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Cai Z, Iso-Touru T, Sanchez MP, Kadri N, Bouwman AC, Chitneedi PK, MacLeod IM, Vander Jagt CJ, Chamberlain AJ, Gredler-Grandl B, Spengeler M, Lund MS, Boichard D, Kühn C, Pausch H, Vilkki J, Sahana G. Meta-analysis of six dairy cattle breeds reveals biologically relevant candidate genes for mastitis resistance. Genet Sel Evol 2024; 56:54. [PMID: 39009986 PMCID: PMC11247842 DOI: 10.1186/s12711-024-00920-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Accepted: 06/26/2024] [Indexed: 07/17/2024] Open
Abstract
BACKGROUND Mastitis is a disease that incurs significant costs in the dairy industry. A promising approach to mitigate its negative effects is to genetically improve the resistance of dairy cattle to mastitis. A meta-analysis of genome-wide association studies (GWAS) across multiple breeds for clinical mastitis (CM) and its indicator trait, somatic cell score (SCS), is a powerful method to identify functional genetic variants that impact mastitis resistance. RESULTS We conducted meta-analyses of eight and fourteen GWAS on CM and SCS, respectively, using 30,689 and 119,438 animals from six dairy cattle breeds. Methods for the meta-analyses were selected to properly account for the multi-breed structure of the GWAS data. Our study revealed 58 lead markers that were associated with mastitis incidence, including 16 loci that did not overlap with previously identified quantitative trait loci (QTL), as curated at the Animal QTLdb. Post-GWAS analysis techniques such as gene-based analysis and genomic feature enrichment analysis enabled prioritization of 31 candidate genes and 14 credible candidate causal variants that affect mastitis. CONCLUSIONS Our list of candidate genes can help to elucidate the genetic architecture underlying mastitis resistance and provide better tools for the prevention or treatment of mastitis, ultimately contributing to more sustainable animal production.
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Affiliation(s)
- Zexi Cai
- Center for Quantitative Genetics and Genomics, Aarhus University, 8000, Aarhus, Denmark.
| | - Terhi Iso-Touru
- Natural Resources Institute Finland (Luke), 31600, Jokioinen, Finland
| | - Marie-Pierre Sanchez
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France
| | - Naveen Kadri
- Animal Genomics, ETH Zurich, 8092, Zurich, Switzerland
| | - Aniek C Bouwman
- Wageningen University and Research, Animal Breeding and Genomics, P.O. Box 338, 6700, AH, Wageningen, The Netherlands
| | - Praveen Krishna Chitneedi
- Institute of Genome Biology, Research Institute for Farm Animal Biology (FBN), 18196, Dummerstorf, Germany
| | - Iona M MacLeod
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, 3083, Australia
| | | | - Amanda J Chamberlain
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC, Australia
| | - Birgit Gredler-Grandl
- Wageningen University and Research, Animal Breeding and Genomics, P.O. Box 338, 6700, AH, Wageningen, The Netherlands
| | | | - Mogens Sandø Lund
- Center for Quantitative Genetics and Genomics, Aarhus University, 8000, Aarhus, Denmark
| | - Didier Boichard
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France
| | - Christa Kühn
- Institute of Genome Biology, Research Institute for Farm Animal Biology (FBN), 18196, Dummerstorf, Germany
- Agricultural and Environmental Faculty, University Rostock, 18059, Rostock, Germany
| | - Hubert Pausch
- Animal Genomics, ETH Zurich, 8092, Zurich, Switzerland
| | - Johanna Vilkki
- Natural Resources Institute Finland (Luke), 31600, Jokioinen, Finland
| | - Goutam Sahana
- Center for Quantitative Genetics and Genomics, Aarhus University, 8000, Aarhus, Denmark
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Ooi E, Xiang R, Chamberlain AJ, Goddard ME. Archetypal clustering reveals physiological mechanisms linking milk yield and fertility in dairy cattle. J Dairy Sci 2024; 107:4726-4742. [PMID: 38369117 DOI: 10.3168/jds.2023-23699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 01/11/2024] [Indexed: 02/20/2024]
Abstract
Fertility in dairy cattle has declined as an unintended consequence of single-trait selection for high milk yield. The unfavorable genetic correlation between milk yield and fertility is now well documented; however, the underlying physiological mechanisms are still uncertain. To understand the relationship between these traits, we developed a method that clusters variants with similar patterns of effects and, after the integration of gene expression data, identifies the genes through which they are likely to act. Biological processes that are enriched in the genes of each cluster were then identified. We identified several clusters with unique patterns of effects. One of the clusters included variants associated with increased milk yield and decreased fertility, where the "archetypal" variant (i.e., the one with the largest effect) was associated with the GC gene, whereas others were associated with TRIM32, LRRK2, and U6-associated snRNA. These genes have been linked to transcription and alternative splicing, suggesting that these processes are likely contributors to the unfavorable relationship between the 2 traits. Another cluster, with archetypal variant near DGAT1 and including variants associated with CDH2, BTRC, SFRP2, ZFHX3, and SLITRK5, appeared to affect milk yield but have little effect on fertility. These genes have been linked to insulin, adipose tissue, and energy metabolism. A third cluster with archetypal variant near ZNF613 and including variants associated with ROBO1, EFNA5, PALLD, GPC6, and PTPRT were associated with fertility but not milk yield. These genes have been linked to GnRH neuronal migration, embryonic development, or ovarian function. The use of archetypal clustering to group variants with similar patterns of effects may assist in identifying the biological processes underlying correlated traits. The method is hypothesis generating and requires experimental confirmation. However, we have uncovered several novel mechanisms potentially affecting milk production and fertility such as GnRH neuronal migration. We anticipate our method to be a starting point for experimental research into novel pathways, which have been previously unexplored within the context of dairy production.
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Affiliation(s)
- E Ooi
- Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, Victoria 3010, Australia; Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, Victoria 3083, Australia.
| | - R Xiang
- Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, Victoria 3010, Australia; Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, Victoria 3083, Australia
| | - A J Chamberlain
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, Victoria 3083, Australia; School of Applied Systems Biology, La Trobe University, Bundoora, Victoria 3083, Australia
| | - M E Goddard
- Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, Victoria 3010, Australia; Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, Victoria 3083, Australia
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3
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Thangaraj SV, Ghnenis A, Pallas B, Vyas AK, Gregg B, Padmanabhan V. Comparative lipidome study of maternal plasma, milk, and lamb plasma in sheep. Sci Rep 2024; 14:7401. [PMID: 38548847 PMCID: PMC10978966 DOI: 10.1038/s41598-024-58116-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 03/25/2024] [Indexed: 04/01/2024] Open
Abstract
Lipids play a critical role in neonate development and breastmilk is the newborn's major source of lipids. Milk lipids directly influence the neonate plasma lipid profile. The milk lipidome is dynamic, influenced by maternal factors and related to the maternal plasma lipidome. The close inter-relationship between the maternal plasma, milk and neonate plasma lipidomes is critical to understanding maternal-child health and nutrition. In this exploratory study, lipidomes of blood and breast milk from Suffolk sheep and matched lamb blood (n = 13), were profiled on day 34 post birth by untargeted mass spectrometry. Comparative multivariate analysis of the three matrices identified distinct differences in lipids and class of lipids amongst them. Paired analysis identified 346 differential lipids (DL) and 31 correlated lipids (CL) in maternal plasma and milk, 340 DL and 32 CL in lamb plasma and milk and 295 DL and 16 CL in maternal plasma and lamb plasma. Conversion of phosphatidic acid to phosphatidyl inositol was the most active pathway in lamb plasma compared to maternal plasma. This exploratory study illustrates the partitioning of lipids across maternal plasma, milk and lamb plasma and the dynamic relationship between them, reiterating the need to study these three matrices as one biological system.
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Affiliation(s)
- Soundara Viveka Thangaraj
- Department of Pediatrics, University of Michigan, 7510 MSRB 1, 1500 W. Medical Center Drive, Ann Arbor, MI, 48109, USA
| | - Adel Ghnenis
- Department of Pediatrics, University of Michigan, 7510 MSRB 1, 1500 W. Medical Center Drive, Ann Arbor, MI, 48109, USA
| | - Brooke Pallas
- Unit for Laboratory Animal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Arpita Kalla Vyas
- Department of Pediatrics, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Brigid Gregg
- Department of Pediatrics, University of Michigan, 7510 MSRB 1, 1500 W. Medical Center Drive, Ann Arbor, MI, 48109, USA
| | - Vasantha Padmanabhan
- Department of Pediatrics, University of Michigan, 7510 MSRB 1, 1500 W. Medical Center Drive, Ann Arbor, MI, 48109, USA.
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Lopdell TJ, Trevarton AJ, Moody J, Prowse-Wilkins C, Knowles S, Tiplady K, Chamberlain AJ, Goddard ME, Spelman RJ, Lehnert K, Snell RG, Davis SR, Littlejohn MD. A common regulatory haplotype doubles lactoferrin concentration in milk. Genet Sel Evol 2024; 56:22. [PMID: 38549172 PMCID: PMC11234695 DOI: 10.1186/s12711-024-00890-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Accepted: 03/12/2024] [Indexed: 04/02/2024] Open
Abstract
BACKGROUND Bovine lactoferrin (Lf) is an iron absorbing whey protein with antibacterial, antiviral, and antifungal activity. Lactoferrin is economically valuable and has an extremely variable concentration in milk, partly driven by environmental influences such as milking frequency, involution, or mastitis. A significant genetic influence has also been previously observed to regulate lactoferrin content in milk. Here, we conducted genetic mapping of lactoferrin protein concentration in conjunction with RNA-seq, ChIP-seq, and ATAC-seq data to pinpoint candidate causative variants that regulate lactoferrin concentrations in milk. RESULTS We identified a highly-significant lactoferrin protein quantitative trait locus (pQTL), as well as a cis lactotransferrin (LTF) expression QTL (cis-eQTL) mapping to the LTF locus. Using ChIP-seq and ATAC-seq datasets representing lactating mammary tissue samples, we also report a number of regions where the openness of chromatin is under genetic influence. Several of these also show highly significant QTL with genetic signatures similar to those highlighted through pQTL and eQTL analysis. By performing correlation analysis between these QTL, we revealed an ATAC-seq peak in the putative promotor region of LTF, that highlights a set of 115 high-frequency variants that are potentially responsible for these effects. One of the 115 variants (rs110000337), which maps within the ATAC-seq peak, was predicted to alter binding sites of transcription factors known to be involved in lactation-related pathways. CONCLUSIONS Here, we report a regulatory haplotype of 115 variants with conspicuously large impacts on milk lactoferrin concentration. These findings could enable the selection of animals for high-producing specialist herds.
