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Harrison BR, Lee MB, Zhang S, Young B, Han K, Sukomol J, Paus V, Tran S, Kim D, Fitchett H, Pan YC, Tesfaye P, Johnson AW, Zhao X, Djukovic D, Raftery D, Promislow DEL. Wide-ranging genetic variation in sensitivity to rapamycin in Drosophila melanogaster. Aging Cell 2024:e14292. [PMID: 39135281 DOI: 10.1111/acel.14292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Revised: 06/18/2024] [Accepted: 07/16/2024] [Indexed: 09/05/2024] Open
Abstract
The progress made in aging research using laboratory organisms is undeniable. Yet, with few exceptions, these studies are conducted in a limited number of isogenic strains. The path from laboratory discoveries to treatment in human populations is complicated by the reality of genetic variation in nature. To model the effect of genetic variation on the action of the drug rapamycin, here we use the growth of Drosophila melanogaster larvae. We screened 140 lines from the Drosophila Genetic References Panel for the extent of developmental delay and found wide-ranging variation in their response, from lines whose development time is nearly doubled by rapamycin, to those that appear to be completely resistant. Sensitivity did not associate with any single genetic marker, nor with any gene. However, variation at the level of genetic pathways was associated with rapamycin sensitivity and might provide insight into sensitivity. In contrast to the genetic analysis, metabolomic analysis showed a strong response of the metabolome to rapamycin, but only among the sensitive larvae. In particular, we found that rapamycin altered levels of amino acids in sensitive larvae, and in a direction strikingly similar to the metabolome response to nutrient deprivation. This work demonstrates the need to evaluate interventions across genetic backgrounds and highlights the potential of omic approaches to reveal biomarkers of drug efficacy and to shed light on mechanisms underlying sensitivity to interventions aimed at increasing lifespan.
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Affiliation(s)
- Benjamin R Harrison
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, Washington, USA
| | | | - Shufan Zhang
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, Washington, USA
| | - Bill Young
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, Washington, USA
| | - Kenneth Han
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, Washington, USA
| | - Jiranut Sukomol
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, Washington, USA
| | - Vanessa Paus
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, Washington, USA
| | - Sarina Tran
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, Washington, USA
| | - David Kim
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, Washington, USA
| | - Hannah Fitchett
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, Washington, USA
| | - Yu-Chen Pan
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, Washington, USA
| | - Philmon Tesfaye
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, Washington, USA
| | - Alia W Johnson
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, Washington, USA
| | - Xiaqing Zhao
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, Washington, USA
| | - Danijel Djukovic
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington School of Medicine, Seattle, Washington, USA
| | - Daniel Raftery
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington School of Medicine, Seattle, Washington, USA
| | - Daniel E L Promislow
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, Washington, USA
- Department of Biology, University of Washington, Seattle, Washington, USA
- Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, Massachusetts, USA
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Han P, Zhang W, Wang D, Wu Y, Li X, Zhao S, Zhu M. Comparative transcriptome analysis of T lymphocyte subpopulations and identification of critical regulators defining porcine thymocyte identity. Front Immunol 2024; 15:1339787. [PMID: 38384475 PMCID: PMC10879363 DOI: 10.3389/fimmu.2024.1339787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 01/18/2024] [Indexed: 02/23/2024] Open
Abstract
Introduction The development and migration of T cells in the thymus and peripheral tissues are crucial for maintaining adaptive immunity in mammals. However, the regulatory mechanisms underlying T cell development and thymocyte identity formation in pigs remain largely underexplored. Method Here, by integrating bulk and single-cell RNA-sequencing data, we investigated regulatory signatures of porcine thymus and lymph node T cells. Results The comparison of T cell subpopulations derived from porcine thymus and lymph nodes revealed that their transcriptomic differences were influenced more by tissue origin than by T cell phenotypes, and that lymph node cells exhibited greater transcriptional diversity than thymocytes. Through weighted gene co-expression network analysis (WGCNA), we identified the key modules and candidate hub genes regulating the heterogeneity of T cell subpopulations. Further, we integrated the porcine thymocyte dataset with peripheral blood mononuclear cell (PBMC) dataset to systematically compare transcriptomic differences between T cell types from different tissues. Based on single-cell datasets, we further identified the key transcription factors (TFs) responsible for maintaining porcine thymocyte identity and unveiled that these TFs coordinately regulated the entire T cell development process. Finally, we performed GWAS of cell type-specific differentially expressed genes (DEGs) and 30 complex traits, and found that the DEGs in thymus-related and peripheral blood-related cell types, especially CD4_SP cluster and CD8-related cluster, were significantly associated with pig productive and reproductive traits. Discussion Our findings provide an insight into T cell development and lay a foundation for further exploring the porcine immune system and genetic mechanisms underlying complex traits in pigs.
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Affiliation(s)
- Pingping Han
- Key Lab of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan, China
| | - Wei Zhang
- Key Lab of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan, China
| | - Daoyuan Wang
- Key Lab of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan, China
| | - Yalan Wu
- Key Lab of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan, China
| | - Xinyun Li
- Key Lab of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan, China
- The Cooperative Innovation Center for Sustainable Pig Production, Huazhong Agricultural University, Wuhan, China
| | - Shuhong Zhao
- Key Lab of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan, China
- The Cooperative Innovation Center for Sustainable Pig Production, Huazhong Agricultural University, Wuhan, China
| | - Mengjin Zhu
- Key Lab of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan, China
- The Cooperative Innovation Center for Sustainable Pig Production, Huazhong Agricultural University, Wuhan, China
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3
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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] [MESH Headings] [Grants] [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
- Key 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
- Key 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
- School of Life Sciences, Westlake University, Hangzhou, China
| | - Shaolei Shi
- Key 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
- Key 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
- Key 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
- Key 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
- Key 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
- Key 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
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
- Center for Quantitative Genetics and Genomics (QGG), Aarhus University, Aarhus, Denmark
| | - Ying Yu
- Key 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
- Key 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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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: 12] [Impact Index Per Article: 6.0] [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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Hao X, Liang A, Plastow G, Zhang C, Wang Z, Liu J, Salzano A, Gasparrini B, Campanile G, Zhang S, Yang L. An Integrative Genomic Prediction Approach for Predicting Buffalo Milk Traits by Incorporating Related Cattle QTLs. Genes (Basel) 2022; 13:genes13081430. [PMID: 36011341 PMCID: PMC9408041 DOI: 10.3390/genes13081430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 08/08/2022] [Accepted: 08/09/2022] [Indexed: 11/16/2022] Open
Abstract
Background: The 90K Axiom Buffalo SNP Array is expected to improve and speed up various genomic analyses for the buffalo (Bubalus bubalis). Genomic prediction is an effective approach in animal breeding to improve selection and reduce costs. As buffalo genome research is lagging behind that of the cow and production records are also limited, genomic prediction performance will be relatively poor. To improve the genomic prediction in buffalo, we introduced a new approach (pGBLUP) for genomic prediction of six buffalo milk traits by incorporating QTL information from the cattle milk traits in order to help improve the prediction performance for buffalo. Results: In simulations, the pGBLUP could outperform BayesR and the GBLUP if the prior biological information (i.e., the known causal loci) was appropriate; otherwise, it performed slightly worse than BayesR and equal to or better than the GBLUP. In real data, the heritability of the buffalo genomic region corresponding to the cattle milk trait QTLs was enriched (fold of enrichment > 1) in four buffalo milk traits (FY270, MY270, PY270, and PM) when the EBV was used as the response variable. The DEBV as the response variable yielded more reliable genomic predictions than the traditional EBV, as has been shown by previous research. The performance of the three approaches (GBLUP, BayesR, and pGBLUP) did not vary greatly in this study, probably due to the limited sample size, incomplete prior biological information, and less artificial selection in buffalo. Conclusions: To our knowledge, this study is the first to apply genomic prediction to buffalo by incorporating prior biological information. The genomic prediction of buffalo traits can be further improved with a larger sample size, higher-density SNP chips, and more precise prior biological information.
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Affiliation(s)
- Xingjie Hao
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
- Correspondence: (X.H.); (L.Y.)
| | - Aixin Liang
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China
| | - Graham Plastow
- Livestock Gentec Center, Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB T6G 2C8, Canada
| | - Chunyan Zhang
- Livestock Gentec Center, Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB T6G 2C8, Canada
| | - Zhiquan Wang
- Livestock Gentec Center, Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB T6G 2C8, Canada
| | - Jiajia Liu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China
| | - Angela Salzano
- Department of Veterinary Medicine and Animal Productions, University of Naples “Federico II”, 80137 Naples, Italy
| | - Bianca Gasparrini
- Department of Veterinary Medicine and Animal Productions, University of Naples “Federico II”, 80137 Naples, Italy
| | - Giuseppe Campanile
- Department of Veterinary Medicine and Animal Productions, University of Naples “Federico II”, 80137 Naples, Italy
| | - Shujun Zhang
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China
| | - Liguo Yang
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China
- Correspondence: (X.H.); (L.Y.)
