1
|
Su P, Gu Y, Wang S, Cao X, Lv X, Getachew T, Li Y, Song Z, Yuan Z, Sun W. FecB Was Associated with Litter Size and Follows Mendel's Laws of Inheritance When It Transited to Next Generation in Suhu Meat Sheep Breeding Population. Genes (Basel) 2024; 15:260. [PMID: 38540319 PMCID: PMC10970568 DOI: 10.3390/genes15030260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 02/09/2024] [Accepted: 02/16/2024] [Indexed: 06/15/2024] Open
Abstract
In order to investigate the effect of FecB on litter size and growth and development traits of Suhu meat sheep and the inheritance patterns of FecB between parents and offspring in the population. In this experiment, 2241 sheep from the Suhu meat sheep population were tested for FecB using capillary electrophoresis. We combined the lambing records of 473 ewes, the growth trait records of 881 sheep at both the birth and weaning (2-month-old) stages, and the complete genealogical records of 643 lambs to analysis the distribution of FecB in the Suhu meat sheep breeding population, its effect on litter size of ewes, growth and development of lambs, and the inheritance patterns of FecB. The results showed that there were three genotypes of FecB in the Suhu meat sheep population, namely the AA genotype, AG genotype, and GG genotype. FecB in this population has a moderate polymorphism (0.25 < PIC < 0.5), and deviates from Hardy-Weinberg disequilibrium (p < 0.05). The litter size of GG genotype ewes was significantly higher than that with the AG and AA genotypes (p < 0.01). A Chi-square test showed that the inheritance patterns of FecB follows Mendel's Laws of Inheritance (p > 0.05). An association analysis of different genotypes of FecB with body weight and body size of Suhu meat sheep at birth and weaning revealed that FecB adversely affects the early growth and development of Suhu meat sheep. In summary, FecB can improve the litter size of ewes but it has negative effects on the early growth and survival rate of lambs in sheep. Therefore, FecB test results and feeding management measures should be comprehensively applied to improve the reproductive performance of ewes, the survival rate and production performance of lambs in sheep production, and thus improve the economic benefits of sheep farms.
Collapse
Affiliation(s)
- Pengwei Su
- Joint International Research Laboratory of Agriculture and Agri-Product Safety of Ministry of Education of China, Yangzhou University, Yangzhou 225009, China; (P.S.); (Y.G.); (X.C.); (X.L.); (Z.Y.)
- International Joint Research Laboratory in Universities of Jiangsu Province of China for Domestic Animal Germplasm Resources and Genetic Improvement, Yangzhou University, Yangzhou 225009, China;
- College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China
| | - Yifei Gu
- Joint International Research Laboratory of Agriculture and Agri-Product Safety of Ministry of Education of China, Yangzhou University, Yangzhou 225009, China; (P.S.); (Y.G.); (X.C.); (X.L.); (Z.Y.)
- International Joint Research Laboratory in Universities of Jiangsu Province of China for Domestic Animal Germplasm Resources and Genetic Improvement, Yangzhou University, Yangzhou 225009, China;
- College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China
| | - Shanhe Wang
- International Joint Research Laboratory in Universities of Jiangsu Province of China for Domestic Animal Germplasm Resources and Genetic Improvement, Yangzhou University, Yangzhou 225009, China;
- College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China
| | - Xiukai Cao
- Joint International Research Laboratory of Agriculture and Agri-Product Safety of Ministry of Education of China, Yangzhou University, Yangzhou 225009, China; (P.S.); (Y.G.); (X.C.); (X.L.); (Z.Y.)
- International Joint Research Laboratory in Universities of Jiangsu Province of China for Domestic Animal Germplasm Resources and Genetic Improvement, Yangzhou University, Yangzhou 225009, China;
| | - Xiaoyang Lv
- Joint International Research Laboratory of Agriculture and Agri-Product Safety of Ministry of Education of China, Yangzhou University, Yangzhou 225009, China; (P.S.); (Y.G.); (X.C.); (X.L.); (Z.Y.)
