kerman, P.O. Box 76169133, Kerman, Iran
| | - Nalini Yasoda Hirimuthugoda
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.,Faculty of Agriculture, University of Ruhuna, Matara, Sri Lanka
| | - Moch Syamsul Arifin Zein
- Museum Zoologicum Bogoriense, Research Center for Biology, Indonesian Institute of Science (LIPI), Cibinong, Bogor, 16911, Indonesia
| | - Szilvia Kusza
- Institute of Animal Husbandry, Biotechnology and Nature Conservation, University of Debrecen, Debrecen, H-4032, Hungary
| | - Hamed Kharrati-Koopaee
- Department of Animal Science, Shahid Bahonar University of Kerman, P.O. Box 76169133, Kerman, Iran.,Institute of Biotechnology, School of Agriculture, Shiraz University, P.O. Box 1585, Shiraz, Iran
| | - Lin Zeng
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, China
| | - Yun-Mei Wang
- Center for Neurobiology and Brain Restoration, Skolkovo Institute of Science and Technology, Moscow, 143026, Russia
| | - Ting-Ting Yin
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, China
| | - Min-Min Yang
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, China
| | - Ming-Li Li
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, China
| | - Xue-Mei Lu
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, China.,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650204, China
| | - Emiliano Lasagna
- Dipartimento di Scienze Agrarie, Alimentarie Ambientali, University of Perugia, 06123, Perugia, Italy
| | - Simone Ceccobelli
- Dipartimento di Scienze Agrarie, Alimentarie Ambientali, University of Perugia, 06123, Perugia, Italy
| | | | | | - Shao-Hong Feng
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.,BGI-Shenzhen, Beishan Industrial Zone, Shenzhen, 518083, China
| | - Hao Zhang
- Laboratory of Animal Genetics, Breeding and Reproduction, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Ministry of Agriculture of China, Beijing, 100193, China
| | | | | | | | - Le Thi Thuy
- National Institute of Animal Husbandry, Hanoi, Vietnam
| | - Okeyo A Mwai
- Livestock Genetics Program, International Livestock Research Institute (ILRI), Nairobi, 00100, Kenya
| | | | - Guojie Zhang
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650204, China.,China National Genebank, BGI-Shenzhen, Shenzhen, 518083, China.,Centre for Social Evolution, Department of Biology, University of Copenhagen, DK-1870, Copenhagen, Denmark
| | - Kai-Xing Qu
- Yunnan Academy of Grassland and Animal Science, Kunming, 650212, China
| | - Olivier Hanotte
- Cells, Organisms and Molecular Genetics, School of Life Sciences, University of Nottingham, Nottingham, NG7 2RD, UK.,Livestock Genetics Program, International Livestock Research Institute (ILRI), P.O. Box 5689, Addis Ababa, Ethiopia
| | - Beth Shapiro
- Howard Hughes Medical Institute, University of California Santa Cruz, Santa Cruz, CA, 95064, USA.,Department of Ecology and Evolutionary Biology, University of California Santa Cruz, Santa Cruz, CA, 95064, USA
| | - Mirte Bosse
- Wageningen University & Research - Animal Breeding and Genomics, 6708 PB, Wageningen, The Netherlands.
| | - Dong-Dong Wu
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China. .,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, China. .,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650204, China.
| | - Jian-Lin Han
- CAAS-ILRI Joint Laboratory on Livestock and Forage Genetic Resources, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100193, China. .,Livestock Genetics Program, International Livestock Research Institute (ILRI), Nairobi, 00100, Kenya.
| | - Ya-Ping Zhang
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China. .,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, China. .,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650204, China. .,State Key Laboratory for Conservation and Utilization of Bio-resource, Yunnan University, Kunming, 650091, China.
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19
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Misztal I, Aguilar I, Lourenco D, Ma L, Steibel JP, Toro M. Emerging issues in genomic selection. J Anim Sci 2021; 99:skab092. [PMID: 33773494 PMCID: PMC8186541 DOI: 10.1093/jas/skab092] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Accepted: 03/26/2021] [Indexed: 12/22/2022] Open
Abstract
Genomic selection (GS) is now practiced successfully across many species. However, many questions remain, such as long-term effects, estimations of genomic parameters, robustness of genome-wide association study (GWAS) with small and large datasets, and stability of genomic predictions. This study summarizes presentations from the authors at the 2020 American Society of Animal Science (ASAS) symposium. The focus of many studies until now is on linkage disequilibrium between two loci. Ignoring higher-level equilibrium may lead to phantom dominance and epistasis. The Bulmer effect leads to a reduction of the additive variance; however, the selection for increased recombination rate can release anew genetic variance. With genomic information, estimates of genetic parameters may be biased by genomic preselection, but costs of estimation can increase drastically due to the dense form of the genomic information. To make the computation of estimates feasible, genotypes could be retained only for the most important animals, and methods of estimation should use algorithms that can recognize dense blocks in sparse matrices. GWASs using small genomic datasets frequently find many marker-trait associations, whereas studies using much bigger datasets find only a few. Most of the current tools use very simple models for GWAS, possibly causing artifacts. These models are adequate for large datasets where pseudo-phenotypes such as deregressed proofs indirectly account for important effects for traits of interest. Artifacts arising in GWAS with small datasets can be minimized by using data from all animals (whether genotyped or not), realistic models, and methods that account for population structure. Recent developments permit the computation of P-values from genomic best linear unbiased prediction (GBLUP), where models can be arbitrarily complex but restricted to genotyped animals only, and single-step GBLUP that also uses phenotypes from ungenotyped animals. Stability was an important part of nongenomic evaluations, where genetic predictions were stable in the absence of new data even with low prediction accuracies. Unfortunately, genomic evaluations for such animals change because all animals with genotypes are connected. A top-ranked animal can easily drop in the next evaluation, causing a crisis of confidence in genomic evaluations. While correlations between consecutive genomic evaluations are high, outliers can have differences as high as 1 SD. A solution to fluctuating genomic evaluations is to base selection decisions on groups of animals. Although many issues in GS have been solved, many new issues that require additional research continue to surface.