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6
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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: 10] [Impact Index Per Article: 5.0] [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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7
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Functional annotation of regulatory elements in cattle genome reveals the roles of extracellular interaction and dynamic change of chromatin states in rumen development during weaning. Genomics 2022; 114:110296. [PMID: 35143887 DOI: 10.1016/j.ygeno.2022.110296] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 12/20/2021] [Accepted: 02/01/2022] [Indexed: 12/24/2022]
Abstract
We profiled landscapes of bovine regulatory elements and explored dynamic changes of chromatin states in rumen development during weaning. The regulatory elements (15 chromatin states) and their coordinated activities in cattle were defined through genome-wide profiling of four histone modifications, CTCF-binding, DNA accessibility, DNA methylation, and transcriptome in rumen epithelial tissues. Each chromatin state presented specific enrichment for sequence ontology, methylation, trait-associated variants, transcription, gene expression-associated variants, selection signatures, and evolutionarily conserved elements. During weaning, weak enhancers and flanking active transcriptional start sites (TSS) were the most dynamic chromatin states and occurred in tandem with significant variations in gene expression and DNA methylation, significantly associated with stature, production, and reproduction economic traits. By comparing with in vitro cultured epithelial cells and in vivo rumen tissues, we showed the commonness and uniqueness of these results, especially the roles of cell interactions and mitochondrial activities in tissue development.
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8
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Zhao B, Luo H, He J, Huang X, Chen S, Fu X, Zeng W, Tian Y, Liu S, Li CJ, Liu GE, Fang L, Zhang S, Tian K. Comprehensive transcriptome and methylome analysis delineates the biological basis of hair follicle development and wool-related traits in Merino sheep. BMC Biol 2021; 19:197. [PMID: 34503498 PMCID: PMC8427949 DOI: 10.1186/s12915-021-01127-9] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 08/18/2021] [Indexed: 12/13/2022] Open
Abstract
Background Characterization of the molecular mechanisms underlying hair follicle development is of paramount importance in the genetic improvement of wool-related traits in sheep and skin-related traits in humans. The Merino is the most important breed of fine-wooled sheep in the world. In this study, we systematically investigated the complexity of sheep hair follicle development by integrating transcriptome and methylome datasets from Merino sheep skin. Results We analysed 72 sequence datasets, including DNA methylome and the whole transcriptome of four gene types, i.e. protein-coding genes (PCGs), lncRNAs, circRNAs, and miRNAs, across four embryonic days (E65, E85, E105, and E135) and two postnatal days (P7 and P30) from the skin tissue of 18 Merino sheep. We revealed distinct expression profiles of these four gene types across six hair follicle developmental stages, and demonstrated their complex interactions with DNA methylation. PCGs with stage-specific expression or regulated by stage-specific lncRNAs, circRNAs, and miRNAs were significantly enriched in epithelial differentiation and hair follicle morphogenesis. Regulatory network and gene co-expression analyses identified key transcripts controlling hair follicle development. We further predicted transcriptional factors (e.g. KLF4, LEF1, HOXC13, RBPJ, VDR, RARA, and STAT3) with stage-specific involvement in hair follicle morphogenesis. Through integrating these stage-specific genomic features with results from genome-wide association studies (GWAS) of five wool-related traits in 7135 Merino sheep, we detected developmental stages and genes that were relevant with wool-related traits in sheep. For instance, genes that were specifically upregulated at E105 were significantly associated with most of wool-related traits. A phenome-wide association study (PheWAS) demonstrated that candidate genes of wool-related traits (e.g. SPHK1, GHR, PPP1R27, CSRP2, EEF1A2, and PTPN1) in sheep were also significantly associated with dermatological, metabolic, and immune traits in humans. Conclusions Our study provides novel insights into the molecular basis of hair follicle morphogenesis and will serve as a foundation to improve breeding for wool traits in sheep. It also indicates the importance of studying gene expression in the normal development of organs in understanding the genetic architecture of economically important traits in livestock. The datasets generated here are useful resources for functionally annotating the sheep genome, and for elucidating early skin development in mammals, including humans. Supplementary Information The online version contains supplementary material available at 10.1186/s12915-021-01127-9.
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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
| | - Junmin He
- 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
| | - Xixia Huang
- College of Animal Science, Xinjiang Agricultural University, Urumqi, China
| | - Siqian Chen
- 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
| | - 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
| | - Weidan Zeng
- College of Animal Science, Xinjiang Agricultural University, 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
| | - Shuli Liu
- 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
| | - Cong-Jun Li
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, Agricultural Research Service, USDA, Beltsville, Maryland, USA
| | - George E Liu
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, Agricultural Research Service, USDA, Beltsville, Maryland, 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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9
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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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10
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Rohde PD, Kristensen TN, Sarup P, Muñoz J, Malmendal A. Prediction of complex phenotypes using the Drosophila melanogaster metabolome. Heredity (Edinb) 2021; 126:717-732. [PMID: 33510469 PMCID: PMC8102504 DOI: 10.1038/s41437-021-00404-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 01/04/2021] [Accepted: 01/04/2021] [Indexed: 01/30/2023] Open
Abstract
Understanding the genotype-phenotype map and how variation at different levels of biological organization is associated are central topics in modern biology. Fast developments in sequencing technologies and other molecular omic tools enable researchers to obtain detailed information on variation at DNA level and on intermediate endophenotypes, such as RNA, proteins and metabolites. This can facilitate our understanding of the link between genotypes and molecular and functional organismal phenotypes. Here, we use the Drosophila melanogaster Genetic Reference Panel and nuclear magnetic resonance (NMR) metabolomics to investigate the ability of the metabolome to predict organismal phenotypes. We performed NMR metabolomics on four replicate pools of male flies from each of 170 different isogenic lines. Our results show that metabolite profiles are variable among the investigated lines and that this variation is highly heritable. Second, we identify genes associated with metabolome variation. Third, using the metabolome gave better prediction accuracies than genomic information for four of five quantitative traits analyzed. Our comprehensive characterization of population-scale diversity of metabolomes and its genetic basis illustrates that metabolites have large potential as predictors of organismal phenotypes. This finding is of great importance, e.g., in human medicine, evolutionary biology and animal and plant breeding.
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Affiliation(s)
- Palle Duun Rohde
- Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark.
| | - Torsten Nygaard Kristensen
- Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
- Department of Animal Science, Aarhus University, Tjele, Denmark
| | - Pernille Sarup
- Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark
- Nordic Seed A/S, Odder, Denmark
| | - Joaquin Muñoz
- Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
| | - Anders Malmendal
- Department of Science and Environment, Roskilde University, Roskilde, Denmark.
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11
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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: 15] [Impact Index Per Article: 5.0] [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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12
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Yan Z, Huang H, Freebern E, Santos DJA, Dai D, Si J, Ma C, Cao J, Guo G, Liu GE, Ma L, Fang L, Zhang Y. Integrating RNA-Seq with GWAS reveals novel insights into the molecular mechanism underpinning ketosis in cattle. BMC Genomics 2020; 21:489. [PMID: 32680461 PMCID: PMC7367229 DOI: 10.1186/s12864-020-06909-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Accepted: 07/13/2020] [Indexed: 01/12/2023] Open
Abstract
Background Ketosis is a common metabolic disease during the transition period in dairy cattle, resulting in long-term economic loss to the dairy industry worldwide. While genetic selection of resistance to ketosis has been adopted by many countries, the genetic and biological basis underlying ketosis is poorly understood. Results We collected a total of 24 blood samples from 12 Holstein cows, including 4 healthy and 8 ketosis-diagnosed ones, before (2 weeks) and after (5 days) calving, respectively. We then generated RNA-Sequencing (RNA-Seq) data and seven blood biochemical indicators (bio-indicators) from leukocytes and plasma in each of these samples, respectively. By employing a weighted gene co-expression network analysis (WGCNA), we detected that 4 out of 16 gene-modules, which were significantly engaged in lipid metabolism and immune responses, were transcriptionally (FDR < 0.05) correlated with postpartum ketosis and several bio-indicators (e.g., high-density lipoprotein and low-density lipoprotein). By conducting genome-wide association signal (GWAS) enrichment analysis among six common health traits (ketosis, mastitis, displaced abomasum, metritis, hypocalcemia and livability), we found that 4 out of 16 modules were genetically (FDR < 0.05) associated with ketosis, among which three were correlated with postpartum ketosis based on WGCNA. We further identified five candidate genes for ketosis, including GRINA, MAF1, MAFA, C14H8orf82 and RECQL4. Our phenome-wide association analysis (Phe-WAS) demonstrated that human orthologues of these candidate genes were also significantly associated with many metabolic, endocrine, and immune traits in humans. For instance, MAFA, which is involved in insulin secretion, glucose response, and transcriptional regulation, showed a significantly higher association with metabolic and endocrine traits compared to other types of traits in humans. Conclusions In summary, our study provides novel insights into the molecular mechanism underlying ketosis in cattle, and highlights that an integrative analysis of omics data and cross-species mapping are promising for illustrating the genetic architecture underpinning complex traits.
