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Song H, Dong T, Wang W, Jiang B, Yan X, Geng C, Bai S, Xu S, Hu H. Cost-effective genomic prediction of critical economic traits in sturgeons through low-coverage sequencing. Genomics 2024; 116:110874. [PMID: 38839024 DOI: 10.1016/j.ygeno.2024.110874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 05/27/2024] [Accepted: 06/01/2024] [Indexed: 06/07/2024]
Abstract
Low-coverage whole-genome sequencing (LCS) offers a cost-effective alternative for sturgeon breeding, especially given the lack of SNP chips and the high costs associated with whole-genome sequencing. In this study, the efficiency of LCS for genotype imputation and genomic prediction was assessed in 643 sequenced Russian sturgeons (∼13.68×). The results showed that using BaseVar+STITCH at a sequencing depth of 2× with a sample size larger than 300 resulted in the highest genotyping accuracy. In addition, when the sequencing depth reached 0.5× and SNP density was reduced to 50 K through linkage disequilibrium pruning, the prediction accuracy was comparable to that of whole sequencing depth. Furthermore, an incremental feature selection method has the potential to improve prediction accuracy. This study suggests that the combination of LCS and imputation can be a cost-effective strategy, contributing to the genetic improvement of economic traits and promoting genetic gains in aquaculture species.
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Affiliation(s)
- Hailiang Song
- Fisheries Science Institute, Beijing Academy of Agriculture and Forestry Sciences & Beijing Key Laboratory of Fisheries Biotechnology, Beijing 100068, China; Key Laboratory of Sturgeon Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Hangzhou 311799, China; National Innovation Center for Digital Seed Industry, Beijing 100097, China
| | - Tian Dong
- Fisheries Science Institute, Beijing Academy of Agriculture and Forestry Sciences & Beijing Key Laboratory of Fisheries Biotechnology, Beijing 100068, China; Key Laboratory of Sturgeon Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Hangzhou 311799, China; National Innovation Center for Digital Seed Industry, Beijing 100097, China
| | - Wei Wang
- Fisheries Science Institute, Beijing Academy of Agriculture and Forestry Sciences & Beijing Key Laboratory of Fisheries Biotechnology, Beijing 100068, China; Key Laboratory of Sturgeon Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Hangzhou 311799, China; National Innovation Center for Digital Seed Industry, Beijing 100097, China
| | - Boyun Jiang
- Key Laboratory of Sturgeon Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Hangzhou 311799, China; Hangzhou Qiandaohu Xunlong Sci-tech Co., Ltd., Hangzhou 311799, China.
| | - Xiaoyu Yan
- Fisheries Science Institute, Beijing Academy of Agriculture and Forestry Sciences & Beijing Key Laboratory of Fisheries Biotechnology, Beijing 100068, China; Key Laboratory of Sturgeon Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Hangzhou 311799, China; National Innovation Center for Digital Seed Industry, Beijing 100097, China
| | - Chenfan Geng
- Fisheries Science Institute, Beijing Academy of Agriculture and Forestry Sciences & Beijing Key Laboratory of Fisheries Biotechnology, Beijing 100068, China; Key Laboratory of Sturgeon Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Hangzhou 311799, China; National Innovation Center for Digital Seed Industry, Beijing 100097, China
| | - Song Bai
- Fisheries Science Institute, Beijing Academy of Agriculture and Forestry Sciences & Beijing Key Laboratory of Fisheries Biotechnology, Beijing 100068, China; Key Laboratory of Sturgeon Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Hangzhou 311799, China; National Innovation Center for Digital Seed Industry, Beijing 100097, China
| | - Shijian Xu
- Key Laboratory of Sturgeon Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Hangzhou 311799, China; Hangzhou Qiandaohu Xunlong Sci-tech Co., Ltd., Hangzhou 311799, China.
| | - Hongxia Hu
- Fisheries Science Institute, Beijing Academy of Agriculture and Forestry Sciences & Beijing Key Laboratory of Fisheries Biotechnology, Beijing 100068, China; Key Laboratory of Sturgeon Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Hangzhou 311799, China; National Innovation Center for Digital Seed Industry, Beijing 100097, China.