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Affiliation(s)
- Ignacy Misztal
- Department of Animal and Dairy Science, University of Georgia, Athens, GA 30602, USA
| | - Ignacio Aguilar
- Instituto Nacional de Investigación Agropecuaria (INIA), 90200 Canelones, Uruguay
| | - Daniela Lourenco
- Department of Animal and Dairy Science, University of Georgia, Athens, GA 30602, USA
| | - Li Ma
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20742, USA
| | - Juan Pedro Steibel
- Department of Animal Science, Michigan State University, East Lansing, MI 48824, USA
| | - Miguel Toro
- Departamento de Producción Agraria, Universidad Politécnica de Madrid, Madrid, Spain
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20
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Gao Y, Fang L, Baldwin RL, Connor EE, Cole JB, Van Tassell CP, Ma L, Li CJ, Liu GE. Single-cell transcriptomic analyses of dairy cattle ruminal epithelial cells during weaning. Genomics 2021; 113:2045-2055. [PMID: 33933592 DOI: 10.1016/j.ygeno.2021.04.039] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 03/20/2021] [Accepted: 04/27/2021] [Indexed: 11/30/2022]
Abstract
Using the 10× Genomics Chromium Controller, we obtained scRNA-seq data of 5064 and 1372 individual cells from two Holstein calf ruminal epithelial tissues before and after weaning, respectively. We detected six distinct cell clusters, designated their cell types, and reported their marker genes. We then examined these clusters' underlining cell types and relationships by performing cell cycle, pseudotime trajectory, regulatory network, weighted gene co-expression network and gene ontology analyses. By integrating these cell marker genes with Holstein GWAS signals, we found they were enriched for animal production and body conformation traits. Finally, we confirmed their cell identities by comparing them with human and mouse stomach epithelial cells. This study presents an initial effort to implement single-cell transcriptomic analysis in cattle, and demonstrates ruminal tissue epithelial cell types and their developments during weaning, opening the door for new discoveries about tissue/cell type roles in complex traits at single-cell resolution.
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Affiliation(s)
- Yahui Gao
- Animal Genomics and Improvement Laboratory, BARC, USDA-ARS, Beltsville, MD 20705, USA; Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20742, USA.
| | - Lingzhao Fang
- MRC Human Genetics Unit at the Medical Research Council Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, United Kingdom.
| | - Ransom L Baldwin
- Animal Genomics and Improvement Laboratory, BARC, USDA-ARS, Beltsville, MD 20705, USA.
| | - Erin E Connor
- Department of Animal and Food Sciences, University of Delaware, Newark, DE 19716, USA.
| | - John B Cole
- Animal Genomics and Improvement Laboratory, BARC, USDA-ARS, Beltsville, MD 20705, USA.
| | - Curtis P Van Tassell
- Animal Genomics and Improvement Laboratory, BARC, USDA-ARS, Beltsville, MD 20705, USA.
| | - Li Ma
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20742, USA.
| | - Cong-Jun Li
- Animal Genomics and Improvement Laboratory, BARC, USDA-ARS, Beltsville, MD 20705, USA.
| | - George E Liu
- Animal Genomics and Improvement Laboratory, BARC, USDA-ARS, Beltsville, MD 20705, USA.
