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de Oliveira LF, Veroneze R, Sousa KRS, Mulim HA, Araujo AC, Huang Y, Johnson JS, Brito LF. Genomic regions, candidate genes, and pleiotropic variants associated with physiological and anatomical indicators of heat stress response in lactating sows. BMC Genomics 2024; 25:467. [PMID: 38741036 DOI: 10.1186/s12864-024-10365-4] [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: 02/12/2024] [Accepted: 04/29/2024] [Indexed: 05/16/2024] Open
Abstract
BACKGROUND Heat stress (HS) poses significant threats to the sustainability of livestock production. Genetically improving heat tolerance could enhance animal welfare and minimize production losses during HS events. Measuring phenotypic indicators of HS response and understanding their genetic background are crucial steps to optimize breeding schemes for improved climatic resilience. The identification of genomic regions and candidate genes influencing the traits of interest, including variants with pleiotropic effects, enables the refinement of genotyping panels used to perform genomic prediction of breeding values and contributes to unraveling the biological mechanisms influencing heat stress response. Therefore, the main objectives of this study were to identify genomic regions, candidate genes, and potential pleiotropic variants significantly associated with indicators of HS response in lactating sows using imputed whole-genome sequence (WGS) data. Phenotypic records for 18 traits and genomic information from 1,645 lactating sows were available for the study. The genotypes from the PorcineSNP50K panel containing 50,703 single nucleotide polymorphisms (SNPs) were imputed to WGS and after quality control, 1,622 animals and 7,065,922 SNPs were included in the analyses. RESULTS A total of 1,388 unique SNPs located on sixteen chromosomes were found to be associated with 11 traits. Twenty gene ontology terms and 11 biological pathways were shown to be associated with variability in ear skin temperature, shoulder skin temperature, rump skin temperature, tail skin temperature, respiration rate, panting score, vaginal temperature automatically measured every 10 min, vaginal temperature measured at 0800 h, hair density score, body condition score, and ear area. Seven, five, six, two, seven, 15, and 14 genes with potential pleiotropic effects were identified for indicators of skin temperature, vaginal temperature, animal temperature, respiration rate, thermoregulatory traits, anatomical traits, and all traits, respectively. CONCLUSIONS Physiological and anatomical indicators of HS response in lactating sows are heritable but highly polygenic. The candidate genes found are associated with important gene ontology terms and biological pathways related to heat shock protein activities, immune response, and cellular oxidative stress. Many of the candidate genes with pleiotropic effects are involved in catalytic activities to reduce cell damage from oxidative stress and cellular mechanisms related to immune response.
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Affiliation(s)
- Letícia Fernanda de Oliveira
- Department of Animal Science, Federal University of Viçosa, Viçosa, MG, Brazil
- Department of Animal Sciences, Purdue University, West Lafayette, IN, USA
| | - Renata Veroneze
- Department of Animal Science, Federal University of Viçosa, Viçosa, MG, Brazil
| | - Katiene Régia Silva Sousa
- Department of Animal Sciences, Purdue University, West Lafayette, IN, USA
- Department of Oceanography and Limnology, Federal University of Maranhão, São Luís, MA, Brazil
| | - Henrique A Mulim
- Department of Animal Sciences, Purdue University, West Lafayette, IN, USA
| | | | | | - Jay S Johnson
- USDA-ARS Livestock Behavior Research Unit, West Lafayette, IN, USA
| | - Luiz F Brito
- Department of Animal Sciences, Purdue University, West Lafayette, IN, USA.
