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Faggion S, Bonfatti V, Carnier P. Genome-Wide Association Study for Weight Loss at the End of Dry-Curing of Hams Produced from Purebred Heavy Pigs. Animals (Basel) 2024; 14:1983. [PMID: 38998095 PMCID: PMC11240668 DOI: 10.3390/ani14131983] [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: 05/31/2024] [Revised: 06/24/2024] [Accepted: 07/03/2024] [Indexed: 07/14/2024] Open
Abstract
Dissecting the genetics of production traits in livestock is of outmost importance, both to understand biological mechanisms underlying those traits and to facilitate the design of selection programs incorporating that information. For the pig industry, traits related to curing are key for protected designation of origin productions. In particular, appropriate ham weight loss after dry-curing ensures high quality of the final product and avoids economic losses. In this study, we analyzed data (N = 410) of ham weight loss after approximately 20 months of dry-curing. The animals used for ham production were purebred pigs belonging to a commercial line. A genome-wide association study (GWAS) of 29,844 SNP markers revealed the polygenic nature of the trait: 221 loci explaining a small percentage of the variance (0.3-1.65%) were identified on almost all Sus scrofa chromosomes. Post-GWAS analyses revealed 32 windows located within regulatory regions and 94 windows located in intronic regions of specific genes. In total, 30 candidate genes encoding receptors and enzymes associated with ham weight loss (MTHFD1L, DUSP8), proteolysis (SPARCL1, MYH8), drip loss (TNNI2), growth (CDCA3, LSP1, CSMD1, AP2A2, TSPAN4), and fat metabolism (AGPAT4, IGF2R, PTDSS2, HRAS, TALDO1, BRSK2, TNNI2, SYT8, GTF2I, GTF2IRD1, LPCAT3, ATN1, GNB3, CMIP, SORCS2, CCSER1, SPP1) were detected.
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Affiliation(s)
- Sara Faggion
- Department of Comparative Biomedicine and Food Science, University of Padova, Viale dell'Università 16, 35020 Padova, Italy
| | - Valentina Bonfatti
- Department of Comparative Biomedicine and Food Science, University of Padova, Viale dell'Università 16, 35020 Padova, Italy
| | - Paolo Carnier
- Department of Comparative Biomedicine and Food Science, University of Padova, Viale dell'Università 16, 35020 Padova, Italy
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Jung JH, Lee SM, Oh SH. A genome-wide association study on growth traits of Korean commercial pig breeds using Bayesian methods. Anim Biosci 2024; 37:807-816. [PMID: 38637973 PMCID: PMC11065719 DOI: 10.5713/ab.23.0443] [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: 10/25/2023] [Revised: 12/01/2023] [Accepted: 03/19/2024] [Indexed: 04/20/2024] Open
Abstract
OBJECTIVE This study aims to identify the significant regions and candidate genes of growth-related traits (adjusted backfat thickness [ABF], average daily gain [ADG], and days to 90 kg [DAYS90]) in Korean commercial GGP pig (Duroc, Landrace, and Yorkshire) populations. METHODS A genome-wide association study (GWAS) was performed using single-nucleotide polymorphism (SNP) markers for imputation to Illumina PorcineSNP60. The BayesB method was applied to calculate thresholds for the significance of SNP markers. The identified windows were considered significant if they explained ≥1% genetic variance. RESULTS A total of 28 window regions were related to genetic growth effects. Bayesian GWAS revealed 28 significant genetic regions including 52 informative SNPs associated with growth traits (ABF, ADG, DAYS90) in Duroc, Landrace, and Yorkshire pigs, with genetic variance ranging from 1.00% to 5.46%. Additionally, 14 candidate genes with previous functional validation were identified for these traits. CONCLUSION The identified SNPs within these regions hold potential value for future markerassisted or genomic selection in pig breeding programs. Consequently, they contribute to an improved understanding of genetic architecture and our ability to genetically enhance pigs. SNPs within the identified regions could prove valuable for future marker-assisted or genomic selection in pig breeding programs.
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Affiliation(s)
| | - Sang Min Lee
- National Institute of Animal Science, RDA, Cheonan, 31000,
Korea
| | - Sang-Hyon Oh
- Division of Animal Science, Gyeongsang National University, Jinju 52725,
Korea
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Feng X, Tong L, Ma L, Mu T, Yu B, Ma R, Li J, Wang C, Zhang J, Gu Y. Mining key circRNA-associated-ceRNA networks for milk fat metabolism in cows with varying milk fat percentages. BMC Genomics 2024; 25:323. [PMID: 38561663 PMCID: PMC10983688 DOI: 10.1186/s12864-024-10252-y] [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: 08/31/2023] [Accepted: 03/26/2024] [Indexed: 04/04/2024] Open
Abstract
BACKGROUND Cow milk fat is an essential indicator for evaluating and measuring milk quality and cow performance. Growing research has identified the molecular functions of circular RNAs (circRNAs) necessary for mammary gland development and lactation in mammals. METHOD The present study analyzed circRNA expression profiling data in mammary epithelial cells (MECs) from cows with highly variable milk fat percentage (MFP) using differential expression analysis and weighted gene co-expression network analysis (WGCNA). RESULTS A total of 309 differentially expressed circRNAs (DE-circRNAs) were identified in the high and low MFP groups. WGCNA analysis revealed that the pink module was significantly associated with MFP (r = - 0.85, P = 0.007). Parental genes of circRNAs in this module were enriched mainly in lipid metabolism-related signaling pathways, such as focal adhesion, ECM-receptor interaction, adherens junction and AMPK. Finally, six DE-circRNAs were screened from the pink module: circ_0010571, circ_0007797, circ_0002746, circ_0003052, circ_0004319, and circ_0012840. Among them, circ_0002746, circ_0003052, circ_0004319, and circ_0012840 had circular structures and were highly expressed in mammary tissues. Subcellular localization revealed that these four DE-circRNAs may play a regulatory role in the mammary glands of dairy cows, mainly as competitive endogenous RNAs (ceRNAs). Seven hub target genes (GNB1, GNG2, PLCB1, PLCG1, ATP6V0C, NDUFS4, and PIGH) were obtained by constructing the regulatory network of their ceRNAs and then analyzed by CytoHubba and MCODE plugins in Cytoscape. Functional enrichment analysis revealed that these genes are crucial and most probable ceRNA regulators in milk fat metabolism. CONCLUSIONS Our study identified several vital circRNAs and ceRNAs affecting milk fat synthesis, providing new research ideas and a theoretical basis for cow lactation, milk quality, and breed improvement.
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Affiliation(s)
- Xiaofang Feng
- Key Laboratory of Ruminant Molecular and Cellular Breeding, School of Agriculture, Ningxia University, 750021, Yinchuan, China
| | - Lijia Tong
- Key Laboratory of Ruminant Molecular and Cellular Breeding, School of Agriculture, Ningxia University, 750021, Yinchuan, China
| | - Lina Ma
- NingXia Academy of Agriculture and Forestry Sciences, 750002, Yinchuan, China
| | - Tong Mu
- School of Life Science, Yan'an University, 716000, Yanan, China
| | - Baojun Yu
- Key Laboratory of Ruminant Molecular and Cellular Breeding, School of Agriculture, Ningxia University, 750021, Yinchuan, China
| | - Ruoshuang Ma
- Key Laboratory of Ruminant Molecular and Cellular Breeding, School of Agriculture, Ningxia University, 750021, Yinchuan, China
| | - Jiwei Li
- Key Laboratory of Ruminant Molecular and Cellular Breeding, School of Agriculture, Ningxia University, 750021, Yinchuan, China
| | - Chuanchuan Wang
- Key Laboratory of Ruminant Molecular and Cellular Breeding, School of Agriculture, Ningxia University, 750021, Yinchuan, China
| | - Juan Zhang
- Key Laboratory of Ruminant Molecular and Cellular Breeding, School of Agriculture, Ningxia University, 750021, Yinchuan, China.
