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Takahashi I, Ohseto H, Ueno F, Oonuma T, Narita A, Obara T, Ishikuro M, Murakami K, Noda A, Hozawa A, Sugawara J, Tamiya G, Kuriyama S. Genome-wide association study based on clustering by obesity-related variables uncovers a genetic architecture of obesity in the Japanese and the UK populations. Heliyon 2024; 10:e36023. [PMID: 39247266 PMCID: PMC11379603 DOI: 10.1016/j.heliyon.2024.e36023] [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: 06/08/2023] [Revised: 08/06/2024] [Accepted: 08/08/2024] [Indexed: 09/10/2024] Open
Abstract
Whether all obesity-related variants contribute to the onset of obesity or one or a few variants cause obesity in genetically heterogeneous populations remains obscure. Here, we investigated the genetic architecture of obesity by clustering the Japanese and British populations with obesity using obesity-related factors. In Step-1, we conducted a genome-wide association study (GWAS) with body mass index (BMI) as the outcome for eligible participants. In Step-2, we assigned participants with obesity (BMI ≥25 kg/m2) to five clusters based on obesity-related factors. Subsequently, participants from each cluster and those with a BMI <25 kg/m2 were combined. A GWAS was conducted for each cluster. Several previously identified obesity-related genes were verified in Step-1. Of the genes detected in Step-1, unique obesity-related genes were detected separately for each cluster in Step-2. Our novel findings suggest that a smaller sample size with increased homogeneity may provide insights into the genetic architecture of obesity.
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Affiliation(s)
- Ippei Takahashi
- Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Hisashi Ohseto
- Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Fumihiko Ueno
- Graduate School of Medicine, Tohoku University, Sendai, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Tomomi Oonuma
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Akira Narita
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Taku Obara
- Graduate School of Medicine, Tohoku University, Sendai, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Tohoku University Hospital, Sendai, Japan
| | - Mami Ishikuro
- Graduate School of Medicine, Tohoku University, Sendai, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Keiko Murakami
- Graduate School of Medicine, Tohoku University, Sendai, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Aoi Noda
- Graduate School of Medicine, Tohoku University, Sendai, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Tohoku University Hospital, Sendai, Japan
| | - Atsushi Hozawa
- Graduate School of Medicine, Tohoku University, Sendai, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Junichi Sugawara
- Graduate School of Medicine, Tohoku University, Sendai, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Gen Tamiya
- Graduate School of Medicine, Tohoku University, Sendai, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
| | - Shinichi Kuriyama
- Graduate School of Medicine, Tohoku University, Sendai, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- International Research Institute of Disaster Science, Tohoku University, Sendai, Japan
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Zhang Y, Zhuang Z, Liu Y, Huang J, Luan M, Zhao X, Dong L, Ye J, Yang M, Zheng E, Cai G, Wu Z, Yang J. Genomic prediction based on preselected single-nucleotide polymorphisms from genome-wide association study and imputed whole-genome sequence data annotation for growth traits in Duroc pigs. Evol Appl 2024; 17:e13651. [PMID: 38362509 PMCID: PMC10868536 DOI: 10.1111/eva.13651] [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: 12/11/2022] [Revised: 10/31/2023] [Accepted: 01/13/2024] [Indexed: 02/17/2024] Open
Abstract
The use of whole-genome sequence (WGS) data is expected to improve genomic prediction (GP) power of complex traits because it may contain mutations that in strong linkage disequilibrium pattern with causal mutations. However, a few previous studies have shown no or small improvement in prediction accuracy using WGS data. Incorporating prior biological information into GP seems to be an attractive strategy that might improve prediction accuracy. In this study, a total of 6334 pigs were genotyped using 50K chips and subsequently imputed to the WGS level. This cohort includes two prior discovery populations that comprise 294 Landrace pigs and 186 Duroc pigs, as well as two validation populations that consist of 3770 American Duroc pigs and 2084 Canadian Duroc pigs. Then we used annotation information and genome-wide association study (GWAS) from the WGS data to make GP for six growth traits in two Duroc pig populations. Based on variant annotation, we partitioned different genomic classes, such as intron, intergenic, and untranslated regions, for imputed WGS data. Based on GWAS results of WGS data, we obtained trait-associated single-nucleotide polymorphisms (SNPs). We then applied the genomic feature best linear unbiased prediction (GFBLUP) and genomic best linear unbiased prediction (GBLUP) models to estimate the genomic estimated breeding values for growth traits with these different variant panels, including six genomic classes and trait-associated SNPs. Compared with 50K chip data, GBLUP with imputed WGS data had no increase in prediction accuracy. Using only annotations resulted in no increase in prediction accuracy compared to GBLUP with 50K, but adding annotation information into the GFBLUP model with imputed WGS data could improve the prediction accuracy with increases of 0.00%-2.82%. In conclusion, a GFBLUP model that incorporated prior biological information might increase the advantage of using imputed WGS data for GP.
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Affiliation(s)
- Yuling Zhang
- College of Animal Science and National Engineering Research Center for Breeding Swine IndustrySouth China Agricultural UniversityGuangzhouChina
- Guangdong Provincial Key Laboratory of Agro‐animal Genomics and Molecular BreedingSouth China Agricultural UniversityGuangzhouChina
| | - Zhanwei Zhuang
- College of Animal Science and National Engineering Research Center for Breeding Swine IndustrySouth China Agricultural UniversityGuangzhouChina
- Guangdong Provincial Key Laboratory of Agro‐animal Genomics and Molecular BreedingSouth China Agricultural UniversityGuangzhouChina
| | - Yiyi Liu
- College of Animal Science and National Engineering Research Center for Breeding Swine IndustrySouth China Agricultural UniversityGuangzhouChina
- Guangdong Provincial Key Laboratory of Agro‐animal Genomics and Molecular BreedingSouth China Agricultural UniversityGuangzhouChina
| | - Jinyan Huang
- College of Animal Science and National Engineering Research Center for Breeding Swine IndustrySouth China Agricultural UniversityGuangzhouChina
- Guangdong Provincial Key Laboratory of Agro‐animal Genomics and Molecular BreedingSouth China Agricultural UniversityGuangzhouChina
| | - Menghao Luan
- College of Animal Science and National Engineering Research Center for Breeding Swine IndustrySouth China Agricultural UniversityGuangzhouChina
- Guangdong Provincial Key Laboratory of Agro‐animal Genomics and Molecular BreedingSouth China Agricultural UniversityGuangzhouChina
| | - Xiang Zhao
- College of Animal Science and National Engineering Research Center for Breeding Swine IndustrySouth China Agricultural UniversityGuangzhouChina
- Guangdong Provincial Key Laboratory of Agro‐animal Genomics and Molecular BreedingSouth China Agricultural UniversityGuangzhouChina
| | - Linsong Dong
- Guangdong Zhongxin Breeding Technology Co., LtdGuangzhouChina
| | - Jian Ye
- Guangdong Zhongxin Breeding Technology Co., LtdGuangzhouChina
| | - Ming Yang
- College of Animal Science and TechnologyZhongkai University of Agriculture and EngineeringGuangzhouChina
| | - Enqin Zheng
- College of Animal Science and National Engineering Research Center for Breeding Swine IndustrySouth China Agricultural UniversityGuangzhouChina
- Guangdong Provincial Key Laboratory of Agro‐animal Genomics and Molecular BreedingSouth China Agricultural UniversityGuangzhouChina
| | - Gengyuan Cai
- College of Animal Science and National Engineering Research Center for Breeding Swine IndustrySouth China Agricultural UniversityGuangzhouChina
- Guangdong Provincial Key Laboratory of Agro‐animal Genomics and Molecular BreedingSouth China Agricultural UniversityGuangzhouChina
| | - Zhenfang Wu
- College of Animal Science and National Engineering Research Center for Breeding Swine IndustrySouth China Agricultural UniversityGuangzhouChina
- Guangdong Provincial Key Laboratory of Agro‐animal Genomics and Molecular BreedingSouth China Agricultural UniversityGuangzhouChina
- Guangdong Zhongxin Breeding Technology Co., LtdGuangzhouChina
| | - Jie Yang
- College of Animal Science and National Engineering Research Center for Breeding Swine IndustrySouth China Agricultural UniversityGuangzhouChina
- Guangdong Provincial Key Laboratory of Agro‐animal Genomics and Molecular BreedingSouth China Agricultural UniversityGuangzhouChina
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Liu Y, Zhang Y, Zhou F, Yao Z, Zhan Y, Fan Z, Meng X, Zhang Z, Liu L, Yang J, Wu Z, Cai G, Zheng E. Increased Accuracy of Genomic Prediction Using Preselected SNPs from GWAS with Imputed Whole-Genome Sequence Data in Pigs. Animals (Basel) 2023; 13:3871. [PMID: 38136908 PMCID: PMC10740755 DOI: 10.3390/ani13243871] [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: 11/02/2023] [Revised: 12/13/2023] [Accepted: 12/14/2023] [Indexed: 12/24/2023] Open
Abstract
Enhancing the accuracy of genomic prediction is a key goal in genomic selection (GS) research. Integrating prior biological information into GS methods using appropriate models can improve prediction accuracy for complex traits. Genome-wide association study (GWAS) is widely utilized to identify potential candidate loci associated with complex traits in livestock and poultry, offering essential genomic insights. In this study, a GWAS was conducted on 685 Duroc × Landrace × Yorkshire (DLY) pigs to extract significant single-nucleotide polymorphisms (SNPs) as genomic features. We compared two GS models, genomic best linear unbiased prediction (GBLUP) and genomic feature BLUP (GFBLUP), by using imputed whole-genome sequencing (WGS) data on 651 Yorkshire pigs. The results revealed that the GBLUP model achieved prediction accuracies of 0.499 for backfat thickness (BFT) and 0.423 for loin muscle area (LMA). By applying the GFBLUP model with GWAS-based SNP preselection, the average prediction accuracies for BFT and LMA traits reached 0.491 and 0.440, respectively. Specifically, the GFBLUP model displayed a 4.8% enhancement in predicting LMA compared to the GBLUP model. These findings suggest that, in certain scenarios, the GFBLUP model may offer superior genomic prediction accuracy when compared to the GBLUP model, underscoring the potential value of incorporating genomic features to refine GS models.
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Affiliation(s)
- Yiyi Liu
- National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Y.L.); (Y.Z.); (F.Z.); (Z.Y.); (Y.Z.); (Z.F.); (X.M.); (Z.Z.); (L.L.); (J.Y.); (Z.W.)
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
| | - Yuling Zhang
- National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Y.L.); (Y.Z.); (F.Z.); (Z.Y.); (Y.Z.); (Z.F.); (X.M.); (Z.Z.); (L.L.); (J.Y.); (Z.W.)