- International Joint Research Laboratory in Universities of Jiangsu Province of China for Domestic Animal Germplasm Resources and Genetic Improvement, Yangzhou University, Yangzhou 225009, China;
| | - Tesfaye Getachew
- International Centre for Agricultural Research in the Dry Areas, Addis Ababa 999047, Ethiopia;
| | - Yutao Li
- CSIRO Agriculture and Food, 306 Carmody Rd, St Lucia, Brisbane, QLD 4067, Australia;
| | - Zhenghai Song
- Dongshan Animal Epidemic Prevention Station of Wuzhong District, Suzhou 215000, China;
| | - Zehu Yuan
- Joint International Research Laboratory of Agriculture and Agri-Product Safety of Ministry of Education of China, Yangzhou University, Yangzhou 225009, China; (P.S.); (Y.G.); (X.C.); (X.L.); (Z.Y.)
- International Joint Research Laboratory in Universities of Jiangsu Province of China for Domestic Animal Germplasm Resources and Genetic Improvement, Yangzhou University, Yangzhou 225009, China;
| | - Wei Sun
- Joint International Research Laboratory of Agriculture and Agri-Product Safety of Ministry of Education of China, Yangzhou University, Yangzhou 225009, China; (P.S.); (Y.G.); (X.C.); (X.L.); (Z.Y.)
- International Joint Research Laboratory in Universities of Jiangsu Province of China for Domestic Animal Germplasm Resources and Genetic Improvement, Yangzhou University, Yangzhou 225009, China;
- College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China
- Innovative China “Belt and Road” International Agricultural Technology Innovation Institute for Evaluation, Protection, and Improvement on Sheep Genetic Resource, Yangzhou 225009, China
| |
Collapse
|
2
|
Hodge MJ, de Las Heras-Saldana S, Rindfleish SJ, Stephen CP, Pant SD. QTLs and Candidate Genes Associated with Semen Traits in Merino Sheep. Animals (Basel) 2023; 13:2286. [PMID: 37508063 PMCID: PMC10376747 DOI: 10.3390/ani13142286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 07/06/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023] Open
Abstract
Ram semen traits play a significant role in conception outcomes, which in turn may influence reproductive efficiency and the overall productivity and profitability of sheep enterprises. Since hundreds of ewes may be inseminated from a single ejaculate, it is important to evaluate semen quality prior to use in sheep breeding programs. Given that semen traits have been found to be heritable, genetic variation likely contributes to the variability observed in these traits. Identifying such genetic variants could provide novel insights into the molecular mechanisms underlying variability in semen traits. Therefore, this study aimed to identify quantitative trait loci (QTLs) associated with semen traits in Merino sheep. A genome-wide association study (GWAS) was undertaken using 4506 semen collection records from 246 Merino rams collected between January 2002 and May 2021. The R package RepeatABEL was used to perform a GWAS for semen volume, gross motility, concentration, and percent post-thaw motility. A total of 35 QTLs, located on 16 Ovis aries autosomes (OARs), were significantly associated with either of the four semen traits in this study. A total of 89, 95, 33, and 73 candidate genes were identified, via modified Bonferroni, within the QTLs significantly associated with volume, gross motility, concentration, and percent post-thaw motility, respectively. Among the candidate genes identified, SORD, SH2B1, and NT5E have been previously described to significantly influence spermatogenesis, spermatozoal motility, and high percent post-thaw motility, respectively. Several candidate genes identified could potentially influence ram semen traits based on existing evidence in the literature. As such, validation of these putative candidates may offer the potential to develop future strategies to improve sheep reproductive efficiency. Furthermore, Merino ram semen traits are lowly heritable (0.071-0.139), and thus may be improved by selective breeding.