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Affiliation(s)
- Thomas J Lopdell
- Research & Development, Livestock Improvement Corporation, Ruakura Road, Hamilton, New Zealand.
| | - Alexander J Trevarton
- School of Biological Sciences, University of Auckland, Private Bag 92019, Auckland, New Zealand
| | - Janelle Moody
- School of Biological Sciences, University of Auckland, Private Bag 92019, Auckland, New Zealand
| | - Claire Prowse-Wilkins
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC, Australia
- Faculty of Veterinarian and Agricultural Science, The University of Melbourne, Parkville, VIC, Australia
| | - Sarah Knowles
- Auckland War Memorial Museum, Victoria Street West, Auckland, New Zealand
| | - Kathryn Tiplady
- Research & Development, Livestock Improvement Corporation, Ruakura Road, Hamilton, New Zealand
| | - Amanda J Chamberlain
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC, Australia
| | - Michael E Goddard
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC, Australia
- Faculty of Veterinarian and Agricultural Science, The University of Melbourne, Parkville, VIC, Australia
| | - Richard J Spelman
- Research & Development, Livestock Improvement Corporation, Ruakura Road, Hamilton, New Zealand
| | - Klaus Lehnert
- School of Biological Sciences, University of Auckland, Private Bag 92019, Auckland, New Zealand
| | - Russell G Snell
- School of Biological Sciences, University of Auckland, Private Bag 92019, Auckland, New Zealand
| | - Stephen R Davis
- Research & Development, Livestock Improvement Corporation, Ruakura Road, Hamilton, New Zealand
| | - Mathew D Littlejohn
- Research & Development, Livestock Improvement Corporation, Ruakura Road, Hamilton, New Zealand
- AL Rae Centre for Genetics and Breeding, Massey University, Palmerston North, New Zealand
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Schneider H, Krizanac AM, Falker-Gieske C, Heise J, Tetens J, Thaller G, Bennewitz J. Genomic dissection of the correlation between milk yield and various health traits using functional and evolutionary information about imputed sequence variants of 34,497 German Holstein cows. BMC Genomics 2024; 25:265. [PMID: 38461236 PMCID: PMC11385139 DOI: 10.1186/s12864-024-10115-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 02/13/2024] [Indexed: 03/11/2024] Open
Abstract
BACKGROUND Over the last decades, it was subject of many studies to investigate the genomic connection of milk production and health traits in dairy cattle. Thereby, incorporating functional information in genomic analyses has been shown to improve the understanding of biological and molecular mechanisms shaping complex traits and the accuracies of genomic prediction, especially in small populations and across-breed settings. Still, little is known about the contribution of different functional and evolutionary genome partitioning subsets to milk production and dairy health. Thus, we performed a uni- and a bivariate analysis of milk yield (MY) and eight health traits using a set of ~34,497 German Holstein cows with 50K chip genotypes and ~17 million imputed sequence variants divided into 27 subsets depending on their functional and evolutionary annotation. In the bivariate analysis, eight trait-combinations were observed that contrasted MY with each health trait. Two genomic relationship matrices (GRM) were included, one consisting of the 50K chip variants and one consisting of each set of subset variants, to obtain subset heritabilities and genetic correlations. In addition, 50K chip heritabilities and genetic correlations were estimated applying merely the 50K GRM. RESULTS In general, 50K chip heritabilities were larger than the subset heritabilities. The largest heritabilities were found for MY, which was 0.4358 for the 50K and 0.2757 for the subset heritabilities. Whereas all 50K genetic correlations were negative, subset genetic correlations were both, positive and negative (ranging from -0.9324 between MY and mastitis to 0.6662 between MY and digital dermatitis). The subsets containing variants which were annotated as noncoding related, splice sites, untranslated regions, metabolic quantitative trait loci, and young variants ranked highest in terms of their contribution to the traits` genetic variance. We were able to show that linkage disequilibrium between subset variants and adjacent variants did not cause these subsets` high effect. CONCLUSION Our results confirm the connection of milk production and health traits in dairy cattle via the animals` metabolic state. In addition, they highlight the potential of including functional information in genomic analyses, which helps to dissect the extent and direction of the observed traits` connection in more detail.
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Affiliation(s)
- Helen Schneider
- Institute of Animal Science, University of Hohenheim, 70599, Stuttgart, Germany.
| | - Ana-Marija Krizanac
- Department of Animal Sciences, University of Göttingen, 37077, Göttingen, Germany
| | | | - Johannes Heise
- Vereinigte Informationssysteme Tierhaltung w.V. (VIT), 27283, Verden, Germany
| | - Jens Tetens
- Department of Animal Sciences, University of Göttingen, 37077, Göttingen, Germany
| | - Georg Thaller
- Institute of Animal Breeding and Husbandry, Christian-Albrechts University of Kiel, 24098, Kiel, Germany
| | - Jörn Bennewitz
- Institute of Animal Science, University of Hohenheim, 70599, Stuttgart, Germany
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6
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Meuwissen T, Eikje LS, Gjuvsland AB. GWABLUP: genome-wide association assisted best linear unbiased prediction of genetic values. Genet Sel Evol 2024; 56:17. [PMID: 38429665 PMCID: PMC11234632 DOI: 10.1186/s12711-024-00881-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 01/31/2024] [Indexed: 03/03/2024] Open
Abstract
BACKGROUND Since the very beginning of genomic selection, researchers investigated methods that improved upon SNP-BLUP (single nucleotide polymorphism best linear unbiased prediction). SNP-BLUP gives equal weight to all SNPs, whereas it is expected that many SNPs are not near causal variants and thus do not have substantial effects. A recent approach to remedy this is to use genome-wide association study (GWAS) findings and increase the weights of GWAS-top-SNPs in genomic predictions. Here, we employ a genome-wide approach to integrate GWAS results into genomic prediction, called GWABLUP. RESULTS GWABLUP consists of the following steps: (1) performing a GWAS in the training data which results in likelihood ratios; (2) smoothing the likelihood ratios over the SNPs; (3) combining the smoothed likelihood ratio with the prior probability of SNPs having non-zero effects, which yields the posterior probability of the SNPs; (4) calculating a weighted genomic relationship matrix using the posterior probabilities as weights; and (5) performing genomic prediction using the weighted genomic relationship matrix. Using high-density genotypes and milk, fat, protein and somatic cell count phenotypes on dairy cows, GWABLUP was compared to GBLUP, GBLUP (topSNPs) with extra weights for GWAS top-SNPs, and BayesGC, i.e. a Bayesian variable selection model. The GWAS resulted in six, five, four, and three genome-wide significant peaks for milk, fat and protein yield and somatic cell count, respectively. GWABLUP genomic predictions were 10, 6, 7 and 1% more reliable than those of GBLUP for milk, fat and protein yield and somatic cell count, respectively. It was also more reliable than GBLUP (topSNPs) for all four traits, and more reliable than BayesGC for three of the traits. Although GWABLUP showed a tendency towards inflation bias for three of the traits, this was not statistically significant. In a multitrait analysis, GWABLUP yielded the highest accuracy for two of the traits. However, for SCC, which was relatively unrelated to the yield traits, including yield trait GWAS-results reduced the reliability compared to a single trait analysis. CONCLUSIONS GWABLUP uses GWAS results to differentially weigh all the SNPs in a weighted GBLUP genomic prediction analysis. GWABLUP yielded up to 10% and 13% more reliable genomic predictions than GBLUP for single and multitrait analyses, respectively. Extension of GWABLUP to single-step analyses is straightforward.
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Affiliation(s)
- Theo Meuwissen
- Faculty of Life Sciences, Norwegian University of Life Sciences, 1432, Ås, Norway.
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Li T, Wan P, Lin Q, Wei C, Guo K, Li X, Lu Y, Zhang Z, Li J. Genome-Wide Association Study Meta-Analysis Elucidates Genetic Structure and Identifies Candidate Genes of Teat Number Traits in Pigs. Int J Mol Sci 2023; 25:451. [PMID: 38203622 PMCID: PMC10779318 DOI: 10.3390/ijms25010451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 12/17/2023] [Accepted: 12/27/2023] [Indexed: 01/12/2024] Open
Abstract
The teat number is a pivotal reproductive trait that significantly influences the survival rate of piglets. A meta-analysis is a robust instrument, enhancing the universality of research findings and improving statistical power by increasing the sample size. This study aimed to identify universal candidate genes associated with teat number traits using a genome-wide association study (GWAS) meta-analysis with three breeds. We identified 21 chromosome threshold significant single-nucleotide polymorphisms (SNPs) associated with five teat number traits in single-breed and cross-breed meta-GWAS analyses. Using a co-localization analysis of expression quantitative trait loci and GWAS loci, we detected four unique genes that were co-localized with cross-breed GWAS loci associated with teat number traits. Through a meta-analysis and integrative analysis, we identified more reliable candidate genes associated with multiple-breed teat number traits. Our research provides new information for exploring the genetic mechanism affecting pig teat number for breeding selection and improvement.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Jiaqi Li
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (T.L.); (P.W.); (Q.L.); (C.W.); (K.G.); (X.L.); (Y.L.); (Z.Z.)
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8
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Wei C, Chang C, Zhang W, Ren D, Cai X, Zhou T, Shi S, Wu X, Si J, Yuan X, Li J, Zhang Z. Preselecting Variants from Large-Scale Genome-Wide Association Study Meta-Analyses Increases the Genomic Prediction Accuracy of Growth and Carcass Traits in Large White Pigs. Animals (Basel) 2023; 13:3746. [PMID: 38136785 PMCID: PMC10740834 DOI: 10.3390/ani13243746] [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: 10/11/2023] [Revised: 11/30/2023] [Accepted: 11/30/2023] [Indexed: 12/24/2023] Open
Abstract
Preselected variants associated with the trait of interest from genome-wide association studies (GWASs) are available to improve genomic prediction in pigs. The objectives of this study were to use preselected variants from a large GWAS meta-analysis to assess the impact of single-nucleotide polymorphism (SNP) preselection strategies on genome prediction of growth and carcass traits in pigs. We genotyped 1018 Large White pigs using medium (50k) SNP arrays and then imputed SNPs to sequence level by utilizing a reference panel of 1602 whole-genome sequencing samples. We tested the effects of different proportions of selected top SNPs across different SNP preselection strategies on genomic prediction. Finally, we compared the prediction accuracies by employing genomic best linear unbiased prediction (GBLUP), genomic feature BLUP and three weighted GBLUP models. SNP preselection strategies showed an average improvement in accuracy ranging from 0.3 to 2% in comparison to the SNP chip data. The accuracy of genomic prediction exhibited a pattern of initial increase followed by decrease, or continuous decrease across various SNP preselection strategies, as the proportion of selected top SNPs increased. The highest level of prediction accuracy was observed when utilizing 1 or 5% of top SNPs. Compared with the GBLUP model, the utilization of estimated marker effects from a GWAS meta-analysis as SNP weights in the BLUP|GA model improved the accuracy of genomic prediction in different SNP preselection strategies. The new SNP preselection strategies gained from this study bring opportunities for genomic prediction in limited-size populations in pigs.