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
| | - Fuchen Zhou
- National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Y.L.); (Y.Z.); (F.Z.); (Z.Y.); (Y.Z.); (Z.F.); (X.M.); (Z.Z.); (L.L.); (J.Y.); (Z.W.)
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
| | - Zekai Yao
- National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Y.L.); (Y.Z.); (F.Z.); (Z.Y.); (Y.Z.); (Z.F.); (X.M.); (Z.Z.); (L.L.); (J.Y.); (Z.W.)
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
| | - Yuexin Zhan
- National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Y.L.); (Y.Z.); (F.Z.); (Z.Y.); (Y.Z.); (Z.F.); (X.M.); (Z.Z.); (L.L.); (J.Y.); (Z.W.)
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
| | - Zhenfei Fan
- National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Y.L.); (Y.Z.); (F.Z.); (Z.Y.); (Y.Z.); (Z.F.); (X.M.); (Z.Z.); (L.L.); (J.Y.); (Z.W.)
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
| | - Xianglun Meng
- National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Y.L.); (Y.Z.); (F.Z.); (Z.Y.); (Y.Z.); (Z.F.); (X.M.); (Z.Z.); (L.L.); (J.Y.); (Z.W.)
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
| | - Zebin Zhang
- National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Y.L.); (Y.Z.); (F.Z.); (Z.Y.); (Y.Z.); (Z.F.); (X.M.); (Z.Z.); (L.L.); (J.Y.); (Z.W.)
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
| | - Langqing Liu
- National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Y.L.); (Y.Z.); (F.Z.); (Z.Y.); (Y.Z.); (Z.F.); (X.M.); (Z.Z.); (L.L.); (J.Y.); (Z.W.)
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
| | - Jie Yang
- National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Y.L.); (Y.Z.); (F.Z.); (Z.Y.); (Y.Z.); (Z.F.); (X.M.); (Z.Z.); (L.L.); (J.Y.); (Z.W.)
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
| | - Zhenfang Wu
- National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Y.L.); (Y.Z.); (F.Z.); (Z.Y.); (Y.Z.); (Z.F.); (X.M.); (Z.Z.); (L.L.); (J.Y.); (Z.W.)
- 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
| | - Gengyuan Cai
- National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Y.L.); (Y.Z.); (F.Z.); (Z.Y.); (Y.Z.); (Z.F.); (X.M.); (Z.Z.); (L.L.); (J.Y.); (Z.W.)
- 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
| | - Enqin Zheng
- National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou 510642, China; (Y.L.); (Y.Z.); (F.Z.); (Z.Y.); (Y.Z.); (Z.F.); (X.M.); (Z.Z.); (L.L.); (J.Y.); (Z.W.)
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
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Deng S, Qiu Y, Zhuang Z, Wu J, Li X, Ruan D, Xu C, Zheng E, Yang M, Cai G, Yang J, Wu Z, Huang S. Genome-Wide Association Study of Body Conformation Traits in a Three-Way Crossbred Commercial Pig Population. Animals (Basel) 2023; 13:2414. [PMID: 37570223 PMCID: PMC10417164 DOI: 10.3390/ani13152414] [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: 05/24/2023] [Revised: 06/28/2023] [Accepted: 07/05/2023] [Indexed: 08/13/2023] Open
Abstract
Body conformation is the most direct production index, which can fully reflect pig growth status and is closely related to critical economic traits. In this study, we conducted a genome-wide association study (GWAS) on body conformation traits in a population of 1518 Duroc × (Landrace × Yorkshire) commercial pigs. These traits included body length (BL), body height (BH), chest circumference (CC), abdominal circumference (AC), and waist circumference (WC). Both the mixed linear model (MLM) and fixed and random model circulating probability unification (FarmCPU) approaches were employed for the analysis. Our findings revealed 60 significant single nucleotide polymorphisms (SNPs) associated with these body conformation traits in the crossbred pig population. Specifically, sixteen SNPs were significantly associated with BL, three SNPs with BH, thirteen SNPs with CC, twelve SNPs with AC, and sixteen SNPs with WC. Moreover, we identified several promising candidate genes located within the genomic regions associated with body conformation traits. These candidate genes include INTS10, KIRREL3, SOX21, BMP2, MAP4K3, SOD3, FAM160B1, ATL2, SPRED2, SEC16B, and RASAL2. Furthermore, our analysis revealed a novel significant quantitative trait locus (QTL) on SSC7 specifically associated with waist circumference, spanning an 84 kb interval. Overall, the identification of these significant SNPs and potential candidate genes in crossbred commercial pigs enhances our understanding of the genetic basis underlying body conformation traits. Additionally, these findings provide valuable genetic resources for pig breeding programs.
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Affiliation(s)
- Shaoxiong Deng
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (S.D.); (Y.Q.); (Z.Z.); (J.W.); (X.L.); (D.R.); (C.X.); (E.Z.); (G.C.); (J.Y.)
| | - Yibin Qiu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (S.D.); (Y.Q.); (Z.Z.); (J.W.); (X.L.); (D.R.); (C.X.); (E.Z.); (G.C.); (J.Y.)
| | - Zhanwei Zhuang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (S.D.); (Y.Q.); (Z.Z.); (J.W.); (X.L.); (D.R.); (C.X.); (E.Z.); (G.C.); (J.Y.)
| | - Jie Wu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (S.D.); (Y.Q.); (Z.Z.); (J.W.); (X.L.); (D.R.); (C.X.); (E.Z.); (G.C.); (J.Y.)
| | - Xuehua Li
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (S.D.); (Y.Q.); (Z.Z.); (J.W.); (X.L.); (D.R.); (C.X.); (E.Z.); (G.C.); (J.Y.)
| | - Donglin Ruan
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (S.D.); (Y.Q.); (Z.Z.); (J.W.); (X.L.); (D.R.); (C.X.); (E.Z.); (G.C.); (J.Y.)
| | - Cineng Xu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (S.D.); (Y.Q.); (Z.Z.); (J.W.); (X.L.); (D.R.); (C.X.); (E.Z.); (G.C.); (J.Y.)
| | - Enqing Zheng
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (S.D.); (Y.Q.); (Z.Z.); (J.W.); (X.L.); (D.R.); (C.X.); (E.Z.); (G.C.); (J.Y.)
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
| | - Ming Yang
- College of Animal Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China;
| | - Gengyuan Cai
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (S.D.); (Y.Q.); (Z.Z.); (J.W.); (X.L.); (D.R.); (C.X.); (E.Z.); (G.C.); (J.Y.)
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
- Yunfu Subcenter of Guangdong Laboratory for Lingnan Modern Agriculture, Yunfu 527400, China
| | - Jie Yang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (S.D.); (Y.Q.); (Z.Z.); (J.W.); (X.L.); (D.R.); (C.X.); (E.Z.); (G.C.); (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; (S.D.); (Y.Q.); (Z.Z.); (J.W.); (X.L.); (D.R.); (C.X.); (E.Z.); (G.C.); (J.Y.)
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
- Yunfu Subcenter of Guangdong Laboratory for Lingnan Modern Agriculture, Yunfu 527400, China
| | - Sixiu Huang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou 510642, China; (S.D.); (Y.Q.); (Z.Z.); (J.W.); (X.L.); (D.R.); (C.X.); (E.Z.); (G.C.); (J.Y.)
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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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Adeniyi OO, Medugorac I, Grochowska E, Düring RA, Lühken G. Single-Locus and Multi-Locus Genome-Wide Association Studies Identify Genes Associated with Liver Cu Concentration in Merinoland Sheep. Genes (Basel) 2023; 14:genes14051053. [PMID: 37239413 DOI: 10.3390/genes14051053] [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: 03/30/2023] [Revised: 05/02/2023] [Accepted: 05/04/2023] [Indexed: 05/28/2023] Open
Abstract
Economic losses due to copper intoxication or deficiency is a problem encountered by sheep farmers. The aim of this study was to investigate the ovine genome for genomic regions and candidate genes responsible for variability in liver copper concentration. Liver samples were collected from slaughtered lambs of the Merinoland breed from two farms, and used for measurement of copper concentration and genome-wide association study (GWAS). A total of 45,511 SNPs and 130 samples were finally used for analysis, in which single-locus and several multi-locus GWAS (SL-GWAS; ML-GWAS) methods were employed. Gene enrichment analysis was performed for identified candidate genes to detect gene ontology (GO) terms significantly associated with hepatic copper levels. The SL-GWAS and a minimum of two ML-GWAS identified two and thirteen significant SNPs, respectively. Within genomic regions surrounding identified SNPs, we observed nine promising candidate genes such as DYNC1I2, VPS35, SLC38A9 and CHMP1A. GO terms such as lysosomal membrane, mitochondrial inner membrane and sodium:proton antiporter activity were significantly enriched. Genes involved in these identified GO terms mediate multivesicular body (MVB) fusion with lysosome for degradation and control mitochondrial membrane permeability. This reveals the polygenic status of this trait and candidate genes for further studies on breeding for copper tolerance in sheep.