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Yan X, Li J, He L, Chen O, Wang N, Wang S, Wang X, Wang Z, Su R. Accuracy of Genomic prediction for fleece traits in Inner Mongolia Cashmere goats. BMC Genomics 2024; 25:349. [PMID: 38589806 PMCID: PMC11000370 DOI: 10.1186/s12864-024-10249-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 03/22/2024] [Indexed: 04/10/2024] Open
Abstract
The fleece traits are important economic traits of goats. With the reduction of sequencing and genotyping cost and the improvement of related technologies, genomic selection for goats has become possible. The research collect pedigree, phenotype and genotype information of 2299 Inner Mongolia Cashmere goats (IMCGs) individuals. We estimate fixed effects, and compare the estimates of variance components, heritability and genomic predictive ability of fleece traits in IMCGs when using the pedigree based Best Linear Unbiased Prediction (ABLUP), Genomic BLUP (GBLUP) or single-step GBLUP (ssGBLUP). The fleece traits considered are cashmere production (CP), cashmere diameter (CD), cashmere length (CL) and fiber length (FL). It was found that year of production, sex, herd and individual ages had highly significant effects on the four fleece traits (P < 0.01). All of these factors should be considered when the genetic parameters of fleece traits in IMCGs are evaluated. The heritabilities of FL, CL, CP and CD with ABLUP, GBLUP and ssGBLUP methods were 0.26 ~ 0.31, 0.05 ~ 0.08, 0.15 ~ 0.20 and 0.22 ~ 0.28, respectively. Therefore, it can be inferred that the genetic progress of CL is relatively slow. The predictive ability of fleece traits in IMCGs with GBLUP (56.18% to 69.06%) and ssGBLUP methods (66.82% to 73.70%) was significantly higher than that of ABLUP (36.73% to 41.25%). For the ssGBLUP method is significantly (29% ~ 33%) higher than that with ABLUP, and which is slightly (4% ~ 14%) higher than that of GBLUP. The ssGBLUP will be as an superiors method for using genomic selection of fleece traits in Inner Mongolia Cashmere goats.
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Affiliation(s)
- Xiaochun Yan
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia Autonomous Region, 010018, China
| | - Jinquan Li
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia Autonomous Region, 010018, China
- Inner Mongolia Key Laboratory of Sheep & Goat Genetics Breeding and Reproduction, Hohhot, Inner Mongolia Autonomous Region, 010018, China
- Key Laboratory Of Mutton Sheep & Goat Genetics And Breeding, Ministry of Agriculture And Rural Affairs, Hohhot, Inner Mongolia Autonomous Region, 010018, China
- Engineering Research Centre for Goat Genetics and Breeding, Inner Mongolia Autonomous Region, Hohhot, Inner Mongolia Autonomous Region, 010018, China
| | - Libing He
- Inner Mongolia Jinlai Livestock Technology Co., Ltd, Hohhot, Inner Mongolia Autonomous Region, 010018, China
| | - Oljibilig Chen
- Inner Mongolia Yiwei White Cashmere Goat Co., Ltd, Ordos, Inner Mongolia Autonomous Region, 010018, China
| | - Na Wang
- Inner Mongolia Yiwei White Cashmere Goat Co., Ltd, Ordos, Inner Mongolia Autonomous Region, 010018, China
| | - Shuai Wang
- Inner Mongolia Yiwei White Cashmere Goat Co., Ltd, Ordos, Inner Mongolia Autonomous Region, 010018, China
| | - Xiuyan Wang
- Livestock Improvement Center of Alxa Left Banner, Alxa League, Inner Mongolia Autonomous Region, 75000, China
| | - Zhiying Wang
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia Autonomous Region, 010018, China.
| | - Rui Su
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia Autonomous Region, 010018, China.