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Affiliation(s)
- Chen Wei
- National Engineering Research Centre for Swine Breeding Industry, Provincial Key Laboratory of Agricultural Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510640, China; (C.W.); (C.C.); (W.Z.); (D.R.); (X.C.); (T.Z.); (S.S.); (X.Y.); (J.L.)
| | - Chengjie Chang
- National Engineering Research Centre for Swine Breeding Industry, Provincial Key Laboratory of Agricultural Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510640, China; (C.W.); (C.C.); (W.Z.); (D.R.); (X.C.); (T.Z.); (S.S.); (X.Y.); (J.L.)
| | - Wenjing Zhang
- National Engineering Research Centre for Swine Breeding Industry, Provincial Key Laboratory of Agricultural Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510640, China; (C.W.); (C.C.); (W.Z.); (D.R.); (X.C.); (T.Z.); (S.S.); (X.Y.); (J.L.)
| | - Duanyang Ren
- National Engineering Research Centre for Swine Breeding Industry, Provincial Key Laboratory of Agricultural Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510640, China; (C.W.); (C.C.); (W.Z.); (D.R.); (X.C.); (T.Z.); (S.S.); (X.Y.); (J.L.)
| | - Xiaodian Cai
- National Engineering Research Centre for Swine Breeding Industry, Provincial Key Laboratory of Agricultural Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510640, China; (C.W.); (C.C.); (W.Z.); (D.R.); (X.C.); (T.Z.); (S.S.); (X.Y.); (J.L.)
| | - Tianru Zhou
- National Engineering Research Centre for Swine Breeding Industry, Provincial Key Laboratory of Agricultural Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510640, China; (C.W.); (C.C.); (W.Z.); (D.R.); (X.C.); (T.Z.); (S.S.); (X.Y.); (J.L.)
| | - Shaolei Shi
- National Engineering Research Centre for Swine Breeding Industry, Provincial Key Laboratory of Agricultural Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510640, China; (C.W.); (C.C.); (W.Z.); (D.R.); (X.C.); (T.Z.); (S.S.); (X.Y.); (J.L.)
| | - Xibo Wu
- Guangxi State Farms Yongxin Animal Husbandry Group Co., Ltd., Nanning 530022, China; (X.W.); (J.S.)
| | - Jinglei Si
- Guangxi State Farms Yongxin Animal Husbandry Group Co., Ltd., Nanning 530022, China; (X.W.); (J.S.)
| | - Xiaolong Yuan
- National Engineering Research Centre for Swine Breeding Industry, Provincial Key Laboratory of Agricultural Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510640, China; (C.W.); (C.C.); (W.Z.); (D.R.); (X.C.); (T.Z.); (S.S.); (X.Y.); (J.L.)
| | - Jiaqi Li
- National Engineering Research Centre for Swine Breeding Industry, Provincial Key Laboratory of Agricultural Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510640, China; (C.W.); (C.C.); (W.Z.); (D.R.); (X.C.); (T.Z.); (S.S.); (X.Y.); (J.L.)
| | - Zhe Zhang
- National Engineering Research Centre for Swine Breeding Industry, Provincial Key Laboratory of Agricultural Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510640, China; (C.W.); (C.C.); (W.Z.); (D.R.); (X.C.); (T.Z.); (S.S.); (X.Y.); (J.L.)
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9
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Gualdrón Duarte JL, Yuan C, Gori AS, Moreira GCM, Takeda H, Coppieters W, Charlier C, Georges M, Druet T. Sequenced-based GWAS for linear classification traits in Belgian Blue beef cattle reveals new coding variants in genes regulating body size in mammals. Genet Sel Evol 2023; 55:83. [PMID: 38017417 PMCID: PMC10683324 DOI: 10.1186/s12711-023-00857-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 11/17/2023] [Indexed: 11/30/2023] Open
Abstract
BACKGROUND Cohorts of individuals that have been genotyped and phenotyped for genomic selection programs offer the opportunity to better understand genetic variation associated with complex traits. Here, we performed an association study for traits related to body size and muscular development in intensively selected beef cattle. We leveraged multiple trait information to refine and interpret the significant associations. RESULTS After a multiple-step genotype imputation to the sequence-level for 14,762 Belgian Blue beef (BBB) cows, we performed a genome-wide association study (GWAS) for 11 traits related to muscular development and body size. The 37 identified genome-wide significant quantitative trait loci (QTL) could be condensed in 11 unique QTL regions based on their position. Evidence for pleiotropic effects was found in most of these regions (e.g., correlated association signals, overlap between credible sets (CS) of candidate variants). Thus, we applied a multiple-trait approach to combine information from different traits to refine the CS. In several QTL regions, we identified strong candidate genes known to be related to growth and height in other species such as LCORL-NCAPG or CCND2. For some of these genes, relevant candidate variants were identified in the CS, including three new missense variants in EZH2, PAPPA2 and ADAM12, possibly two additional coding variants in LCORL, and candidate regulatory variants linked to CCND2 and ARMC12. Strikingly, four other QTL regions associated with dimension or muscular development traits were related to five (recessive) deleterious coding variants previously identified. CONCLUSIONS Our study further supports that a set of common genes controls body size across mammalian species. In particular, we added new genes to the list of those associated with height in both humans and cattle. We also identified new strong candidate causal variants in some of these genes, strengthening the evidence of their causality. Several breed-specific recessive deleterious variants were identified in our QTL regions, probably as a result of the extreme selection for muscular development in BBB cattle.
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Affiliation(s)
- José Luis Gualdrón Duarte
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège, Avenue de l'Hôpital, 1, Liège, 4000, Belgium.
- Walloon Breeders Association, Rue des Champs Elysées, 4, 5590, Ciney, Belgium.
| | - Can Yuan
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège, Avenue de l'Hôpital, 1, Liège, 4000, Belgium
| | - Ann-Stephan Gori
- Walloon Breeders Association, Rue des Champs Elysées, 4, 5590, Ciney, Belgium
| | - Gabriel C M Moreira
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège, Avenue de l'Hôpital, 1, Liège, 4000, Belgium
| | - Haruko Takeda
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège, Avenue de l'Hôpital, 1, Liège, 4000, Belgium
| | - Wouter Coppieters
- GIGA Genomic Platform, GIGA-R, University of Liège, Avenue de l'Hôpital, 1, 4000, Liège, Belgium
| | - Carole Charlier
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège, Avenue de l'Hôpital, 1, Liège, 4000, Belgium
| | - Michel Georges
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège, Avenue de l'Hôpital, 1, Liège, 4000, Belgium
| | - Tom Druet
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège, Avenue de l'Hôpital, 1, Liège, 4000, Belgium
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10
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Sallam AM, Abou-Souliman I, Reyer H, Wimmers K, Rabee AE. New insights into the genetic predisposition of brucellosis and its effect on the gut and vaginal microbiota in goats. Sci Rep 2023; 13:20086. [PMID: 37973848 PMCID: PMC10654701 DOI: 10.1038/s41598-023-46997-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 11/07/2023] [Indexed: 11/19/2023] Open
Abstract
Goats contribute significantly to the global food security and industry. They constitute a main supplier of meat and milk for large proportions of people in Egypt and worldwide. Brucellosis is a zoonotic infectious disease that causes a significant economic loss in animal production. A case-control genome-wide association analysis (GWAS) was conducted using the infectious status of the animal as a phenotype. The does that showed abortion during the last third period of pregnancy and which were positive to both rose bengal plate and serum tube agglutination tests, were considered as cases. Otherwise, they were considered as controls. All animals were genotyped using the Illumina 65KSNP BeadChip. Additionally, the diversity and composition of vaginal and fecal microbiota in cases and controls were investigated using PCR-amplicone sequencing of the V4 region of 16S rDNA. After applying quality control criteria, 35,818 markers and 66 does were available for the GWAS test. The GWAS revealed a significantly associated SNP (P = 5.01 × 10-7) located on Caprine chromosome 15 at 29 megabases. Four other markers surpassed the proposed threshold (P = 2.5 × 10-5). Additionally, fourteen genomic regions accounted for more than 0.1% of the variance explained by all genome windows. Corresponding markers were located within or in close vicinity to several candidate genes, such as ARRB1, RELT, ATG16L2, IGSF21, UBR4, ULK1, DCN, MAPB1, NAIP, CD26, IFIH1, NDFIP2, DOK4, MAF, IL2RB, USP18, ARID5A, ZAP70, CNTN5, PIK3AP1, DNTT, BLNK, and NHLRC3. These genes play important roles in the regulation of immune responses to the infections through several biological pathways. Similar vaginal bacterial community was observed in both cases and controls while the fecal bacterial composition and diversity differed between the groups (P < 0.05). Faeces from the control does showed a higher relative abundance of the phylum Bacteroidota compared to cases (P < 0.05), while the latter showed more Firmicutes, Spirochaetota, Planctomycetota, and Proteobacteria. On the genus level, the control does exhibited higher abundances of Rikenellaceae RC9 gut group and Christensenellaceae R-7 group (P < 0.05), while the infected does revealed higher Bacteroides, Alistipes, and Prevotellaceae UCG-003 (P < 0.05). This information increases our understanding of the genetics of the susceptibility to Brucella in goats and may be useful in breeding programs and selection schemes that aim at controlling the disease in livestock.
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Affiliation(s)
- Ahmed M Sallam
- Animal and Poultry Breeding Department, Desert Research Center, Cairo, Egypt.
| | | | - Henry Reyer
- Research Institute for Farm Animal Biology (FBN), Wilhelm-Stahl-Allee 2, 18196, Dummerstorf, Germany
| | - Klaus Wimmers
- Research Institute for Farm Animal Biology (FBN), Wilhelm-Stahl-Allee 2, 18196, Dummerstorf, Germany
| | - Alaa Emara Rabee
- Animal and Poultry Nutrition Department, Desert Research Center, Cairo, Egypt
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11
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Sanchez MP, Tribout T, Kadri NK, Chitneedi PK, Maak S, Hozé C, Boussaha M, Croiseau P, Philippe R, Spengeler M, Kühn C, Wang Y, Li C, Plastow G, Pausch H, Boichard D. Sequence-based GWAS meta-analyses for beef production traits. Genet Sel Evol 2023; 55:70. [PMID: 37828440 PMCID: PMC10568825 DOI: 10.1186/s12711-023-00848-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Accepted: 10/04/2023] [Indexed: 10/14/2023] Open
Abstract
BACKGROUND Combining the results of within-population genome-wide association studies (GWAS) based on whole-genome sequences into a single meta-analysis (MA) is an accurate and powerful method for identifying variants associated with complex traits. As part of the H2020 BovReg project, we performed sequence-level MA for beef production traits. Five partners from France, Switzerland, Germany, and Canada contributed summary statistics from sequence-based GWAS conducted with 54,782 animals from 15 purebred or crossbred populations. We combined the summary statistics for four growth, nine morphology, and 15 carcass traits into 16 MA, using both fixed effects and z-score methods. RESULTS The fixed-effects method was generally more informative to provide indication on potentially causal variants, although we combined substantially different traits in each MA. In comparison with within-population GWAS, this approach highlighted (i) a larger number of quantitative trait loci (QTL), (ii) QTL more frequently located in genomic regions known for their effects on growth and meat/carcass traits, (iii) a smaller number of genomic variants within the QTL, and (iv) candidate variants that were more frequently located in genes. MA pinpointed variants in genes, including MSTN, LCORL, and PLAG1 that have been previously associated with morphology and carcass traits. We also identified dozens of other variants located in genes associated with growth and carcass traits, or with a function that may be related to meat production (e.g., HS6ST1, HERC2, WDR75, COL3A1, SLIT2, MED28, and ANKAR). Some of these variants overlapped with expression or splicing QTL reported in the cattle Genotype-Tissue Expression atlas (CattleGTEx) and could therefore regulate gene expression. CONCLUSIONS By identifying candidate genes and potential causal variants associated with beef production traits in cattle, MA demonstrates great potential for investigating the biological mechanisms underlying these traits. As a complement to within-population GWAS, this approach can provide deeper insights into the genetic architecture of complex traits in beef cattle.