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Affiliation(s)
- Olusegun O Adeniyi
- Institute of Animal Breeding and Genetics, Justus Liebig University Giessen, Ludwigstrasse 21, 35390 Giessen, Germany
| | - Ivica Medugorac
- Population Genomics Group, Department of Veterinary Sciences, Ludwig Maximilian University Munich, Lena-Christ-Str. 48, 82152 Martinsried, Germany
| | - Ewa Grochowska
- Department of Animal Biotechnology and Genetics, Bydgoszcz University of Science and Technology, Mazowiecka 28 St., 85-084 Bydgoszcz, Poland
| | - Rolf-Alexander Düring
- Institute of Soil Science and Soil Conservation, Interdisciplinary Research Center for Biosystems, Land Use and Nutrition (IFZ), Justus Liebig University Giessen, Heinrich-Buff-Ring 26-32, 35392 Giessen, Germany
| | - Gesine Lühken
- Institute of Animal Breeding and Genetics, Justus Liebig University Giessen, Ludwigstrasse 21, 35390 Giessen, Germany
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Zhao YX, Gao GX, Zhou Y, Guo CX, Li B, El-Ashram S, Li ZL. Genome-wide association studies uncover genes associated with litter traits in the pig. Animal 2022; 16:100672. [PMID: 36410176 DOI: 10.1016/j.animal.2022.100672] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 10/17/2022] [Accepted: 10/18/2022] [Indexed: 12/24/2022] Open
Abstract
Litter traits are critical economic variables in the pig industry as they represent a production indicator that can serve to determine sow fertility. In this study, a genome-wide association study on litter traits, including total number born (TNB), number born alive (NBA), litter birth weight (LBW), average birth weight (ABW), and piglet uniformity (PU), was carried out on two pig breeds (Yorkshire and Landrace). A total of 3 637 pigs of both breeds were genotyped using the GeneSeek GGP Porcine 50K SNP BeadChip. A mixed linear model (MLM) and fixed and random model circulating probability unification (FarmCPU) were employed in the genome-wide association studies for litter traits using combined data from the two pig breeds and data from each breed separately. Additionally, the heritability of traits was estimated using three methods-pedigree-based best linear unbiased prediction (PBLUP), genomic best linear unbiased prediction (GBLUP), and single-step best linear unbiased prediction (ssGBLUP)-and was found to lie between 0.065 and 0.1289, 0.0478 and 0.0938, 0.0793 and 0.0935, 0.1862 and 0.2163, and 0.0327 and 0.0419 for TNB, NBA, LBW, ABW, and PU, respectively. We also compared the genomic prediction accuracies and unbiasedness for litter traits of the three BLUP models. Our results indicated that the ssGBLUP method provided higher predictive accuracies and more rational unbiasedness compared with the PBLUP and GBLUP methodologies. Furthermore, based on their possible roles, eight candidate genes (INHBA, LEPR, HDHD2, CTNND2, RNF216, HMX1, PAPPA2, and NTN1) were identified as being linked with litter traits. In the middle of the test, these genes were found to be connected with pig metabolism and ovulation rate. Our results provide the insights into the genetic architecture of litter traits in pigs, and the potential single nucleotide polymorphisms (SNPs) and candidate genes identified may benefit economic profits in pig-breeding industry and contribute to improve litter traits.
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Affiliation(s)
- Y X Zhao
- School of Life Science and Engineering, Foshan University, Foshan, Guangdong 528000, China; Guangxi Yangxiang Agricultural and Animal Husbandry Co, Ltd, Guigang, Guangxi 537100, China
| | - G X Gao
- School of Life Science and Engineering, Foshan University, Foshan, Guangdong 528000, China
| | - Y Zhou
- Guangxi Yangxiang Agricultural and Animal Husbandry Co, Ltd, Guigang, Guangxi 537100, China
| | - C X Guo
- Guangxi Yangxiang Agricultural and Animal Husbandry Co, Ltd, Guigang, Guangxi 537100, China
| | - B Li
- Guangxi Yangxiang Agricultural and Animal Husbandry Co, Ltd, Guigang, Guangxi 537100, China
| | - S El-Ashram
- Faculty of Science, Kafrelsheikh University, Kafr El-Sheikh 33516, Egypt
| | - Z L Li
- School of Life Science and Engineering, Foshan University, Foshan, Guangdong 528000, China.
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8
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Shi L, Wang L, Fang L, Li M, Tian J, Wang L, Zhao F. Integrating genome-wide association studies and population genomics analysis reveals the genetic architecture of growth and backfat traits in pigs. Front Genet 2022; 13:1078696. [PMID: 36506319 PMCID: PMC9732542 DOI: 10.3389/fgene.2022.1078696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 11/14/2022] [Indexed: 11/26/2022] Open
Abstract
Growth and fat deposition are complex traits, which can affect economical income in the pig industry. Due to the intensive artificial selection, a significant genetic improvement has been observed for growth and fat deposition in pigs. Here, we first investigated genomic-wide association studies (GWAS) and population genomics (e.g., selection signature) to explore the genetic basis of such complex traits in two Large White pig lines (n = 3,727) with the GeneSeek GGP Porcine HD array (n = 50,915 SNPs). Ten genetic variants were identified to be associated with growth and fatness traits in two Large White pig lines from different genetic backgrounds by performing both within-population GWAS and cross-population GWAS analyses. These ten significant loci represented eight candidate genes, i.e., NRG4, BATF3, IRS2, ANO1, ANO9, RNF152, KCNQ5, and EYA2. One of them, ANO1 gene was simultaneously identified for both two lines in BF100 trait. Compared to single-population GWAS, cross-population GWAS was less effective for identifying SNPs with population-specific effect, but more powerful for detecting SNPs with population-shared effects. We further detected genomic regions specifically selected in each of two populations, but did not observe a significant enrichment for the heritability of growth and backfat traits in such regions. In summary, the candidate genes will provide an insight into the understanding of the genetic architecture of growth-related traits and backfat thickness, and may have a potential use in the genomic breeding programs in pigs.
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Affiliation(s)
- Liangyu Shi
- Key Laboratory of Animal Genetics, Breeding and Reproduction (Poultry) of Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China,Laboratory of Genetic Breeding, Reproduction and Precision Livestock Farming, School of Animal Science and Nutritional Engineering, Wuhan Polytechnic University, Wuhan, China
| | - Ligang Wang
- Key Laboratory of Animal Genetics, Breeding and Reproduction (Poultry) of Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Lingzhao Fang
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Mianyan Li
- Key Laboratory of Animal Genetics, Breeding and Reproduction (Poultry) of Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Jingjing Tian
- Key Laboratory of Animal Genetics, Breeding and Reproduction (Poultry) of Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Lixian Wang
- Key Laboratory of Animal Genetics, Breeding and Reproduction (Poultry) of Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China,*Correspondence: Lixian Wang, ; Fuping Zhao,
| | - Fuping Zhao
- Key Laboratory of Animal Genetics, Breeding and Reproduction (Poultry) of Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China,*Correspondence: Lixian Wang, ; Fuping Zhao,
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9
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Zhao Y, Pu Y, Liang B, Bai T, Liu Y, Jiang L, Ma Y. A study using single-locus and multi-locus genome-wide association study to identify genes associated with teat number in Hu sheep. Anim Genet 2022; 53:203-211. [PMID: 35040155 PMCID: PMC9303709 DOI: 10.1111/age.13169] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Revised: 11/08/2021] [Accepted: 12/24/2021] [Indexed: 01/19/2023]
Abstract
The multiple teats trait is common in many species of mammals and is considered related to lactation ability in swine. However, in Hu sheep, related gene research is still relatively limited. In this study, a genome‐wide association study was used to identify genetic markers and genes related to the number of teats in the Hu sheep population, a native Chinese sheep breed. A single marker method and several multi‐locus methods were utilized. A total of 61 SNPs were found to be related to the number of teats. Among these, 11 SNPs and one SNP were consistently detected by two and three multi‐locus models respectively. Four SNPs were concordantly identified between the single marker and multi‐locus methods. We also performed quantitative real‐time PCR testing of these identified candidate genes, identifying three genes with significantly different expression. Our study suggested that the LHFP, DPYSL2, and TDP‐43 genes may be related to the number of teats in sheep. The combination of single and multi‐locus GWAS detected additional SNPs not found with only one model. Our results provide new and important insights into the genetic mechanisms of the mammalian multiparous teat phenotype. These findings may be useful for future breeding and understanding the genetics of sheep and other livestock.
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Affiliation(s)
- Yuhetian Zhao
- National Germplasm Center of Domestic Animal Resources, Institute of Animal Science (IAS), Chinese Academy of Agricultural Sciences (CAAS), Haidian, Beijing, China
| | - Yabin Pu
- National Germplasm Center of Domestic Animal Resources, Institute of Animal Science (IAS), Chinese Academy of Agricultural Sciences (CAAS), Haidian, Beijing, China
| | - Benmeng Liang
- National Germplasm Center of Domestic Animal Resources, Institute of Animal Science (IAS), Chinese Academy of Agricultural Sciences (CAAS), Haidian, Beijing, China
| | - Tianyou Bai
- National Germplasm Center of Domestic Animal Resources, Institute of Animal Science (IAS), Chinese Academy of Agricultural Sciences (CAAS), Haidian, Beijing, China
| | - Yue Liu
- National Germplasm Center of Domestic Animal Resources, Institute of Animal Science (IAS), Chinese Academy of Agricultural Sciences (CAAS), Haidian, Beijing, China
| | - Lin Jiang
- National Germplasm Center of Domestic Animal Resources, Institute of Animal Science (IAS), Chinese Academy of Agricultural Sciences (CAAS), Haidian, Beijing, China
| | - Yuehui Ma
- National Germplasm Center of Domestic Animal Resources, Institute of Animal Science (IAS), Chinese Academy of Agricultural Sciences (CAAS), Haidian, Beijing, China
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10
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Sell-Kubiak E, Knol EF, Lopes M. Evaluation of the phenotypic and genomic background of variability based on litter size of Large White pigs. Genet Sel Evol 2022; 54:1. [PMID: 34979897 PMCID: PMC8722267 DOI: 10.1186/s12711-021-00692-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 12/15/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The genetic background of trait variability has captured the interest of ecologists and animal breeders because the genes that control it could be involved in buffering various environmental effects. Phenotypic variability of a given trait can be assessed by studying the heterogeneity of the residual variance, and the quantitative trait loci (QTL) that are involved in the control of this variability are described as variance QTL (vQTL). This study focuses on litter size (total number born, TNB) and its variability in a Large White pig population. The variability of TNB was evaluated either using a simple method, i.e. analysis of the log-transformed variance of residuals (LnVar), or the more complex double hierarchical generalized linear model (DHGLM). We also performed a single-SNP (single nucleotide polymorphism) genome-wide association study (GWAS). To our knowledge, this is only the second study that reports vQTL for litter size in pigs and the first one that shows GWAS results when using two methods to evaluate variability of TNB: LnVar and DHGLM. RESULTS Based on LnVar, three candidate vQTL regions were detected, on Sus scrofa chromosomes (SSC) 1, 7, and 18, which comprised 18 SNPs. Based on the DHGLM, three candidate vQTL regions were detected, i.e. two on SSC7 and one on SSC11, which comprised 32 SNPs. Only one candidate vQTL region overlapped between the two methods, on SSC7, which also contained the most significant SNP. Within this vQTL region, two candidate genes were identified, ADGRF1, which is involved in neurodevelopment of the brain, and ADGRF5, which is involved in the function of the respiratory system and in vascularization. The correlation between estimated breeding values based on the two methods was 0.86. Three-fold cross-validation indicated that DHGLM yielded EBV that were much more accurate and had better prediction of missing observations than LnVar. CONCLUSIONS The results indicated that the LnVar and DHGLM methods resulted in genetically different traits. Based on their validation, we recommend the use of DHGLM over the simpler method of log-transformed variance of residuals. These conclusions can be useful for future studies on the evaluation of the variability of any trait in any species.