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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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Fraslin C, Robledo D, Kause A, Houston RD. Potential of low-density genotype imputation for cost-efficient genomic selection for resistance to Flavobacterium columnare in rainbow trout (Oncorhynchus mykiss). Genet Sel Evol 2023; 55:59. [PMID: 37580697 PMCID: PMC10424455 DOI: 10.1186/s12711-023-00832-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 07/26/2023] [Indexed: 08/16/2023] Open
Abstract
BACKGROUND Flavobacterium columnare is the pathogen agent of columnaris disease, a major emerging disease that affects rainbow trout aquaculture. Selective breeding using genomic selection has potential to achieve cumulative improvement of the host resistance. However, genomic selection is expensive partly because of the cost of genotyping large numbers of animals using high-density single nucleotide polymorphism (SNP) arrays. The objective of this study was to assess the efficiency of genomic selection for resistance to F. columnare using in silico low-density (LD) panels combined with imputation. After a natural outbreak of columnaris disease, 2874 challenged fish and 469 fish from the parental generation (n = 81 parents) were genotyped with 27,907 SNPs. The efficiency of genomic prediction using LD panels was assessed for 10 panels of different densities, which were created in silico using two sampling methods, random and equally spaced. All LD panels were also imputed to the full 28K HD panel using the parental generation as the reference population, and genomic predictions were re-evaluated. The potential of prioritizing SNPs that are associated with resistance to F. columnare was also tested for the six lower-density panels. RESULTS The accuracies of both imputation and genomic predictions were similar with random and equally-spaced sampling of SNPs. Using LD panels of at least 3000 SNPs or lower-density panels (as low as 300 SNPs) combined with imputation resulted in accuracies that were comparable to those of the 28K HD panel and were 11% higher than the pedigree-based predictions. CONCLUSIONS Compared to using the commercial HD panel, LD panels combined with imputation may provide a more affordable approach to genomic prediction of breeding values, which supports a more widespread adoption of genomic selection in aquaculture breeding programmes.
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Affiliation(s)
- Clémence Fraslin
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, UK.
| | - Diego Robledo
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, UK
| | - Antti Kause
- Natural Resources Institute Finland (Luke), Myllytie 1, 31600, Jokioinen, Finland
| | - Ross D Houston
- Benchmark Genetics, Edinburgh Technopole, 1 Pioneer Building, Penicuik, EH26 0GB, UK
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Kriaridou C, Tsairidou S, Fraslin C, Gorjanc G, Looseley ME, Johnston IA, Houston RD, Robledo D. Evaluation of low-density SNP panels and imputation for cost-effective genomic selection in four aquaculture species. Front Genet 2023; 14:1194266. [PMID: 37252666 PMCID: PMC10213886 DOI: 10.3389/fgene.2023.1194266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Accepted: 04/26/2023] [Indexed: 05/31/2023] Open
Abstract
Genomic selection can accelerate genetic progress in aquaculture breeding programmes, particularly for traits measured on siblings of selection candidates. However, it is not widely implemented in most aquaculture species, and remains expensive due to high genotyping costs. Genotype imputation is a promising strategy that can reduce genotyping costs and facilitate the broader uptake of genomic selection in aquaculture breeding programmes. Genotype imputation can predict ungenotyped SNPs in populations genotyped at a low-density (LD), using a reference population genotyped at a high-density (HD). In this study, we used datasets of four aquaculture species (Atlantic salmon, turbot, common carp and Pacific oyster), phenotyped for different traits, to investigate the efficacy of genotype imputation for cost-effective genomic selection. The four datasets had been genotyped at HD, and eight LD panels (300-6,000 SNPs) were generated in silico. SNPs were selected to be: i) evenly distributed according to physical position ii) selected to minimise the linkage disequilibrium between adjacent SNPs or iii) randomly selected. Imputation was performed with three different software packages (AlphaImpute2, FImpute v.3 and findhap v.4). The results revealed that FImpute v.3 was faster and achieved higher imputation accuracies. Imputation accuracy increased with increasing panel density for both SNP selection methods, reaching correlations greater than 0.95 in the three fish species and 0.80 in Pacific oyster. In terms of genomic prediction accuracy, the LD and the imputed panels performed similarly, reaching values very close to the HD panels, except in the pacific oyster dataset, where the LD panel performed better than the imputed panel. In the fish species, when LD panels were used for genomic prediction without imputation, selection of markers based on either physical or genetic distance (instead of randomly) resulted in a high prediction accuracy, whereas imputation achieved near maximal prediction accuracy independently of the LD panel, showing higher reliability. Our results suggests that, in fish species, well-selected LD panels may achieve near maximal genomic selection prediction accuracy, and that the addition of imputation will result in maximal accuracy independently of the LD panel. These strategies represent effective and affordable methods to incorporate genomic selection into most aquaculture settings.