Collapse
Affiliation(s)
- Marnie J Hodge
- School of Agricultural, Environmental and Veterinary Sciences, Charles Sturt University, Wagga Wagga, NSW 2678, Australia
- Apiam Animal Health, Apiam Genetic Services, Dubbo, NSW 2830, Australia
| | - Sara de Las Heras-Saldana
- Animal Genetics and Breeding Unit, a Joint Venture of NSW Department of Primary Industries and University of New England, Armidale, NSW 2351, Australia
| | | | - Cyril P Stephen
- School of Agricultural, Environmental and Veterinary Sciences, Charles Sturt University, Wagga Wagga, NSW 2678, Australia
- Gulbali Institute, Charles Sturt University, Boorooma Street, Wagga Wagga, NSW 2678, Australia
| | - Sameer D Pant
- School of Agricultural, Environmental and Veterinary Sciences, Charles Sturt University, Wagga Wagga, NSW 2678, Australia
- Gulbali Institute, Charles Sturt University, Boorooma Street, Wagga Wagga, NSW 2678, Australia
| |
Collapse
|
3
|
Herry F, Hérault F, Lecerf F, Lagoutte L, Doublet M, Picard-Druet D, Bardou P, Varenne A, Burlot T, Le Roy P, Allais S. Restriction site-associated DNA sequencing technologies as an alternative to low-density SNP chips for genomic selection: a simulation study in layer chickens. BMC Genomics 2023; 24:271. [PMID: 37208589 DOI: 10.1186/s12864-023-09321-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 04/18/2023] [Indexed: 05/21/2023] Open
Abstract
BACKGROUND To reduce the cost of genomic selection, a low-density (LD) single nucleotide polymorphism (SNP) chip can be used in combination with imputation for genotyping selection candidates instead of using a high-density (HD) SNP chip. Next-generation sequencing (NGS) techniques have been increasingly used in livestock species but remain expensive for routine use for genomic selection. An alternative and cost-efficient solution is to use restriction site-associated DNA sequencing (RADseq) techniques to sequence only a fraction of the genome using restriction enzymes. From this perspective, use of RADseq techniques followed by an imputation step on HD chip as alternatives to LD chips for genomic selection was studied in a pure layer line. RESULTS Genome reduction and sequencing fragments were identified on reference genome using four restriction enzymes (EcoRI, TaqI, AvaII and PstI) and a double-digest RADseq (ddRADseq) method (TaqI-PstI). The SNPs contained in these fragments were detected from the 20X sequence data of the individuals in our population. Imputation accuracy on HD chip with these genotypes was assessed as the mean correlation between true and imputed genotypes. Several production traits were evaluated using single-step GBLUP methodology. The impact of imputation errors on the ranking of the selection candidates was assessed by comparing a genomic evaluation based on ancestry using true HD or imputed HD genotyping. The relative accuracy of genomic estimated breeding values (GEBVs) was investigated by considering the GEBVs estimated on offspring as a reference. With AvaII or PstI and ddRADseq with TaqI and PstI, more than 10 K SNPs were detected in common with the HD SNP chip, resulting in an imputation accuracy greater than 0.97. The impact of imputation errors on genomic evaluation of the breeders was reduced, with a Spearman correlation greater than 0.99. Finally, the relative accuracy of GEBVs was equivalent. CONCLUSIONS RADseq approaches can be interesting alternatives to low-density SNP chips for genomic selection. With more than 10 K SNPs in common with the SNPs of the HD SNP chip, good imputation and genomic evaluation results can be obtained. However, with real data, heterogeneity between individuals with missing data must be considered.
Collapse
Affiliation(s)
- Florian Herry
- NOVOGEN, 5 rue des Compagnons, Secteur du Vau Ballier, Plédran, 22960, France
- PEGASE, INRAE, Institut Agro, Saint-Gilles, 35590, France
| | | | | | | | | | | | - Philippe Bardou
- SIGENAE, GenPhySE, Université de Toulouse, INRA, ENVT, 24 chemin de Borde-Rouge - Auzeville Tolosane, Castanet Tolosan, 31326, France
| | - Amandine Varenne
- NOVOGEN, 5 rue des Compagnons, Secteur du Vau Ballier, Plédran, 22960, France
| | - Thierry Burlot
- NOVOGEN, 5 rue des Compagnons, Secteur du Vau Ballier, Plédran, 22960, France
| | - Pascale Le Roy
- PEGASE, INRAE, Institut Agro, Saint-Gilles, 35590, France
| | - Sophie Allais
- PEGASE, INRAE, Institut Agro, Saint-Gilles, 35590, France.