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Affiliation(s)
- Ewa Sell-Kubiak
- Department of Genetics and Animal Breeding, Poznan University of Life Sciences, Poznań, Poland.
| | - Egbert F Knol
- Topigs Norsvin Research Centre, Beuningen, The Netherlands
| | - Marcos Lopes
- Topigs Norsvin Research Centre, Beuningen, The Netherlands.,Topigs Norsvin, Curitiba, Brazil
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11
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Zhang H, Zhuang Z, Yang M, Ding R, Quan J, Zhou S, Gu T, Xu Z, Zheng E, Cai G, Yang J, Wu Z. Genome-Wide Detection of Genetic Loci and Candidate Genes for Body Conformation Traits in Duroc × Landrace × Yorkshire Crossbred Pigs. Front Genet 2021; 12:664343. [PMID: 34707635 PMCID: PMC8542986 DOI: 10.3389/fgene.2021.664343] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 09/16/2021] [Indexed: 11/30/2022] Open
Abstract
The Duroc × (Landrace × Yorkshire) hybrid pigs (DLY) are the most popular commercial pigs, providing consumers with the largest source of pork. In order to gain more insights into the genetic architecture of economically important traits in pigs, we performed a genome-wide association study (GWAS) using the GeneSeek Porcine 50 K SNP Chip to map the genetic markers and genes associated with body conformation traits (BCT) in 311 DLY pigs. The quantitative traits analyzed included body weight (BW), carcass length (CL), body length (BL), body height (BH), and body mass index (BMI). BMI was defined as BMICL, BMIBL, and BMIBH, respectively, based on CL, BL, and BH phenotypic data. We identified 82 SNPs for the seven traits by GEMMA-based and FarmCPU-based GWASs. Both methods detected two quantitative trait loci (QTL) on SSC8 and SSC17 for body conformation traits. Several candidate genes (such as TNFAIP3, KDM4C, HSPG2, BMP2, PLCB4, and GRM5) were found to be associated with body weight and body conformation traits in pigs. Notably, the BMP2 gene had pleiotropic effects on CL, BL, BH, BMICL, and BMIBL and is proposed as a strong candidate gene for body size due to its involvement in growth and bone development. Furthermore, gene set enrichment analysis indicated that most of the pathway terms are associated with regulation of cell growth, negative regulation of cell population proliferation, and chondrocyte differentiation. We anticipate that these results further advance our understanding of the genetic architecture of body conformation traits in the popular commercial DLY pigs and provide new insights into the genetic architecture of BMI in pigs.
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Affiliation(s)
- Hui Zhang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, China
| | - Zhanwei Zhuang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, China
| | - Ming Yang
- College of Animal Science and Technology, Zhongkai University of Agriculture and Engineering, Guangdong, China
| | - Rongrong Ding
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, China
| | - Jianping Quan
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, China
| | - Shenping Zhou
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, China
| | - Ting Gu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, China
| | - Zheng Xu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, China
| | - Enqin Zheng
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, China
| | - Gengyuan Cai
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, China
| | - Jie Yang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, China
| | - Zhenfang Wu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, China
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12
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Shi L, Li Y, Liu Q, Zhang L, Wang L, Liu X, Gao H, Hou X, Zhao F, Yan H, Wang L. Identification of SNPs and Candidate Genes for Milk Production Ability in Yorkshire Pigs. Front Genet 2021; 12:724533. [PMID: 34675963 PMCID: PMC8523896 DOI: 10.3389/fgene.2021.724533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Accepted: 09/22/2021] [Indexed: 12/01/2022] Open
Abstract
Sow milk production ability is an important limiting factor impacting suboptimal growth and the survival of piglets. Through pig genetic improvement, litter sizes have been increased. Larger litters need more suckling mammary glands, which results in increased milk from the lactating sow. Hence, there is much significance to exploring sow lactation performance. For milk production ability, it is not practical to directly measure the milk yield, we used litter weight gain (LWG) throughout sow lactation as an indicator. In this study, we estimated the heritability of LWG, namely, 0.18 ± 0.07. We then performed a GWAS, and detected seven significant SNPs, namely, Sus scrofa Chromosome (SSC) 2: ASGA0010040 (p = 7.73E-11); SSC2:MARC0029355 (p = 1.30E-08), SSC6: WU_10.2_6_65751151 (p = 1.32E-10), SSC7: MARC0058875 (p = 4.99E-09), SSC10: WU_10.2_10_49571394 (p = 6.79E-08), SSC11: M1GA0014659 (p = 1.19E-07), and SSC15: MARC0042106 (p = 1.16E-07). We performed the distribution of phenotypes corresponding to the genotypes of seven significant SNPs and showed that ASGA0010040, MARC0029355, MARC0058875, WU_10.2_10_49571394, M1GA0014659, and MARC0042106 had extreme phenotypic values that corresponded to the homozygous genotypes, while the intermediate values corresponded to the heterozygous genotypes. We screened for flanking regions ± 200 kb nearby the seven significant SNPs, and identified 38 genes in total. Among them, 28 of the candidates were involved in lactose metabolism, colostrum immunity, milk protein, and milk fat by functional enrichment analysis. Through the combined analysis between 28 candidate genes and transcriptome data of the sow mammary gland, we found nine commons (ANO3, MUC15, DISP3, FBXO6, CLCN6, HLA-DRA, SLA-DRB1, SLA-DQB1, and SLA-DQA1). Furthermore, by comparing the chromosome positions of the candidate genes with the quantitative trait locus (QTLs) as previously reported, a total of 17 genes were found to be within 0.86–94.02 Mb of the reported QTLs for sow milk production ability, in which, NAV2 was found to be located with 0.86 Mb of the QTL region ssc2: 40936355. In conclusion, we identified seven significant SNPs located on SSC2, 6, 7, 10, 11, and 15, and propose 28 candidate genes for the ability to produce milk in Yorkshire pigs, 10 of which were key candidates.
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Affiliation(s)
- Lijun Shi
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yang Li
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Qian Liu
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Longchao Zhang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Ligang Wang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xin Liu
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Hongmei Gao
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xinhua Hou
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Fuping Zhao
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Hua Yan
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Lixian Wang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
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13
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Qiu Y, Ding R, Zhuang Z, Wu J, Yang M, Zhou S, Ye Y, Geng Q, Xu Z, Huang S, Cai G, Wu Z, Yang J. Genome-wide detection of CNV regions and their potential association with growth and fatness traits in Duroc pigs. BMC Genomics 2021; 22:332. [PMID: 33964879 PMCID: PMC8106131 DOI: 10.1186/s12864-021-07654-7] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Accepted: 04/27/2021] [Indexed: 11/19/2022] Open
Abstract
Background In the process of pig breeding, the average daily gain (ADG), days to 100 kg (AGE), and backfat thickness (BFT) are directly related to growth rate and fatness. However, the genetic mechanisms involved are not well understood. Copy number variation (CNV), an important source of genetic diversity, can affect a variety of complex traits and diseases and has gradually been thrust into the limelight. In this study, we reported the genome-wide CNVs of Duroc pigs using SNP genotyping data from 6627 animals. We also performed a copy number variation region (CNVR)-based genome-wide association studies (GWAS) for growth and fatness traits in two Duroc populations. Results Our study identified 953 nonredundant CNVRs in U.S. and Canadian Duroc pigs, covering 246.89 Mb (~ 10.90%) of the pig autosomal genome. Of these, 802 CNVRs were in U.S. Duroc pigs with 499 CNVRs were in Canadian Duroc pigs, indicating 348 CNVRs were shared by the two populations. Experimentally, 77.8% of nine randomly selected CNVRs were validated through quantitative PCR (qPCR). We also identified 35 CNVRs with significant association with growth and fatness traits using CNVR-based GWAS. Ten of these CNVRs were associated with both ADG and AGE traits in U.S. Duroc pigs. Notably, four CNVRs showed significant associations with ADG, AGE, and BFT, indicating that these CNVRs may play a pleiotropic role in regulating pig growth and fat deposition. In Canadian Duroc pigs, nine CNVRs were significantly associated with both ADG and AGE traits. Further bioinformatic analysis identified a subset of potential candidate genes, including PDGFA, GPER1, PNPLA2 and BSCL2. Conclusions The present study provides a necessary supplement to the CNV map of the Duroc genome through large-scale population genotyping. In addition, the CNVR-based GWAS results provide a meaningful way to elucidate the genetic mechanisms underlying complex traits. The identified CNVRs can be used as molecular markers for genetic improvement in the molecular-guided breeding of modern commercial pigs. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-021-07654-7.
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Affiliation(s)
- Yibin Qiu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, 510642, People's Republic of China
| | - Rongrong Ding
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, 510642, People's Republic of China.,Guangdong Wens Breeding Swine Technology Co., Ltd., Yunfu, Guangdong, 527400, People's Republic of China
| | - Zhanwei Zhuang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, 510642, People's Republic of China
| | - Jie Wu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, 510642, People's Republic of China
| | - Ming Yang
- Guangdong Wens Breeding Swine Technology Co., Ltd., Yunfu, Guangdong, 527400, People's Republic of China
| | - Shenping Zhou
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, 510642, People's Republic of China
| | - Yong Ye
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, 510642, People's Republic of China
| | - Qian Geng
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, 510642, People's Republic of China
| | - Zheng Xu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, 510642, People's Republic of China.,Lingnan Guangdong Laboratory of Modern Agriculture, Guangzhou, 510642, People's Republic of China
| | - Sixiu Huang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, 510642, People's Republic of China
| | - Gengyuan Cai
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, 510642, People's Republic of China.,Guangdong Wens Breeding Swine Technology Co., Ltd., Yunfu, Guangdong, 527400, People's Republic of China.,Lingnan Guangdong Laboratory of Modern Agriculture, Guangzhou, 510642, People's Republic of China
| | - Zhenfang Wu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, 510642, People's Republic of China. .,Guangdong Wens Breeding Swine Technology Co., Ltd., Yunfu, Guangdong, 527400, People's Republic of China. .,Lingnan Guangdong Laboratory of Modern Agriculture, Guangzhou, 510642, People's Republic of China.
| | - Jie Yang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, 510642, People's Republic of China. .,Lingnan Guangdong Laboratory of Modern Agriculture, Guangzhou, 510642, People's Republic of China.