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Affiliation(s)
- Christina Kriaridou
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, United Kingdom
| | - Smaragda Tsairidou
- Global Academy of Agriculture and Food Systems, University of Edinburgh, Edinburgh, United Kingdom
| | - Clémence Fraslin
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, United Kingdom
| | - Gregor Gorjanc
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, United Kingdom
| | | | | | - Ross D. Houston
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, United Kingdom
- Benchmark Genetics, Penicuik, United Kingdom
| | - Diego Robledo
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, United Kingdom
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Zhuang Z, Wu J, Qiu Y, Ruan D, Ding R, Xu C, Zhou S, Zhang Y, Liu Y, Ma F, Yang J, Sun Y, Zheng E, Yang M, Cai G, Yang J, Wu Z. Improving the accuracy of genomic prediction for meat quality traits using whole genome sequence data in pigs. J Anim Sci Biotechnol 2023; 14:67. [PMID: 37161604 PMCID: PMC10170792 DOI: 10.1186/s40104-023-00863-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 03/05/2023] [Indexed: 05/11/2023] Open
Abstract
BACKGROUND Pork quality can directly affect customer purchase tendency and meat quality traits have become valuable in modern pork production. However, genetic improvement has been slow due to high phenotyping costs. In this study, whole genome sequence (WGS) data was used to evaluate the prediction accuracy of genomic best linear unbiased prediction (GBLUP) for meat quality in large-scale crossbred commercial pigs. RESULTS We produced WGS data (18,695,907 SNPs and 2,106,902 INDELs exceed quality control) from 1,469 sequenced Duroc × (Landrace × Yorkshire) pigs and developed a reference panel for meat quality including meat color score, marbling score, L* (lightness), a* (redness), and b* (yellowness) of genomic prediction. The prediction accuracy was defined as the Pearson correlation coefficient between adjusted phenotypes and genomic estimated breeding values in the validation population. Using different marker density panels derived from WGS data, accuracy differed substantially among meat quality traits, varied from 0.08 to 0.47. Results showed that MultiBLUP outperform GBLUP and yielded accuracy increases ranging from 17.39% to 75%. We optimized the marker density and found medium- and high-density marker panels are beneficial for the estimation of heritability for meat quality. Moreover, we conducted genotype imputation from 50K chip to WGS level in the same population and found average concordance rate to exceed 95% and r2 = 0.81. CONCLUSIONS Overall, estimation of heritability for meat quality traits can benefit from the use of WGS data. This study showed the superiority of using WGS data to genetically improve pork quality in genomic prediction.
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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, 510642, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China
| | - Jie Wu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China
| | - Yibin Qiu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China
| | - Donglin Ruan
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China
| | - Rongrong Ding
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China
| | - Cineng Xu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China
| | - Shenping Zhou
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China
| | - Yuling Zhang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China
| | - Yiyi Liu
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China
| | - Fucai Ma
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China
| | - Jifei Yang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China
| | - Ying Sun
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China
| | - Enqin Zheng
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, 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
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China
| | - Jie Yang
- College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China.
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, 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.
- Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, Guangzhou, 510642, China.
- Yunfu Subcenter of Guangdong Laboratory for Lingnan Modern Agriculture, Yunfu, 527400, China.