| |
Collapse
|
4
|
Ye H, Zhang Z, Ren D, Cai X, Zhu Q, Ding X, Zhang H, Zhang Z, Li J. Genomic Prediction Using LD-Based Haplotypes in Combined Pig Populations. Front Genet 2022; 13:843300. [PMID: 35754827 PMCID: PMC9218795 DOI: 10.3389/fgene.2022.843300] [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: 12/25/2021] [Accepted: 05/02/2022] [Indexed: 11/13/2022] Open
Abstract
The size of reference population is an important factor affecting genomic prediction. Thus, combining different populations in genomic prediction is an attractive way to improve prediction ability. However, combining multireference population roughly cannot increase the prediction accuracy as well as expected in pig. This may be due to different linkage disequilibrium (LD) pattern differences between population. In this study, we used the imputed whole-genome sequencing (WGS) data to construct LD-based haplotypes for genomic prediction in combined population to explore the impact of different single-nucleotide polymorphism (SNP) densities, variant representation (SNPs or haplotype alleles), and reference population size on the prediction accuracy for reproduction traits. Our results showed that genomic best linear unbiased prediction (GBLUP) using the WGS data can improve prediction accuracy in multi-population but not within-population. Not only the genomic prediction accuracy of the haplotype method using 80 K chip data in multi-population but also GBLUP for the multi-population (3.4–5.9%) was higher than that within-population (1.2–4.3%). More importantly, we have found that using the haplotype method based on the WGS data in multi-population has better genomic prediction performance, and our results showed that building haploblock in this scenario based on low LD threshold (r2 = 0.2–0.3) produced an optimal set of variables for reproduction traits in Yorkshire pig population. Our results suggested that whether the use of the haplotype method based on the chip data or GBLUP (individual SNP method) based on the WGS data were beneficial for genomic prediction in multi-population, while simultaneously combining the haplotype method and WGS data was a better strategy for multi-population genomic evaluation.
Collapse
Affiliation(s)
- Haoqiang Ye
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, National Engineering Research Centre for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Zipeng Zhang
- Key Laboratory of Animal Genetics and Breeding of Ministry of Agriculture and Rural Affairs, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Duanyang Ren
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, National Engineering Research Centre for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Xiaodian Cai
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, National Engineering Research Centre for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Qianghui Zhu
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, National Engineering Research Centre for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Xiangdong Ding
- Key Laboratory of Animal Genetics and Breeding of Ministry of Agriculture and Rural Affairs, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Hao Zhang
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, National Engineering Research Centre for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Zhe Zhang
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, National Engineering Research Centre for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Jiaqi Li
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, National Engineering Research Centre for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, China
| |
Collapse
|
5
|
Genotyping, the Usefulness of Imputation to Increase SNP Density, and Imputation Methods and Tools. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2467:113-138. [PMID: 35451774 DOI: 10.1007/978-1-0716-2205-6_4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Imputation has become a standard practice in modern genetic research to increase genome coverage and improve accuracy of genomic selection and genome-wide association study as a large number of samples can be genotyped at lower density (and lower cost) and, imputed up to denser marker panels or to sequence level, using information from a limited reference population. Most genotype imputation algorithms use information from relatives and population linkage disequilibrium. A number of software for imputation have been developed originally for human genetics and, more recently, for animal and plant genetics considering pedigree information and very sparse SNP arrays or genotyping-by-sequencing data. In comparison to human populations, the population structures in farmed species and their limited effective sizes allow to accurately impute high-density genotypes or sequences from very low-density SNP panels and a limited set of reference individuals. Whatever the imputation method, the imputation accuracy, measured by the correct imputation rate or the correlation between true and imputed genotypes, increased with the increasing relatedness of the individual to be imputed with its denser genotyped ancestors and as its own genotype density increased. Increasing the imputation accuracy pushes up the genomic selection accuracy whatever the genomic evaluation method. Given the marker densities, the most important factors affecting imputation accuracy are clearly the size of the reference population and the relationship between individuals in the reference and target populations.