| | - Yaling Gu
- Key Laboratory of Ruminant Molecular and Cellular Breeding, School of Agriculture, Ningxia University, 750021, Yinchuan, China
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Park J. Genome-wide association study to reveal new candidate genes using single-step approaches for productive traits of Yorkshire pig in Korea. Anim Biosci 2024; 37:451-460. [PMID: 38271983 PMCID: PMC10915189 DOI: 10.5713/ab.23.0255] [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: 06/11/2023] [Revised: 09/25/2023] [Accepted: 11/08/2023] [Indexed: 01/27/2024] Open
Abstract
OBJECTIVE The objective is to identify genomic regions and candidate genes associated with age to 105 kg (AGE), average daily gain (ADG), backfat thickness (BF), and eye muscle area (EMA) in Yorkshire pig. METHODS This study used a total of 104,380 records and 11,854 single nucleotide polymorphism (SNP) data obtained from Illumina porcine 60K chip. The estimated genomic breeding values (GEBVs) and SNP effects were estimated by single-step genomic best linear unbiased prediction (ssGBLUP). RESULTS The heritabilities of AGE, ADG, BF, and EMA were 0.50, 0.49, 0.49, and 0.23, respectively. We identified significant SNP markers surpassing the Bonferroni correction threshold (1.68×10-6), with a total of 9 markers associated with both AGE and ADG, and 4 markers associated with BF and EMA. Genome-wide association study (GWAS) analyses revealed notable chromosomal regions linked to AGE and ADG on Sus scrofa chromosome (SSC) 1, 6, 8, and 16; BF on SSC 2, 5, and 8; and EMA on SSC 1. Additionally, we observed strong linkage disequilibrium on SSC 1. Finally, we performed enrichment analyses using gene ontology and Kyoto encyclopedia of genes and genomes (KEGG), which revealed significant enrichments in eight biological processes, one cellular component, one molecular function, and one KEGG pathway. CONCLUSION The identified SNP markers for productive traits are expected to provide valuable information for genetic improvement as an understanding of their expression.
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Affiliation(s)
- Jun Park
- Department of Animal Biotechnology, Jeonbuk National University, Jeonju 54896,
Korea
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Wu Z, Dou T, Bai L, Han J, Yang F, Wang K, Han X, Qiao R, Li XL, Li XJ. Genomic prediction and genome-wide association studies for additive and dominance effects for body composition traits using 50 K and imputed high-density SNP genotypes in Yunong-black pigs. J Anim Breed Genet 2024; 141:124-137. [PMID: 37822282 DOI: 10.1111/jbg.12830] [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: 06/21/2023] [Revised: 10/03/2023] [Accepted: 10/04/2023] [Indexed: 10/13/2023]
Abstract
Body composition traits are complex traits controlled by minor genes and, in hybrid populations, are impacted by additive and nonadditive effects. We aimed to identify candidate genes and increase the accuracy of genomic prediction of body composition traits in crossbred pigs by including dominance genetic effects. Genomic selection (GS) and genome-wide association studies were performed on seven body composition traits in 807 Yunong-black pigs using additive genomic models (AM) and additive-dominance genomic models (ADM) with an imputed high-density single nucleotide polymorphism (SNP) array and the Illumina Porcine SNP50 BeadChip. The results revealed that the additive heritabilities estimated for AM and ADM using the 50 K SNP data ranged from 0.20 to 0.34 and 0.11 to 0.30, respectively. However, the ranges of additive heritability for AM and ADM in the imputed data ranged from 0.20 to 0.36 and 0.12 to 0.30, respectively. The dominance variance accounted for 23% and 27% of the total variance for the 50 K and imputed data, respectively. The accuracy of genomic prediction improved by 5% on average for 50 K and imputed data when dominance effect were considered. Without the dominance effect, the accuracies for 50 K and imputed data were 0.35 and 0.38, respectively, and 0.41 and 0.43, respectively, upon considering it. A total of 12 significant SNP and 16 genomic regions were identified in the AM, and 14 significant SNP and 21 genomic regions were identified in the ADM for both the 50 K and imputed data. There were five overlapping SNP in the 50 K and imputed data. In the AM, a significant SNP (CNC10041568) was found in both body length and backfat thickness traits, which was in the PLAG1 gene strongly and significantly associated with body length and backfat thickness in pigs. Moreover, a significant SNP (CNC10031356) with a heterozygous dominant genotype was present in the ADM. Furthermore, several functionally related genes were associated with body composition traits, including MOS, RPS20, LYN, TGS1, TMEM68, XKR4, SEMA4D and ARNT2. These findings provide insights into molecular markers and GS breeding for the Yunong-black pigs.
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Affiliation(s)
- Ziyi Wu
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, Henan, China
| | - Tengfei Dou
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, Henan, China
| | - Liyao Bai
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, Henan, China
| | - Jinyi Han
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, Henan, China
| | - Feng Yang
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, Henan, China
| | - Kejun Wang
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, Henan, China
| | - Xuelei Han
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, Henan, China
| | - Ruimin Qiao
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, Henan, China
| | - Xiu-Ling Li
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, Henan, China
| | - Xin-Jian Li
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, Henan, China
- Sanya Institute, Hainan Academy of Agricultural Science, Sanya, Hainan, China
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Zhou F, Lin D, Dong L, Hong Y, Zeng H, Cai G, Ye J, Wu Z. Genetic evaluation for production and body size traits using different animal models in purebred-Duroc pigs. Front Vet Sci 2023; 10:1274266. [PMID: 38164395 PMCID: PMC10758212 DOI: 10.3389/fvets.2023.1274266] [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: 08/08/2023] [Accepted: 11/27/2023] [Indexed: 01/03/2024] Open
Abstract
Duroc pigs are popular crossbred terminal sires, and accurate assessment of genetic parameters in the population can help to rationalize breeding programmes. The principle aim of this study were to evaluate the genetic parameters of production (birth weight, BW; age at 115 kg, AGE; feed conversion ratio, FCR) and body size (body length, BL; body height, BH; front cannon circumference, FCC) traits of Duroc pigs. The second objective was to analyze the fit of different genetic assessment models. The variance components and correlations of BW (28,348 records), AGE (28,335 records), FCR (11,135 records), BL (31,544 records), BH (21,862 records), and FCC (14,684 records) traits were calculated by using DMU and AIREMLF90 from BLUPF90 package. In the common environment model, the heritability of BW, AGE, FCR, BL, BH, and FCC traits were 0.17 ± 0.014, 0.30 ± 0.019, 0.28 ± 0.024, 0.16 ± 0.013, 0.14 ± 0.017, and 0.081 ± 0.016, with common litter effect values of 0.25, 0.20, 0.18, 0.23, 0.19, and 0.16, respectively. According to the results of the Akaike information criterion (AIC) calculations, models with smaller AIC values have a better fit. We found that the common environment model with litter effects as random effects for estimating genetic parameters had a better fit. In this Model, the estimated genetic correlations between AGE with BW, FCR, BL, BH, and FCC traits were -0.28 (0.040), 0.76 (0.038), -0.71 (0.036), -0.44 (0.060), and -0.60 (0.073), respectively, with phenotypic correlations of -0.17, 0.52, -0.22, -0.13 and -0.24, respectively. In our analysis of genetic trends for six traits in the Duroc population from 2012 to 2021, we observed significant genetic trends for AGE, BL, and BH. Particularly noteworthy is the rapid decline in the genetic trend for AGE, indicating an enhancement in the pig's growth rate through selective breeding. Therefore, we believe that some challenging-to-select traits can benefit from the genetic correlations between traits. By selecting easily measurable traits, they can gain from synergistic selection effects, leading to genetic progress. Conducting population genetic parameter analysis can assist us in devising breeding strategies.