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da Silva Neto JB, Peripoli E, Pereira ASC, Stafuzza NB, Lôbo RB, Fukumasu H, Ferraz JBS, Baldi F. Weighted genomic prediction for growth and carcass-related traits in Nelore cattle. Anim Genet 2023; 54:271-283. [PMID: 36856051 DOI: 10.1111/age.13310] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 02/07/2023] [Accepted: 02/09/2023] [Indexed: 03/02/2023]
Abstract
This study aimed to assess the impact of differential weighting in genomic regions harboring candidate causal loci on the genomic prediction accuracy and dispersion for growth and carcass-related traits in Nelore cattle. The dataset contained 168 793 phenotypic records for adjusted weight at 450 days of age (W450), 83 624 for rib eye area (REA), 24 480 for marbling (MAR) and 82 981 for subcutaneous backfat thickness (BFT) and rump fat thickness (RFT). The pedigree harbored information from 244 254 animals born between 1977 and 2016, including 6283 sires and 50 742 dams. Animals (n = 7769) were genotyped with the low-density panel (Clarifide® Nelore 3.0), and the genotypes were imputed to a panel containing 735 044 markers. A linear animal model was applied to estimate the genetic parameters and to perform the weighted single-step genome-wide association study (WssGWAS). A total of seven models for genomic prediction were evaluated combining the SNP weights obtained in the iterations of the WssGWAS and the candidate QTL. The heritability estimated for W450 (0.35) was moderate, and for carcass-related traits, the estimates were moderate for REA (0.27), MAR (0.28) and RFT (0.28), and low for BFT (0.18). The prediction accuracy for W450 incorporating reported QTL previously described in the literature along with different SNPs weights was like those described for the default ssGBLUP model. The use of the ssGWAS to weight the SNP effects displayed limited advantages for the REA prediction accuracy. Comparing the ssGBLUP with the BLUP model, a meaningful improvement in the prediction accuracy from 0.09 to 0.63 (700%) was observed for MAR. The highest prediction accuracy was obtained for BFT and RFT in all evaluated models. The application of information obtained from the WssGWAS is an alternative to reduce the genomic prediction dispersion for growth and carcass-related traits, except for MAR. Furthermore, the results obtained herein pointed out that is possible to improve the prediction accuracy and reduce the genomic prediction dispersion for growth and carcass-related traits in young animals.
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Affiliation(s)
- João Barbosa da Silva Neto
- Department of Animal Science, São Paulo State University - Júlio de Mesquita Filho (UNESP), Jaboticabal, Brazil
| | - Elisa Peripoli
- Department of Veterinary Medicine, Faculty of Animal Science and Food Engineering, University of São Paulo, Pirassununga, Brazil
| | - Angelica S C Pereira
- Department of Veterinary Medicine, Faculty of Animal Science and Food Engineering, University of São Paulo, Pirassununga, Brazil
| | | | - Raysildo B Lôbo
- National Association of Breeders and Researchers, Ribeirão Preto, Brazil
| | - Heigde Fukumasu
- Department of Veterinary Medicine, Faculty of Animal Science and Food Engineering, University of São Paulo, Pirassununga, Brazil
| | - José Bento Sterman Ferraz
- Department of Veterinary Medicine, Faculty of Animal Science and Food Engineering, University of São Paulo, Pirassununga, Brazil
| | - Fernando Baldi
- Department of Animal Science, São Paulo State University - Júlio de Mesquita Filho (UNESP), Jaboticabal, Brazil
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Ye H, Xu Z, Bello SF, Zhu Q, Kong S, Zheng M, Fang X, Jia X, Xu H, Zhang X, Nie Q. Haplotype analysis of genomic prediction by incorporating genomic pathway information based on high-density SNP marker in Chinese yellow-feathered chicken. Poult Sci 2023; 102:102549. [PMID: 36907129 PMCID: PMC10024239 DOI: 10.1016/j.psj.2023.102549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 01/16/2023] [Accepted: 01/27/2023] [Indexed: 02/09/2023] Open
Abstract
Genomic selection using single nucleotide polymorphism (SNP) markers is now intensively investigated in breeding and has been widely utilized for genetic improvement. Currently, several studies have used haplotype (consisting of multiallelic SNPs) for genomic prediction and revealed its performance advantage. In this study, we comprehensively evaluated the performance of haplotype models for genomic prediction in 15 traits, including 6 growth, 5 carcass, and 4 feeding traits in a Chinese yellow-feathered chicken population. We adopted 3 methods to define haplotypes from high-density SNP panels, and our strategy included combining Kyoto Encyclopedia of Genes and Genomes pathway information and considering linkage disequilibrium (LD) information. Our results showed an increase in prediction accuracy due to haplotypes ranging from -0.04∼27.16% in all traits, where the significant improvements were found in 12 traits. The estimates of haplotype epistasis heritability were strongly correlated with the accuracy increase by haplotype models. In addition, incorporating genomic annotation information could further increase the accuracy of the haplotype model, where the further increase in accuracy is significantly relative to the increase of relative haplotype epistasis heritability. The genomic prediction using LD information for constructing haplotypes has the best prediction performance among the 4 traits. These results uncovered that haplotype methods were beneficial for genomic prediction, and the accuracy could be further increased by incorporating genomic annotation information. Moreover, using LD information would potentially improve the performance of genomic prediction.