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Affiliation(s)
- Ze Yan
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Hetian Huang
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China.,College of Animal Science and Veterinary Medicine, Henan Agricultural University, Zhengzhou, 450002, China
| | - Ellen Freebern
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD, 20742, USA
| | - Daniel J A Santos
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD, 20742, USA
| | - Dongmei Dai
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Jingfang Si
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Chong Ma
- Department of Clinical Veterinary Medicine, College of Veterinary Medicine, China Agricultural University, Beijing, 100193, China
| | - Jie Cao
- Department of Clinical Veterinary Medicine, College of Veterinary Medicine, China Agricultural University, Beijing, 100193, China
| | - Gang Guo
- Beijing Sunlon Livestock Development Co Ltd., Beijing, 100076, China
| | - George E Liu
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, Agricultural Research Service, USDA, Beltsville, MD, 20705, USA
| | - Li Ma
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD, 20742, USA.
| | - Lingzhao Fang
- MRC Human Genetics Unit at the Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK.
| | - Yi Zhang
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China.
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13
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Liu S, Yu Y, Zhang S, Cole JB, Tenesa A, Wang T, McDaneld TG, Ma L, Liu GE, Fang L. Epigenomics and genotype-phenotype association analyses reveal conserved genetic architecture of complex traits in cattle and human. BMC Biol 2020; 18:80. [PMID: 32620158 PMCID: PMC7334855 DOI: 10.1186/s12915-020-00792-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Accepted: 05/12/2020] [Indexed: 02/01/2023] Open
Abstract
Background Lack of comprehensive functional annotations across a wide range of tissues and cell types severely hinders the biological interpretations of phenotypic variation, adaptive evolution, and domestication in livestock. Here we used a combination of comparative epigenomics, genome-wide association study (GWAS), and selection signature analysis, to shed light on potential adaptive evolution in cattle. Results We cross-mapped 8 histone marks of 1300 samples from human to cattle, covering 178 unique tissues/cell types. By uniformly analyzing 723 RNA-seq and 40 whole genome bisulfite sequencing (WGBS) datasets in cattle, we validated that cross-mapped histone marks captured tissue-specific expression and methylation, reflecting tissue-relevant biology. Through integrating cross-mapped tissue-specific histone marks with large-scale GWAS and selection signature results, we for the first time detected relevant tissues and cell types for 45 economically important traits and artificial selection in cattle. For instance, immune tissues are significantly associated with health and reproduction traits, multiple tissues for milk production and body conformation traits (reflecting their highly polygenic architecture), and thyroid for the different selection between beef and dairy cattle. Similarly, we detected relevant tissues for 58 complex traits and diseases in humans and observed that immune and fertility traits in humans significantly correlated with those in cattle in terms of relevant tissues, which facilitated the identification of causal genes for such traits. For instance, PIK3CG, a gene highly specifically expressed in mononuclear cells, was significantly associated with both age-at-menopause in human and daughter-still-birth in cattle. ICAM, a T cell-specific gene, was significantly associated with both allergic diseases in human and metritis in cattle. Conclusion Collectively, our results highlighted that comparative epigenomics in conjunction with GWAS and selection signature analyses could provide biological insights into the phenotypic variation and adaptive evolution. Cattle may serve as a model for human complex traits, by providing additional information beyond laboratory model organisms, particularly when more novel phenotypes become available in the near future.
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Affiliation(s)
- Shuli Liu
- Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, BARC-East, Beltsville, MD, 20705, USA.,College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - 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
| | - John B Cole
- Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, BARC-East, Beltsville, MD, 20705, USA
| | - Albert Tenesa
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK.,The Roslin Institute, University of Edinburgh, Edinburgh, EH25 9RG, UK
| | - Ting Wang
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Tara G McDaneld
- US Meat Animal Research Center, Agricultural Research Service, USDA, Clay Center, NE, 68933, USA
| | - Li Ma
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD, 20742, USA.
| | - George E Liu
- Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, BARC-East, Beltsville, MD, 20705, USA.
| | - Lingzhao Fang
- Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, BARC-East, Beltsville, MD, 20705, USA. .,MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK. .,Department of Animal and Avian Sciences, University of Maryland, College Park, MD, 20742, USA.
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14
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Fang L, Cai W, Liu S, Canela-Xandri O, Gao Y, Jiang J, Rawlik K, Li B, Schroeder SG, Rosen BD, Li CJ, Sonstegard TS, Alexander LJ, Van Tassell CP, VanRaden PM, Cole JB, Yu Y, Zhang S, Tenesa A, Ma L, Liu GE. Comprehensive analyses of 723 transcriptomes enhance genetic and biological interpretations for complex traits in cattle. Genome Res 2020; 30:790-801. [PMID: 32424068 PMCID: PMC7263193 DOI: 10.1101/gr.250704.119] [Citation(s) in RCA: 77] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Accepted: 05/01/2020] [Indexed: 12/13/2022]
Abstract
By uniformly analyzing 723 RNA-seq data from 91 tissues and cell types, we built a comprehensive gene atlas and studied tissue specificity of genes in cattle. We demonstrated that tissue-specific genes significantly reflected the tissue-relevant biology, showing distinct promoter methylation and evolution patterns (e.g., brain-specific genes evolve slowest, whereas testis-specific genes evolve fastest). Through integrative analyses of those tissue-specific genes with large-scale genome-wide association studies, we detected relevant tissues/cell types and candidate genes for 45 economically important traits in cattle, including blood/immune system (e.g., CCDC88C) for male fertility, brain (e.g., TRIM46 and RAB6A) for milk production, and multiple growth-related tissues (e.g., FGF6 and CCND2) for body conformation. We validated these findings by using epigenomic data across major somatic tissues and sperm. Collectively, our findings provided novel insights into the genetic and biological mechanisms underlying complex traits in cattle, and our transcriptome atlas can serve as a primary source for biological interpretation, functional validation, studies of adaptive evolution, and genomic improvement in livestock.
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Affiliation(s)
- Lingzhao Fang
- 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, Maryland 20742, USA
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian EH25 9RG, United Kingdom
- Medical Research Council Human Genetics Unit at the Medical Research Council Institute of Genetics and Molecular Medicine, The University of Edinburgh, Edinburgh EH4 2XU, United Kingdom
| | - Wentao Cai
- Department of Animal and Avian Sciences, University of Maryland, College Park, Maryland 20742, USA
- College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - 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
| | - Oriol Canela-Xandri
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian EH25 9RG, United Kingdom
- Medical Research Council Human Genetics Unit at the Medical Research Council Institute of Genetics and Molecular Medicine, The University of Edinburgh, Edinburgh EH4 2XU, United Kingdom
| | - 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, Maryland 20742, USA
| | - Jicai Jiang
- Department of Animal and Avian Sciences, University of Maryland, College Park, Maryland 20742, USA
| | - Konrad Rawlik
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian EH25 9RG, United Kingdom
| | - Bingjie Li
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, Maryland 20705, USA
| | - Steven G Schroeder
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, Maryland 20705, USA
| | - Benjamin D Rosen
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, Maryland 20705, USA
| | - Cong-Jun Li
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, Maryland 20705, USA
| | | | - Leeson J Alexander
- Fort Keogh Livestock and Range Research Laboratory, Agricultural Research Service, USDA, Miles City, Montana 59301, USA
| | - Curtis P Van Tassell
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, Maryland 20705, USA
| | - Paul M VanRaden
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, Maryland 20705, USA
| | - John B Cole
- 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
| | - Albert Tenesa
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian EH25 9RG, United Kingdom
- Medical Research Council Human Genetics Unit at the Medical Research Council Institute of Genetics and Molecular Medicine, The University of Edinburgh, Edinburgh EH4 2XU, United Kingdom
| | - Li Ma
- Department of Animal and Avian Sciences, University of Maryland, College Park, Maryland 20742, USA
| | - George E Liu
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, Maryland 20705, USA
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15
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Reay WR, Cairns MJ. The role of the retinoids in schizophrenia: genomic and clinical perspectives. Mol Psychiatry 2020; 25:706-718. [PMID: 31666680 PMCID: PMC7156347 DOI: 10.1038/s41380-019-0566-2] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Revised: 09/23/2019] [Accepted: 10/17/2019] [Indexed: 12/13/2022]
Abstract
Signalling by retinoid compounds is vital for embryonic development, with particular importance for neurogenesis in the human brain. Retinoids, metabolites of vitamin A, exert influence over the expression of thousands of transcripts genome wide, and thus, act as master regulators of many important biological processes. A significant body of evidence in the literature now supports dysregulation of the retinoid system as being involved in the aetiology of schizophrenia. This includes mechanistic insights from large-scale genomic, transcriptomic and, proteomic studies, which implicate disruption of disparate aspects of retinoid biology such as transport, metabolism, and signalling. As a result, retinoids may present a valuable clinical opportunity in schizophrenia via novel pharmacotherapies and dietary intervention. Further work, however, is required to expand on the largely observational data collected thus far and confirm causality. This review will highlight the fundamentals of retinoid biology and examine the evidence for retinoid dysregulation in schizophrenia.