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14
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Donaldson B, Villagomez DAF, King WA. Classical, Molecular, and Genomic Cytogenetics of the Pig, a Clinical Perspective. Animals (Basel) 2021; 11:1257. [PMID: 33925534 PMCID: PMC8146943 DOI: 10.3390/ani11051257] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Revised: 04/16/2021] [Accepted: 04/23/2021] [Indexed: 02/06/2023] Open
Abstract
The chromosomes of the domestic pig (Sus scrofa domesticus) are known to be prone to reciprocal chromosome translocations and other balanced chromosome rearrangements with concomitant fertility impairment of carriers. In response to the remarkable prevalence of chromosome rearrangements in swine herds, clinical cytogenetics laboratories have been established in several countries in order to screen young boars for chromosome rearrangements prior to service. At present, clinical cytogenetics laboratories typically apply classical cytogenetics techniques such as giemsa-trypsin (GTG)-banding to produce high-quality karyotypes and reveal large-scale chromosome ectopic exchanges. Further refinements to clinical cytogenetics practices have led to the implementation of molecular cytogenetics techniques such as fluorescent in-situ hybridization (FISH), allowing for rearrangements to be visualized and breakpoints refined using fluorescently labelled painting probes. The next-generation of clinical cytogenetics include the implementation of DNA microarrays, and next-generation sequencing (NGS) technologies such as DNA sequencing to better explore tentative genome architecture changes. The implementation of these cytogenomics techniques allow the genomes of rearrangement carriers to be deciphered at the highest resolution, allowing rearrangements to be detected; breakpoints to be delineated; and, most importantly, potential gene implications of those chromosome rearrangements to be interrogated. Clinical cytogenetics has become an integral tool in the livestock industry, identifying rearrangements and allowing breeders to make informed breeding decisions.
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Affiliation(s)
- Brendan Donaldson
- Department of Biomedical Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada;
| | | | - W. Allan King
- Department of Biomedical Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada;
- Karyotekk Inc., Box 363 OVC, University of Guelph, Guelph, ON N1G 2W1, Canada
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15
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Tao L, He X, Jiang Y, Liu Y, Ouyang Y, Shen Y, Hong Q, Chu M. Genome-Wide Analyses Reveal Genetic Convergence of Prolificacy between Goats and Sheep. Genes (Basel) 2021; 12:480. [PMID: 33810234 PMCID: PMC8065816 DOI: 10.3390/genes12040480] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 03/16/2021] [Accepted: 03/23/2021] [Indexed: 12/20/2022] Open
Abstract
The litter size of domestic goats and sheep is an economically important trait that shows variation within breeds. Strenuous efforts have been made to understand the genetic mechanisms underlying prolificacy in goats and sheep. However, there has been a paucity of research on the genetic convergence of prolificacy between goats and sheep, which likely arose because of similar natural and artificial selection forces. Here, we performed comparative genomic and transcriptomic analyses to identify the genetic convergence of prolificacy between goats and sheep. By combining genomic and transcriptomic data for the first time, we identified this genetic convergence in (1) positively selected genes (CHST11 and SDCCAG8), (2) differentially expressed genes (SERPINA14, RSAD2, and PPIG at follicular phase, and IGF1, GPRIN3, LIPG, SLC7A11, and CHST15 at luteal phase), and (3) biological pathways (genomic level: osteoclast differentiation, ErbB signaling pathway, and relaxin signaling pathway; transcriptomic level: the regulation of viral genome replication at follicular phase, and protein kinase B signaling and antigen processing and presentation at luteal phase). These results indicated the potential physiological convergence and enhanced our understanding of the overlapping genetic makeup underlying litter size in goats and sheep.
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Affiliation(s)
- Lin Tao
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China; (L.T.); (X.H.); (Y.L.)
| | - Xiaoyun He
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China; (L.T.); (X.H.); (Y.L.)
| | - Yanting Jiang
- Yunnan Animal Science and Veterinary Institute, Kunming 650224, China; (Y.J.); (Y.O.)
| | - Yufang Liu
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China; (L.T.); (X.H.); (Y.L.)
- College of Life Science and Food Engineering, Hebei University of Engineering, Handan 056038, China
| | - Yina Ouyang
- Yunnan Animal Science and Veterinary Institute, Kunming 650224, China; (Y.J.); (Y.O.)
| | - Yezhen Shen
- Annoroad Gene Technology Co., Ltd., Beijing 100176, China;
| | - Qionghua Hong
- Yunnan Animal Science and Veterinary Institute, Kunming 650224, China; (Y.J.); (Y.O.)
| | - Mingxing Chu
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China; (L.T.); (X.H.); (Y.L.)
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16
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Narita A, Nagai M, Mizuno S, Ogishima S, Tamiya G, Ueki M, Sakurai R, Makino S, Obara T, Ishikuro M, Yamanaka C, Matsubara H, Kuniyoshi Y, Murakami K, Ueno F, Noda A, Kobayashi T, Kobayashi M, Usuzaki T, Ohseto H, Hozawa A, Kikuya M, Metoki H, Kure S, Kuriyama S. Clustering by phenotype and genome-wide association study in autism. Transl Psychiatry 2020; 10:290. [PMID: 32807774 PMCID: PMC7431539 DOI: 10.1038/s41398-020-00951-x] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 07/15/2020] [Accepted: 07/22/2020] [Indexed: 12/12/2022] Open
Abstract
Autism spectrum disorder (ASD) has phenotypically and genetically heterogeneous characteristics. A simulation study demonstrated that attempts to categorize patients with a complex disease into more homogeneous subgroups could have more power to elucidate hidden heritability. We conducted cluster analyses using the k-means algorithm with a cluster number of 15 based on phenotypic variables from the Simons Simplex Collection (SSC). As a preliminary study, we conducted a conventional genome-wide association study (GWAS) with a data set of 597 ASD cases and 370 controls. In the second step, we divided cases based on the clustering results and conducted GWAS in each of the subgroups vs controls (cluster-based GWAS). We also conducted cluster-based GWAS on another SSC data set of 712 probands and 354 controls in the replication stage. In the preliminary study, which was conducted in conventional GWAS design, we observed no significant associations. In the second step of cluster-based GWASs, we identified 65 chromosomal loci, which included 30 intragenic loci located in 21 genes and 35 intergenic loci that satisfied the threshold of P < 5.0 × 10-8. Some of these loci were located within or near previously reported candidate genes for ASD: CDH5, CNTN5, CNTNAP5, DNAH17, DPP10, DSCAM, FOXK1, GABBR2, GRIN2A5, ITPR1, NTM, SDK1, SNCA, and SRRM4. Of these 65 significant chromosomal loci, rs11064685 located within the SRRM4 gene had a significantly different distribution in the cases vs controls in the replication cohort. These findings suggest that clustering may successfully identify subgroups with relatively homogeneous disease etiologies. Further cluster validation and replication studies are warranted in larger cohorts.
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Affiliation(s)
- Akira Narita
- grid.69566.3a0000 0001 2248 6943Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan ,grid.69566.3a0000 0001 2248 6943Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Masato Nagai
- grid.69566.3a0000 0001 2248 6943Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan ,grid.69566.3a0000 0001 2248 6943Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Satoshi Mizuno
- grid.69566.3a0000 0001 2248 6943Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan ,grid.69566.3a0000 0001 2248 6943Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Soichi Ogishima
- grid.69566.3a0000 0001 2248 6943Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan ,grid.69566.3a0000 0001 2248 6943Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Gen Tamiya
- grid.69566.3a0000 0001 2248 6943Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan ,grid.69566.3a0000 0001 2248 6943Graduate School of Medicine, Tohoku University, Sendai, Japan ,grid.7597.c0000000094465255RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
| | - Masao Ueki
- grid.69566.3a0000 0001 2248 6943Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan ,grid.69566.3a0000 0001 2248 6943Graduate School of Medicine, Tohoku University, Sendai, Japan ,grid.7597.c0000000094465255RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
| | - Rieko Sakurai
- grid.69566.3a0000 0001 2248 6943Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan ,grid.69566.3a0000 0001 2248 6943Graduate School of Medicine, Tohoku University, Sendai, Japan ,grid.7597.c0000000094465255RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
| | - Satoshi Makino
- grid.69566.3a0000 0001 2248 6943Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan ,grid.69566.3a0000 0001 2248 6943Graduate School of Medicine, Tohoku University, Sendai, Japan ,grid.7597.c0000000094465255RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
| | - Taku Obara
- grid.69566.3a0000 0001 2248 6943Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan ,grid.69566.3a0000 0001 2248 6943Graduate School of Medicine, Tohoku University, Sendai, Japan ,grid.69566.3a0000 0001 2248 6943Tohoku University Hospital, Tohoku University, Sendai, Japan
| | - Mami Ishikuro
- grid.69566.3a0000 0001 2248 6943Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan ,grid.69566.3a0000 0001 2248 6943Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Chizuru Yamanaka
- grid.69566.3a0000 0001 2248 6943Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan ,grid.69566.3a0000 0001 2248 6943Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Hiroko Matsubara
- grid.69566.3a0000 0001 2248 6943Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan ,grid.69566.3a0000 0001 2248 6943Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Yasutaka Kuniyoshi
- grid.69566.3a0000 0001 2248 6943Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Keiko Murakami
- grid.69566.3a0000 0001 2248 6943Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan ,grid.69566.3a0000 0001 2248 6943Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Fumihiko Ueno
- grid.69566.3a0000 0001 2248 6943Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan ,grid.69566.3a0000 0001 2248 6943Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Aoi Noda
- grid.69566.3a0000 0001 2248 6943Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan ,grid.69566.3a0000 0001 2248 6943Graduate School of Medicine, Tohoku University, Sendai, Japan ,grid.69566.3a0000 0001 2248 6943Tohoku University Hospital, Tohoku University, Sendai, Japan
| | - Tomoko Kobayashi
- grid.69566.3a0000 0001 2248 6943Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan ,grid.69566.3a0000 0001 2248 6943Graduate School of Medicine, Tohoku University, Sendai, Japan ,grid.69566.3a0000 0001 2248 6943Tohoku University Hospital, Tohoku University, Sendai, Japan
| | - Mika Kobayashi
- grid.69566.3a0000 0001 2248 6943Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Takuma Usuzaki
- grid.69566.3a0000 0001 2248 6943Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Hisashi Ohseto
- grid.69566.3a0000 0001 2248 6943Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Atsushi Hozawa
- grid.69566.3a0000 0001 2248 6943Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan ,grid.69566.3a0000 0001 2248 6943Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Masahiro Kikuya
- grid.69566.3a0000 0001 2248 6943Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan ,grid.69566.3a0000 0001 2248 6943Graduate School of Medicine, Tohoku University, Sendai, Japan ,grid.264706.10000 0000 9239 9995School of Medicine, Teikyo University, Tokyo, Japan
| | - Hirohito Metoki
- grid.69566.3a0000 0001 2248 6943Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan ,grid.69566.3a0000 0001 2248 6943Graduate School of Medicine, Tohoku University, Sendai, Japan ,grid.412755.00000 0001 2166 7427School of Medicine, Tohoku Medical and Pharmaceutical University, Sendai, Japan
| | - Shigeo Kure
- grid.69566.3a0000 0001 2248 6943Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan ,grid.69566.3a0000 0001 2248 6943Graduate School of Medicine, Tohoku University, Sendai, Japan ,grid.69566.3a0000 0001 2248 6943Tohoku University Hospital, Tohoku University, Sendai, Japan
| | - Shinichi Kuriyama
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan. .,Graduate School of Medicine, Tohoku University, Sendai, Japan. .,International Research Institute of Disaster Science, Tohoku University, Sendai, Miyagi, Japan.