Collapse
|
6
|
Ye S, Song H, Ding X, Zhang Z, Li J. Pre-selecting markers based on fixation index scores improved the power of genomic evaluations in a combined Yorkshire pig population. Animal 2020; 14:1555-1564. [PMID: 32209149 DOI: 10.1017/s1751731120000506] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
Combining different swine populations in genomic prediction can be an important tool, leading to an increased accuracy of genomic prediction using single nucleotide polymorphism (SNP) chip data compared with within-population genomic. However, the expected higher accuracy of multi-population genomic prediction has not been realized. This may be due to an inconsistent linkage disequilibrium (LD) between SNPs and quantitative trait loci (QTL) across populations, and the weak genetic relationships across populations. In this study, we determined the impact of different genomic relationship matrices, SNP density and pre-selected variants on prediction accuracy using a combined Yorkshire pig population. Our objective was to provide useful strategies for improving the accuracy of genomic prediction within a combined population. Results showed that the accuracy of genomic best linear unbiased prediction (GBLUP) using imputed whole-genome sequencing (WGS) data in the combined population was always higher than that within populations. Furthermore, the use of imputed WGS data always resulted in a higher accuracy of GBLUP than the use of 80K chip data for the combined population. Additionally, the accuracy of GBLUP with a non-linear genomic relationship matrix was markedly increased (0.87% to 15.17% for 80K chip data, and 0.43% to 4.01% for imputed WGS data) compared with that obtained with a linear genomic relationship matrix, except for the prediction of XD population in the combined population using imputed WGS data. More importantly, the application of pre-selected variants based on fixation index (Fst) scores improved the accuracy of multi-population genomic prediction, especially for 80K chip data. For BLUP|GA (BLUP approach given the genetic architecture), the use of a linear method with an appropriate weight to build a weight-relatedness matrix led to a higher prediction accuracy compared with the use of only pre-selected SNPs for genomic evaluations, especially for the total number of piglets born. However, for the non-linear method, BLUP|GA showed only a small increase or even a decrease in prediction accuracy compared with the use of only pre-selected SNPs. Overall, the best genomic evaluation strategy for reproduction-related traits for a combined population was found to be GBLUP performed with a non-linear genomic relationship matrix using variants pre-selected from the 80K chip data based on Fst scores.
Collapse
Affiliation(s)
- S Ye
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, National Engineering Research Centre for Breeding Swine Industry, College of Animal Science, South China Agricultural University, No. 483, Wushan Road, Tianhe District, 510642Guangzhou, China
| | - H Song
- 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, No. 2, Yuanmingyuan West Road, Haidian District, 100193Beijing, China
| | - X Ding
- 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, No. 2, Yuanmingyuan West Road, Haidian District, 100193Beijing, China
| | - Z Zhang
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, National Engineering Research Centre for Breeding Swine Industry, College of Animal Science, South China Agricultural University, No. 483, Wushan Road, Tianhe District, 510642Guangzhou, China
| | - J Li
- Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, National Engineering Research Centre for Breeding Swine Industry, College of Animal Science, South China Agricultural University, No. 483, Wushan Road, Tianhe District, 510642Guangzhou, China
| |
Collapse
|
7
|
Interest of using imputation for genomic evaluation in layer chicken. Poult Sci 2020; 99:2324-2336. [PMID: 32359567 PMCID: PMC7597443 DOI: 10.1016/j.psj.2020.01.004] [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: 07/25/2019] [Revised: 12/27/2019] [Accepted: 01/01/2020] [Indexed: 11/21/2022] Open
Abstract
With the availability of the 600K Affymetrix Axiom high-density (HD) single nucleotide polymorphism (SNP) chip, genomic selection has been implemented in broiler and layer chicken. However, the cost of this SNP chip is too high to genotype all selection candidates. A solution is to develop a low-density SNP chip, at a lower price, and to impute all missing markers. But to routinely implement this solution, the impact of imputation on genomic evaluation accuracy must be studied. It is also interesting to study the consequences of the use of low-density SNP chips in genomic evaluation accuracy. In this perspective, the interest of using imputation in genomic selection was studied in a pure layer line. Two low-density SNP chip designs were compared: an equidistant methodology and a methodology based on linkage disequilibrium. Egg weight, egg shell color, egg shell strength, and albumen height were evaluated with single-step genomic best linear unbiased prediction methodology. The impact of imputation errors or the absence of imputation on the ranking of the male selection candidates was assessed with a genomic evaluation based on ancestry. Thus, genomic estimated breeding values (GEBV) obtained with imputed HD genotypes or low-density genotypes were compared with GEBV obtained with the HD SNP chip. The relative accuracy of GEBV was also investigated by considering as reference GEBV estimated on the offspring. A limited reordering of the breeders, selected on a multitrait index, was observed. Spearman correlations between GEBV on HD genotypes and GEBV on low-density genotypes (with or without imputation) were always higher than 0.94 with more than 3K SNP. For the genetically closer, top 150 individuals for a specific trait, with imputation, the reordering was reduced with correlation higher than 0.94 with more than 3K SNP. Without imputation, the correlations remained lower than 0.85 with less than 3K and 16K SNP for equidistant and linkage disequilibrium methodology, respectively. The differences in GEBV correlations between both methodologies were never significant. The conclusions were the same for all studied traits.