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Affiliation(s)
- Fuchen Zhou
- National Engineering Research Center for Breeding Swine Industry and College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Danyang Lin
- National Engineering Research Center for Breeding Swine Industry and College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Linsong Dong
- National Engineering Research Center for Breeding Swine Industry, Wens Foodstuff Group Co., Ltd., Yunfu, China
| | - Yifeng Hong
- National Engineering Research Center for Breeding Swine Industry and College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Haiyu Zeng
- National Engineering Research Center for Breeding Swine Industry, Wens Foodstuff Group Co., Ltd., Yunfu, China
| | - Gengyuan Cai
- National Engineering Research Center for Breeding Swine Industry and College of Animal Science, South China Agricultural University, Guangzhou, China
- National Engineering Research Center for Breeding Swine Industry, Wens Foodstuff Group Co., Ltd., Yunfu, China
| | - Jian Ye
- National Engineering Research Center for Breeding Swine Industry, Wens Foodstuff Group Co., Ltd., Yunfu, China
| | - Zhenfang Wu
- National Engineering Research Center for Breeding Swine Industry and College of Animal Science, South China Agricultural University, Guangzhou, China
- National Engineering Research Center for Breeding Swine Industry, Wens Foodstuff Group Co., Ltd., Yunfu, China
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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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Fang X, Ye H, Zhang S, Guo L, Xu Y, Zhang D, Nie Q. Investigation of potential genetic factors for growth traits in yellow-feather broilers using weighted single-step genome-wide association study. Poult Sci 2023; 102:103034. [PMID: 37657249 PMCID: PMC10480639 DOI: 10.1016/j.psj.2023.103034] [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: 05/26/2023] [Revised: 08/04/2023] [Accepted: 08/11/2023] [Indexed: 09/03/2023] Open
Abstract
Yellow-feather broilers take a large portion of poultry industry in China due to its meat characteristics. Improving the growth traits of yellow-feathered broilers will have great significance for the Chinese poultry market. The current study was designed to investigate the potential genetic factors using the weighted single-step genome-wide association study (wssGWAS) method, which takes consideration of more factors including pedigree, sex, environment and has more accuracy than traditional GWAS. The yellow-feather dwarf chickens from Wens Nanfang Poultry Breeding Co. Ltd. were revolved to recode 9 growth traits: Average daily gain (ADG), body weight (BW) at 45 d, 49 d, 56 d, 63 d, 70 d, 77 d, 84 d, 91 d for analysis. For the results, the region 4.63 to 5.03 Mb on chromosome 15, which was the QTL overlapped in BW45, BW49, BW56, BW63, BW84, might be the crucial genetic region for growth traits. Seven GO terms and 3 KEGG pathways, GO:0005200, GO:0005882, GO:0045111, GO:0099513, GO:0099081, GO:0099512, GO:0099080, KEGG:gga04020, KEGG:gga04540, KEGG:gga04210, were detected to relevant with growth traits. The genes enriched in these biological processes (NRAS, TUBB1, ADORA2B, NTRK3, NGF, TNNC2, F-KER, LOC429492, LOC431325, LOC431324, LOC396480) might have the function in growth of yellow-feather broilers.
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Affiliation(s)
- Xiang Fang
- State Key Laboratory of Livestock and Poultry Breeding, & Lingnan Guangdong Laboratory of Agriculture, South China Agricultural University, & Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affair, South China Agricultural University, Guangzhou 510642, China
| | - Haoqiang Ye
- State Key Laboratory of Livestock and Poultry Breeding, & Lingnan Guangdong Laboratory of Agriculture, South China Agricultural University, & Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affair, South China Agricultural University, Guangzhou 510642, China
| | - Siyu Zhang
- State Key Laboratory of Livestock and Poultry Breeding, & Lingnan Guangdong Laboratory of Agriculture, South China Agricultural University, & Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affair, South China Agricultural University, Guangzhou 510642, China
| | - Lijin Guo
- State Key Laboratory of Livestock and Poultry Breeding, & Lingnan Guangdong Laboratory of Agriculture, South China Agricultural University, & Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affair, South China Agricultural University, Guangzhou 510642, China; Henry Fok School of Biology and Agriculture, Shaoguan University, Shaoguan 512005, China
| | - Yibin Xu
- State Key Laboratory of Livestock and Poultry Breeding, & Lingnan Guangdong Laboratory of Agriculture, South China Agricultural University, & Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affair, South China Agricultural University, Guangzhou 510642, China
| | - Dexiang Zhang
- State Key Laboratory of Livestock and Poultry Breeding, & Lingnan Guangdong Laboratory of Agriculture, South China Agricultural University, & Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affair, South China Agricultural University, Guangzhou 510642, China
| | - Qinghua Nie
- State Key Laboratory of Livestock and Poultry Breeding, & Lingnan Guangdong Laboratory of Agriculture, South China Agricultural University, & Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affair, South China Agricultural University, Guangzhou 510642, China; Henry Fok School of Biology and Agriculture, Shaoguan University, Shaoguan 512005, China.
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Zhou F, Quan J, Ruan D, Qiu Y, Ding R, Xu C, Ye Y, Cai G, Liu L, Zhang Z, Yang J, Wu Z, Zheng E. Identification of Candidate Genes for Economically Important Carcass Cutting in Commercial Pigs through GWAS. Animals (Basel) 2023; 13:3243. [PMID: 37893967 PMCID: PMC10603759 DOI: 10.3390/ani13203243] [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: 09/12/2023] [Revised: 10/08/2023] [Accepted: 10/16/2023] [Indexed: 10/29/2023] Open
Abstract
During the process of pork production, the carcasses of pigs are divided and sold, which provides better economic benefits and market competitiveness for pork production than selling the carcass as a whole. Due to the significant cost of post-slaughter phenotypic measurement, the genetic architecture of tenderloin weight (TLNW) and rib weight (RIBW)-important components of pig carcass economic value-remain unknown. In this study, we conducted genome-wide association studies (GWAS) for TLNW and RIBW traits in a population of 431 Duroc × Landrace × Yorkshire (DLY) pigs. In our study, the most significant single nucleotide polymorphism (SNP) associated with TLNW was identified as ASGA0085853 (3.28 Mb) on Sus scrofa chromosome 12 (SSC12), while for RIBW, it was Affx-1115046258 (172.45 Mb) on SSC13. Through haplotype block analysis, we discovered a novel quantitative trait locus (QTL) associated with TLNW, spanning a 5 kb region on SSC12, and a novel RIBW-associated QTL spanning 1.42 Mb on SSC13. Furthermore, we hypothesized that three candidate genes, TIMP2 and EML1, and SMN1, are associated with TLNW and RIBW, respectively. Our research not only addresses the knowledge gap regarding TLNW, but also serves as a valuable reference for studying RIBW. The identified SNP loci strongly associated with TLNW and RIBW may prove useful for marker-assisted selection in pig breeding programs.
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Affiliation(s)
- Fuchen Zhou
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (F.Z.); (J.Q.); (D.R.); (Y.Q.); (R.D.); (C.X.); (Y.Y.); (G.C.); (L.L.); (Z.Z.); (J.Y.)
| | - Jianping Quan
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (F.Z.); (J.Q.); (D.R.); (Y.Q.); (R.D.); (C.X.); (Y.Y.); (G.C.); (L.L.); (Z.Z.); (J.Y.)
| | - Donglin Ruan
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (F.Z.); (J.Q.); (D.R.); (Y.Q.); (R.D.); (C.X.); (Y.Y.); (G.C.); (L.L.); (Z.Z.); (J.Y.)
| | - Yibin Qiu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (F.Z.); (J.Q.); (D.R.); (Y.Q.); (R.D.); (C.X.); (Y.Y.); (G.C.); (L.L.); (Z.Z.); (J.Y.)
| | - Rongrong Ding
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (F.Z.); (J.Q.); (D.R.); (Y.Q.); (R.D.); (C.X.); (Y.Y.); (G.C.); (L.L.); (Z.Z.); (J.Y.)
| | - Cineng Xu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (F.Z.); (J.Q.); (D.R.); (Y.Q.); (R.D.); (C.X.); (Y.Y.); (G.C.); (L.L.); (Z.Z.); (J.Y.)
| | - Yong Ye
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (F.Z.); (J.Q.); (D.R.); (Y.Q.); (R.D.); (C.X.); (Y.Y.); (G.C.); (L.L.); (Z.Z.); (J.Y.)
| | - Gengyuan Cai
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (F.Z.); (J.Q.); (D.R.); (Y.Q.); (R.D.); (C.X.); (Y.Y.); (G.C.); (L.L.); (Z.Z.); (J.Y.)