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17
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Zhuang Z, Ding R, Peng L, Wu J, Ye Y, Zhou S, Wang X, Quan J, Zheng E, Cai G, Huang W, Yang J, Wu Z. Genome-wide association analyses identify known and novel loci for teat number in Duroc pigs using single-locus and multi-locus models. BMC Genomics 2020; 21:344. [PMID: 32380955 PMCID: PMC7204245 DOI: 10.1186/s12864-020-6742-6] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Accepted: 04/16/2020] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND More teats are necessary for sows to nurse larger litters to provide immunity and nutrient for piglets prior to weaning. Previous studies have reported the strong effect of an insertion mutation in the Vertebrae Development Associated (VRTN) gene on Sus scrofa chromosome 7 (SSC7) that increased the number of thoracic vertebrae and teat number in pigs. We used genome-wide association studies (GWAS) to map genetic markers and genes associated with teat number in two Duroc pig populations with different genetic backgrounds. A single marker method and several multi-locus methods were utilized. A meta-analysis that combined the effects and P-values of 34,681 single nucleotide polymorphisms (SNPs) that were common in the results of single marker GWAS of American and Canadian Duroc pigs was conducted. We also performed association tests between the VRTN insertion and teat number in the same populations. RESULTS A total of 97 SNPs were found to be associated with teat number. Among these, six, eight and seven SNPs were consistently detected with two, three and four multi-locus methods, respectively. Seven SNPs were concordantly identified between single marker and multi-locus methods. Moreover, 26 SNPs were newly found by multi-locus methods to be associated with teat number. Notably, we detected one consistent quantitative trait locus (QTL) on SSC7 for teat number using single-locus and meta-analysis of GWAS and the top SNP (rs692640845) explained 8.68% phenotypic variance of teat number in the Canadian Duroc pigs. The associations between the VRTN insertion and teat number in two Duroc pig populations were substantially weaker. Further analysis revealed that the effect of VRTN on teat number may be mediated by its LD with the true causal mutation. CONCLUSIONS Our study suggested that VRTN insertion may not be a strong or the only candidate causal mutation for the QTL on SSC7 for teat number in the analyzed Duroc pig populations. The combination of single-locus and multi-locus GWAS detected additional SNPs that were absent using only one model. The identified SNPs will be useful for the genetic improvement of teat number in pigs by assigning higher weights to associated SNPs in genomic selection.
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Affiliation(s)
- Zhanwei Zhuang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, 510642, People's Republic of China
| | - Rongrong Ding
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, 510642, People's Republic of China
| | - Longlong Peng
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, 510642, People's Republic of China
| | - Jie Wu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, 510642, People's Republic of China
| | - Yong Ye
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, 510642, People's Republic of China
| | - Shenping Zhou
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, 510642, People's Republic of China
| | - Xingwang Wang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, 510642, People's Republic of China
| | - Jianping Quan
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, 510642, People's Republic of China
| | - Enqin Zheng
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, 510642, People's Republic of China
| | - Gengyuan Cai
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, 510642, People's Republic of China
| | - Wen Huang
- Department of animal science, Michigan State University, East Lansing, MI, USA
| | - Jie Yang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, 510642, People's Republic of China.
| | - Zhenfang Wu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, Guangdong, 510642, People's Republic of China.
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18
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Li Y, Li B, Yang M, Han H, Chen T, Wei Q, Miao Z, Yin L, Wang R, Shen J, Li X, Xu X, Fang M, Zhao S. Genome-Wide Association Study and Fine Mapping Reveals Candidate Genes for Birth Weight of Yorkshire and Landrace Pigs. Front Genet 2020; 11:183. [PMID: 32292414 PMCID: PMC7118202 DOI: 10.3389/fgene.2020.00183] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 02/14/2020] [Indexed: 12/19/2022] Open
Abstract
Birth weight of pigs is an important economic factor in the livestock industry. The identification of the genes and variants that underlie birth weight is of great importance. In this study, we integrated two genotyping methods, single nucleotide polymorphism (SNP) chip analysis and restriction site associated DNA sequencing (RAD-seq) to genotype genome-wide SNPs. In total, 45,175 and 139,634 SNPs were detected with the SNP chip and RAD-seq, respectively. The genome-wide association study (GWAS) of the combined SNP panels identified two significant loci located at chr1: 97,745,041 and chr4: 112,031,589, that explained 6.36% and 4.25% of the phenotypic variance respectively. To reduce interval containing causal variants, we imputed sequence-level SNPs in the GWAS identified regions and fine-mapped the causative variants into two narrower genomic intervals: a ∼100 kb interval containing 71 SNPs and a broader ∼870 kb interval with 432 SNPs. This fine-mapping highlighted four promising candidate genes, SKOR2, SMAD2, VAV3, and NTNG1. Additionally, the functional genes, SLC25A24, PRMT6 and STXBP3, are also located near the fine-mapping region. These results suggest that these candidate genes may have contribute substantially to the birth weight of pigs.
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Affiliation(s)
- Yong Li
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, The Cooperative Innovation Center for Sustainable Pig Production, Ministry of Education, Huazhong Agricultural University, Wuhan, China.,Shenzhen Engineering Laboratory for Genomics - Assisted Animal Breeding, BGI Institute of Applied Agriculture, BGI-Shenzhen, Shenzhen, China
| | - Bin Li
- Shenzhen Engineering Laboratory for Genomics - Assisted Animal Breeding, BGI Institute of Applied Agriculture, BGI-Shenzhen, Shenzhen, China
| | - Manman Yang
- Shenzhen Engineering Laboratory for Genomics - Assisted Animal Breeding, BGI Institute of Applied Agriculture, BGI-Shenzhen, Shenzhen, China
| | - Hu Han
- Shenzhen Engineering Laboratory for Genomics - Assisted Animal Breeding, BGI Institute of Applied Agriculture, BGI-Shenzhen, Shenzhen, China
| | - Tao Chen
- Shenzhen Engineering Laboratory for Genomics - Assisted Animal Breeding, BGI Institute of Applied Agriculture, BGI-Shenzhen, Shenzhen, China
| | - Qiang Wei
- Shenzhen Engineering Laboratory for Genomics - Assisted Animal Breeding, BGI Institute of Applied Agriculture, BGI-Shenzhen, Shenzhen, China
| | - Zepu Miao
- Shenzhen Engineering Laboratory for Genomics - Assisted Animal Breeding, BGI Institute of Applied Agriculture, BGI-Shenzhen, Shenzhen, China
| | - Lilin Yin
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, The Cooperative Innovation Center for Sustainable Pig Production, Ministry of Education, Huazhong Agricultural University, Wuhan, China
| | - Ran Wang
- Shenzhen Engineering Laboratory for Genomics - Assisted Animal Breeding, BGI Institute of Applied Agriculture, BGI-Shenzhen, Shenzhen, China
| | - Junran Shen
- Shenzhen Engineering Laboratory for Genomics - Assisted Animal Breeding, BGI Institute of Applied Agriculture, BGI-Shenzhen, Shenzhen, China
| | - Xinyun Li
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, The Cooperative Innovation Center for Sustainable Pig Production, Ministry of Education, Huazhong Agricultural University, Wuhan, China
| | - Xuewen Xu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, The Cooperative Innovation Center for Sustainable Pig Production, Ministry of Education, Huazhong Agricultural University, Wuhan, China
| | - Ming Fang
- Key Laboratory of Healthy Mariculture for the East China Sea, Ministry of Agriculture and Rural Affairs, Fisheries College, Jimei University, Xiamen, China
| | - Shuhong Zhao
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, The Cooperative Innovation Center for Sustainable Pig Production, Ministry of Education, Huazhong Agricultural University, Wuhan, China
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19
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Iung LHDS, Carvalheiro R, Neves HHDR, Mulder HA. Genetics and genomics of uniformity and resilience in livestock and aquaculture species: A review. J Anim Breed Genet 2019; 137:263-280. [PMID: 31709657 DOI: 10.1111/jbg.12454] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Revised: 10/15/2019] [Accepted: 10/16/2019] [Indexed: 01/29/2023]
Abstract
Genetic control of residual variance offers opportunities to increase uniformity and resilience of livestock and aquaculture species. Improving uniformity and resilience of animals will improve health and welfare of animals and lead to more homogenous products. Our aims in this review were to summarize the current models and methods to study genetic control of residual variance, genetic parameters and genomic results for residual variance and discuss future research directions. Typically, the genetic coefficient of variation is high (median = 0.27; range 0-0.86) and the heritability of residual variance is low (median = 0.01; range 0-0.10). Higher heritabilities can be achieved when increasing the number of records per animal. Divergent selection experiments have supported the feasibility of selecting for high or low residual variance. Genomic studies have revealed associations in regions related to stress, including those from the heat shock protein family. Although the number of studies is growing, genetic control of residual variance is still poorly understood, but big data and genomics offer great opportunities.
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Affiliation(s)
- Laiza Helena de Souza Iung
- School of Agricultural and Veterinarian Sciences, São Paulo State University (Unesp), Jaboticabal, Brazil.,CRV Lagoa, Sertãozinho, Brazil
| | - Roberto Carvalheiro
- School of Agricultural and Veterinarian Sciences, São Paulo State University (Unesp), Jaboticabal, Brazil.,National Council for Scientific and Technological Development (CNPq), Brasília, Brazil
| | | | - Herman Arend Mulder
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
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20
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Wang Y, Zhang T, Wang C. Detection and analysis of genome-wide copy number variation in the pig genome using an 80 K SNP Beadchip. J Anim Breed Genet 2019; 137:166-176. [PMID: 31506991 DOI: 10.1111/jbg.12435] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Revised: 08/02/2019] [Accepted: 08/05/2019] [Indexed: 12/23/2022]
Abstract
Copy number variation (CNV) is an important source of genetic variability in human or animal genomes and play key roles in phenotypic diversity and disease susceptibility. In the present study, we performed a genome-wide analysis for CNV detection using SNP genotyping data of 857 Large White pigs. A total of 312 CNV regions (CNVRs) were detected with the PennCNV algorithm, which covered 57.76 Mb of the pig genome and correspond to 2.36% of the genome sequence. The length of the CNVRs on autosomes ranged from 1.77 Kb to 1.76 Mb with an average of 185.11 Kb. Of these, 220 completely or partially overlapped with 1,092 annotated genes, which enriched a wide variety of biological processes. Comparisons with previously reported pig CNVR revealed 92 (29.49%) novel CNVRs. Experimentally, 80% of CNVRs selected randomly were validated by quantitative PCR (qPCR). We also performed an association analysis between some of the CNVRs and reproductive traits, with results demonstrating the potential importance of CNVR61 and CNVR283 associated with litter sizes. Notably, the GPER1 gene located in CNVR61 plays a key role in reproduction. Our study is an important complement to the CNV map in the pig genome and provides valuable information for investigating the association between genomic variation and economic traits.