Collapse
|
8
|
Wu P, Wang K, Zhou J, Chen D, Yang Q, Yang X, Liu Y, Feng B, Jiang A, Shen L, Xiao W, Jiang Y, Zhu L, Zeng Y, Xu X, Li X, Tang G. GWAS on Imputed Whole-Genome Resequencing From Genotyping-by-Sequencing Data for Farrowing Interval of Different Parities in Pigs. Front Genet 2019; 10:1012. [PMID: 31681435 PMCID: PMC6813215 DOI: 10.3389/fgene.2019.01012] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2019] [Accepted: 09/23/2019] [Indexed: 12/30/2022] Open
Abstract
The whole-genome sequencing (WGS) data can potentially discover all genetic variants. Studies have shown the power of WGS for genome-wide association study (GWAS) lies in the ability to identify quantitative trait loci and nucleotides (QTNs). However, the resequencing of thousands of target individuals is expensive. Genotype imputation is a powerful approach for WGS and to identify causal mutations. This study aimed to evaluate the imputation accuracy from genotyping-by-sequencing (GBS) to WGS in two pig breeds using a resequencing reference population and to detect single-nucleotide polymorphisms (SNPs) and candidate genes for farrowing interval (FI) of different parities using the data before and after imputation for GWAS. Six hundred target pigs, 300 Landrace and 300 Large White pigs, were genotyped by GBS, and 60 reference pigs, 20 Landrace and 40 Large White pigs, were sequenced by whole-genome resequencing. Imputation for pigs was conducted using Beagle software. The average imputation accuracy (allelic R 2) from GBS to WGS was 0.42 for Landrace pigs and 0.45 for Large White pigs. For Landrace pigs (Large White pigs), 4,514,934 (5,533,290) SNPs had an accuracy >0.3, resulting an average accuracy of 0.73 (0.72), and 2,093,778 (2,468,645) SNPs had an accuracy >0.8, resulting an average accuracy of 0.94 (0.93). Association studies with data before and after imputation were performed for FI of different parities in two populations. Before imputation, 18 and 128 significant SNPs were detected for FI in Landrace and Large White pigs, respectively. After imputation, 125 and 27 significant SNPs were identified for dataset with an accuracy >0.3 and 0.8 in Large White pigs, and 113 and 18 SNPs were found among imputed sequence variants. Among these significant SNPs, six top SNPs were detected in both GBS data and imputed WGS data, namely, SSC2: 136127645, SSC5: 103426443, SSC6: 27811226, SSC10: 3609429, SSC14: 15199253, and SSC15: 150297519. Overall, many candidate genes could be involved in FI of different parities in pigs. Although imputation from GBS to WGS data resulted in a low imputation accuracy, association analyses with imputed WGS data were optimized to detect QTNs for complex trait. The obtained results provide new insight into genotype imputation, genetic architecture, and candidate genes for FI of different parities in Landrace and Large White pigs.
Collapse
Affiliation(s)
- Pingxian Wu
- Farm Animal Genetic Resources Exploration and Innovation, Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
| | - Kai Wang
- Farm Animal Genetic Resources Exploration and Innovation, Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
| | - Jie Zhou
- Farm Animal Genetic Resources Exploration and Innovation, Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
| | - Dejuan Chen
- Farm Animal Genetic Resources Exploration and Innovation, Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
| | - Qiang Yang
- Farm Animal Genetic Resources Exploration and Innovation, Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
| | - Xidi Yang
- Farm Animal Genetic Resources Exploration and Innovation, Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
| | - Yihui Liu
- Sichuan Province Department of Agriculture and Rural Affairs, Sichuan Animal Husbandry Station, Chengdu, China
| | - Bo Feng
- Sichuan Province Department of Agriculture and Rural Affairs, Sichuan Animal Husbandry Station, Chengdu, China
| | - Anan Jiang
- Farm Animal Genetic Resources Exploration and Innovation, Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
| | - Linyuan Shen
- Farm Animal Genetic Resources Exploration and Innovation, Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
| | - Weihang Xiao
- Farm Animal Genetic Resources Exploration and Innovation, Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
| | - Yanzhi Jiang
- College of Life Science, Sichuan Agricultural University, Yaan, China
| | - Li Zhu
- Farm Animal Genetic Resources Exploration and Innovation, Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
| | - Yangshuang Zeng
- Sichuan Province Department of Agriculture and Rural Affairs, Sichuan Animal Husbandry Station, Chengdu, China
| | - Xu Xu
- Sichuan Province Department of Agriculture and Rural Affairs, Sichuan Animal Husbandry Station, Chengdu, China
| | - Xuewei Li
- Farm Animal Genetic Resources Exploration and Innovation, Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
| | - Guoqing Tang
- Farm Animal Genetic Resources Exploration and Innovation, Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
| |
Collapse
|
9
|
Ye S, Gao N, Zheng R, Chen Z, Teng J, Yuan X, Zhang H, Chen Z, Zhang X, Li J, Zhang Z. Strategies for Obtaining and Pruning Imputed Whole-Genome Sequence Data for Genomic Prediction. Front Genet 2019; 10:673. [PMID: 31379929 PMCID: PMC6650575 DOI: 10.3389/fgene.2019.00673] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2019] [Accepted: 06/27/2019] [Indexed: 11/13/2022] Open
Abstract