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Affiliation(s)
- Haoqiang Ye
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou, 510642 China; Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou, 510642 China
| | - Zhenqiang Xu
- Wen's Nanfang Poultry Breeding Co. Ltd, Guangdong Province, Yunfu 527400, China
| | - Semiu Folaniyi Bello
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou, 510642 China; Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou, 510642 China
| | - Qianghui Zhu
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou, 510642 China
| | - Shaofen Kong
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou, 510642 China; Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou, 510642 China
| | - Ming Zheng
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou, 510642 China; Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou, 510642 China
| | - Xiang Fang
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou, 510642 China; Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou, 510642 China
| | - Xinzheng Jia
- Guangdong Provincial Key Laboratory of Animal Molecular Design and Precise Breeding, Foshan University, Foshan, 528225 China
| | - Haiping Xu
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou, 510642 China; Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou, 510642 China
| | - Xiquan Zhang
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou, 510642 China; Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou, 510642 China
| | - Qinghua Nie
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou, 510642 China; Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou, 510642 China.
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9
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Wang K, Yang B, Li Q, Liu S. Systematic Evaluation of Genomic Prediction Algorithms for Genomic Prediction and Breeding of Aquatic Animals. Genes (Basel) 2022; 13:genes13122247. [PMID: 36553514 PMCID: PMC9778314 DOI: 10.3390/genes13122247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 11/18/2022] [Accepted: 11/25/2022] [Indexed: 12/04/2022] Open
Abstract
The extensive use of genomic selection (GS) in livestock and crops has led to a series of genomic-prediction (GP) algorithms despite the lack of a single algorithm that can suit all the species and traits. A systematic evaluation of available GP algorithms is thus necessary to identify the optimal GP algorithm for selective breeding in aquaculture species. In this study, a systematic comparison of ten GP algorithms, including both traditional and machine-learning algorithms, was conducted using publicly available genotype and phenotype data of eight traits, including weight and disease resistance traits, from five aquaculture species. The study aimed to provide insights into the optimal algorithm for GP in aquatic animals. Notably, no algorithm showed the best performance in all traits. However, reproducing kernel Hilbert space (RKHS) and support-vector machine (SVM) algorithms achieved relatively high prediction accuracies in most of the tested traits. Bayes A and random forest (RF) better prevented noise interference in the phenotypic data compared to the other algorithms. The prediction performances of GP algorithms in the Crassostrea gigas dataset were improved by using a genome-wide association study (GWAS) to select subsets of significant SNPs. An R package, "ASGS," which integrates the commonly used traditional and machine-learning algorithms for efficiently finding the optimal algorithm, was developed to assist the application of genomic selection breeding of aquaculture species. This work provides valuable information and a tool for optimizing algorithms for GP, aiding genetic breeding in aquaculture species.
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Affiliation(s)
- Kuiqin Wang
- Key Laboratory of Mariculture, Ministry of Education, College of Fisheries, Ocean University of China, Qingdao 266003, China
| | - Ben Yang
- Key Laboratory of Mariculture, Ministry of Education, College of Fisheries, Ocean University of China, Qingdao 266003, China
| | - Qi Li
- Key Laboratory of Mariculture, Ministry of Education, College of Fisheries, Ocean University of China, Qingdao 266003, China
- Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China
| | - Shikai Liu
- Key Laboratory of Mariculture, Ministry of Education, College of Fisheries, Ocean University of China, Qingdao 266003, China
- Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China
- Correspondence: ; Tel.: +86-0532-82032595
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