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Affiliation(s)
- Yuan Wang
- College of Animal Science and Technology, Qingdao Agricultural University, Qingdao, China.,Key Laboratory of Animal Genetics and Breeding of Ministry of Agriculture, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Tingrong Zhang
- College of Animal Science and Technology, Qingdao Agricultural University, Qingdao, China
| | - Chuduan Wang
- Key Laboratory of Animal Genetics and Breeding of Ministry of Agriculture, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
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21
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Zhang S, Zhang J, Olasege BS, Ma P, Qiu X, Gao H, Wang C, Wang Y, Zhang Q, Yang H, Wang Z, Ding X, Pan Y. Estimation of genetic parameters for reproductive traits in connectedness groups of Duroc, Landrace and Yorkshire pigs in China. J Anim Breed Genet 2019; 137:211-222. [PMID: 31468579 DOI: 10.1111/jbg.12431] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2019] [Revised: 07/24/2019] [Accepted: 07/27/2019] [Indexed: 11/30/2022]
Abstract
The objective of this study was to estimate group- and breed-specific genetic parameters for reproductive traits in Chinese Duroc, Landrace, and Yorkshire populations. Records for reproductive traits between April 1998 and December 2017 from 92 nucleus pig breeding farms, which were involved in the China Swine Genetic Improvement Program, were analysed. Due to weak genetic connectedness across all farms, connectedness groups consisting of related farms were used. Three, two and four connectedness groups for Duroc, Landrace and Yorkshire were firstly established according to the genetic connectedness rating among farms. For each connectedness group a five-trait animal model was implemented, and via restricted maximum likelihood procedure the genetic parameters were estimated for five reproductive traits i.e., total number born (TNB), number born alive (NBA), litter weight at farrowing (LWF), farrowing interval (FI) and age at first farrowing (AFF). The average of heritabilities among connectedness groups ranged from .01 (for FI in Yorkshire) to .30 (for AFF in Duroc). Estimates of repeatability for litter traits ranged from .14 to .20 and were consistent for each breed, and for FI, the estimates varied from .01 to .11 across breeds and groups. The estimated genetic correlations among litter traits (i.e., TNB, NBA and LWF) were all significantly high (>.56) and similar across breeds. Averaged genetic correlations over three breeds were -.25, -.27, -.18, -.04, -.10, -.02, and .28 for FI-TNB, FI-NBA, FI-LWF, AFF-TNB, AFF-NBA, AFF-LWF and FI-AFF, respectively. The standard errors of the estimates were all very low (<0.01) in most situations. Results from this study suggest that selection based on TNB which is currently used in dam line selection index can improve NBA and LWF simultaneously. However, care should be taken on FI and AFF as they are both greatly influenced by non-genetic factors such as management and measurement.
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Affiliation(s)
- Suoyu Zhang
- School of Agriculture and Biology, Department of Animal Science, Shanghai Jiao Tong University, Shanghai, China.,National Animal Husbandry Service, Beijing, China
| | - Jinxin Zhang
- National Animal Husbandry Service, Beijing, China.,College of Animal Science and Technology, National Engineering Laboratory for Animal Breeding & Key Laboratory of Animal Genetics and Breeding of Ministry of Agriculture, China Agricultural University, Beijing, China
| | - Babatunde Shittu Olasege
- School of Agriculture and Biology, Department of Animal Science, Shanghai Jiao Tong University, Shanghai, China
| | - Peipei Ma
- School of Agriculture and Biology, Department of Animal Science, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaotian Qiu
- National Animal Husbandry Service, Beijing, China
| | - Hong Gao
- National Animal Husbandry Service, Beijing, China.,College of Animal Science and Technology, National Engineering Laboratory for Animal Breeding & Key Laboratory of Animal Genetics and Breeding of Ministry of Agriculture, China Agricultural University, Beijing, China
| | | | - Yuan Wang
- National Animal Husbandry Service, Beijing, China.,College of Animal Science and Technology, National Engineering Laboratory for Animal Breeding & Key Laboratory of Animal Genetics and Breeding of Ministry of Agriculture, China Agricultural University, Beijing, China
| | - Qin Zhang
- College of Animal Science and Technology, National Engineering Laboratory for Animal Breeding & Key Laboratory of Animal Genetics and Breeding of Ministry of Agriculture, China Agricultural University, Beijing, China
| | - Hongjie Yang
- National Animal Husbandry Service, Beijing, China
| | - Zhigang Wang
- National Animal Husbandry Service, Beijing, China
| | - Xiangdong Ding
- College of Animal Science and Technology, National Engineering Laboratory for Animal Breeding & Key Laboratory of Animal Genetics and Breeding of Ministry of Agriculture, China Agricultural University, Beijing, China
| | - Yuchun Pan
- School of Agriculture and Biology, Department of Animal Science, Shanghai Jiao Tong University, Shanghai, China.,Shanghai Key Laboratory of Veterinary Biotechnology, Shanghai, China
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22
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Zhuang Z, Li S, Ding R, Yang M, Zheng E, Yang H, Gu T, Xu Z, Cai G, Wu Z, Yang J. Meta-analysis of genome-wide association studies for loin muscle area and loin muscle depth in two Duroc pig populations. PLoS One 2019; 14:e0218263. [PMID: 31188900 PMCID: PMC6561594 DOI: 10.1371/journal.pone.0218263] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Accepted: 05/29/2019] [Indexed: 01/07/2023] Open
Abstract
Loin muscle area (LMA) and loin muscle depth (LMD) are important traits influencing the production performance of breeding pigs. However, the genetic architecture of these two traits is still poorly understood. To discern the genetic architecture of LMA and LMD, a material consisting of 6043 Duroc pigs belonging to two populations with different genetic backgrounds was collected and applied in genome-wide association studies (GWAS) with a genome-wide distributed panel of 50K single nucleotide polymorphisms (SNPs). To improve the power of detection for common SNPs, we conducted a meta-analysis in these two pig populations and uncovered additional significant SNPs. As a result, we identified 75 significant SNPs for LMA and LMD on SSC6, 7, 12, 16, and 18. Among them, 25 common SNPs were associated with LMA and LMD. One pleiotropic quantitative trait locus (QTL), which was located on SSC7 with a 283 kb interval, was identified to affect LMA and LMD. Marker ALGA0040260 is a key SNP for this QTL, explained 1.77% and 2.48% of the phenotypic variance for LMA and LMD, respectively. Another genetic region on SSC16 (709 kb) was detected and displayed prominent association with LMA and the peak SNP, WU_10.2_16_35829257, contributed 1.83% of the phenotypic variance for LMA. Further bioinformatics analysis determined eight promising candidate genes (GCLC, GPX8, DAXX, FGF21, TAF11, SPDEF, NUDT3, and PACSIN1) with functions in glutathione metabolism, adipose and muscle tissues development and lipid metabolism. This study provides the first GWAS for the LMA and LMD of Duroc breed to analyze the underlying genetic variants through a large sample size. The findings further advance our understanding and help elucidate the genetic architecture of LMA, LMD and growth-related traits in pigs.
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Affiliation(s)
- Zhanwei Zhuang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, P.R. China
| | - Shaoyun Li
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, P.R. China
| | - Rongrong Ding
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, P.R. China
| | - Ming Yang
- National Engineering Research Center for Breeding Swine Industry, Guangdong Wens Foodstuffs Group Co., Ltd, Guangdong, P.R. China
| | - Enqin Zheng
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, P.R. China
| | - Huaqiang Yang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, P.R. China
| | - Ting Gu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, P.R. China
| | - Zheng Xu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, P.R. China
| | - Gengyuan Cai
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, P.R. China
| | - Zhenfang Wu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, P.R. China
- National Engineering Research Center for Breeding Swine Industry, Guangdong Wens Foodstuffs Group Co., Ltd, Guangdong, P.R. China
- * E-mail: (JY); (ZW)
| | - Jie Yang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, P.R. China
- * E-mail: (JY); (ZW)
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23
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Tang Z, Xu J, Yin L, Yin D, Zhu M, Yu M, Li X, Zhao S, Liu X. Genome-Wide Association Study Reveals Candidate Genes for Growth Relevant Traits in Pigs. Front Genet 2019; 10:302. [PMID: 31024621 PMCID: PMC6459934 DOI: 10.3389/fgene.2019.00302] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2018] [Accepted: 03/19/2019] [Indexed: 12/02/2022] Open
Abstract
Improvement of the growth rate is a challenge in the pig industry, the Average Daily Gain (ADG) and Days (AGE) to 100 kg are directly related to growth performance. We performed genome-wide association study (GWAS) and genetic parameters estimation for ADG and AGE using the genomic and phonemic from four breed (Duroc, Yorkshire, Landrace, and Pietrain) populations. All analyses were performed by a multi-loci GWAS model, FarmCPU. The GWAS results of all four breeds indicate that five genome-wide significant SNPs were associated with ADG, and the nearby genomic regions explained 4.08% of the genetic variance and 1.90% of the phenotypic variance, respectively. For AGE, six genome-wide significant SNPs were detected, and the nearby genomic regions explained 8.09% of the genetic variance and 3.52% of phenotypic variance, respectively. In total, nine candidate genes were identified to be associated with growth and metabolism. Among them, TRIB3 was reported to associate with pig growth, GRP, TTR, CNR1, GLP1R, BRD2, HCRTR2, SEC11C, and ssc-mir-122 were reported to associate with growth traits in human and mouse. The newly detected candidate genes will advance the understanding of growth related traits and the identification of the novel variants will suggest a potential use in pig genomic breeding programs.