Genomic prediction with imputed whole-genome sequencing (WGS) data is an attractive approach to improve predictive ability with low cost. However, high accuracy has not been realized using this method in livestock. In this study, we imputed 435 individuals from 600K single nucleotide polymorphism (SNP) chip data to WGS data using different reference panels. We also investigated the prediction accuracy of genomic best linear unbiased prediction (GBLUP) using imputed WGS data from different reference panels, linkage disequilibrium (LD)-based marker pruning, and pre-selected variants based on Genome-wide association society (GWAS) results. Results showed that the imputation accuracies from 600K to WGS data were 0.873 ± 0.038, 0.906 ± 0.036, and 0.979 ± 0.010 for the internal, external, and combined reference panels, respectively. In most traits of chickens, the prediction accuracy of imputed WGS data obtained from the internal reference panel was greater than or equal to that of the combined reference panel; the external reference panel had the lowest prediction accuracy. Compared with 600K chip data, GBLUP with imputed WGS data had only a small increase (1-3%) in prediction accuracy. Using only variants selected from imputed WGS data based on GWAS results resulted in almost no increase for most traits and even increased the bias of the regression coefficient. The impact of the degree of LD of selected and remaining variants on prediction accuracy was different. For average daily gain (ADG), residual feed intake (RFI), intestine length (IL), and body weight in 91 days (BW91), the accuracy of GBLUP increased as the degree of LD of selected variants decreased, but the opposite relationship occurred for the remaining variants. But for breast muscle weight (BMW) and average daily feed intake (ADFI), the accuracy of GBLUP increased as the degree of LD of selected variants increased, and the degree of LD of remaining variants had a small effect on prediction accuracy. Overall, the optimal imputation strategy to obtain WGS data for genomic prediction should consider the relationship between selected individuals and target population individuals to avoid heterogeneity of imputation. LD-based marker pruning can be used to improve the accuracy of genomic prediction using imputed WGS data.
Collapse
Affiliation(s)
- Shaopan Ye
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Ning Gao
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Rongrong Zheng
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Zitao Chen
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Jinyan Teng
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Xiaolong Yuan
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Hao Zhang
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Zanmou Chen
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Xiquan Zhang
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Jiaqi Li
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, China
| | - Zhe Zhang
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, China
| |
Collapse
|
10
|
O'Brien AC, Judge MM, Fair S, Berry DP. High imputation accuracy from informative low-to-medium density single nucleotide polymorphism genotypes is achievable in sheep1. J Anim Sci 2019; 97:1550-1567. [PMID: 30722011 DOI: 10.1093/jas/skz043] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Accepted: 01/30/2019] [Indexed: 12/29/2022] Open
Abstract
The objective of the present study was to quantify the accuracy of imputing medium-density single nucleotide polymorphism (SNP) genotypes from lower-density panels (384 to 12,000 SNPs) derived using alternative selection methods to select the most informative SNPs. Four different selection methods were used to select SNPs based on genomic characteristics (i.e., minor allele frequency (MAF) and linkage disequilibrium (LD)) within five sheep breeds (642 Belclare, 645 Charollais, 715 Suffolk, 440 Texel, and 620 Vendeen) separately. Selection methods evaluated included (i) random, (ii) splitting the genome into blocks of equal length and selecting SNPs within block based on MAF and LD patterns, (iii) equidistant location while optimizing MAF, (iv) a combination of MAF, distance from already selected SNPs, and weak LD with the SNP(s) already selected. All animals were genotyped on the Illumina OvineSNP50 Beadchip containing 51,135 SNPs of which 44,040 remained after edits. Within each breed separately, the youngest 100 animals were assumed to represent the validation population; the remaining animals represented the reference population. Imputation was undertaken under three different conditions: (i) SNPs were selected within a given breed and imputed for all breeds individually, (ii) all breeds were collectively used to select SNPs and were included as the reference population, and (iii) the SNPs were selected for each breed separately and imputation was undertaken for all breeds but excluding from the reference population, the breed from which the SNPs were selected. Regardless of SNP selection method, mean animal allele concordance rate improved at a diminishing rate while the variability in mean animal allele concordance rate reduced as the panel density increased. The SNP selection method impacted the accuracy of imputation although the effect reduced as the density of the panel increased. Overall, the most accurate SNP selection method for panels with <9,000 SNPs was that based on MAF and LD pattern within genomic blocks. The mean animal allele concordance rate varied from 0.89 in Texel to 0.97 in Vendeen. Greater imputation accuracy was achieved when SNPs were selected and imputed within each breed individually compared with when SNPs were selected across all breeds and imputed using a multi-breed reference population. In all, results indicate that accurate genotype imputation to medium density is achievable with low-density genotype panels with at least 6,000 SNPs.