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Affiliation(s)
- Marie-Pierre Sanchez
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France.
| | - Thierry Tribout
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France
| | | | - Praveen K Chitneedi
- Research Institute for Farm Animal Biology (FBN), 18196, Dummerstorf, Germany
| | - Steffen Maak
- Research Institute for Farm Animal Biology (FBN), 18196, Dummerstorf, Germany
| | - Chris Hozé
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France
- Eliance, 75595, Paris, France
| | - Mekki Boussaha
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France
| | - Pascal Croiseau
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France
| | - Romain Philippe
- INRAE, USC1061 GAMAA, Université de Limoges, 87060, Limoges, France
| | | | - Christa Kühn
- Research Institute for Farm Animal Biology (FBN), 18196, Dummerstorf, Germany
- Agricultural and Environmental faculty, University Rostock, 18059, Rostock, Germany
- Friedrich-Loeffler-Institut (FLI), 17493, Greifswald, Insel Riems, Germany
| | - Yining Wang
- Lacombe Research and Development Centre, Agriculture and Agri-Food Canada, Lacombe, AB, T4L 1W1, Canada
| | - Changxi Li
- Lacombe Research and Development Centre, Agriculture and Agri-Food Canada, Lacombe, AB, T4L 1W1, Canada
- Department of Agricultural, Food and Nutritional Science, Livestock Gentec, University of Alberta, Edmonton, AB, T6G 2HI, Canada
| | - Graham Plastow
- Department of Agricultural, Food and Nutritional Science, Livestock Gentec, University of Alberta, Edmonton, AB, T6G 2HI, Canada
| | - Hubert Pausch
- Animal Genomics, ETH Zurich, 8092, Zurich, Switzerland
| | - Didier Boichard
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France
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12
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Xiang R, Fang L, Liu S, Macleod IM, Liu Z, Breen EJ, Gao Y, Liu GE, Tenesa A, Mason BA, Chamberlain AJ, Wray NR, Goddard ME. Gene expression and RNA splicing explain large proportions of the heritability for complex traits in cattle. CELL GENOMICS 2023; 3:100385. [PMID: 37868035 PMCID: PMC10589627 DOI: 10.1016/j.xgen.2023.100385] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 08/10/2022] [Accepted: 07/26/2023] [Indexed: 10/24/2023]
Abstract
Many quantitative trait loci (QTLs) are in non-coding regions. Therefore, QTLs are assumed to affect gene regulation. Gene expression and RNA splicing are primary steps of transcription, so DNA variants changing gene expression (eVariants) or RNA splicing (sVariants) are expected to significantly affect phenotypes. We quantify the contribution of eVariants and sVariants detected from 16 tissues (n = 4,725) to 37 traits of ∼120,000 cattle (average magnitude of genetic correlation between traits = 0.13). Analyzed in Bayesian mixture models, averaged across 37 traits, cis and trans eVariants and sVariants detected from 16 tissues jointly explain 69.2% (SE = 0.5%) of heritability, 44% more than expected from the same number of random variants. This 69.2% includes an average of 24% from trans e-/sVariants (14% more than expected). Averaged across 56 lipidomic traits, multi-tissue cis and trans e-/sVariants also explain 71.5% (SE = 0.3%) of heritability, demonstrating the essential role of proximal and distal regulatory variants in shaping mammalian phenotypes.
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Affiliation(s)
- Ruidong Xiang
- Faculty of Veterinary & Agricultural Science, the University of Melbourne, Parkville, VIC 3052, Australia
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC 3083, Australia
- Cambridge-Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
| | - Lingzhao Fang
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, the University of Edinburgh, Edinburgh, UK
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Shuli Liu
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
| | - Iona M. Macleod
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC 3083, Australia
| | - Zhiqian Liu
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC 3083, Australia
| | - Edmond J. Breen
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC 3083, Australia
| | - Yahui Gao
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, MD 20705, USA
| | - George E. Liu
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, MD 20705, USA
| | - Albert Tenesa
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, the University of Edinburgh, Edinburgh, UK
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, the University of Edinburgh, Midlothian EH25 9RG, UK
| | - CattleGTEx Consortium
- Faculty of Veterinary & Agricultural Science, the University of Melbourne, Parkville, VIC 3052, Australia
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC 3083, Australia
- Cambridge-Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, the University of Edinburgh, Edinburgh, UK
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, MD 20705, USA
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, the University of Edinburgh, Midlothian EH25 9RG, UK
- Institute for Molecular Bioscience, the University of Queensland, Brisbane, QLD 4072, Australia
- Queensland Brain Institute, the University of Queensland, Brisbane, QLD 4072, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia
| | - Brett A. Mason
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC 3083, Australia
| | - Amanda J. Chamberlain
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC 3083, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia
| | - Naomi R. Wray
- Institute for Molecular Bioscience, the University of Queensland, Brisbane, QLD 4072, Australia
- Queensland Brain Institute, the University of Queensland, Brisbane, QLD 4072, Australia
| | - Michael E. Goddard
- Faculty of Veterinary & Agricultural Science, the University of Melbourne, Parkville, VIC 3052, Australia
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC 3083, Australia
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13
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Sahana G, Cai Z, Sanchez MP, Bouwman AC, Boichard D. Invited review: Good practices in genome-wide association studies to identify candidate sequence variants in dairy cattle. J Dairy Sci 2023:S0022-0302(23)00357-0. [PMID: 37349208 DOI: 10.3168/jds.2022-22694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 02/01/2023] [Indexed: 06/24/2023]
Abstract
Genotype data from dairy cattle selection programs have greatly facilitated GWAS to identify variants related to economic traits. Results can enhance the accuracy of genomic prediction, analyze more complex models that go beyond additive effects, elucidate the genetic architecture of a trait, and finally, decipher the underlying biology of traits. The entire process, comprising data generation, quality control, statistical analyses, interpretation of association results, and linking results to biology should be designed and executed to minimize the generation of false-positive and false-negative associations and misleading links to biological processes. This review aims to provide general guidelines for data analysis that address data quality control, association tests, adjustment for population stratification, and significance evaluation to improve the reliability of conclusions. We also provide guidance on post-GWAS strategy and the interpretation of results. These guidelines are tailored to dairy cattle, which are characterized by long-range linkage disequilibrium, large half-sib families, and routinely collected phenotypes, requiring different approaches than those applied in human GWAS. We discuss common limitations and challenges that have been overlooked in the analysis and interpretation of GWAS to identify candidate sequence variants in dairy cattle.
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Affiliation(s)
- G Sahana
- Aarhus University, Center for Quantitative Genetic and Genomics, 8830 Tjele, Denmark.
| | - Z Cai
- Aarhus University, Center for Quantitative Genetic and Genomics, 8830 Tjele, Denmark
| | - M P Sanchez
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350 Jouy-en-Josas, France
| | - A C Bouwman
- Wageningen University & Research, Animal Breeding and Genomics, 6700 AH Wageningen, the Netherlands
| | - D Boichard
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350 Jouy-en-Josas, France
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14
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Sanchez MP, Escouflaire C, Baur A, Bottin F, Hozé C, Boussaha M, Fritz S, Capitan A, Boichard D. X-linked genes influence various complex traits in dairy cattle. BMC Genomics 2023; 24:338. [PMID: 37337145 DOI: 10.1186/s12864-023-09438-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 06/08/2023] [Indexed: 06/21/2023] Open
Abstract
BACKGROUND The search for quantitative trait loci (QTL) affecting traits of interest in mammals is frequently limited to autosomes, with the X chromosome excluded because of its hemizygosity in males. This study aimed to assess the importance of the X chromosome in the genetic determinism of 11 complex traits related to milk production, milk composition, mastitis resistance, fertility, and stature in 236,496 cows from three major French dairy breeds (Holstein, Montbéliarde, and Normande) and three breeds of regional importance (Abondance, Tarentaise, and Vosgienne). RESULTS Estimates of the proportions of heritability due to autosomes and X chromosome (h²X) were consistent among breeds. On average over the 11 traits, h²X=0.008 and the X chromosome explained ~ 3.5% of total genetic variance. GWAS was performed within-breed at the sequence level (~ 200,000 genetic variants) and then combined in a meta-analysis. QTL were identified for most breeds and traits analyzed, with the exception of Tarentaise and Vosgienne and two fertility traits. Overall, 3, 74, 59, and 71 QTL were identified in Abondance, Montbéliarde, Normande, and Holstein, respectively, and most were associated with the most-heritable traits (milk traits and stature). The meta-analyses, which assessed a total of 157 QTL for the different traits, highlighted new QTL and refined the positions of some QTL found in the within-breed analyses. Altogether, our analyses identified a number of functional candidate genes, with the most notable being GPC3, MBNL3, HS6ST2, and DMD for dairy traits; TMEM164, ACSL4, ENOX2, HTR2C, AMOT, and IRAK1 for udder health; MAMLD1 and COL4A6 for fertility; and NRK, ESX1, GPR50, GPC3, and GPC4 for stature. CONCLUSIONS This study demonstrates the importance of the X chromosome in the genetic determinism of complex traits in dairy cattle and highlights new functional candidate genes and variants for these traits. These results could potentially be extended to other species as many X-linked genes are shared among mammals.
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Affiliation(s)
- Marie-Pierre Sanchez
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, Jouy-en-Josas, 78350, France.
| | | | | | - Fiona Bottin
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, Jouy-en-Josas, 78350, France
| | | | - Mekki Boussaha
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, Jouy-en-Josas, 78350, France
| | | | - Aurélien Capitan
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, Jouy-en-Josas, 78350, France
| | - Didier Boichard
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, Jouy-en-Josas, 78350, France
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15
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Pausch H, Mapel XM. Review: Genetic mutations affecting bull fertility. Animal 2023; 17 Suppl 1:100742. [PMID: 37567657 DOI: 10.1016/j.animal.2023.100742] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 01/23/2023] [Accepted: 01/24/2023] [Indexed: 08/13/2023] Open
Abstract
Cattle are a well-suited "model organism" to study the genetic underpinnings of variation in male reproductive performance. The adoption of artificial insemination and genomic prediction in many cattle breeds provide access to microarray-derived genotypes and repeated measurements for semen quality and insemination success in several thousand bulls. Similar-sized mapping cohorts with phenotypes for male fertility are not available for most other species precluding powerful association testing. The repeated measurements of the artificial insemination bulls' semen quality enable the differentiation between transient and biologically relevant trait fluctuations, and thus, are an ideal source of phenotypes for variance components estimation and genome-wide association testing. Genome-wide case-control association testing involving bulls with either aberrant sperm quality or low insemination success revealed several causal recessive loss-of-function alleles underpinning monogenic reproductive disorders. These variants are routinely monitored with customised genotyping arrays in the male selection candidates to avoid the use of subfertile or infertile bulls for artificial insemination and natural service. Genome-wide association studies with quantitative measurements of semen quality and insemination success revealed quantitative trait loci for male fertility, but the underlying causal variants remain largely unknown. Moreover, these loci explain only a small part of the heritability of male fertility. Integrating genome-wide association studies with gene expression and other omics data from male reproductive tissues is required for the fine-mapping of candidate causal variants underlying variation in male reproductive performance in cattle.