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Affiliation(s)
- William R. Reay
- 0000 0000 8831 109Xgrid.266842.cSchool of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW Australia ,grid.413648.cCentre for Brain and Mental Health Research, Hunter Medical Research Institute, Newcastle, NSW Australia
| | - Murray J. Cairns
- 0000 0000 8831 109Xgrid.266842.cSchool of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW Australia ,grid.413648.cCentre for Brain and Mental Health Research, Hunter Medical Research Institute, Newcastle, NSW Australia
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16
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Rohde PD, Fourie Sørensen I, Sørensen P. qgg: an R package for large-scale quantitative genetic analyses. Bioinformatics 2019; 36:2614-2615. [DOI: 10.1093/bioinformatics/btz955] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Revised: 12/16/2019] [Accepted: 12/23/2019] [Indexed: 01/03/2023] Open
Abstract
Abstract
Summary
Here, we present the R package qgg, which provides an environment for large-scale genetic analyses of quantitative traits and diseases. The qgg package provides an infrastructure for efficient processing of large-scale genetic data and functions for estimating genetic parameters, and performing single and multiple marker association analyses and genomic-based predictions of phenotypes.
Availability and implementation
The qgg package is freely available. For the latest updates, user guides and example scripts, consult the main page http://psoerensen.github.io/qgg. The current release is available from CRAN (https://CRAN.R-project.org/package=qgg) for all major operating systems.
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Palle Duun Rohde
- Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark
| | | | - Peter Sørensen
- Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark
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17
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Affiliation(s)
- Palle Duun Rohde
- Department of Molecular Biology & Genetics, Aarhus University, Aarhus, Denmark
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18
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Rohde PD, Jensen IR, Sarup PM, Ørsted M, Demontis D, Sørensen P, Kristensen TN. Genetic Signatures of Drug Response Variability in Drosophila melanogaster. Genetics 2019; 213:633-650. [PMID: 31455722 PMCID: PMC6781897 DOI: 10.1534/genetics.119.302381] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Accepted: 08/26/2019] [Indexed: 12/27/2022] Open
Abstract
Knowledge of the genetic basis underlying variation in response to environmental exposures or treatments is important in many research areas. For example, knowing the set of causal genetic variants for drug responses could revolutionize personalized medicine. We used Drosophila melanogaster to investigate the genetic signature underlying behavioral variability in response to methylphenidate (MPH), a drug used in the treatment of attention-deficit/hyperactivity disorder. We exposed a wild-type D. melanogaster population to MPH and a control treatment, and observed an increase in locomotor activity in MPH-exposed individuals. Whole-genome transcriptomic analyses revealed that the behavioral response to MPH was associated with abundant gene expression alterations. To confirm these patterns in a different genetic background and to further advance knowledge on the genetic signature of drug response variability, we used a system of inbred lines, the Drosophila Genetic Reference Panel (DGRP). Based on the DGRP, we showed that the behavioral response to MPH was strongly genotype-dependent. Using an integrative genomic approach, we incorporated known gene interactions into the genomic analyses of the DGRP, and identified putative candidate genes for variability in drug response. We successfully validated 71% of the investigated candidate genes by gene expression knockdown. Furthermore, we showed that MPH has cross-generational behavioral and transcriptomic effects. Our findings establish a foundation for understanding the genetic mechanisms driving genotype-specific responses to medical treatment, and highlight the opportunities that integrative genomic approaches have in optimizing medical treatment of complex diseases.
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Affiliation(s)
- Palle Duun Rohde
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8000 Aarhus C, Denmark
- Center for Integrative Sequencing, Aarhus University, 8000, Denmark
| | - Iben Ravnborg Jensen
- Section for Biology and Environmental Science, Department of Chemistry and Bioscience, Aalborg University, 9220, Denmark
| | - Pernille Merete Sarup
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark
| | - Michael Ørsted
- Section for Biology and Environmental Science, Department of Chemistry and Bioscience, Aalborg University, 9220, Denmark
| | - Ditte Demontis
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8000 Aarhus C, Denmark
- Center for Integrative Sequencing, Aarhus University, 8000, Denmark
- Department of Biomedicine, Aarhus University, 8000, Denmark
| | - Peter Sørensen
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark
| | - Torsten Nygaard Kristensen
- Section for Biology and Environmental Science, Department of Chemistry and Bioscience, Aalborg University, 9220, Denmark
- Section for Genetics, Ecology and Evolution, Department of Bioscience, Aarhus University, 8000, Denmark
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19
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Li B, Fang L, Null DJ, Hutchison JL, Connor EE, VanRaden PM, VandeHaar MJ, Tempelman RJ, Weigel KA, Cole JB. High-density genome-wide association study for residual feed intake in Holstein dairy cattle. J Dairy Sci 2019; 102:11067-11080. [PMID: 31563317 DOI: 10.3168/jds.2019-16645] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Accepted: 07/19/2019] [Indexed: 01/27/2023]
Abstract
Improving feed efficiency (FE) of dairy cattle may boost farm profitability and reduce the environmental footprint of the dairy industry. Residual feed intake (RFI), a candidate FE trait in dairy cattle, can be defined to be genetically uncorrelated with major energy sink traits (e.g., milk production, body weight) by including genomic predicted transmitting ability of such traits in genetic analyses for RFI. We examined the genetic basis of RFI through genome-wide association (GWA) analyses and post-GWA enrichment analyses and identified candidate genes and biological pathways associated with RFI in dairy cattle. Data were collected from 4,823 lactations of 3,947 Holstein cows in 9 research herds in the United States. Of these cows, 3,555 were genotyped and were imputed to a high-density list of 312,614 SNP. We used a single-step GWA method to combine information from genotyped and nongenotyped animals with phenotypes as well as their ancestors' information. The estimated genomic breeding values from a single-step genomic BLUP were back-solved to obtain the individual SNP effects for RFI. The proportion of genetic variance explained by each 5-SNP sliding window was also calculated for RFI. Our GWA analyses suggested that RFI is a highly polygenic trait regulated by many genes with small effects. The closest genes to the top SNP and sliding windows were associated with dry matter intake (DMI), RFI, energy homeostasis and energy balance regulation, digestion and metabolism of carbohydrates and proteins, immune regulation, leptin signaling, mitochondrial ATP activities, rumen development, skeletal muscle development, and spermatogenesis. The region of 40.7 to 41.5 Mb on BTA25 (UMD3.1 reference genome) was the top associated region for RFI. The closest genes to this region, CARD11 and EIF3B, were previously shown to be related to RFI of dairy cattle and FE of broilers, respectively. Another candidate region, 57.7 to 58.2 Mb on BTA18, which is associated with DMI and leptin signaling, was also associated with RFI in this study. Post-GWA enrichment analyses used a sum-based marker-set test based on 4 public annotation databases: Gene Ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, Reactome pathways, and medical subject heading (MeSH) terms. Results of these analyses were consistent with those from the top GWA signals. Across the 4 databases, GWA signals for RFI were highly enriched in the biosynthesis and metabolism of amino acids and proteins, digestion and metabolism of carbohydrates, skeletal development, mitochondrial electron transport, immunity, rumen bacteria activities, and sperm motility. Our findings offer novel insight into the genetic basis of RFI and identify candidate regions and biological pathways associated with RFI in dairy cattle.