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
- Guangdong Zhongxin Breeding Technology Co., Ltd., Guangzhou 510642, China
| | - Langqing Liu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (F.Z.); (J.Q.); (D.R.); (Y.Q.); (R.D.); (C.X.); (Y.Y.); (G.C.); (L.L.); (Z.Z.); (J.Y.)
| | - Zebin Zhang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (F.Z.); (J.Q.); (D.R.); (Y.Q.); (R.D.); (C.X.); (Y.Y.); (G.C.); (L.L.); (Z.Z.); (J.Y.)
| | - Jie Yang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (F.Z.); (J.Q.); (D.R.); (Y.Q.); (R.D.); (C.X.); (Y.Y.); (G.C.); (L.L.); (Z.Z.); (J.Y.)
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
| | - Zhenfang Wu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (F.Z.); (J.Q.); (D.R.); (Y.Q.); (R.D.); (C.X.); (Y.Y.); (G.C.); (L.L.); (Z.Z.); (J.Y.)
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
- Guangdong Zhongxin Breeding Technology Co., Ltd., Guangzhou 510642, China
- Yunfu Subcenter of Guangdong Laboratory for Lingnan Modern Agriculture, Yunfu 527400, China
| | - Enqin Zheng
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (F.Z.); (J.Q.); (D.R.); (Y.Q.); (R.D.); (C.X.); (Y.Y.); (G.C.); (L.L.); (Z.Z.); (J.Y.)
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
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10
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Tong X, Chen D, Hu J, Lin S, Ling Z, Ai H, Zhang Z, Huang L. Accurate haplotype construction and detection of selection signatures enabled by high quality pig genome sequences. Nat Commun 2023; 14:5126. [PMID: 37612277 PMCID: PMC10447580 DOI: 10.1038/s41467-023-40434-3] [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: 07/06/2022] [Accepted: 07/27/2023] [Indexed: 08/25/2023] Open
Abstract
High-quality whole-genome resequencing in large-scale pig populations with pedigree structure and multiple breeds would enable accurate construction of haplotype and robust selection-signature detection. Here, we sequence 740 pigs, combine with 149 of our previously published resequencing data, retrieve 207 resequencing datasets, and form a panel of worldwide distributed wild boars, aboriginal and highly selected pigs with pedigree structures, amounting to 1096 genomes from 43 breeds. Combining with their haplotype-informative reads and pedigree structure, we accurately construct a panel of 1874 haploid genomes with 41,964,356 genetic variants. We further demonstrate its valuable applications in GWAS by identifying five novel loci for intramuscular fat content, and in genomic selection by increasing the accuracy of estimated breeding value by 36.7%. In evolutionary selection, we detect MUC13 gene under a long-term balancing selection, as well as NPR3 gene under positive selection for pig stature. Our study provides abundant genomic variations for robust selection-signature detection and accurate haplotypes for deciphering complex traits in pigs.
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Affiliation(s)
- Xinkai Tong
- National Key Laboratory for Swine genetic improvement and production technology, Ministry of Science and Technology of China, Jiangxi Agricultural University, NanChang, Jiangxi Province, PR China
- College of Life Sciences, Jiangxi Normal University, NanChang, Jiangxi Province, PR China
| | - Dong Chen
- National Key Laboratory for Swine genetic improvement and production technology, Ministry of Science and Technology of China, Jiangxi Agricultural University, NanChang, Jiangxi Province, PR China
| | - Jianchao Hu
- National Key Laboratory for Swine genetic improvement and production technology, Ministry of Science and Technology of China, Jiangxi Agricultural University, NanChang, Jiangxi Province, PR China
| | - Shiyao Lin
- National Key Laboratory for Swine genetic improvement and production technology, Ministry of Science and Technology of China, Jiangxi Agricultural University, NanChang, Jiangxi Province, PR China
| | - Ziqi Ling
- National Key Laboratory for Swine genetic improvement and production technology, Ministry of Science and Technology of China, Jiangxi Agricultural University, NanChang, Jiangxi Province, PR China
| | - Huashui Ai
- National Key Laboratory for Swine genetic improvement and production technology, Ministry of Science and Technology of China, Jiangxi Agricultural University, NanChang, Jiangxi Province, PR China
| | - Zhiyan Zhang
- National Key Laboratory for Swine genetic improvement and production technology, Ministry of Science and Technology of China, Jiangxi Agricultural University, NanChang, Jiangxi Province, PR China.
| | - Lusheng Huang
- National Key Laboratory for Swine genetic improvement and production technology, Ministry of Science and Technology of China, Jiangxi Agricultural University, NanChang, Jiangxi Province, PR China.
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11
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Zhou P, Yin C, Wang Y, Yin Z, Liu Y. Genomic Association Analysis of Growth and Backfat Traits in Large White Pigs. Genes (Basel) 2023; 14:1258. [PMID: 37372438 DOI: 10.3390/genes14061258] [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: 05/11/2023] [Revised: 06/09/2023] [Accepted: 06/12/2023] [Indexed: 06/29/2023] Open
Abstract
The pig industry is significantly influenced by complex traits such as growth rate and fat deposition, which have substantial implications for economic returns. Over the years, remarkable genetic advancements have been achieved through intense artificial selection to enhance these traits in pigs. In this study, we aimed to investigate the genetic factors that contribute to growth efficiency and lean meat percentages in Large White pigs. Specifically, we focused on analyzing two key traits: age at 100 kg live weight (AGE100) and backfat thickness at 100 kg (BF100), in three distinct Large White pig populations-500 Canadian, 295 Danish, and 1500 American Large White pigs. By employing population genomic techniques, we observed significant population stratification among these pig populations. Utilizing imputed whole-genome sequencing data, we conducted single population genome-wide association studies (GWAS) as well as a combined meta-analysis across the three populations to identify genetic markers associated with the aforementioned traits. Our analyses highlighted several candidate genes, such as CNTN1-which has been linked to weight loss in mice and is potentially influential for AGE100-and MC4R, which is associated with obesity and appetite and may impact both traits. Additionally, we identified other genes-namely, PDZRN4, LIPM, and ANKRD22-which play a partial role in fat growth. Our findings provide valuable insights into the genetic basis of these important traits in Large White pigs, which may inform breeding strategies for improved production efficiency and meat quality.