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Affiliation(s)
- Hubert Pausch
- Animal Genomics, Department of Environmental Systems Science, ETH Zurich, Universitaetstrasse 2, 8092 Zurich, Switzerland.
| | - Xena Marie Mapel
- Animal Genomics, Department of Environmental Systems Science, ETH Zurich, Universitaetstrasse 2, 8092 Zurich, Switzerland
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16
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Lopdell TJ. Using QTL to Identify Genes and Pathways Underlying the Regulation and Production of Milk Components in Cattle. Animals (Basel) 2023; 13:ani13050911. [PMID: 36899768 PMCID: PMC10000085 DOI: 10.3390/ani13050911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 02/23/2023] [Accepted: 02/28/2023] [Indexed: 03/06/2023] Open
Abstract
Milk is a complex liquid, and the concentrations of many of its components are under genetic control. Many genes and pathways are known to regulate milk composition, and the purpose of this review is to highlight how the discoveries of quantitative trait loci (QTL) for milk phenotypes can elucidate these pathways. The main body of this review focuses primarily on QTL discovered in cattle (Bos taurus) as a model species for the biology of lactation, and there are occasional references to sheep genetics. The following section describes a range of techniques that can be used to help identify the causative genes underlying QTL when the underlying mechanism involves the regulation of gene expression. As genotype and phenotype databases continue to grow and diversify, new QTL will continue to be discovered, and although proving the causality of underlying genes and variants remains difficult, these new data sets will further enhance our understanding of the biology of lactation.
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17
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Teng J, Wang D, Zhao C, Zhang X, Chen Z, Liu J, Sun D, Tang H, Wang W, Li J, Mei C, Yang Z, Ning C, Zhang Q. Longitudinal genome-wide association studies of milk production traits in Holstein cattle using whole-genome sequence data imputed from medium-density chip data. J Dairy Sci 2023; 106:2535-2550. [PMID: 36797187 DOI: 10.3168/jds.2022-22277] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 10/20/2022] [Indexed: 02/16/2023]
Abstract
Longitudinal traits, such as milk production traits in dairy cattle, are featured by having phenotypic values at multiple time points, which change dynamically over time. In this study, we first imputed SNP chip (50-100K) data to whole-genome sequence (WGS) data in a Chinese Holstein population consisting of 6,470 cows. The imputation accuracies were 0.88 to 0.97 on average after quality control. We then performed longitudinal GWAS in this population based on a random regression test-day model using the imputed WGS data. The longitudinal GWAS revealed 16, 39, and 75 quantitative trait locus regions associated with milk yield, fat percentage, and protein percentage, respectively. We estimated the 95% confidence intervals (CI) for these quantitative trait locus regions using the logP drop method and identified 581 genes involved in these CI. Further, we focused on the CI that covered or overlapped with only 1 gene or the CI that contained an extremely significant top SNP. Twenty-eight candidate genes were identified in these CI. Most of them have been reported in the literature to be associated with milk production traits, such as DGAT1, HSF1, MGST1, GHR, ABCG2, ADCK5, and CSN1S1. Among the unreported novel genes, some also showed good potential as candidate genes, such as CCSER1, CUX2, SNTB1, RGS7, OSR2, and STK3, and are worth being further investigated. Our study provided not only new insights into the candidate genes for milk production traits, but also a general framework for longitudinal GWAS based on random regression test-day model using WGS data.
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Affiliation(s)
- Jun Teng
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Tai'an 271018, China
| | - Dan Wang
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Tai'an 271018, China
| | - Changheng Zhao
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Tai'an 271018, China
| | - Xinyi Zhang
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Tai'an 271018, China
| | - Zhi Chen
- College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China
| | - Jianfeng Liu
- College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Dongxiao Sun
- College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Hui Tang
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Tai'an 271018, China
| | - Wenwen Wang
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Tai'an 271018, China
| | - Jianbin Li
- Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences, Jinan 250100, China
| | - Cheng Mei
- Dongying Shenzhou AustAsia Modern Dairy Farm Co. Ltd., Dongying 257200, China
| | - Zhangping Yang
- College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China
| | - Chao Ning
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Tai'an 271018, China.
| | - Qin Zhang
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Tai'an 271018, China.
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18
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Schneider H, Segelke D, Tetens J, Thaller G, Bennewitz J. A genomic assessment of the correlation between milk production traits and claw and udder health traits in Holstein dairy cattle. J Dairy Sci 2023; 106:1190-1205. [PMID: 36460501 DOI: 10.3168/jds.2022-22312] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 09/01/2022] [Indexed: 11/30/2022]
Abstract
Claw diseases and mastitis represent the most important disease traits in dairy cattle with increasing incidences and a frequently mentioned connection to milk yield. Yet, many studies aimed to detect the genetic background of both trait complexes via fine-mapping of quantitative trait loci. However, little is known about genomic regions that simultaneously affect milk production and disease traits. For this purpose, several tools to detect local genetic correlations have been developed. In this study, we attempted a detailed analysis of milk production and disease traits as well as their interrelationship using a sample of 34,497 50K genotyped German Holstein cows with milk production and claw and udder disease traits records. We performed a pedigree-based quantitative genetic analysis to estimate heritabilities and genetic correlations. Additionally, we generated GWAS summary statistics, paying special attention to genomic inflation, and used these data to identify shared genomic regions, which affect various trait combinations. The heritability on the liability scale of the disease traits was low, between 0.02 for laminitis and 0.19 for interdigital hyperplasia. The heritabilities for milk production traits were higher (between 0.27 for milk energy yield and 0.48 for fat-protein ratio). Global genetic correlations indicate the shared genetic effect between milk production and disease traits on a whole genome level. Most of these estimates were not significantly different from zero, only mastitis showed a positive one to milk (0.18) and milk energy yield (0.13), as well as a negative one to fat-protein ratio (-0.07). The genomic analysis revealed significant SNPs for milk production traits that were enriched on Bos taurus autosome 5, 6, and 14. For digital dermatitis, we found significant hits, predominantly on Bos taurus autosome 5, 10, 22, and 23, whereas we did not find significantly trait-associated SNPs for the other disease traits. Our results confirm the known genetic background of disease and milk production traits. We further detected 13 regions that harbor strong concordant effects on a trait combination of milk production and disease traits. This detailed investigation of genetic correlations reveals additional knowledge about the localization of regions with shared genetic effects on these trait complexes, which in turn enables a better understanding of the underlying biological pathways and putatively the utilization for a more precise design of breeding schemes.
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Affiliation(s)
- Helen Schneider
- Institute of Animal Science, University of Hohenheim, 70599 Stuttgart, Germany.
| | - Dierck Segelke
- Vereinigte Informationssysteme Tierhaltung w.V. (VIT), 27283 Verden, Germany
| | - Jens Tetens
- Department of Animal Sciences, University of Göttingen, 37077 Göttingen, Germany
| | - Georg Thaller
- Institute of Animal Breeding and Husbandry, Christian-Albrechts University of Kiel, 24098 Kiel, Germany
| | - Jörn Bennewitz
- Institute of Animal Science, University of Hohenheim, 70599 Stuttgart, Germany
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19
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Neumann GB, Korkuć P, Arends D, Wolf MJ, May K, König S, Brockmann GA. Genomic diversity and relationship analyses of endangered German Black Pied cattle (DSN) to 68 other taurine breeds based on whole-genome sequencing. Front Genet 2023; 13:993959. [PMID: 36712857 PMCID: PMC9875303 DOI: 10.3389/fgene.2022.993959] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 11/28/2022] [Indexed: 01/05/2023] Open
Abstract
German Black Pied cattle (Deutsches Schwarzbuntes Niederungsrind, DSN) are an endangered dual-purpose cattle breed originating from the North Sea region. The population comprises about 2,500 cattle and is considered one of the ancestral populations of the modern Holstein breed. The current study aimed at defining the breeds closest related to DSN cattle, characterizing their genomic diversity and inbreeding. In addition, the detection of selection signatures between DSN and Holstein was a goal. Relationship analyses using fixation index (FST), phylogenetic, and admixture analyses were performed between DSN and 68 other breeds from the 1000 Bull Genomes Project. Nucleotide diversity, observed heterozygosity, and expected heterozygosity were calculated as metrics for genomic diversity. Inbreeding was measured as excess of homozygosity (FHom) and genomic inbreeding (FRoH) through runs of homozygosity (RoHs). Region-wide FST and cross-population-extended haplotype homozygosity (XP-EHH) between DSN and Holstein were used to detect selection signatures between the two breeds, and RoH islands were used to detect selection signatures within DSN and Holstein. DSN showed a close genetic relationship with breeds from the Netherlands, Belgium, Northern Germany, and Scandinavia, such as Dutch Friesian Red, Dutch Improved Red, Belgian Red White Campine, Red White Dual Purpose, Modern Angler, Modern Danish Red, and Holstein. The nucleotide diversity in DSN (0.151%) was higher than in Holstein (0.147%) and other breeds, e.g., Norwegian Red (0.149%), Red White Dual Purpose (0.149%), Swedish Red (0.149%), Hereford (0.145%), Angus (0.143%), and Jersey (0.136%). The FHom and FRoH values in DSN were among the lowest. Regions with high FST between DSN and Holstein, significant XP-EHH regions, and RoH islands detected in both breeds harbor candidate genes that were previously reported for milk, meat, fertility, production, and health traits, including one QTL detected in DSN for endoparasite infection resistance. The selection signatures between DSN and Holstein provide evidence of regions responsible for the dual-purpose properties of DSN and the milk type of Holstein. Despite the small population size, DSN has a high level of diversity and low inbreeding. FST supports its relatedness to breeds from the same geographic origin and provides information on potential gene pools that could be used to maintain diversity in DSN.