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Affiliation(s)
- B Li
- Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD 20705-2350
| | - L Fang
- Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD 20705-2350; Department of Animal and Avian Sciences, University of Maryland, College Park 20742; Medical Research Council Human Genetics Unit at the Medical Research Council Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, United Kingdom
| | - D J Null
- Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD 20705-2350
| | - J L Hutchison
- Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD 20705-2350
| | - E E Connor
- Department of Animal and Food Sciences, University of Delaware, Newark 19716
| | - P M VanRaden
- Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD 20705-2350
| | - M J VandeHaar
- Department of Animal Science, Michigan State University, East Lansing 48824
| | - R J Tempelman
- Department of Animal Science, Michigan State University, East Lansing 48824
| | - K A Weigel
- Department of Dairy Science, University of Wisconsin, Madison 53706
| | - J B Cole
- Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD 20705-2350.
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20
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Fang L, Liu S, Liu M, Kang X, Lin S, Li B, Connor EE, Baldwin RL, Tenesa A, Ma L, Liu GE, Li CJ. Functional annotation of the cattle genome through systematic discovery and characterization of chromatin states and butyrate-induced variations. BMC Biol 2019; 17:68. [PMID: 31419979 PMCID: PMC6698049 DOI: 10.1186/s12915-019-0687-8] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Accepted: 08/05/2019] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND The functional annotation of genomes, including chromatin accessibility and modifications, is important for understanding and effectively utilizing the increased amount of genome sequences reported. However, while such annotation has been well explored in a diverse set of tissues and cell types in human and model organisms, relatively little data are available for livestock genomes, hindering our understanding of complex trait variation, domestication, and adaptive evolution. Here, we present the first complete global landscape of regulatory elements in cattle and explore the dynamics of chromatin states in rumen epithelial cells induced by the rumen developmental regulator-butyrate. RESULTS We established the first global map of regulatory elements (15 chromatin states) and defined their coordinated activities in cattle, through genome-wide profiling for six histone modifications, RNA polymerase II, CTCF-binding sites, DNA accessibility, DNA methylation, and transcriptome in rumen epithelial primary cells (REPC), rumen tissues, and Madin-Darby bovine kidney epithelial cells (MDBK). We demonstrated that each chromatin state exhibited specific enrichment for sequence ontology, transcription, methylation, trait-associated variants, gene expression-associated variants, selection signatures, and evolutionarily conserved elements, implying distinct biological functions. After butyrate treatments, we observed that the weak enhancers and flanking active transcriptional start sites (TSS) were the most dynamic chromatin states, occurred concomitantly with significant alterations in gene expression and DNA methylation, which was significantly associated with heifer conception rate and stature economic traits. CONCLUSION Our results demonstrate the crucial role of functional genome annotation for understanding genome regulation, complex trait variation, and adaptive evolution in livestock. Using butyrate to induce the dynamics of the epigenomic landscape, we were able to establish the correlation among nutritional elements, chromatin states, gene activities, and phenotypic outcomes.
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Affiliation(s)
- Lingzhao Fang
- Animal Genomics and Improvement Laboratory, BARC, Agricultural Research Service, USDA, Beltsville, MD 20705 USA
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20742 USA
| | - Shuli Liu
- Animal Genomics and Improvement Laboratory, BARC, Agricultural Research Service, USDA, Beltsville, MD 20705 USA
- College of Animal Science and Technology, China Agricultural University, Beijing, 100193 China
| | - Mei Liu
- Animal Genomics and Improvement Laboratory, BARC, Agricultural Research Service, USDA, Beltsville, MD 20705 USA
- College of Animal Science and Technology, Shaanxi Key Laboratory of Agricultural Molecular Biology, Northwest A&F University, Yangling, 712100 Shaanxi China
| | - Xiaolong Kang
- Animal Genomics and Improvement Laboratory, BARC, Agricultural Research Service, USDA, Beltsville, MD 20705 USA
- College of Agriculture, Ningxia University, Yinchuan, 750021 China
| | - Shudai Lin
- Animal Genomics and Improvement Laboratory, BARC, Agricultural Research Service, USDA, Beltsville, MD 20705 USA
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science of South China Agricultural University, Guangzhou, 510642 China
| | - Bingjie Li
- Animal Genomics and Improvement Laboratory, BARC, Agricultural Research Service, USDA, Beltsville, MD 20705 USA
| | - Erin E. Connor
- Animal Genomics and Improvement Laboratory, BARC, Agricultural Research Service, USDA, Beltsville, MD 20705 USA
| | - Ransom L. Baldwin
- Animal Genomics and Improvement Laboratory, BARC, Agricultural Research Service, USDA, Beltsville, MD 20705 USA
| | - Albert Tenesa
- The Roslin Institute, University of Edinburgh, Edinburgh, EH4 2XU UK
| | - Li Ma
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20742 USA
| | - George E. Liu
- Animal Genomics and Improvement Laboratory, BARC, Agricultural Research Service, USDA, Beltsville, MD 20705 USA
| | - Cong-jun Li
- Animal Genomics and Improvement Laboratory, BARC, Agricultural Research Service, USDA, Beltsville, MD 20705 USA
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21
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Fang L, Zhou Y, Liu S, Jiang J, Bickhart DM, Null DJ, Li B, Schroeder SG, Rosen BD, Cole JB, Van Tassell CP, Ma L, Liu GE. Integrating Signals from Sperm Methylome Analysis and Genome-Wide Association Study for a Better Understanding of Male Fertility in Cattle. EPIGENOMES 2019; 3:epigenomes3020010. [PMID: 34968233 PMCID: PMC8594688 DOI: 10.3390/epigenomes3020010] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Revised: 05/03/2019] [Accepted: 05/11/2019] [Indexed: 01/18/2023] Open
Abstract
Decreased male fertility is a big concern in both human society and the livestock industry. Sperm DNA methylation is commonly believed to be associated with male fertility. However, due to the lack of accurate male fertility records (i.e., limited mating times), few studies have investigated the comprehensive impacts of sperm DNA methylation on male fertility in mammals. In this study, we generated 10 sperm DNA methylomes and performed a preliminary correlation analysis between signals from sperm DNA methylation and signals from large-scale (n = 27,214) genome-wide association studies (GWAS) of 35 complex traits (including 12 male fertility-related traits). We detected genomic regions, which experienced DNA methylation alterations in sperm and were associated with aging and extreme fertility phenotypes (e.g., sire-conception rate or SCR). In dynamic hypomethylated regions (HMRs) and partially methylated domains (PMDs), we found genes (e.g., HOX gene clusters and microRNAs) that were involved in the embryonic development. We demonstrated that genomic regions, which gained rather than lost methylations during aging, and in animals with low SCR were significantly and selectively enriched for GWAS signals of male fertility traits. Our study discovered 16 genes as the potential candidate markers for male fertility, including SAMD5 and PDE5A. Collectively, this initial effort supported a hypothesis that sperm DNA methylation may contribute to male fertility in cattle and revealed the usefulness of functional annotations in enhancing biological interpretation and genomic prediction for complex traits and diseases.
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Affiliation(s)
- Lingzhao Fang
- Animal Genomics and Improvement Laboratory, BARC, Agricultural Research Service, USDA, Beltsville, MD 20705, USA
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20742, USA
| | - Yang Zhou
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Education Ministry of China, Huazhong Agricultural University, Wuhan 430070, China
| | - Shuli Liu
- Animal Genomics and Improvement Laboratory, BARC, Agricultural Research Service, USDA, Beltsville, MD 20705, USA
- 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
| | - Jicai Jiang
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20742, USA
| | - Derek M. Bickhart
- Dairy Forage Research Center, Agricultural Research Service, USDA, Madison, WI 53718, USA
| | - Daniel J. Null
- Animal Genomics and Improvement Laboratory, BARC, Agricultural Research Service, USDA, Beltsville, MD 20705, USA
| | - Bingjie Li
- Animal Genomics and Improvement Laboratory, BARC, Agricultural Research Service, USDA, Beltsville, MD 20705, USA
| | - Steven G. Schroeder
- Animal Genomics and Improvement Laboratory, BARC, Agricultural Research Service, USDA, Beltsville, MD 20705, USA
| | - Benjamin D. Rosen
- Animal Genomics and Improvement Laboratory, BARC, Agricultural Research Service, USDA, Beltsville, MD 20705, USA
| | - John B. Cole
- Animal Genomics and Improvement Laboratory, BARC, Agricultural Research Service, USDA, Beltsville, MD 20705, USA
| | - Curtis P. Van Tassell
- Animal Genomics and Improvement Laboratory, BARC, Agricultural Research Service, USDA, Beltsville, MD 20705, USA
| | - Li Ma
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20742, USA
- Correspondence: (L.M.); (G.E.L.); Tel.: +1-301-405-1389 (L.M.); +1-301-504-9843 (G.E.L.); Fax: +1-301-504-8414 (G.E.L.)
| | - George E. Liu
- Animal Genomics and Improvement Laboratory, BARC, Agricultural Research Service, USDA, Beltsville, MD 20705, USA
- Correspondence: (L.M.); (G.E.L.); Tel.: +1-301-405-1389 (L.M.); +1-301-504-9843 (G.E.L.); Fax: +1-301-504-8414 (G.E.L.)