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Tan WLA, Hudson NJ, Porto Neto LR, Reverter A, Afonso J, Fortes MRS. An association weight matrix identified biological pathways associated with bull fertility traits in a multi-breed population. Anim Genet 2024. [PMID: 38692842 DOI: 10.1111/age.13431] [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: 11/16/2023] [Revised: 02/26/2024] [Accepted: 04/01/2024] [Indexed: 05/03/2024]
Abstract
Using seven indicator traits, we investigated the genetic basis of bull fertility and predicted gene interactions from SNP associations. We used percent normal sperm as the key phenotype for the association weight matrix-partial correlation information theory (AWM-PCIT) approach. Beyond a simple list of candidate genes, AWM-PCIT predicts significant gene interactions and associations for the selected traits. These interactions formed a network of 537 genes: 38 genes were transcription cofactors, and 41 genes were transcription factors. The network displayed two distinct clusters, one with 294 genes and another with 243 genes. The network is enriched in fertility-associated pathways: steroid biosynthesis, p53 signalling, and the pentose phosphate pathway. Enrichment analysis also highlighted gene ontology terms associated with 'regulation of neurotransmitter secretion' and 'chromatin formation'. Our network recapitulates some genes previously implicated in another network built with lower-density genotypes. Sequence-level data also highlights additional candidate genes relevant to bull fertility, such as FOXO4, FOXP3, GATA1, CYP27B1, and EBP. A trio of regulatory genes-KDM5C, LRRK2, and PME-was deemed core to the network because of their overarching connections. This trio probably influences bull fertility through their interaction with genes, both known and unknown as to their role in male fertility. Future studies may target the trio and their target genes to enrich our understanding of male fertility further.
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Affiliation(s)
- Wei Liang Andre Tan
- School of Chemistry and Molecular Bioscience, The University of Queensland, St Lucia, Queensland, Australia
| | - Nicholas James Hudson
- School of Agriculture and Food Sustainability, The University of Queensland, Gatton, Queensland, Australia
| | | | | | - Juliano Afonso
- School of Chemistry and Molecular Bioscience, The University of Queensland, St Lucia, Queensland, Australia
- Empresa Brasileira de Pesquisa Agropecuária, Pecuária Sudeste, São Carlos, São Paulo, Brazil
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Jové-Juncà T, Crespo-Piazuelo D, González-Rodríguez O, Pascual M, Hernández-Banqué C, Reixach J, Quintanilla R, Ballester M. Genomic architecture of carcass and pork traits and their association with immune capacity. Animal 2024; 18:101043. [PMID: 38113634 DOI: 10.1016/j.animal.2023.101043] [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: 05/25/2023] [Revised: 11/16/2023] [Accepted: 11/20/2023] [Indexed: 12/21/2023] Open
Abstract
Carcass and pork traits have traditionally been considered of prime importance in pig breeding programmes. However, the changing conditions in modern farming, coupled with antimicrobial resistance issues, are raising the importance of health and robustness-related traits. Here, we explore the genetic architecture of carcass and pork traits and their relationship with immunity phenotypes in a commercial Duroc pig population. A total of nine traits related to fatness, lean content and meat pH were measured at slaughter (∼190 d of age) in 378 pigs previously phenotyped (∼70 d of age) for 36 immunity-related traits, including plasma concentrations of immunoglobulins, acute-phase proteins, leukocytes subpopulations and phagocytosis. Our study showed medium to high heritabilities and strong genetic correlations between fatness, lean content and meat pH at 24 h postmortem. Genetic correlations were found between carcass and pork traits and white blood cells. pH showed strong positive genetic correlations with leukocytes and eosinophils, and strong negative genetic correlations with haemoglobin, haematocrit and cytotoxic T cell proportion. In addition, genome-wide association studies (GWASs) pointed out four significantly associated genomic regions for lean meat percentages in different muscles, ham fat, backfat thickness, and semimembranosus pH at 24 h. The functional annotation of genes located in these regions reported a total of 14 candidate genes, with BGN, DPP10, LEPR, LEPROT, PDE4B and SLC6A8 being the strongest candidates. After performing an expression GWAS for the expression of these genes in muscle, two signals were detected in cis for the BGN and SLC6A8 genes. Our results indicate a genetic relationship between carcass fatness, lean content and meat pH with a variety of immunity-related traits that should be considered to improve immunocompetence without impairing production traits.