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Affiliation(s)
- Guilherme B. Neumann
- Animal Breeding Biology and Molecular Genetics, Albrecht Daniel Thaer-Institute for Agricultural and Horticultural Sciences, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Paula Korkuć
- Animal Breeding Biology and Molecular Genetics, Albrecht Daniel Thaer-Institute for Agricultural and Horticultural Sciences, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Danny Arends
- Animal Breeding Biology and Molecular Genetics, Albrecht Daniel Thaer-Institute for Agricultural and Horticultural Sciences, Humboldt-Universität zu Berlin, Berlin, Germany,Department of Applied Sciences, Northumbria University, Newcastle Upon Tyne, United Kingdom
| | - Manuel J. Wolf
- Institute of Animal Breeding and Genetics, Justus-Liebig-Universität, Giessen, Germany
| | - Katharina May
- Institute of Animal Breeding and Genetics, Justus-Liebig-Universität, Giessen, Germany
| | - Sven König
- Institute of Animal Breeding and Genetics, Justus-Liebig-Universität, Giessen, Germany
| | - Gudrun A. Brockmann
- Animal Breeding Biology and Molecular Genetics, Albrecht Daniel Thaer-Institute for Agricultural and Horticultural Sciences, Humboldt-Universität zu Berlin, Berlin, Germany,*Correspondence: Gudrun A. Brockmann,
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20
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Shi L, Wang L, Fang L, Li M, Tian J, Wang L, Zhao F. Integrating genome-wide association studies and population genomics analysis reveals the genetic architecture of growth and backfat traits in pigs. Front Genet 2022; 13:1078696. [PMID: 36506319 PMCID: PMC9732542 DOI: 10.3389/fgene.2022.1078696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 11/14/2022] [Indexed: 11/26/2022] Open
Abstract
Growth and fat deposition are complex traits, which can affect economical income in the pig industry. Due to the intensive artificial selection, a significant genetic improvement has been observed for growth and fat deposition in pigs. Here, we first investigated genomic-wide association studies (GWAS) and population genomics (e.g., selection signature) to explore the genetic basis of such complex traits in two Large White pig lines (n = 3,727) with the GeneSeek GGP Porcine HD array (n = 50,915 SNPs). Ten genetic variants were identified to be associated with growth and fatness traits in two Large White pig lines from different genetic backgrounds by performing both within-population GWAS and cross-population GWAS analyses. These ten significant loci represented eight candidate genes, i.e., NRG4, BATF3, IRS2, ANO1, ANO9, RNF152, KCNQ5, and EYA2. One of them, ANO1 gene was simultaneously identified for both two lines in BF100 trait. Compared to single-population GWAS, cross-population GWAS was less effective for identifying SNPs with population-specific effect, but more powerful for detecting SNPs with population-shared effects. We further detected genomic regions specifically selected in each of two populations, but did not observe a significant enrichment for the heritability of growth and backfat traits in such regions. In summary, the candidate genes will provide an insight into the understanding of the genetic architecture of growth-related traits and backfat thickness, and may have a potential use in the genomic breeding programs in pigs.
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Affiliation(s)
- Liangyu Shi
- Key Laboratory of Animal Genetics, Breeding and Reproduction (Poultry) of Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China,Laboratory of Genetic Breeding, Reproduction and Precision Livestock Farming, School of Animal Science and Nutritional Engineering, Wuhan Polytechnic University, Wuhan, China
| | - Ligang Wang
- Key Laboratory of Animal Genetics, Breeding and Reproduction (Poultry) of Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Lingzhao Fang
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Mianyan Li
- Key Laboratory of Animal Genetics, Breeding and Reproduction (Poultry) of Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Jingjing Tian
- Key Laboratory of Animal Genetics, Breeding and Reproduction (Poultry) of Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Lixian Wang
- Key Laboratory of Animal Genetics, Breeding and Reproduction (Poultry) of Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China,*Correspondence: Lixian Wang, ; Fuping Zhao,
| | - Fuping Zhao
- Key Laboratory of Animal Genetics, Breeding and Reproduction (Poultry) of Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China,*Correspondence: Lixian Wang, ; Fuping Zhao,
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21
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Ros-Freixedes R, Johnsson M, Whalen A, Chen CY, Valente BD, Herring WO, Gorjanc G, Hickey JM. Genomic prediction with whole-genome sequence data in intensely selected pig lines. GENETICS SELECTION EVOLUTION 2022; 54:65. [PMID: 36153511 PMCID: PMC9509613 DOI: 10.1186/s12711-022-00756-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 09/05/2022] [Indexed: 12/03/2022]
Abstract
Background Early simulations indicated that whole-genome sequence data (WGS) could improve the accuracy of genomic predictions within and across breeds. However, empirical results have been ambiguous so far. Large datasets that capture most of the genomic diversity in a population must be assembled so that allele substitution effects are estimated with high accuracy. The objectives of this study were to use a large pig dataset from seven intensely selected lines to assess the benefits of using WGS for genomic prediction compared to using commercial marker arrays and to identify scenarios in which WGS provides the largest advantage. Methods We sequenced 6931 individuals from seven commercial pig lines with different numerical sizes. Genotypes of 32.8 million variants were imputed for 396,100 individuals (17,224 to 104,661 per line). We used BayesR to perform genomic prediction for eight complex traits. Genomic predictions were performed using either data from a standard marker array or variants preselected from WGS based on association tests. Results The accuracies of genomic predictions based on preselected WGS variants were not robust across traits and lines and the improvements in prediction accuracy that we achieved so far with WGS compared to standard marker arrays were generally small. The most favourable results for WGS were obtained when the largest training sets were available and standard marker arrays were augmented with preselected variants with statistically significant associations to the trait. With this method and training sets of around 80k individuals, the accuracy of within-line genomic predictions was on average improved by 0.025. With multi-line training sets, improvements of 0.04 compared to marker arrays could be expected. Conclusions Our results showed that WGS has limited potential to improve the accuracy of genomic predictions compared to marker arrays in intensely selected pig lines. Thus, although we expect that larger improvements in accuracy from the use of WGS are possible with a combination of larger training sets and optimised pipelines for generating and analysing such datasets, the use of WGS in the current implementations of genomic prediction should be carefully evaluated against the cost of large-scale WGS data on a case-by-case basis. Supplementary Information The online version contains supplementary material available at 10.1186/s12711-022-00756-0.
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22
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Barden M, Li B, Griffiths BE, Anagnostopoulos A, Bedford C, Psifidi A, Banos G, Oikonomou G. Genetic parameters and genome-wide association study of digital cushion thickness in Holstein cows. J Dairy Sci 2022; 105:8237-8256. [PMID: 36028347 PMCID: PMC9511494 DOI: 10.3168/jds.2022-22035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 05/27/2022] [Indexed: 11/19/2022]
Abstract
The digital cushion is linked to the development of claw horn lesions (CHL) in dairy cattle. The objectives of this study were to (1) estimate genetic parameters for digital cushion thickness (DCT), (2) estimate the genetic correlation between DCT and CHL, and (3) identify candidate genes associated with DCT. A cohort of 2,352 Holstein dairy cows were prospectively enrolled on 4 farms and assessed at 4 time points: before calving, immediately after calving, in early lactation, and in late lactation. At each time point, CHL was recorded by veterinary surgeons, and ultrasonographic images of the digital cushion were stored and retrospectively measured at 2 anatomical locations. Animals were genotyped and pedigree details extracted from the national database. Genetic parameters were estimated following a single-step approach implemented in AIREMLF90. Four traits were analyzed: the 2 DCT measurements, sole lesions (sole hemorrhage and sole ulcers), and white line lesions. All traits were analyzed with univariate linear mixed models; bivariate models were fit to estimate the genetic correlation between traits within and between time points. Single-marker and window-based genome-wide association analyses of DCT traits were conducted at each time point; candidate genes were mapped near (<0.2 Mb) or within the genomic markers or windows with the largest effects. Heritability estimates of DCT ranged from 0.14 to 0.44 depending on the location of DCT measurement and assessment time point. The genetic correlation between DCT and sole lesions was generally negative, notably between DCT immediately after calving and sole lesions in early or late lactation, and between DCT in early or late lactation and sole lesion severity in early or late lactation. Digital cushion thickness was not genetically correlated with white line lesions. A polygenic background to DCT was found; genes associated with inflammation, fat metabolism, and bone development were mapped near or within the top markers and windows. The moderate heritability of DCT provides an opportunity to use selective breeding to change DCT in a population. The negative genetic correlation between DCT and sole lesions at different stages of production lends support to current hypotheses of sole lesion pathogenesis. Highlighted candidate genes provide information regarding the complex genetic background of DCT in Holstein cows, but further studies are needed to explore and corroborate these findings.
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Affiliation(s)
- Matthew Barden
- Department of Livestock and One Health, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Leahurst Campus, Liverpool, CH64 7TE, United Kingdom.
| | - Bingjie Li
- Animal & Veterinary Sciences, SRUC, Roslin Institute Building, Easter Bush, Midlothian, EH25 9RG, United Kingdom
| | - Bethany E Griffiths
- Department of Livestock and One Health, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Leahurst Campus, Liverpool, CH64 7TE, United Kingdom
| | - Alkiviadis Anagnostopoulos
- Department of Livestock and One Health, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Leahurst Campus, Liverpool, CH64 7TE, United Kingdom
| | - Cherry Bedford
- Department of Livestock and One Health, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Leahurst Campus, Liverpool, CH64 7TE, United Kingdom
| | - Androniki Psifidi
- Department of Clinical Science and Services, Royal Veterinary College, North Mymms, Hertfordshire, AL9 7TA, United Kingdom
| | - Georgios Banos
- Animal & Veterinary Sciences, SRUC, Roslin Institute Building, Easter Bush, Midlothian, EH25 9RG, United Kingdom
| | - Georgios Oikonomou
- Department of Livestock and One Health, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Leahurst Campus, Liverpool, CH64 7TE, United Kingdom
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23
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Rare and population-specific functional variation across pig lines. Genet Sel Evol 2022; 54:39. [PMID: 35659233 PMCID: PMC9164375 DOI: 10.1186/s12711-022-00732-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 05/17/2022] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND It is expected that functional, mainly missense and loss-of-function (LOF), and regulatory variants are responsible for most phenotypic differences between breeds and genetic lines of livestock species that have undergone diverse selection histories. However, there is still limited knowledge about the existing missense and LOF variation in commercial livestock populations, in particular regarding population-specific variation and how it can affect applications such as across-breed genomic prediction. METHODS We re-sequenced the whole genome of 7848 individuals from nine commercial pig lines (average sequencing coverage: 4.1×) and imputed whole-genome genotypes for 440,610 pedigree-related individuals. The called variants were categorized according to predicted functional annotation (from LOF to intergenic) and prevalence level (number of lines in which the variant segregated; from private to widespread). Variants in each category were examined in terms of their distribution along the genome, alternative allele frequency, per-site Wright's fixation index (FST), individual load, and association to production traits. RESULTS Of the 46 million called variants, 28% were private (called in only one line) and 21% were widespread (called in all nine lines). Genomic regions with a low recombination rate were enriched with private variants. Low-prevalence variants (called in one or a few lines only) were enriched for lower allele frequencies, lower FST, and putatively functional and regulatory roles (including LOF and deleterious missense variants). On average, individuals carried fewer private deleterious missense alleles than expected compared to alleles with other predicted consequences. Only a small subset of the low-prevalence variants had intermediate allele frequencies and explained small fractions of phenotypic variance (up to 3.2%) of production traits. The significant low-prevalence variants had higher per-site FST than the non-significant ones. These associated low-prevalence variants were tagged by other more widespread variants in high linkage disequilibrium, including intergenic variants. CONCLUSIONS Most low-prevalence variants have low minor allele frequencies and only a small subset of low-prevalence variants contributed detectable fractions of phenotypic variance of production traits. Accounting for low-prevalence variants is therefore unlikely to noticeably benefit across-breed analyses, such as the prediction of genomic breeding values in a population using reference populations of a different genetic background.