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Affiliation(s)
- Zhenshuang Tang
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China.,Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, Huazhong Agricultural University, Wuhan, China
| | - Jingya Xu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China.,Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, Huazhong Agricultural University, Wuhan, China
| | - Lilin Yin
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China.,Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, Huazhong Agricultural University, Wuhan, China
| | - Dong Yin
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China.,Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, Huazhong Agricultural University, Wuhan, China
| | - Mengjin Zhu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China.,Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, Huazhong Agricultural University, Wuhan, China
| | - Mei Yu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China.,Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, Huazhong Agricultural University, Wuhan, China
| | - Xinyun Li
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China.,Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, Huazhong Agricultural University, Wuhan, China
| | - Shuhong Zhao
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China.,Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, Huazhong Agricultural University, Wuhan, China
| | - Xiaolei Liu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China.,Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, Huazhong Agricultural University, Wuhan, China
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24
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Abstract
Piglet mortality has a negative impact on animal welfare and public acceptance. Moreover, the number of weaned piglets per sow mainly determines the profitability of piglet production. Increased litter sizes are associated with lower birth weights and piglet survival. Decreased survival rates and performance of piglets make the control of diseases and infections within pig production even more crucial. Consequently, selection for immunocompetence becomes an important key aspect within modern breeding programmes. However, the phenotypic recording of immune traits is difficult and expensive to realize within farm routines. Even though immune traits show genetic variability, only few examples exist on their respective suitability within a breeding programme and their relationships to economically important production traits. The analysis of immune traits for an evaluation of immunocompetence to gain a generally improved immune response is promising. Generally, in-depth knowledge of the genetic background of the immune system is needed to gain helpful insights about its possible incorporation into breeding programmes. Possible physiological drawbacks for enhanced immunocompetence must be considered with regards to the allocation theory and possible trade-offs between the immune system and performance. This review aims to discuss the relationships between the immunocompetence of the pig, piglet survival as well as the potential of these traits to be included into a breeding strategy for improved robustness.
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25
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Iung LHDS, Mulder HA, Neves HHDR, Carvalheiro R. Genomic regions underlying uniformity of yearling weight in Nellore cattle evaluated under different response variables. BMC Genomics 2018; 19:619. [PMID: 30115034 PMCID: PMC6097312 DOI: 10.1186/s12864-018-5003-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Accepted: 08/08/2018] [Indexed: 02/22/2023] Open
Abstract
BACKGROUND In livestock, residual variance has been studied because of the interest to improve uniformity of production. Several studies have provided evidence that residual variance is partially under genetic control; however, few investigations have elucidated genes that control it. The aim of this study was to identify genomic regions associated with within-family residual variance of yearling weight (YW; N = 423) in Nellore bulls with high density SNP data, using different response variables. For this, solutions from double hierarchical generalized linear models (DHGLM) were used to provide the response variables, as follows: a DGHLM assuming non-null genetic correlation between mean and residual variance (rmv ≠ 0) to obtain deregressed EBV for mean (dEBVm) and residual variance (dEBVv); and a DHGLM assuming rmv = 0 to obtain two alternative response variables for residual variance, dEBVv_r0 and log-transformed variance of estimated residuals (ln_[Formula: see text]). RESULTS The dEBVm and dEBVv were highly correlated, resulting in common regions associated with mean and residual variance of YW. However, higher effects on variance than the mean showed that these regions had effects on the variance beyond scale effects. More independent association results between mean and residual variance were obtained when null rmv was assumed. While 13 and 4 single nucleotide polymorphisms (SNPs) showed a strong association (Bayes Factor > 20) with dEBVv and ln_[Formula: see text], respectively, only suggestive signals were found for dEBVv_r0. All overlapping 1-Mb windows among top 20 between dEBVm and dEBVv were previously associated with growth traits. The potential candidate genes for uniformity are involved in metabolism, stress, inflammatory and immune responses, mineralization, neuronal activity and bone formation. CONCLUSIONS It is necessary to use a strategy like assuming null rmv to obtain genomic regions associated with uniformity that are not associated with the mean. Genes involved not only in metabolism, but also stress, inflammatory and immune responses, mineralization, neuronal activity and bone formation were the most promising biological candidates for uniformity of YW. Although no clear evidence of using a specific response variable was found, we recommend consider different response variables to study uniformity to increase evidence on candidate regions and biological mechanisms behind it.
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Affiliation(s)
- Laiza Helena de Souza Iung
- School of Agricultural and Veterinarian Sciences, São Paulo State University (Unesp), Via de Acesso Prof. Paulo Donato Castelane, S/N, Vila Industrial, FCAV/UNESP, Jaboticabal, São Paulo, 14884-900 Brazil
| | - Herman Arend Mulder
- Wageningen University & Research Animal Breeding and Genomics, P.O. Box 338, 6700 AH Wageningen, The Netherlands
| | | | - Roberto Carvalheiro
- School of Agricultural and Veterinarian Sciences, São Paulo State University (Unesp), Via de Acesso Prof. Paulo Donato Castelane, S/N, Vila Industrial, FCAV/UNESP, Jaboticabal, São Paulo, 14884-900 Brazil
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26
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Pradhan M, Pal A, Samanta AK, Banerjee S, Samanta R. Mutations in cytochrome B gene effects female reproduction of Ghungroo pig. Theriogenology 2018; 119:121-130. [PMID: 30006127 DOI: 10.1016/j.theriogenology.2018.05.015] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2017] [Revised: 05/14/2018] [Accepted: 05/14/2018] [Indexed: 12/20/2022]
Abstract
Cytochrome B is an important polypeptide of the mitochondria helpful in energy metabolism through oxidative phosphorylation. Cytochrome B plays an immense role in the reproduction of animals and due to its mutation prone nature, it can affect the basic physiology of animals. Cytochrome B affects reproductive system in males and equally plays an important role in transferring and providing energy in the development of the embryo, zygote, and oocytes precisely in females. The present study was conducted on Ghungroo pig to study their molecular and reproductive traits and the effect of the cytochrome B gene in the female reproduction of the Ghungroo pig. Although studies are available for cytochrome B gene analysis for evolutionary studies through phylogenetic analysis. This is the first report for the study of Cytochrome B gene on reproduction in pigs. Cytochrome B gene was sequenced and seven SNPs were observed out of which three were non-synonymous. INDEL mutation was detected in Variant B which had lead to Frame Shift mutation resulting in a stop codon AGA. The effect in the reproductive traits of the sow was studied due to the occurrence of nucleotide substitution. Bioinformatics analysis (I-mutant, PROVEAN, and SIFT) had revealed that the mutations were deleterious for the mutant type. Mutation leading to alterations in post-translational modification sites as phosphorylation site, leucine-rich nuclear export signal, occurrence of transmembrane helices, arginine and lysine peptide cleavage site for the mutant variant had resulted in a reduced physiological response. 3 D protein structure, (predicted through bioinformatics analysis) for cytochrome B has revealed distinct structural differences in mutated form with truncated protein by RMSD analysis through TM-Align software. Associated studies of genotype variants with reproductive traits have revealed the significant effect of variants of cytochrome B gene on reproductive traits namely litter size at first, second and third furrowing, piglet mortality, age at first furrowing and furrowing interval. Mitochondrial gene as Cytochrome B variants might be used as a marker for studying female reproduction of Ghungroo sow in future.
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Affiliation(s)
- Meenakshi Pradhan
- West Bengal University of Animal and Fishery Sciences, 37, K.B. Sarani, Kolkata-37, West Bengal, India
| | - Aruna Pal
- West Bengal University of Animal and Fishery Sciences, 37, K.B. Sarani, Kolkata-37, West Bengal, India.
| | - A K Samanta
- West Bengal University of Animal and Fishery Sciences, 37, K.B. Sarani, Kolkata-37, West Bengal, India
| | - Samiddha Banerjee
- West Bengal University of Animal and Fishery Sciences, 37, K.B. Sarani, Kolkata-37, West Bengal, India
| | - R Samanta
- West Bengal University of Animal and Fishery Sciences, 37, K.B. Sarani, Kolkata-37, West Bengal, India
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27
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Ding R, Yang M, Wang X, Quan J, Zhuang Z, Zhou S, Li S, Xu Z, Zheng E, Cai G, Liu D, Huang W, Yang J, Wu Z. Genetic Architecture of Feeding Behavior and Feed Efficiency in a Duroc Pig Population. Front Genet 2018; 9:220. [PMID: 29971093 PMCID: PMC6018414 DOI: 10.3389/fgene.2018.00220] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2018] [Accepted: 05/29/2018] [Indexed: 11/13/2022] Open
Abstract
Increasing feed efficiency is a major goal of breeders as it can reduce production cost and energy consumption. However, the genetic architecture of feeding behavior and feed efficiency traits remains elusive. To investigate the genetic architecture of feed efficiency in pigs, three feeding behavior traits (daily feed intake, number of daily visits to feeder, and duration of each visit) and two feed efficiency traits (feed conversion ratio and residual feed intake) were considered. We performed genome-wide association studies (GWASs) of the five traits using a population of 1,008 Duroc pigs genotyped with an Illumina Porcine SNP50K BeadChip. A total of 9 genome-wide (P < 1.54E-06) and 35 suggestive (P < 3.08E-05) single nucleotide polymorphisms (SNPs) were detected. Two pleiotropic quantitative trait loci (QTLs) on SSC 1 and SSC 7 were found to affect more than one trait. Markers WU_10.2_7_18377044 and DRGA0001676 are two key SNPs for these two pleiotropic QTLs. Marker WU_10.2_7_18377044 on SSC 7 contributed 2.16 and 2.37% of the observed phenotypic variance for DFI and RFI, respectively. The other SNP DRGA0001676 on SSC 1 explained 3.22 and 5.46% of the observed phenotypic variance for FCR and RFI, respectively. Finally, functions of candidate genes and gene set enrichment analysis indicate that most of the significant pathways are associated with hormonal and digestive gland secretion during feeding. This study advances our understanding of the genetic mechanisms of feeding behavior and feed efficiency traits and provide an opportunity for increasing feeding efficiency using marker-assisted selection or genomic selection in pigs.
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Affiliation(s)
- Rongrong Ding
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, China
| | - Ming Yang
- National Engineering Research Center for Breeding Swine Industry, Guangdong Wens Foodstuffs Group, Co., Ltd., Guangdong, China
| | - Xingwang Wang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, China
| | - Jianping Quan
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, China
| | - Zhanwei Zhuang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, China
| | - Shenping Zhou
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, China
| | - Shaoyun Li
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, China
| | - Zheng Xu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, China
| | - Enqin Zheng
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, China
| | - Gengyuan Cai
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, China.,National Engineering Research Center for Breeding Swine Industry, Guangdong Wens Foodstuffs Group, Co., Ltd., Guangdong, China
| | - Dewu Liu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, China
| | - Wen Huang
- Department of Animal Science, Michigan State University, East Lansing, MI, United States
| | - Jie Yang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, China
| | - Zhenfang Wu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, China.,National Engineering Research Center for Breeding Swine Industry, Guangdong Wens Foodstuffs Group, Co., Ltd., Guangdong, China
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