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Affiliation(s)
- Peng Zhou
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing 210095, China
| | - Chang Yin
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing 210095, China
| | - Yuwei Wang
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing 210095, China
| | - Zongjun Yin
- College of Animal Science and Technology, Anhui Agricultural University, Hefei 230036, China
| | - Yang Liu
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing 210095, China
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12
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Xi Y, Wu Q, Zeng Y, Qi J, Li J, He H, Xu H, Hu J, Yan X, Bai L, Han C, Hu S, Wang J, Liu H, Li L. Identification of the genetic basis of the duck growth rate in multiple growth stages using genome-wide association analysis. BMC Genomics 2023; 24:285. [PMID: 37237371 DOI: 10.1186/s12864-023-09302-8] [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: 11/23/2022] [Accepted: 04/09/2023] [Indexed: 05/28/2023] Open
Abstract
BACKGROUND The genetic locus responsible for duck body size has been fully explained before, but the growth trait-related genetic basis is still waiting to be explored. For example, the genetic site related to growth rate, an important economic trait affecting marketing weight and feeding cost, is still unclear. Here, we performed genome wide association study (GWAS) to identify growth rate-associated genes and mutations. RESULT In the current study, the body weight data of 358 ducks were recorded every 10 days from hatching to 120 days of age. According to the growth curve, we evaluated the relative and absolute growth rates (RGR and AGR) of 5 stages during the early rapid growth period. GWAS results for RGRs identified 31 significant SNPs on autosomes, and these SNPs were annotated by 24 protein-coding genes. Fourteen autosomal SNPs were significantly associated with AGRs. In addition, 4 shared significant SNPs were identified as having an association with both AGR and RGR, which were Chr2: 11483045 C>T, Chr2: 13750217 G>A, Chr2: 42508231 G>A and Chr2: 43644612 C>T. Among them, Chr2: 11483045 C>T, Chr2: 42508231 G>A, and Chr2: 43644612 C>T were annotated by ASAP1, LYN and CABYR, respectively. ASAP1 and LYN have already been proven to play roles in the growth and development of other species. In addition, we genotyped every duck using the most significant SNP (Chr2: 42508231 G>A) and compared the growth rate difference among each genotype population. The results showed that the growth rates of individuals carrying the Chr2: 42508231 A allele were significantly lower than those without this allele. Moreover, the results of the Mendelian randomization (MR) analysis supported the idea that the growth rate and birth weight had a causal effect on the adult body weight, with the growth rate having a greater effect size. CONCLUSION In this study, 41 SNPs significantly related to growth rate were identified. In addition, we considered that the ASAP1 and LYN genes are essential candidate genes affecting the duck growth rate. The growth rate also showed the potential to be used as a reliable predictor of adult weight, providing a theoretical reference for preselection.
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Affiliation(s)
- Yang Xi
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, People's Republic of China
| | - Qifan Wu
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, People's Republic of China
| | - Yutian Zeng
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, People's Republic of China
| | - Jingjing Qi
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, People's Republic of China
| | - Junpeng Li
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, People's Republic of China
| | - Hua He
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, People's Republic of China
| | - Hengyong Xu
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, People's Republic of China
| | - Jiwei Hu
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, People's Republic of China
| | - Xiping Yan
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, People's Republic of China
| | - Lili Bai
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, People's Republic of China
| | - Chunchun Han
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, People's Republic of China
| | - Shenqiang Hu
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, People's Republic of China
| | - Jiwen Wang
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, People's Republic of China
| | - Hehe Liu
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, People's Republic of China.
| | - Liang Li
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, People's Republic of China.
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13
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Wei C, Zeng H, Zhong Z, Cai X, Teng J, Liu Y, Zhao Y, Wu X, Li J, Zhang Z. Integration of non-additive genome-wide association study with a multi-tissue transcriptome analysis of growth and carcass traits in Duroc pigs. Animal 2023; 17:100817. [PMID: 37196577 DOI: 10.1016/j.animal.2023.100817] [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: 12/08/2022] [Revised: 04/03/2023] [Accepted: 04/06/2023] [Indexed: 05/19/2023] Open
Abstract
Growth and carcass traits are of economic importance in the pig production, which affect pork quality and profitability of finishing pig production. This study used whole-genome and transcriptome sequencing technologies to identify potential candidate genes affecting growth and carcass traits in Duroc pigs. The medium (50-60 k) single nucleotide polymorphism (SNP) arrays of 4 154 Duroc pigs from three populations were imputed to whole-genome sequence data, yielding 10 463 227 markers on 18 autosomes. The dominance heritabilities estimated for growth and carcass traits ranged from 0.000 ± 0.041 to 0.161 ± 0.054. Using non-additive genome-wide association study (GWAS), we identified 80 dominance quantitative trait loci for growth and carcass traits at genome-wide significance (false discovery rate < 5%), 15 of which were also detected in our additive GWAS. After fine mapping, 31 candidate genes for dominance GWAS were annotated, and 8 of them were highlighted that have been previously reported to be associated with growth and development (e.g. SNX14, RELN and ENPP2), autosomal recessive diseases (e.g. AMPH, SNX14, RELN and CACNB4) and immune response (e.g. UNC93B1 and PPM1D). By integrating the lead SNPs with RNA-seq data of 34 pig tissues from the Pig Genotype-Tissue Expression project (https://piggtex.farmgtex.org/), we found that the rs691128548, rs333063869, and rs1110730611 have significantly dominant effects for the expression of SNX14, AMPH and UNC93B1 genes in tissues related to growth and development for pig, respectively. Finally, the identified candidate genes were significantly enriched for biological processes involved in the cell and organ development, lipids catabolic process and phosphatidylinositol 3-kinase signalling (P < 0.05). These results provide new molecular markers for meat production and quality selection of pig as well as basis for deciphering the genetic mechanisms of growth and carcass traits.
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Affiliation(s)
- Chen Wei
- 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, PR China
| | - Haonan Zeng
- 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, PR China
| | - Zhanming Zhong
- 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, PR China
| | - Xiaodian Cai
- 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, PR China
| | - Jingyan Teng
- 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, PR China
| | - Yuqiang Liu
- 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, PR China
| | - Yunxiang Zhao
- School of Life Science and Engineering, Foshan University, Foshan 528225, PR China
| | - Xibo Wu
- Guangxi Guiken Yongxin Animal Husbandry Group Co. Ltd, Nanning 530000, PR China
| | - Jiaqi Li
- 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, PR China
| | - Zhe Zhang
- 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, PR China.
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14
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Bertolini F, Ribani A, Capoccioni F, Buttazzoni L, Bovo S, Schiavo G, Caggiano M, Rothschild MF, Fontanesi L. Whole Genome Sequencing Provides Information on the Genomic Architecture and Diversity of Cultivated Gilthead Seabream (Sparus aurata) Broodstock Nuclei. Genes (Basel) 2023; 14:genes14040839. [PMID: 37107597 PMCID: PMC10137967 DOI: 10.3390/genes14040839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 03/21/2023] [Accepted: 03/27/2023] [Indexed: 04/03/2023] Open
Abstract
The gilthead seabream (Sparus aurata) is a species of relevance for the Mediterranean aquaculture industry. Despite the advancement of genetic tools for the species, breeding programs still do not often include genomics. In this study, we designed a genomic strategy to identify signatures of selection and genomic regions of high differentiation among populations of farmed fish stocks. A comparative DNA pooling sequencing approach was applied to identify signatures of selection in gilthead seabream from the same hatchery and from different nuclei that had not been subjected to genetic selection. Identified genomic regions were further investigated to detect SNPs with predicted high impact. The analyses underlined major genomic differences in the proportion of fixed alleles among the investigated nuclei. Some of these differences highlighted genomic regions, including genes involved in general metabolism and development already detected in QTL for growth, size, skeletal deformity, and adaptation to variation of oxygen levels in other teleosts. The obtained results pointed out the need to control the genetic effect of breeding programs in this species to avoid the reduction of genetic variability within populations and the increase in inbreeding level that, in turn, might lead to an increased frequency of alleles with deleterious effects.