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24
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Xiang R, Fang L, Sanchez MP, Cheng H, Zhang Z. Editorial: Multi-Layered Genome-Wide Association/Prediction in Animals. Front Genet 2022; 13:877748. [PMID: 35464854 PMCID: PMC9023786 DOI: 10.3389/fgene.2022.877748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 03/07/2022] [Indexed: 12/02/2022] Open
Affiliation(s)
- Ruidong Xiang
- Faculty of Veterinary and Agricultural Science, The University of Melbourne, Parkville, VIC, Australia
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC, Australia
- *Correspondence: Ruidong Xiang,
| | - Lingzhao Fang
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, United Kingdom
| | | | - Hao Cheng
- Department of Animal Science, University of California, Davis, Davis, CA, United States
| | - Zhe Zhang
- College of Animal Science, South China Agricultural University, Guangzhou, China
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25
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Singh A, Kumar A, Gondro C, Pandey AK, Dutt T, Mishra BP. Genome Wide Scan to Identify Potential Genomic Regions Associated With Milk Protein and Minerals in Vrindavani Cattle. Front Vet Sci 2022; 9:760364. [PMID: 35359668 PMCID: PMC8960298 DOI: 10.3389/fvets.2022.760364] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 02/11/2022] [Indexed: 12/02/2022] Open
Abstract
In this study, genome-wide association study (GWAS) was conducted for identifying significantly associated genomic regions/SNPs with milk protein and minerals in the 96 taurine-indicine crossbred (Vrindavani) cows using 50K SNP Chip. After quality control, a total of 41,427 SNPs were retained and were further analyzed using a single-SNP additive linear model. Lactation stage, parity, test day milk yield and proportion of exotic inheritance were included as fixed effects in GWAS model. Across all traits, 13 genome-wide significant (p < 1.20 x 10−06) and 49 suggestive significant (p < 2.41 x 10−05) SNPs were identified which were located on 18 different autosomes. The strongest association for protein percentage, calcium (Ca), phosphorus (P), copper (Cu), zinc (Zn), and iron (Fe) were found on BTA 18, 7, 2, 3, 14, and 2, respectively. No significant SNP was detected for manganese (Mn). Several significant SNPs identified were within or close proximity to CDH13, BHLHE40, EDIL3, HAPLN1, INHBB, USP24, ZFAT, and IKZF2 gene, respectively. Enrichment analysis of the identified candidate genes elucidated biological processes, cellular components, and molecular functions involved in metal ion binding, ion transportation, transmembrane protein, and signaling pathways. This study provided a groundwork to characterize the molecular mechanism for the phenotypic variation in milk protein percentage and minerals in crossbred cattle. Further work is required on a larger sample size with fine mapping of identified QTL to validate potential candidate regions.
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Affiliation(s)
- Akansha Singh
- Animal Genetics Division, Indian Council of Agricultural Research-Indian Veterinary Research Institute, Bareilly, India
| | - Amit Kumar
- Animal Genetics Division, Indian Council of Agricultural Research-Indian Veterinary Research Institute, Bareilly, India
- *Correspondence: Amit Kumar
| | - Cedric Gondro
- Department of Animal Science, Michigan State University, East Lansing, MI, United States
| | - A. K. Pandey
- Animal Genetics Division, Indian Council of Agricultural Research-Indian Veterinary Research Institute, Bareilly, India
| | - Triveni Dutt
- Livestock Production and Management, Indian Council of Agricultural Research-Indian Veterinary Research Institute, Bareilly, India
| | - B. P. Mishra
- Division of Animal Biotechnology, Indian Council of Agricultural Research-Indian Veterinary Research Institute, Bareilly, India
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Taherkhani L, Banabazi MH, EmamJomeh-Kashan N, Noshary A, Imumorin I. The Candidate Chromosomal Regions Responsible for Milk Yield of Cow: A GWAS Meta-Analysis. Animals (Basel) 2022; 12:ani12050582. [PMID: 35268150 PMCID: PMC8909671 DOI: 10.3390/ani12050582] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 02/15/2022] [Accepted: 02/22/2022] [Indexed: 12/10/2022] Open
Abstract
Milk yield (MY) is highly heritable and an economically important trait in dairy livestock species. To increase power to detect candidate genomic regions for this trait, we carried out a meta-analysis of genome-wide association studies (GWAS). In the present study, we identified 19 studies in PubMed for the meta-analysis. After review of the studies, 16 studies passed the filters for meta-analysis, and the number of chromosomes, detected markers and their positions, number of animals, and p-values were extracted from these studies and recorded. The final data set based on 16 GWAS studies had 353,698 cows and 3950 markers and was analyzed using METAL software. Our findings revealed 1712 significant (p-value < 2.5 × 10−6) genomic loci related to MY, with markers associated with MY found on all autosomes and sex chromosomes and the majority of them found on chromosome 14. Furthermore, gene ontology (GO) annotation was used to explore biological functions of the genes associated with MY; therefore, different regions of this chromosome may be suitable as genomic regions for further research into gene expression.
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Affiliation(s)
- Lida Taherkhani
- Department of Animal Science, Science and Research Branch, Islamic Azad University, Tehran 1477893855, Iran; (L.T.); (N.E.-K.)
| | - Mohammad Hossein Banabazi
- Department of Biotechnology, Animal Science Research Institute of Iran (ASRI), Agricultural Research, Education & Extension Organization (AREEO), Karaj 3146618361, Iran
- Department of Animal Breeding and Genetics (HGEN), Center for Veterinary Medicine and Animal Science (VHC), Swedish University of Agricultural Sciences (SLU), 75007 Uppsala, Sweden
- Correspondence: ; Tel.: +98-9352470999
| | - Nasser EmamJomeh-Kashan
- Department of Animal Science, Science and Research Branch, Islamic Azad University, Tehran 1477893855, Iran; (L.T.); (N.E.-K.)
| | - Alireza Noshary
- Department of Animal Science, Karaj Branch, Islamic Azad University, Karaj 3187644511, Iran;
| | - Ikhide Imumorin
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA;
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27
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Sustainable Intensification of Beef Production in the Tropics: The Role of Genetically Improving Sexual Precocity of Heifers. Animals (Basel) 2022; 12:ani12020174. [PMID: 35049797 PMCID: PMC8772995 DOI: 10.3390/ani12020174] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 01/07/2022] [Accepted: 01/08/2022] [Indexed: 12/16/2022] Open
Abstract
Simple Summary Tropical pasture-based beef production systems play a vital role in global food security. The importance of promoting sustainable intensification of such systems has been debated worldwide. Demand for beef is growing together with concerns over the impact of its production on the environment. Implementing sustainable livestock intensification programs relies on animal genetic improvement. In tropical areas, the lack of sexual precocity is a bottleneck for cattle efficiency, directly impacting the sustainability of production systems. In the present review we present and discuss the state of the art of genetic evaluation for sexual precocity in Bos indicus beef cattle, covering the definition of measurable traits, genetic parameter estimates, genomic analyses, and a case study of selection for sexual precocity in Nellore breeding programs. Abstract Increasing productivity through continued animal genetic improvement is a crucial part of implementing sustainable livestock intensification programs. In Zebu cattle, the lack of sexual precocity is one of the main obstacles to improving beef production efficiency. Puberty-related traits are complex, but large-scale data sets from different “omics” have provided information on specific genes and biological processes with major effects on the expression of such traits, which can greatly increase animal genetic evaluation. In addition, genetic parameter estimates and genomic predictions involving sexual precocity indicator traits and productive, reproductive, and feed-efficiency related traits highlighted the feasibility and importance of direct selection for anticipating heifer reproductive life. Indeed, the case study of selection for sexual precocity in Nellore breeding programs presented here show that, in 12 years of selection for female early precocity and improved management practices, the phenotypic means of age at first calving showed a strong decreasing trend, changing from nearly 34 to less than 28 months, with a genetic trend of almost −2 days/year. In this period, the percentage of early pregnancy in the herds changed from around 10% to more than 60%, showing that the genetic improvement of heifer’s sexual precocity allows optimizing the productive cycle by reducing the number of unproductive animals in the herd. It has a direct impact on sustainability by better use of resources. Genomic selection breeding programs accounting for genotype by environment interaction represent promising tools for accelerating genetic progress for sexual precocity in tropical beef cattle.
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Gholizadeh M, Esmaeili-Fard SM. Meta-analysis of genome-wide association studies for litter size in sheep. Theriogenology 2021; 180:103-112. [PMID: 34968818 DOI: 10.1016/j.theriogenology.2021.12.025] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 12/19/2021] [Accepted: 12/19/2021] [Indexed: 01/01/2023]
Abstract
Litter size and ovulation rate are important reproduction traits in sheep and have important impacts on the profitability of farm animals. To investigate the genetic architecture of litter size, we report the first meta-analysis of genome-wide association studies (GWAS) using 522 ewes and 564,377 SNPs from six sheep breeds. We identified 29 significant associations for litter size which 27 of which have not been reported in individual GWAS for each population. However, we could confirm the role of BMPR1B in prolificacy. Our gene set analysis discovered biological pathways related to cell signaling, communication, and adhesion. Functional clustering and enrichment using protein databases identified epidermal growth factor-like domain affecting litter size. Through analyzing protein-protein interaction data, we could identify hub genes like CASK, PLCB4, RPTOR, GRIA2, and PLCB1 that were enriched in most of the significant pathways. These genes have a role in cell proliferation, cell adhesion, cell growth and survival, and autophagy. Notably, identified SNPs were scattered on several different chromosomes implying different genetic mechanisms underlying variation of prolificacy in each breed. Given the different layers that make up the follicles and the need for communication and transfer of hormones and nutrients through these layers to the oocyte, the significance of pathways related to cell signaling and communication seems logical. Our results provide genetic insights into the litter size variation in different sheep breeds.