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21
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Wang M, Ibeagha-Awemu EM. Impacts of Epigenetic Processes on the Health and Productivity of Livestock. Front Genet 2021; 11:613636. [PMID: 33708235 PMCID: PMC7942785 DOI: 10.3389/fgene.2020.613636] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 12/21/2020] [Indexed: 12/23/2022] Open
Abstract
The dynamic changes in the epigenome resulting from the intricate interactions of genetic and environmental factors play crucial roles in individual growth and development. Numerous studies in plants, rodents, and humans have provided evidence of the regulatory roles of epigenetic processes in health and disease. There is increasing pressure to increase livestock production in light of increasing food needs of an expanding human population and environment challenges, but there is limited related epigenetic data on livestock to complement genomic information and support advances in improvement breeding and health management. This review examines the recent discoveries on epigenetic processes due to DNA methylation, histone modification, and chromatin remodeling and their impacts on health and production traits in farm animals, including bovine, swine, sheep, goat, and poultry species. Most of the reports focused on epigenome profiling at the genome-wide or specific genic regions in response to developmental processes, environmental stressors, nutrition, and disease pathogens. The bulk of available data mainly characterized the epigenetic markers in tissues/organs or in relation to traits and detection of epigenetic regulatory mechanisms underlying livestock phenotype diversity. However, available data is inadequate to support gainful exploitation of epigenetic processes for improved animal health and productivity management. Increased research effort, which is vital to elucidate how epigenetic mechanisms affect the health and productivity of livestock, is currently limited due to several factors including lack of adequate analytical tools. In this review, we (1) summarize available evidence of the impacts of epigenetic processes on livestock production and health traits, (2) discuss the application of epigenetics data in livestock production, and (3) present gaps in livestock epigenetics research. Knowledge of the epigenetic factors influencing livestock health and productivity is vital for the management and improvement of livestock productivity.
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Affiliation(s)
- Mengqi Wang
- Agriculture and Agri-Food Canada, Sherbrooke Research and Development Centre, Sherbrooke, QC, Canada
- Department of Animal Science, Laval University, Quebec, QC, Canada
| | - Eveline M. Ibeagha-Awemu
- Agriculture and Agri-Food Canada, Sherbrooke Research and Development Centre, Sherbrooke, QC, Canada
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22
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Diniz WJS, Crouse MS, Cushman RA, McLean KJ, Caton JS, Dahlen CR, Reynolds LP, Ward AK. Cerebrum, liver, and muscle regulatory networks uncover maternal nutrition effects in developmental programming of beef cattle during early pregnancy. Sci Rep 2021; 11:2771. [PMID: 33531552 PMCID: PMC7854659 DOI: 10.1038/s41598-021-82156-w] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 01/13/2021] [Indexed: 01/30/2023] Open
Abstract
The molecular basis underlying fetal programming in response to maternal nutrition remains unclear. Herein, we investigated the regulatory relationships between genes in fetal cerebrum, liver, and muscle tissues to shed light on the putative mechanisms that underlie the effects of early maternal nutrient restriction on bovine developmental programming. To this end, cerebrum, liver, and muscle gene expression were measured with RNA-Seq in 14 fetuses collected on day 50 of gestation from dams fed a diet initiated at breeding to either achieve 60% (RES, n = 7) or 100% (CON, n = 7) of energy requirements. To build a tissue-to-tissue gene network, we prioritized tissue-specific genes, transcription factors, and differentially expressed genes. Furthermore, we built condition-specific networks to identify differentially co-expressed or connected genes. Nutrient restriction led to differential tissue regulation between the treatments. Myogenic factors differentially regulated by ZBTB33 and ZNF131 may negatively affect myogenesis. Additionally, nutrient-sensing pathways, such as mTOR and PI3K/Akt, were affected by gene expression changes in response to nutrient restriction. By unveiling the network properties, we identified major regulators driving gene expression. However, further research is still needed to determine the impact of early maternal nutrition and strategic supplementation on pre- and post-natal performance.
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Affiliation(s)
- Wellison J. S. Diniz
- grid.261055.50000 0001 2293 4611Department of Animal Sciences, Center for Nutrition and Pregnancy, North Dakota State University, Fargo, ND USA
| | - Matthew S. Crouse
- grid.463419.d0000 0001 0946 3608USDA, ARS, U.S. Meat Animal Research Center, Clay Center, NE USA
| | - Robert A. Cushman
- grid.463419.d0000 0001 0946 3608USDA, ARS, U.S. Meat Animal Research Center, Clay Center, NE USA
| | - Kyle J. McLean
- grid.411461.70000 0001 2315 1184Department of Animal Science, University of Tennessee, Knoxville, TN USA
| | - Joel S. Caton
- grid.261055.50000 0001 2293 4611Department of Animal Sciences, Center for Nutrition and Pregnancy, North Dakota State University, Fargo, ND USA
| | - Carl R. Dahlen
- grid.261055.50000 0001 2293 4611Department of Animal Sciences, Center for Nutrition and Pregnancy, North Dakota State University, Fargo, ND USA
| | - Lawrence P. Reynolds
- grid.261055.50000 0001 2293 4611Department of Animal Sciences, Center for Nutrition and Pregnancy, North Dakota State University, Fargo, ND USA
| | - Alison K. Ward
- grid.261055.50000 0001 2293 4611Department of Animal Sciences, Center for Nutrition and Pregnancy, North Dakota State University, Fargo, ND USA
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