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Affiliation(s)
- Francesca Bertolini
- Department of Agricultural and Food Sciences, Division of Animal Sciences, University of Bologna, Viale G. Fanin 46, 40127 Bologna, Italy
- Correspondence:
| | - Anisa Ribani
- Department of Agricultural and Food Sciences, Division of Animal Sciences, University of Bologna, Viale G. Fanin 46, 40127 Bologna, Italy
| | - Fabrizio Capoccioni
- Centro di Ricerca “Zootecnia e Acquacoltura”, Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria (CREA), 00198 Roma, Italy
| | - Luca Buttazzoni
- Centro di Ricerca “Zootecnia e Acquacoltura”, Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria (CREA), 00198 Roma, Italy
| | - Samuele Bovo
- Department of Agricultural and Food Sciences, Division of Animal Sciences, University of Bologna, Viale G. Fanin 46, 40127 Bologna, Italy
| | - Giuseppina Schiavo
- Department of Agricultural and Food Sciences, Division of Animal Sciences, University of Bologna, Viale G. Fanin 46, 40127 Bologna, Italy
| | - Massimo Caggiano
- Panittica Italia Società Agricola Srl, Torre Canne di Fasano, 72016 Brindisi, Italy
| | - Max F. Rothschild
- Department of Animal Science, Iowa State University, Ames, IA 50011-3150, USA
| | - Luca Fontanesi
- Department of Agricultural and Food Sciences, Division of Animal Sciences, University of Bologna, Viale G. Fanin 46, 40127 Bologna, Italy
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15
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Wang X, Wang L, Shi L, Zhang P, Li Y, Li M, Tian J, Wang L, Zhao F. GWAS of Reproductive Traits in Large White Pigs on Chip and Imputed Whole-Genome Sequencing Data. Int J Mol Sci 2022; 23:13338. [PMID: 36362120 PMCID: PMC9656588 DOI: 10.3390/ijms232113338] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 10/27/2022] [Accepted: 10/28/2022] [Indexed: 12/09/2023] Open
Abstract
Total number born (TNB), number of stillborn (NSB), and gestation length (GL) are economically important traits in pig production, and disentangling the molecular mechanisms associated with traits can provide valuable insights into their genetic structure. Genotype imputation can be used as a practical tool to improve the marker density of single-nucleotide polymorphism (SNP) chips based on sequence data, thereby dramatically improving the power of genome-wide association studies (GWAS). In this study, we applied Beagle software to impute the 50 K chip data to the whole-genome sequencing (WGS) data with average imputation accuracy (R2) of 0.876. The target pigs, 2655 Large White pigs introduced from Canadian and French lines, were genotyped by a GeneSeek Porcine 50K chip. The 30 Large White reference pigs were the key ancestral individuals sequenced by whole-genome resequencing. To avoid population stratification, we identified genetic variants associated with reproductive traits by performing within-population GWAS and cross-population meta-analyses with data before and after imputation. Finally, several genes were detected and regarded as potential candidate genes for each of the traits: for the TNB trait: NOTCH2, KLF3, PLXDC2, NDUFV1, TLR10, CDC14A, EPC2, ORC4, ACVR2A, and GSC; for the NSB trait: NUB1, TGFBR3, ZDHHC14, FGF14, BAIAP2L1, EVI5, TAF1B, and BCAR3; for the GL trait: PPP2R2B, AMBP, MALRD1, HOXA11, and BICC1. In conclusion, expanding the size of the reference population and finding an optimal imputation strategy to ensure that more loci are obtained for GWAS under high imputation accuracy will contribute to the identification of causal mutations in pig breeding.
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Affiliation(s)
- Xiaoqing Wang
- Key Laboratory of Animal Genetics, Breeding and Reproduction (Poultry) of Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Ligang Wang
- Key Laboratory of Animal Genetics, Breeding and Reproduction (Poultry) of Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Liangyu Shi
- Key Laboratory of Animal Genetics, Breeding and Reproduction (Poultry) of Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
- Laboratory of Genetic Breeding, Reproduction and Precision Livestock Farming, School of Animal Science and Nutritional Engineering, Wuhan Polytechnic University, Wuhan 430023, China
| | - Pengfei Zhang
- Key Laboratory of Animal Genetics, Breeding and Reproduction (Poultry) of Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Yang Li
- Key Laboratory of Animal Genetics, Breeding and Reproduction (Poultry) of Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Mianyan Li
- Key Laboratory of Animal Genetics, Breeding and Reproduction (Poultry) of Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Jingjing Tian
- Key Laboratory of Animal Genetics, Breeding and Reproduction (Poultry) of Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Lixian Wang
- Key Laboratory of Animal Genetics, Breeding and Reproduction (Poultry) of Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Fuping Zhao
- Key Laboratory of Animal Genetics, Breeding and Reproduction (Poultry) of Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
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Genome-Wide Association Study of Growth Traits in a Four-Way Crossbred Pig Population. Genes (Basel) 2022; 13:genes13111990. [DOI: 10.3390/genes13111990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 10/28/2022] [Accepted: 10/28/2022] [Indexed: 11/04/2022] Open
Abstract
Growth traits are crucial economic traits in the commercial pig industry and have a substantial impact on pig production. However, the genetic mechanism of growth traits is not very clear. In this study, we performed a genome-wide association study (GWAS) based on the specific-locus amplified fragment sequencing (SLAF-seq) to analyze ten growth traits on 223 four-way intercross pigs. A total of 227,921 highly consistent single nucleotide polymorphisms (SNPs) uniformly dispersed throughout the entire genome were used to conduct GWAS. A total of 53 SNPs were identified for ten growth traits using the mixed linear model (MLM), of which 18 SNPs were located in previously reported quantitative trait loci (QTL) regions. Two novel QTLs on SSC4 and SSC7 were related to average daily gain from 30 to 60 kg (ADG30–60) and body length (BL), respectively. Furthermore, 13 candidate genes (ATP5O, GHRHR, TRIM55, EIF2AK1, PLEKHA1, BRAP, COL11A2, HMGA1, NHLRC1, SGSM1, NFATC2, MAML1, and PSD3) were found to be associated with growth traits in pigs. The GWAS findings will enhance our comprehension of the genetic architecture of growth traits. We suggested that these detected SNPs and corresponding candidate genes might provide a biological foundation for improving the growth and production performance of pigs in swine breeding.
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17
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Integrated Omics analysis of pig muscle metabolism under the effects of dietary Chlorella vulgaris and exogenous enzymes. Sci Rep 2022; 12:16992. [PMID: 36216870 PMCID: PMC9551059 DOI: 10.1038/s41598-022-21466-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 09/27/2022] [Indexed: 12/29/2022] Open
Abstract
Monogastric feeding is dependent on costly conventional feedstuffs. Microalgae such as Chlorella vulgaris are a sustainable alternative; however, its recalcitrant cell wall hinders monogastric digestion. Carbohydrate Active Enzyme (CAZyme) supplementation is a possible solution. The objective of this work was to evaluate the effect of 5% dietary C. vulgaris (CV) and enzymatic supplementation (CV + R-Rovabio® Excel AP; CV + M-four CAZyme mix) on muscle transcriptome and proteome of finishing pigs, in an integrated approach. Control pigs increased the abundance of contractile apparatus (MYH1, MYH2, MYH4) and energy metabolism (CKMT1, NDUFS3) proteins, demonstrating increased nutrient availability. They had increased expression of SCD, characteristic of increased glucose availability, via the activation of SREBP-1c and ChREBP. CV and CV + R pigs upregulated proteolytic and apoptotic genes (BAX, DDA1), whilst increasing the abundance of glucose (UQCRFS1) and fatty acid catabolism (ACADS) proteins. CV + R pigs upregulated ACOT8 and SIRT3 genes as a response to reduced nutrient availability, maintaining energy homeostasis. The cell wall specific CAZyme mix, CV + M, was able to comparatively reduce Omics alterations in the muscle, thereby reducing endogenous nutrient catabolism compared to the CV + R and CV.