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Affiliation(s)
- T Jové-Juncà
- Animal Breeding and Genetics Program, IRTA, Torre Marimon, 08140 Caldes de Montbui, Barcelona, Spain
| | - D Crespo-Piazuelo
- Animal Breeding and Genetics Program, IRTA, Torre Marimon, 08140 Caldes de Montbui, Barcelona, Spain
| | - O González-Rodríguez
- Animal Breeding and Genetics Program, IRTA, Torre Marimon, 08140 Caldes de Montbui, Barcelona, Spain
| | - M Pascual
- Animal Breeding and Genetics Program, IRTA, Torre Marimon, 08140 Caldes de Montbui, Barcelona, Spain
| | - C Hernández-Banqué
- Animal Breeding and Genetics Program, IRTA, Torre Marimon, 08140 Caldes de Montbui, Barcelona, Spain
| | - J Reixach
- Selección Batallé S.A., Av. dels Segadors s/n, 17421 Riudarenes, Girona, Spain
| | - R Quintanilla
- Animal Breeding and Genetics Program, IRTA, Torre Marimon, 08140 Caldes de Montbui, Barcelona, Spain
| | - M Ballester
- Animal Breeding and Genetics Program, IRTA, Torre Marimon, 08140 Caldes de Montbui, Barcelona, Spain.
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Fan S, Kong C, Chen Y, Zheng X, Zhou R, Zhang X, Wu X, Zhang W, Ding Y, Yin Z. Copy Number Variation Analysis Revealed the Evolutionary Difference between Chinese Indigenous Pigs and Asian Wild Boars. Genes (Basel) 2023; 14:472. [PMID: 36833399 PMCID: PMC9957247 DOI: 10.3390/genes14020472] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 02/08/2023] [Accepted: 02/09/2023] [Indexed: 02/15/2023] Open
Abstract
Copy number variation (CNV) has been widely used to study the evolution of different species. We first discovered different CNVs in 24 Anqingliubai pigs and 6 Asian wild boars using next-generation sequencing at the whole-genome level with 10× depth to understand the relationship between genetic evolution and production traits in wild boars and domestic pigs. A total of 97,489 CNVs were identified and divided into 10,429 copy number variation regions (CNVRs), occupying 32.06% of the porcine genome. Chromosome 1 had the most CNVRs, and chromosome 18 had the least. Ninety-six CNVRs were selected using VST 1% based on the signatures of all CNVRs, and sixty-five genes were identified in the selected regions. These genes were strongly correlated with traits distinguishing groups by enrichment in Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways, such as growth (CD36), reproduction (CIT, RLN), detoxification (CYP3A29), and fatty acid metabolism (ELOVL6). The QTL overlapping regions were associated with meat traits, growth, and immunity, which was consistent with CNV analysis. Our findings increase the understanding of evolved genome structural variations between wild boars and domestic pigs, and provide new molecular biomarkers to guide breeding and the efficient use of available genetic resources.
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Affiliation(s)
- Shuhao Fan
- College of Animal Science and Technology, Anhui Agricultural University, Hefei 230036, China
| | - Chengcheng Kong
- College of Animal Science and Technology, Anhui Agricultural University, Hefei 230036, China
- School of Pharmacy, Anhui University of Chinese Medicine, Hefei 230036, China
| | - Yige Chen
- College of Animal Science and Technology, Anhui Agricultural University, Hefei 230036, China
| | - Xianrui Zheng
- College of Animal Science and Technology, Anhui Agricultural University, Hefei 230036, China
| | - Ren Zhou
- College of Animal Science and Technology, Anhui Agricultural University, Hefei 230036, China
| | - Xiaodong Zhang
- College of Animal Science and Technology, Anhui Agricultural University, Hefei 230036, China
| | - Xudong Wu
- Key Laboratory of Pig Molecular Quantitative Genetics of Anhui Academy of Agricultural Sciences, Anhui Provincial Key Laboratory of Livestock and Poultry Product Safety Engineering, Institute of Animal Husbandry and Veterinary Medicine, Anhui Academy of Agricultural Sciences, Hefei 230031, China
| | - Wei Zhang
- Key Laboratory of Pig Molecular Quantitative Genetics of Anhui Academy of Agricultural Sciences, Anhui Provincial Key Laboratory of Livestock and Poultry Product Safety Engineering, Institute of Animal Husbandry and Veterinary Medicine, Anhui Academy of Agricultural Sciences, Hefei 230031, China
| | - Yueyun Ding
- College of Animal Science and Technology, Anhui Agricultural University, Hefei 230036, China
| | - Zongjun Yin
- College of Animal Science and Technology, Anhui Agricultural University, Hefei 230036, China
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