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22
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Fang L, Zhou Y, Liu S, Jiang J, Bickhart DM, Null DJ, Li B, Schroeder SG, Rosen BD, Cole JB, Van Tassell CP, Ma L, Liu GE. Comparative analyses of sperm DNA methylomes among human, mouse and cattle provide insights into epigenomic evolution and complex traits. Epigenetics 2019; 14:260-276. [PMID: 30810461 PMCID: PMC6557555 DOI: 10.1080/15592294.2019.1582217] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Sperm DNA methylation is crucial for fertility and viability of offspring but epigenome evolution in mammals is largely understudied. By comparing sperm DNA methylomes and large-scale genome-wide association study (GWAS) signals between human and cattle, we aimed to examine the DNA methylome evolution and its associations with complex phenotypes in mammals. Our analysis revealed that genes with conserved non-methylated promoters (e.g., ANKS1A and WNT7A) among human and cattle were involved in common system and embryo development, and enriched for GWAS signals of body conformation traits in both species, while genes with conserved hypermethylated promoters (e.g., TCAP and CD80) were engaged in immune responses and highlighted by immune-related traits. On the other hand, genes with human-specific hypomethylated promoters (e.g., FOXP2 and HYDIN) were engaged in neuron system development and enriched for GWAS signals of brain-related traits, while genes with cattle-specific hypomethylated promoters (e.g., LDHB and DGAT2) mainly participated in lipid storage and metabolism. We validated our findings using sperm-retained nucleosome, preimplantation transcriptome, and adult tissue transcriptome data, as well as sequence evolutionary features, including motif binding sites, mutation rates, recombination rates and evolution signatures. In conclusion, our results demonstrate important roles of epigenome evolution in shaping the genetic architecture underlying complex phenotypes, hence enhance signal prioritization in GWAS and provide valuable information for human neurological disorders and livestock genetic improvement.
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Affiliation(s)
- Lingzhao Fang
- a Animal Genomics and Improvement Laboratory, BARC , Agricultural Research Service, USDA , Beltsville , MD , USA.,b Department of Animal and Avian Sciences , University of Maryland , College Park , MD , USA
| | - Yang Zhou
- c Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Education Ministry of China , Huazhong Agricultural University , Wuhan , Hubei , China
| | - Shuli Liu
- a Animal Genomics and Improvement Laboratory, BARC , Agricultural Research Service, USDA , Beltsville , MD , USA.,d 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 , China
| | - Jicai Jiang
- b Department of Animal and Avian Sciences , University of Maryland , College Park , MD , USA
| | - Derek M Bickhart
- e Dairy Forage Research Center , Agricultural Research Service, USDA , Madison , WI , USA
| | - Daniel J Null
- a Animal Genomics and Improvement Laboratory, BARC , Agricultural Research Service, USDA , Beltsville , MD , USA
| | - Bingjie Li
- a Animal Genomics and Improvement Laboratory, BARC , Agricultural Research Service, USDA , Beltsville , MD , USA
| | - Steven G Schroeder
- a Animal Genomics and Improvement Laboratory, BARC , Agricultural Research Service, USDA , Beltsville , MD , USA
| | - Benjamin D Rosen
- a Animal Genomics and Improvement Laboratory, BARC , Agricultural Research Service, USDA , Beltsville , MD , USA
| | - John B Cole
- a Animal Genomics and Improvement Laboratory, BARC , Agricultural Research Service, USDA , Beltsville , MD , USA
| | - Curtis P Van Tassell
- a Animal Genomics and Improvement Laboratory, BARC , Agricultural Research Service, USDA , Beltsville , MD , USA
| | - Li Ma
- b Department of Animal and Avian Sciences , University of Maryland , College Park , MD , USA
| | - George E Liu
- a Animal Genomics and Improvement Laboratory, BARC , Agricultural Research Service, USDA , Beltsville , MD , USA
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23
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Fang L, Jiang J, Li B, Zhou Y, Freebern E, Vanraden PM, Cole JB, Liu GE, Ma L. Genetic and epigenetic architecture of paternal origin contribute to gestation length in cattle. Commun Biol 2019; 2:100. [PMID: 30886909 PMCID: PMC6418173 DOI: 10.1038/s42003-019-0341-6] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Accepted: 02/06/2019] [Indexed: 12/19/2022] Open
Abstract
The length of gestation can affect offspring health and performance. Both maternal and fetal effects contribute to gestation length; however, paternal contributions to gestation length remain elusive. Using genome-wide association study (GWAS) in 27,214 Holstein bulls with millions of gestation records, here we identify nine paternal genomic loci associated with cattle gestation length. We demonstrate that these GWAS signals are enriched in pathways relevant to embryonic development, and in differentially methylated regions between sperm samples with long and short gestation length. We reveal that gestation length shares genetic and epigenetic architecture in sperm with calving ability, body depth, and conception rate. While several candidate genes are detected in our fine-mapping analysis, we provide evidence indicating ZNF613 as a promising candidate for cattle gestation length. Collectively, our findings support that the paternal genome and epigenome can impact gestation length potentially through regulation of the embryonic development. Lingzhao Fang et al. studied the paternal genetic variants that affect gestational length in cattle. They found that paternal genes from pathways involved in embryonic development were associated with gestation length, and that these were often found in differentially methylated regions of the genome.
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Affiliation(s)
- Lingzhao Fang
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD, 20742, USA.,Animal Genomics and Improvement Laboratory, BARC, Agricultural Research Service, USDA, Beltsville, MD, 20705, USA
| | - Jicai Jiang
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD, 20742, USA
| | - Bingjie Li
- Animal Genomics and Improvement Laboratory, BARC, Agricultural Research Service, USDA, Beltsville, MD, 20705, USA
| | - Yang Zhou
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Education Ministry of China, Huazhong Agricultural University, 430070, Wuhan, Hubei, China
| | - Ellen Freebern
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD, 20742, USA
| | - Paul M Vanraden
- Animal Genomics and Improvement Laboratory, BARC, Agricultural Research Service, USDA, Beltsville, MD, 20705, USA
| | - John B Cole
- Animal Genomics and Improvement Laboratory, BARC, Agricultural Research Service, USDA, Beltsville, MD, 20705, USA
| | - George E Liu
- Animal Genomics and Improvement Laboratory, BARC, Agricultural Research Service, USDA, Beltsville, MD, 20705, USA
| | - Li Ma
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD, 20742, USA.
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MicroRNA-guided prioritization of genome-wide association signals reveals the importance of microRNA-target gene networks for complex traits in cattle. Sci Rep 2018; 8:9345. [PMID: 29921979 PMCID: PMC6008395 DOI: 10.1038/s41598-018-27729-y] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2018] [Accepted: 06/06/2018] [Indexed: 02/07/2023] Open
Abstract
MicroRNAs (miRNA) are key modulators of gene expression and so act as putative fine-tuners of complex phenotypes. Here, we hypothesized that causal variants of complex traits are enriched in miRNAs and miRNA-target networks. First, we conducted a genome-wide association study (GWAS) for seven functional and milk production traits using imputed sequence variants (13~15 million) and >10,000 animals from three dairy cattle breeds, i.e., Holstein (HOL), Nordic red cattle (RDC) and Jersey (JER). Second, we analyzed for enrichments of association signals in miRNAs and their miRNA-target networks. Our results demonstrated that genomic regions harboring miRNA genes were significantly (P < 0.05) enriched with GWAS signals for milk production traits and mastitis, and that enrichments within miRNA-target gene networks were significantly higher than in random gene-sets for the majority of traits. Furthermore, most between-trait and across-breed correlations of enrichments with miRNA-target networks were significantly greater than with random gene-sets, suggesting pleiotropic effects of miRNAs. Intriguingly, genes that were differentially expressed in response to mammary gland infections were significantly enriched in the miRNA-target networks associated with mastitis. All these findings were consistent across three breeds. Collectively, our observations demonstrate the importance of miRNAs and their targets for the expression of complex traits.