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18
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Genetic Parameter Estimation and Genome-Wide Association Analysis of Social Genetic Effects on Average Daily Gain in Purebreds and Crossbreds. Animals (Basel) 2022; 12:ani12172300. [PMID: 36078021 PMCID: PMC9454713 DOI: 10.3390/ani12172300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 08/23/2022] [Accepted: 09/01/2022] [Indexed: 11/29/2022] Open
Abstract
Simple Summary Average daily gain (ADG) is influenced by both an individual’s direct genetic effect (DGE) and by a social genetic effect (SGE) derived from pen mates. Therefore, identifying the DGE and SGE on ADG is essential for a better understanding of pig breeding systems. We conducted this study to elucidate the genetic characteristics and relationships of DGE and SGE on ADG using purebred and crossbred pigs. We found that the DGE and SGE both contributed to ADG in both populations. In addition, the SGE of purebred pigs was highly correlated with the DGE of crossbred pigs. Furthermore, we identified several genomic regions that may be associated with the DGE and SGE on ADG. Our findings will contribute to future genomic evaluation studies of socially affected traits. Abstract Average daily gain (ADG) is an important growth trait in the pig industry. The direct genetic effect (DGE) has been studied mainly to assess the association between genetic information and economic traits. The social genetic effect (SGE) has been shown to affect ADG simultaneously with the DGE because of group housing systems. We conducted this study to elucidate the genetic characteristics and relationships of the DGE and SGE of purebred Korean Duroc and crossbred pigs by single-step genomic best linear unbiased prediction and a genome-wide association study. We used the genotype, phenotype, and pedigree data of 1779, 6022, and 7904 animals, respectively. Total heritabilities on ADG were 0.19 ± 0.04 and 0.39 ± 0.08 for purebred and crossbred pigs, respectively. The genetic correlation was the greatest (0.77 ± 0.12) between the SGE of purebred and DGE of crossbred pigs. We found candidate genes located in the quantitative trait loci (QTLs) for the SGE that were associated with behavior and neurodegenerative diseases, and candidate genes in the QTLs for DGE that were related to body mass, size of muscle fiber, and muscle hypertrophy. These results suggest that the genomic selection of purebred animals could be applied for crossbred performance.
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19
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Martins TF, Braga Magalhães AF, Verardo LL, Santos GC, Silva Fernandes AA, Gomes Vieira JI, Irano N, dos Santos DB. Functional analysis of litter size and number of teats in pigs: From GWAS to post-GWAS. Theriogenology 2022; 193:157-166. [DOI: 10.1016/j.theriogenology.2022.09.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 09/03/2022] [Accepted: 09/05/2022] [Indexed: 10/31/2022]
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20
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Genome-Wide Association Study Reveals Additive and Non-Additive Effects on Growth Traits in Duroc Pigs. Genes (Basel) 2022; 13:genes13081454. [PMID: 36011365 PMCID: PMC9407794 DOI: 10.3390/genes13081454] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 08/12/2022] [Accepted: 08/13/2022] [Indexed: 12/24/2022] Open
Abstract
Growth rate plays a critical role in the pig industry and is related to quantitative traits controlled by many genes. Here, we aimed to identify causative mutations and candidate genes responsible for pig growth traits. In this study, 2360 Duroc pigs were used to detect significant additive, dominance, and epistatic effects associated with growth traits. As a result, a total number of 32 significant SNPs for additive or dominance effects were found to be associated with various factors, including adjusted age at a specified weight (AGE), average daily gain (ADG), backfat thickness (BF), and loin muscle depth (LMD). In addition, the detected additive significant SNPs explained 2.49%, 3.02%, 3.18%, and 1.96% of the deregressed estimated breeding value (DEBV) variance for AGE, ADG, BF, and LMD, respectively, while significant dominance SNPs could explain 2.24%, 13.26%, and 4.08% of AGE, BF, and LMD, respectively. Meanwhile, a total of 805 significant epistatic effects SNPs were associated with one of ADG, AGE, and LMD, from which 11 sub-networks were constructed. In total, 46 potential genes involved in muscle development, fat deposition, and regulation of cell growth were considered as candidates for growth traits, including CD55 and NRIP1 for AGE and ADG, TRIP11 and MIS2 for BF, and VRTN and ZEB2 for LMD, respectively. Generally, in this study, we detected both new and reported variants and potential candidate genes for growth traits of Duroc pigs, which might to be taken into account in future molecular breeding programs to improve the growth performance of pigs.
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21
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Wang H, Wang X, Yan D, Sun H, Chen Q, Li M, Dong X, Pan Y, Lu S. Genome-wide association study identifying genetic variants associated with carcass backfat thickness, lean percentage and fat percentage in a four-way crossbred pig population using SLAF-seq technology. BMC Genomics 2022; 23:594. [PMID: 35971078 PMCID: PMC9380336 DOI: 10.1186/s12864-022-08827-8] [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: 05/25/2022] [Accepted: 08/05/2022] [Indexed: 12/12/2022] Open
Abstract
Background Carcass backfat thickness (BFT), carcass lean percentage (CLP) and carcass fat percentage (CFP) are important to the commercial pig industry. Nevertheless, the genetic architecture of BFT, CLP and CFP is still elusive. Here, we performed a genome-wide association study (GWAS) based on specific-locus amplified fragment sequencing (SLAF-seq) to analyze seven fatness-related traits, including five BFTs, CLP, and CFP on 223 four-way crossbred pigs. Results A total of 227, 921 highly consistent single nucleotide polymorphisms (SNPs) evenly distributed throughout the genome were used to perform GWAS. Using the mixed linear model (MLM), a total of 20 SNP loci significantly related to these traits were identified on ten Sus scrofa chromosomes (SSC), of which 10 SNPs were located in previously reported quantitative trait loci (QTL) regions. On SSC7, two SNPs (SSC7:29,503,670 and rs1112937671) for average backfat thickness (ABFT) exceeded 1% and 10% Bonferroni genome-wide significance levels, respectively. These two SNP loci were located within an intron region of the COL21A1 gene, which was a protein-coding gene that played an important role in the porcine backfat deposition by affecting extracellular matrix (ECM) remodeling. In addition, based on the other three significant SNPs on SSC7, five candidate genes, ZNF184, ZNF391, HMGA1, GRM4 and NUDT3 were proposed to influence BFT. On SSC9, two SNPs for backfat thickness at 6–7 ribs (67RBFT) and one SNP for CLP were in the same locus region (19 kb interval). These three SNPs were located in the PGM2L1 gene, which encoded a protein that played an indispensable role in glycogen metabolism, glycolysis and gluconeogenesis as a key enzyme. Finally, one significant SNP on SSC14 for CLP was located within the PLBD2 gene, which participated in the lipid catabolic process. Conclusions A total of two regions on SSC7 and SSC9 and eight potential candidate genes were found for fatness-related traits in pigs. The results of this GWAS based on SLAF-seq will greatly advance our understanding of the genetic architecture of BFT, CLP, and CFP traits. These identified SNP loci and candidate genes might serve as a biological basis for improving the important fatness-related traits of pigs. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-022-08827-8.
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Affiliation(s)
- Huiyu Wang
- Faculty of Animal Science and Technology, Yunnan Agricultural University, No. 95 of Jinhei Road, Kunming, 650201, Yunnan, China.,Faculty of Animal Science, Xichang University, Xichang, 615000, Sichuan, China
| | - Xiaoyi Wang
- Faculty of Animal Science and Technology, Yunnan Agricultural University, No. 95 of Jinhei Road, Kunming, 650201, Yunnan, China
| | - Dawei Yan
- Faculty of Animal Science and Technology, Yunnan Agricultural University, No. 95 of Jinhei Road, Kunming, 650201, Yunnan, China
| | - Hao Sun
- Faculty of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Qiang Chen
- Faculty of Animal Science and Technology, Yunnan Agricultural University, No. 95 of Jinhei Road, Kunming, 650201, Yunnan, China
| | - Mingli Li
- Faculty of Animal Science and Technology, Yunnan Agricultural University, No. 95 of Jinhei Road, Kunming, 650201, Yunnan, China
| | - Xinxing Dong
- Faculty of Animal Science and Technology, Yunnan Agricultural University, No. 95 of Jinhei Road, Kunming, 650201, Yunnan, China
| | - Yuchun Pan
- Faculty of Animal Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China.
| | - Shaoxiong Lu
- Faculty of Animal Science and Technology, Yunnan Agricultural University, No. 95 of Jinhei Road, Kunming, 650201, Yunnan, China.