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Affiliation(s)
- Mohsen Gholizadeh
- Department of Animal Science, Faculty of Animal Science and Fisheries, Sari Agricultural Sciences and Natural Resources University, Sari, Iran.
| | - Seyed Mehdi Esmaeili-Fard
- Department of Animal Science, Faculty of Animal Science and Fisheries, Sari Agricultural Sciences and Natural Resources University, Sari, Iran
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Cendron F, Franzoi M, Penasa M, De Marchi M, Cassandro M. Effects of β- and κ-casein, and β-lactoglobulin single and composite genotypes on milk composition and milk coagulation properties of Italian Holsteins assessed by FT-MIR. ITALIAN JOURNAL OF ANIMAL SCIENCE 2021. [DOI: 10.1080/1828051x.2021.2011442] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Affiliation(s)
- Filippo Cendron
- Dipartimento di Agronomia, Animali, Alimenti, Risorse naturali e Ambiente, University of Padova, Legnaro, Italy
| | - Marco Franzoi
- Dipartimento di Agronomia, Animali, Alimenti, Risorse naturali e Ambiente, University of Padova, Legnaro, Italy
| | - Mauro Penasa
- Dipartimento di Agronomia, Animali, Alimenti, Risorse naturali e Ambiente, University of Padova, Legnaro, Italy
| | - Massimo De Marchi
- Dipartimento di Agronomia, Animali, Alimenti, Risorse naturali e Ambiente, University of Padova, Legnaro, Italy
| | - Martino Cassandro
- Dipartimento di Agronomia, Animali, Alimenti, Risorse naturali e Ambiente, University of Padova, Legnaro, Italy
- Federazione delle Associazioni Nazionali di Razza e di Specie, Roma, Italy
- Associazione Nazionale Allevatori di Razza Frisona Bruna Jersey Italiana, Cremona, Italy
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Hartfield M, Poulsen NA, Guldbrandtsen B, Bataillon T. Using singleton densities to detect recent selection in Bos taurus. Evol Lett 2021; 5:595-606. [PMID: 34917399 PMCID: PMC8645200 DOI: 10.1002/evl3.263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 10/07/2021] [Accepted: 10/08/2021] [Indexed: 11/05/2022] Open
Abstract
Many quantitative traits are subject to polygenic selection, where several genomic regions undergo small, simultaneous changes in allele frequency that collectively alter a phenotype. The widespread availability of genome data, along with novel statistical techniques, has made it easier to detect these changes. We apply one such method, the "Singleton Density Score" (SDS), to the Holstein breed of Bos taurus to detect recent selection (arising up to around 740 years ago). We identify several genes as candidates for targets of recent selection, including some relating to cell regulation, catabolic processes, neural-cell adhesion and immunity. We do not find strong evidence that three traits that are important to humans-milk protein content, milk fat content, and stature-have been subject to directional selection. Simulations demonstrate that because B. taurus recently experienced a population bottleneck, singletons are depleted so the power of SDS methods is reduced. These results inform on which genes underlie recent genetic change in B. taurus, while providing information on how polygenic selection can be best investigated in future studies.
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Affiliation(s)
- Matthew Hartfield
- Bioinformatics Research CentreAarhus UniversityAarhusDK‐8000Denmark
- Institute of Evolutionary BiologyUniversity of EdinburghEdinburghEH9 3FLUnited Kingdom
| | | | - Bernt Guldbrandtsen
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and GeneticsAarhus UniversityTjeleDK‐8830Denmark
- Rheinische Friedrich‐Wilhelms‐Universität BonnInstitut für TierwissenschaftenBonnDE‐53115Germany
- Department of Veterinary SciencesCopenhagen UniversityFrederiksberg CDK‐1870Denmark
| | - Thomas Bataillon
- Bioinformatics Research CentreAarhus UniversityAarhusDK‐8000Denmark
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Pedrosa VB, Schenkel FS, Chen SY, Oliveira HR, Casey TM, Melka MG, Brito LF. Genomewide Association Analyses of Lactation Persistency and Milk Production Traits in Holstein Cattle Based on Imputed Whole-Genome Sequence Data. Genes (Basel) 2021; 12:1830. [PMID: 34828436 PMCID: PMC8624223 DOI: 10.3390/genes12111830] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Revised: 11/13/2021] [Accepted: 11/17/2021] [Indexed: 12/22/2022] Open
Abstract
Lactation persistency and milk production are among the most economically important traits in the dairy industry. In this study, we explored the association of over 6.1 million imputed whole-genome sequence variants with lactation persistency (LP), milk yield (MILK), fat yield (FAT), fat percentage (FAT%), protein yield (PROT), and protein percentage (PROT%) in North American Holstein cattle. We identified 49, 3991, 2607, 4459, 805, and 5519 SNPs significantly associated with LP, MILK, FAT, FAT%, PROT, and PROT%, respectively. Various known associations were confirmed while several novel candidate genes were also revealed, including ARHGAP35, NPAS1, TMEM160, ZC3H4, SAE1, ZMIZ1, PPIF, LDB2, ABI3, SERPINB6, and SERPINB9 for LP; NIM1K, ZNF131, GABRG1, GABRA2, DCHS1, and SPIDR for MILK; NR6A1, OLFML2A, EXT2, POLD1, GOT1, and ETV6 for FAT; DPP6, LRRC26, and the KCN gene family for FAT%; CDC14A, RTCA, HSTN, and ODAM for PROT; and HERC3, HERC5, LALBA, CCL28, and NEURL1 for PROT%. Most of these genes are involved in relevant gene ontology (GO) terms such as fatty acid homeostasis, transporter regulator activity, response to progesterone and estradiol, response to steroid hormones, and lactation. The significant genomic regions found contribute to a better understanding of the molecular mechanisms related to LP and milk production in North American Holstein cattle.
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Affiliation(s)
- Victor B. Pedrosa
- Department of Animal Sciences, Purdue University, West Lafayette, IN 47907, USA; (V.B.P.); (S.-Y.C.); (H.R.O.); (T.M.C.)
- Department of Animal Sciences, State University of Ponta Grossa, Ponta Grossa 84030-900, Brazil
| | - Flavio S. Schenkel
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G2W1, Canada;
| | - Shi-Yi Chen
- Department of Animal Sciences, Purdue University, West Lafayette, IN 47907, USA; (V.B.P.); (S.-Y.C.); (H.R.O.); (T.M.C.)
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science & Technology, Sichuan Agricultural University, Chengdu 611130, China
| | - Hinayah R. Oliveira
- Department of Animal Sciences, Purdue University, West Lafayette, IN 47907, USA; (V.B.P.); (S.-Y.C.); (H.R.O.); (T.M.C.)
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G2W1, Canada;
| | - Theresa M. Casey
- Department of Animal Sciences, Purdue University, West Lafayette, IN 47907, USA; (V.B.P.); (S.-Y.C.); (H.R.O.); (T.M.C.)
| | - Melkaye G. Melka
- Department of Animal and Food Science, University of Wisconsin River Falls, River Falls, WI 54022, USA;
| | - Luiz F. Brito
- Department of Animal Sciences, Purdue University, West Lafayette, IN 47907, USA; (V.B.P.); (S.-Y.C.); (H.R.O.); (T.M.C.)
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Meta-analysis of genome-wide association studies and gene networks analysis for milk production traits in Holstein cows. Livest Sci 2021. [DOI: 10.1016/j.livsci.2021.104605] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Meuwissen T, van den Berg I, Goddard M. On the use of whole-genome sequence data for across-breed genomic prediction and fine-scale mapping of QTL. Genet Sel Evol 2021; 53:19. [PMID: 33637049 PMCID: PMC7908738 DOI: 10.1186/s12711-021-00607-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Accepted: 01/25/2021] [Indexed: 11/10/2022] Open
Abstract
Background Whole-genome sequence (WGS) data are increasingly available on large numbers of individuals in animal and plant breeding and in human genetics through second-generation resequencing technologies, 1000 genomes projects, and large-scale genotype imputation from lower marker densities. Here, we present a computationally fast implementation of a variable selection genomic prediction method, that could handle WGS data on more than 35,000 individuals, test its accuracy for across-breed predictions and assess its quantitative trait locus (QTL) mapping precision. Methods The Monte Carlo Markov chain (MCMC) variable selection model (Bayes GC) fits simultaneously a genomic best linear unbiased prediction (GBLUP) term, i.e. a polygenic effect whose correlations are described by a genomic relationship matrix (G), and a Bayes C term, i.e. a set of single nucleotide polymorphisms (SNPs) with large effects selected by the model. Computational speed is improved by a Metropolis–Hastings sampling that directs computations to the SNPs, which are, a priori, most likely to be included into the model. Speed is also improved by running many relatively short MCMC chains. Memory requirements are reduced by storing the genotype matrix in binary form. The model was tested on a WGS dataset containing Holstein, Jersey and Australian Red cattle. The data contained 4,809,520 genotypes on 35,549 individuals together with their milk, fat and protein yields, and fat and protein percentage traits. Results The prediction accuracies of the Jersey individuals improved by 1.5% when using across-breed GBLUP compared to within-breed predictions. Using WGS instead of 600 k SNP-chip data yielded on average a 3% accuracy improvement for Australian Red cows. QTL were fine-mapped by locating the SNP with the highest posterior probability of being included in the model. Various QTL known from the literature were rediscovered, and a new SNP affecting milk production was discovered on chromosome 20 at 34.501126 Mb. Due to the high mapping precision, it was clear that many of the discovered QTL were the same across the five dairy traits. Conclusions Across-breed Bayes GC genomic prediction improved prediction accuracies compared to GBLUP. The combination of across-breed WGS data and Bayesian genomic prediction proved remarkably effective for the fine-mapping of QTL.
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Affiliation(s)
- Theo Meuwissen
- Norwegian University of Life Sciences, Box 5003, 1432, Ås, Norway.
| | | | - Mike Goddard
- Agriculture Victoria, Bundoora, Australia.,Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, Australia
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van den Berg I, Ho PN, Haile-Mariam M, Beatson PR, O'Connor E, Pryce JE. Genetic parameters of blood urea nitrogen and milk urea nitrogen concentration in dairy cattle managed in pasture-based production systems of New Zealand and Australia. ANIMAL PRODUCTION SCIENCE 2021. [DOI: 10.1071/an21049] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Context
Urinary nitrogen excretion by grazing cattle causes environmental pollution. Selecting for cows with a lower concentration of urinary nitrogen excretion may reduce the environmental impact. While urinary nitrogen excretion is difficult to measure, blood urea nitrogen (BUN), mid-infrared spectroscopy (MIR)-predicted BUN (MBUN), which is predicted from MIR spectra measured on milk samples, and milk urea nitrogen (MUN) are potential indicator traits. Australia and New Zealand have increasing datasets of cows with urea records, with 18 120 and 15 754 cows with urea records in Australia and New Zealand respectively. A collaboration between Australia and New Zealand could further increase the size of the dataset by sharing data.
Aims
Our aims were to estimate genetic parameters for urea traits within country, and genetic correlations between countries to gauge the benefit of having a joint reference population for genomic prediction of an indicator trait that is potentially suitable for selection to reduce urinary nitrogen excretion for both countries.
Methods
Genetic parameters were estimated within country (Australia and New Zealand) in Holstein, Jersey and a multibreed population, for BUN, MBUN and MUN in Australia and MUN in New Zealand, using high-density genotypes. Genetic correlations were also estimated between the urea traits recorded in Australia and MUN in New Zealand. Analyses used the first record available for each cow or within days-in-milk (DIM) intervals.
Key results
Heritabilities ranged from 0.08 to 0.32 for the various urea traits. Higher heritabilities were obtained for Jersey than for Holstein, and for the New Zealand cows than for the Australian cows. While urea traits were highly correlated within Australia (0.71–0.94), genetic correlations between Australia and New Zealand were small to moderate (0.08–0.58).
Conclusions
Our results showed that the heritability for urea traits differs among trait, breed, and country. While urea traits are highly correlated within country, genetic correlations between urea traits in Australia and MUN in New Zealand were only low to moderate.
Implications
Further study is required to identify the underlying causes of the difference in heritabilities observed, to compare the accuracies of different reference populations, and to estimate genetic correlations between urea traits and other traits such as fertility and feed intake. Larger datasets may help estimate genetic correlations more accurately between countries.
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