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25
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Rohde PD, Østergaard S, Kristensen TN, Sørensen P, Loeschcke V, Mackay TFC, Sarup P. Functional Validation of Candidate Genes Detected by Genomic Feature Models. G3 (BETHESDA, MD.) 2018; 8:1659-1668. [PMID: 29519937 PMCID: PMC5940157 DOI: 10.1534/g3.118.200082] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2018] [Accepted: 03/07/2018] [Indexed: 12/11/2022]
Abstract
Understanding the genetic underpinnings of complex traits requires knowledge of the genetic variants that contribute to phenotypic variability. Reliable statistical approaches are needed to obtain such knowledge. In genome-wide association studies, variants are tested for association with trait variability to pinpoint loci that contribute to the quantitative trait. Because stringent genome-wide significance thresholds are applied to control the false positive rate, many true causal variants can remain undetected. To ameliorate this problem, many alternative approaches have been developed, such as genomic feature models (GFM). The GFM approach tests for association of set of genomic markers, and predicts genomic values from genomic data utilizing prior biological knowledge. We investigated to what degree the findings from GFM have biological relevance. We used the Drosophila Genetic Reference Panel to investigate locomotor activity, and applied genomic feature prediction models to identify gene ontology (GO) categories predictive of this phenotype. Next, we applied the covariance association test to partition the genomic variance of the predictive GO terms to the genes within these terms. We then functionally assessed whether the identified candidate genes affected locomotor activity by reducing gene expression using RNA interference. In five of the seven candidate genes tested, reduced gene expression altered the phenotype. The ranking of genes within the predictive GO term was highly correlated with the magnitude of the phenotypic consequence of gene knockdown. This study provides evidence for five new candidate genes for locomotor activity, and provides support for the reliability of the GFM approach.
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Affiliation(s)
- Palle Duun Rohde
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8000 Aarhus, Denmark
- Center for Integrative Sequencing, Aarhus University, 8000 Aarhus, Denmark
| | - Solveig Østergaard
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark
| | - Torsten Nygaard Kristensen
- Section for Genetics, Ecology and Evolution, Department of Bioscience, Aarhus University, 8000 Aarhus, Denmark
- Section for Biology and Environmental Science, Department of Chemistry and Bioscience, Aalborg University, 9220 Aalborg, Denmark
| | - Peter Sørensen
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark
| | - Volker Loeschcke
- Section for Genetics, Ecology and Evolution, Department of Bioscience, Aarhus University, 8000 Aarhus, Denmark
| | - Trudy F C Mackay
- Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27695
- Program in Genetics, North Carolina State University, Raleigh, North Carolina 27695
- W. M. Keck Center for Behavioral Biology, North Carolina State University, Raleigh, North Carolina 27695
| | - Pernille Sarup
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark
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Ørsted M, Rohde PD, Hoffmann AA, Sørensen P, Kristensen TN. Environmental variation partitioned into separate heritable components. Evolution 2017; 72:136-152. [DOI: 10.1111/evo.13391] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2017] [Revised: 10/30/2017] [Accepted: 10/31/2017] [Indexed: 12/16/2022]
Affiliation(s)
- Michael Ørsted
- Section of Biology and Environmental Science, Department of Chemistry and Bioscience; Aalborg University; Fredrik Bajers Vej 7H 9220 Aalborg E Denmark
- School of Biosciences, Bio21 Molecular Science and Biotechnology Institute; The University of Melbourne; Parkville Victoria 3052 Australia
| | - Palle Duun Rohde
- Center for Quantitative Genetics and Genomics; Department of Molecular Biology and Genetics; Aarhus University; Blichers Allé 20 8830 Tjele Denmark
- i PSYCH; The Lundbeck Foundation Initiative for Integrative Psychiatric Research; 8000 Aarhus C Denmark
- i SEQ, Center for Integrative Sequencing; Aarhus University; Bartholins Allé 6 8000 Aarhus C Denmark
| | - Ary Anthony Hoffmann
- Section of Biology and Environmental Science, Department of Chemistry and Bioscience; Aalborg University; Fredrik Bajers Vej 7H 9220 Aalborg E Denmark
- School of Biosciences, Bio21 Molecular Science and Biotechnology Institute; The University of Melbourne; Parkville Victoria 3052 Australia
| | - Peter Sørensen
- Center for Quantitative Genetics and Genomics; Department of Molecular Biology and Genetics; Aarhus University; Blichers Allé 20 8830 Tjele Denmark
| | - Torsten Nygaard Kristensen
- Section of Biology and Environmental Science, Department of Chemistry and Bioscience; Aalborg University; Fredrik Bajers Vej 7H 9220 Aalborg E Denmark
- Section of Genetics, Ecology and Evolution, Department of Bioscience; Aarhus University; 8000 Aarhus C Denmark
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Fang L, Sahana G, Ma P, Su G, Yu Y, Zhang S, Lund MS, Sørensen P. Use of biological priors enhances understanding of genetic architecture and genomic prediction of complex traits within and between dairy cattle breeds. BMC Genomics 2017; 18:604. [PMID: 28797230 PMCID: PMC5553760 DOI: 10.1186/s12864-017-4004-z] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2016] [Accepted: 08/02/2017] [Indexed: 02/08/2023] Open
Abstract
Background A better understanding of the genetic architecture underlying complex traits (e.g., the distribution of causal variants and their effects) may aid in the genomic prediction. Here, we hypothesized that the genomic variants of complex traits might be enriched in a subset of genomic regions defined by genes grouped on the basis of “Gene Ontology” (GO), and that incorporating this independent biological information into genomic prediction models might improve their predictive ability. Results Four complex traits (i.e., milk, fat and protein yields, and mastitis) together with imputed sequence variants in Holstein (HOL) and Jersey (JER) cattle were analysed. We first carried out a post-GWAS analysis in a HOL training population to assess the degree of enrichment of the association signals in the gene regions defined by each GO term. We then extended the genomic best linear unbiased prediction model (GBLUP) to a genomic feature BLUP (GFBLUP) model, including an additional genomic effect quantifying the joint effect of a group of variants located in a genomic feature. The GBLUP model using a single random effect assumes that all genomic variants contribute to the genomic relationship equally, whereas GFBLUP attributes different weights to the individual genomic relationships in the prediction equation based on the estimated genomic parameters. Our results demonstrate that the immune-relevant GO terms were more associated with mastitis than milk production, and several biologically meaningful GO terms improved the prediction accuracy with GFBLUP for the four traits, as compared with GBLUP. The improvement of the genomic prediction between breeds (the average increase across the four traits was 0.161) was more apparent than that it was within the HOL (the average increase across the four traits was 0.020). Conclusions Our genomic feature modelling approaches provide a framework to simultaneously explore the genetic architecture and genomic prediction of complex traits by taking advantage of independent biological knowledge. Electronic supplementary material The online version of this article (doi:10.1186/s12864-017-4004-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Lingzhao Fang
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark. .,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.
| | - Goutam Sahana
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark
| | - Peipei Ma
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark
| | - Guosheng Su
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark
| | - 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
| | - Shengli 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
| | - Mogens Sandø Lund
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark
| | - Peter Sørensen
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark
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Rohde PD, Gaertner B, Ward K, Sørensen P, Mackay TFC. Genomic Analysis of Genotype-by-Social Environment Interaction for Drosophila melanogaster Aggressive Behavior. Genetics 2017; 206:1969-1984. [PMID: 28550016 PMCID: PMC5560801 DOI: 10.1534/genetics.117.200642] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2017] [Accepted: 05/22/2017] [Indexed: 02/06/2023] Open
Abstract
Human psychiatric disorders such as schizophrenia, bipolar disorder, and attention-deficit/hyperactivity disorder often include adverse behaviors including increased aggressiveness. Individuals with psychiatric disorders often exhibit social withdrawal, which can further increase the probability of conducting a violent act. Here, we used the inbred, sequenced lines of the Drosophila Genetic Reference Panel (DGRP) to investigate the genetic basis of variation in male aggressive behavior for flies reared in a socialized and socially isolated environment. We identified genetic variation for aggressive behavior, as well as significant genotype-by-social environmental interaction (GSEI); i.e., variation among DGRP genotypes in the degree to which social isolation affected aggression. We performed genome-wide association (GWA) analyses to identify genetic variants associated with aggression within each environment. We used genomic prediction to partition genetic variants into gene ontology (GO) terms and constituent genes, and identified GO terms and genes with high prediction accuracies in both social environments and for GSEI. The top predictive GO terms significantly increased the proportion of variance explained, compared to prediction models based on all segregating variants. We performed genomic prediction across environments, and identified genes in common between the social environments that turned out to be enriched for genome-wide associated variants. A large proportion of the associated genes have previously been associated with aggressive behavior in Drosophila and mice. Further, many of these genes have human orthologs that have been associated with neurological disorders, indicating partially shared genetic mechanisms underlying aggression in animal models and human psychiatric disorders.