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22
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Davoudi P, Do DN, Colombo SM, Rathgeber B, Miar Y. Application of Genetic, Genomic and Biological Pathways in Improvement of Swine Feed Efficiency. Front Genet 2022; 13:903733. [PMID: 35754793 PMCID: PMC9220306 DOI: 10.3389/fgene.2022.903733] [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: 03/24/2022] [Accepted: 05/20/2022] [Indexed: 12/24/2022] Open
Abstract
Despite the significant improvement of feed efficiency (FE) in pigs over the past decades, feed costs remain a major challenge for producers profitability. Improving FE is a top priority for the global swine industry. A deeper understanding of the biology underlying FE is crucial for making progress in genetic improvement of FE traits. This review comprehensively discusses the topics related to the FE in pigs including: measurements, genetics, genomics, biological pathways and the advanced technologies and methods involved in FE improvement. We first provide an update of heritability for different FE indicators and then characterize the correlations of FE traits with other economically important traits. Moreover, we present the quantitative trait loci (QTL) and possible candidate genes associated with FE in pigs and outline the most important biological pathways related to the FE traits in pigs. Finally, we present possible ways to improve FE in swine including the implementation of genomic selection, new technologies for measuring the FE traits, and the potential use of genome editing and omics technologies.
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Affiliation(s)
- Pourya Davoudi
- Department of Animal Science and Aquaculture, Dalhousie University, Truro, NS, Canada
| | - Duy Ngoc Do
- Department of Animal Science and Aquaculture, Dalhousie University, Truro, NS, Canada
| | - Stefanie M Colombo
- Department of Animal Science and Aquaculture, Dalhousie University, Truro, NS, Canada
| | - Bruce Rathgeber
- Department of Animal Science and Aquaculture, Dalhousie University, Truro, NS, Canada
| | - Younes Miar
- Department of Animal Science and Aquaculture, Dalhousie University, Truro, NS, Canada
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23
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Chakraborty D, Sharma N, Kour S, Sodhi SS, Gupta MK, Lee SJ, Son YO. Applications of Omics Technology for Livestock Selection and Improvement. Front Genet 2022; 13:774113. [PMID: 35719396 PMCID: PMC9204716 DOI: 10.3389/fgene.2022.774113] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Accepted: 05/16/2022] [Indexed: 12/16/2022] Open
Abstract
Conventional animal selection and breeding methods were based on the phenotypic performance of the animals. These methods have limitations, particularly for sex-limited traits and traits expressed later in the life cycle (e.g., carcass traits). Consequently, the genetic gain has been slow with high generation intervals. With the advent of high-throughput omics techniques and the availability of multi-omics technologies and sophisticated analytic packages, several promising tools and methods have been developed to estimate the actual genetic potential of the animals. It has now become possible to collect and access large and complex datasets comprising different genomics, transcriptomics, proteomics, metabolomics, and phonemics data as well as animal-level data (such as longevity, behavior, adaptation, etc.,), which provides new opportunities to better understand the mechanisms regulating animals’ actual performance. The cost of omics technology and expertise of several fields like biology, bioinformatics, statistics, and computational biology make these technology impediments to its use in some cases. The population size and accurate phenotypic data recordings are other significant constraints for appropriate selection and breeding strategies. Nevertheless, omics technologies can estimate more accurate breeding values (BVs) and increase the genetic gain by assisting the section of genetically superior, disease-free animals at an early stage of life for enhancing animal productivity and profitability. This manuscript provides an overview of various omics technologies and their limitations for animal genetic selection and breeding decisions.
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Affiliation(s)
- Dibyendu Chakraborty
- Division of Animal Genetics and Breeding, Faculty of Veterinary Sciences and Animal Husbandry, Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu, Ranbir Singh Pura, India
| | - Neelesh Sharma
- Division of Veterinary Medicine, Faculty of Veterinary Sciences and Animal Husbandry, Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu, Ranbir Singh Pura, India
- *Correspondence: Neelesh Sharma, ; Young Ok Son,
| | - Savleen Kour
- Division of Veterinary Medicine, Faculty of Veterinary Sciences and Animal Husbandry, Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu, Ranbir Singh Pura, India
| | - Simrinder Singh Sodhi
- Department of Animal Biotechnology, College of Animal Biotechnology, Guru Angad Dev Veterinary and Animal Sciences University, Ludhiana, India
| | - Mukesh Kumar Gupta
- Department of Biotechnology and Medical Engineering, National Institute of Technology, Rourkela, India
| | - Sung Jin Lee
- Department of Animal Biotechnology, College of Animal Life Sciences, Kangwon National University, Chuncheon-si, South Korea
| | - Young Ok Son
- Department of Animal Biotechnology, Faculty of Biotechnology, College of Applied Life Sciences and Interdisciplinary Graduate Program in Advanced Convergence Technology and Science, Jeju National University, Jeju, South Korea
- *Correspondence: Neelesh Sharma, ; Young Ok Son,
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24
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Zhuang Z, Ding R, Qiu Y, Wu J, Zhou S, Quan J, Zheng E, Li Z, Wu Z, Yang J. A large-scale genome-wide association analysis reveals QTL and candidate genes for intramuscular fat content in Duroc pigs. Anim Genet 2021; 52:518-522. [PMID: 34060118 DOI: 10.1111/age.13069] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/10/2021] [Indexed: 01/30/2023]
Abstract
This study aimed at identifying genomic regions and genes associated with intramuscular fat content (IMF) in Duroc pigs using a weighted single-step GWAS. Data from 3912 pigs, of which 3770 animals were genotyped with GeneSeek Porcine 50K Bead chip, were used for the association analysis. We identified 19 genomic regions that each explained >1% of the additive genetic variance associated with IMF. Notably, a consistent QTL on SSC7 (117.42-117.92 Mb) was confirmed, explaining 3.70% of the additive genetic variance, and two genes, BDKRB2 and ATG2B, were highlighted as promising candidates for IMF. Two QTL (SSC7, 94.19-94.64 Mb; SSC14, 123.25-123.75 Mb), which harbored MED6 and MAP3K9 genes and TCF7L2 gene respectively, were newly identified as associated with IMF. In conclusion, we identified a consistent QTL and additional genomic regions and genes that contributed to the genetic variance of IMF using a large-scale sample size of genotyped pigs and genealogical information.
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Affiliation(s)
- Z Zhuang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China.,Guangdong Provincial Laboratory of Lingnan Modern Agricultural Science and Technology, Guangzhou, 510642, China
| | - R Ding
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China.,Guangdong Provincial Laboratory of Lingnan Modern Agricultural Science and Technology, Guangzhou, 510642, China
| | - Y Qiu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China.,Guangdong Provincial Laboratory of Lingnan Modern Agricultural Science and Technology, Guangzhou, 510642, China
| | - J Wu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China.,Guangdong Provincial Laboratory of Lingnan Modern Agricultural Science and Technology, Guangzhou, 510642, China
| | - S Zhou
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China.,Guangdong Provincial Laboratory of Lingnan Modern Agricultural Science and Technology, Guangzhou, 510642, China
| | - J Quan
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China.,Guangdong Provincial Laboratory of Lingnan Modern Agricultural Science and Technology, Guangzhou, 510642, China
| | - E Zheng
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China.,Guangdong Provincial Laboratory of Lingnan Modern Agricultural Science and Technology, Guangzhou, 510642, China.,Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China
| | - Z Li
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China.,Guangdong Provincial Laboratory of Lingnan Modern Agricultural Science and Technology, Guangzhou, 510642, China.,State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, Guangzhou, 510642, China.,Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China
| | - Z Wu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China.,Guangdong Provincial Laboratory of Lingnan Modern Agricultural Science and Technology, Guangzhou, 510642, China.,Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China
| | - J Yang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China.,Guangdong Provincial Laboratory of Lingnan Modern Agricultural Science and Technology, Guangzhou, 510642, China.,State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, Guangzhou, 510642, China.,Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China
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