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Affiliation(s)
- Palle Duun Rohde
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, 8000 Aarhus, Denmark
- ISEQ, Center for Integrative Sequencing, Aarhus University, 8000 Aarhus, Denmark
| | - Bryn Gaertner
- Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27695
- Program in Genetics, North Carolina State University, Raleigh, North Carolina 27695
- W.M. Keck Center for Behavioral Biology, North Carolina State University, Raleigh, North Carolina 27695
| | - Kirsty Ward
- Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27695
- Program in Genetics, North Carolina State University, Raleigh, North Carolina 27695
- W.M. Keck Center for Behavioral Biology, North Carolina State University, Raleigh, North Carolina 27695
| | - Peter Sørensen
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark
| | - Trudy F C Mackay
- Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27695
- Program in Genetics, North Carolina State University, Raleigh, North Carolina 27695
- W.M. Keck Center for Behavioral Biology, North Carolina State University, Raleigh, North Carolina 27695
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29
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Sørensen IF, Edwards SM, Rohde PD, Sørensen P. Multiple Trait Covariance Association Test Identifies Gene Ontology Categories Associated with Chill Coma Recovery Time in Drosophila melanogaster. Sci Rep 2017; 7:2413. [PMID: 28546557 PMCID: PMC5445101 DOI: 10.1038/s41598-017-02281-3] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2017] [Accepted: 04/10/2017] [Indexed: 12/29/2022] Open
Abstract
The genomic best linear unbiased prediction (GBLUP) model has proven to be useful for prediction of complex traits as well as estimation of population genetic parameters. Improved inference and prediction accuracy of GBLUP may be achieved by identifying genomic regions enriched for causal genetic variants. We aimed at searching for patterns in GBLUP-derived single-marker statistics, by including them in genetic marker set tests, that could reveal associations between a set of genetic markers (genomic feature) and a complex trait. GBLUP-derived set tests proved to be powerful for detecting genomic features, here defined by gene ontology (GO) terms, enriched for causal variants affecting a quantitative trait in a population with low degree of relatedness. Different set test approaches were compared using simulated data illustrating the impact of trait- and genomic feature-specific factors on detection power. We extended the most powerful single trait set test, covariance association test (CVAT), to a multiple trait setting. The multiple trait CVAT (MT-CVAT) identified functionally relevant GO categories associated with the quantitative trait, chill coma recovery time, in the unrelated, sequenced inbred lines of the Drosophila melanogaster Genetic Reference Panel.
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Affiliation(s)
- Izel Fourie Sørensen
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830, Tjele, Denmark.
| | - Stefan M Edwards
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830, Tjele, Denmark.,The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush, Midlothian, Scotland, UK
| | - Palle Duun Rohde
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830, Tjele, Denmark.,Centre for Integrative Sequencing, iSEQ, Aarhus University, 8000, Aarhus, Denmark.,iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, 8000, Aarhus, Denmark
| | - Peter Sørensen
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830, Tjele, Denmark
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30
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Fang L, Sahana G, Ma P, Su G, Yu Y, Zhang S, Lund MS, Sørensen P. Exploring the genetic architecture and improving genomic prediction accuracy for mastitis and milk production traits in dairy cattle by mapping variants to hepatic transcriptomic regions responsive to intra-mammary infection. Genet Sel Evol 2017; 49:44. [PMID: 28499345 PMCID: PMC5427631 DOI: 10.1186/s12711-017-0319-0] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2016] [Accepted: 05/03/2017] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND A better understanding of the genetic architecture of complex traits can contribute to improve genomic prediction. We hypothesized that genomic variants associated with mastitis and milk production traits in dairy cattle are enriched in hepatic transcriptomic regions that are responsive to intra-mammary infection (IMI). Genomic markers [e.g. single nucleotide polymorphisms (SNPs)] from those regions, if included, may improve the predictive ability of a genomic model. RESULTS We applied a genomic feature best linear unbiased prediction model (GFBLUP) to implement the above strategy by considering the hepatic transcriptomic regions responsive to IMI as genomic features. GFBLUP, an extension of GBLUP, includes a separate genomic effect of SNPs within a genomic feature, and allows differential weighting of the individual marker relationships in the prediction equation. Since GFBLUP is computationally intensive, we investigated whether a SNP set test could be a computationally fast way to preselect predictive genomic features. The SNP set test assesses the association between a genomic feature and a trait based on single-SNP genome-wide association studies. We applied these two approaches to mastitis and milk production traits (milk, fat and protein yield) in Holstein (HOL, n = 5056) and Jersey (JER, n = 1231) cattle. We observed that a majority of genomic features were enriched in genomic variants that were associated with mastitis and milk production traits. Compared to GBLUP, the accuracy of genomic prediction with GFBLUP was marginally improved (3.2 to 3.9%) in within-breed prediction. The highest increase (164.4%) in prediction accuracy was observed in across-breed prediction. The significance of genomic features based on the SNP set test were correlated with changes in prediction accuracy of GFBLUP (P < 0.05). CONCLUSIONS GFBLUP provides a framework for integrating multiple layers of biological knowledge to provide novel insights into the biological basis of complex traits, and to improve the accuracy of genomic prediction. The SNP set test might be used as a first-step to improve GFBLUP models. Approaches like GFBLUP and SNP set test will become increasingly useful, as the functional annotations of genomes keep accumulating for a range of species and traits.
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Affiliation(s)
- Lingzhao Fang
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark. .,Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China.
| | - Goutam Sahana
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark
| | - Peipei Ma
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark
| | - Guosheng Su
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark
| | - Ying Yu
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Shengli Zhang
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Mogens Sandø Lund
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark
| | - Peter Sørensen
- Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark
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Fang L, Sahana G, Su G, Yu Y, Zhang S, Lund MS, Sørensen P. Integrating Sequence-based GWAS and RNA-Seq Provides Novel Insights into the Genetic Basis of Mastitis and Milk Production in Dairy Cattle. Sci Rep 2017; 7:45560. [PMID: 28358110 PMCID: PMC5372096 DOI: 10.1038/srep45560] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2016] [Accepted: 02/28/2017] [Indexed: 02/06/2023] Open
Abstract
Connecting genome-wide association study (GWAS) to biological mechanisms underlying complex traits is a major challenge. Mastitis resistance and milk production are complex traits of economic importance in the dairy sector and are associated with intra-mammary infection (IMI). Here, we integrated IMI-relevant RNA-Seq data from Holstein cattle and sequence-based GWAS data from three dairy cattle breeds (i.e., Holstein, Nordic red cattle, and Jersey) to explore the genetic basis of mastitis resistance and milk production using post-GWAS analyses and a genomic feature linear mixed model. At 24 h post-IMI, genes responsive to IMI in the mammary gland were preferentially enriched for genetic variants associated with mastitis resistance rather than milk production. Response genes in the liver were mainly enriched for variants associated with mastitis resistance at an early time point (3 h) post-IMI, whereas responsive genes at later stages were enriched for associated variants with milk production. The up- and down-regulated genes were enriched for associated variants with mastitis resistance and milk production, respectively. The patterns were consistent across breeds, indicating that different breeds shared similarities in the genetic basis of these traits. Our approaches provide a framework for integrating multiple layers of data to understand the genetic architecture underlying complex traits.
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Affiliation(s)
- Lingzhao Fang
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark.,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, 100193, Beijing, China
| | - Goutam Sahana
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark
| | - Guosheng Su
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark
| | - 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, 100193, Beijing, China
| | - Shengli 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, 100193, Beijing, China
| | - Mogens Sandø Lund
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark
| | - Peter Sørensen
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, 8830 Tjele, Denmark
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