Collapse
Affiliation(s)
- Aine C O'Brien
- Animal and Grassland Research and Innovation Centre, Teagasc, Moorepark, Fermoy, Co. Cork, Ireland.,Laboratory of Animal Reproduction, Department of Biological Sciences, Faculty of Science and Engineering, University of Limerick, Limerick, Ireland
| | - Michelle M Judge
- Animal and Grassland Research and Innovation Centre, Teagasc, Moorepark, Fermoy, Co. Cork, Ireland
| | - Sean Fair
- Laboratory of Animal Reproduction, Department of Biological Sciences, Faculty of Science and Engineering, University of Limerick, Limerick, Ireland
| | - Donagh P Berry
- Animal and Grassland Research and Innovation Centre, Teagasc, Moorepark, Fermoy, Co. Cork, Ireland
| |
Collapse
|
11
|
Perez BC, Balieiro JCC, Carvalheiro R, Tirelo F, Oliveira Junior GA, Dementshuk JM, Eler JP, Ferraz JBS, Ventura RV. Accounting for population structure in selective cow genotyping strategies. J Anim Breed Genet 2018; 136:23-39. [DOI: 10.1111/jbg.12369] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Revised: 11/09/2018] [Accepted: 11/12/2018] [Indexed: 11/26/2022]
Affiliation(s)
- Bruno C. Perez
- Faculdade de Zootecnia e Engenharia de Alimentos; Universidade de São Paulo; Pirassununga Brasil
| | - Julio C. C. Balieiro
- Faculdade de Medicina Veterinária e Zootecnia; Universidade de São Paulo; Pirassununga Brasil
| | - Roberto Carvalheiro
- Departamento de Zootecnia; Universidade Estadual Paulista Julio de Mesquita Filho; Jaboticabal Brasil
| | | | - Gerson A. Oliveira Junior
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences; University of Guelph; Guelph ON Canada
| | - Juliana M. Dementshuk
- Departamento de Zootecnia; Universidade Federal do Rio Grande do Sul; Porto Alegre Brasil
| | - Joanir P. Eler
- Grupo de Melhoramento Animal e Biotecnologia, Departmento de Ciências Veterinárias, Faculdade de Zootecnia e Engenharia de Alimentos; Universidade de São Paulo (GMAB-FZEA/USP); Pirassununga Brasil
| | - José B. S. Ferraz
- Grupo de Melhoramento Animal e Biotecnologia, Departmento de Ciências Veterinárias, Faculdade de Zootecnia e Engenharia de Alimentos; Universidade de São Paulo (GMAB-FZEA/USP); Pirassununga Brasil
| | - Ricardo V. Ventura
- Faculdade de Medicina Veterinária e Zootecnia; Universidade de São Paulo; Pirassununga Brasil
| |
Collapse
|
12
|
|
13
|
Chassier M, Barrey E, Robert C, Duluard A, Danvy S, Ricard A. Genotype imputation accuracy in multiple equine breeds from medium- to high-density genotypes. J Anim Breed Genet 2018; 135:420-431. [DOI: 10.1111/jbg.12358] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2018] [Revised: 08/17/2018] [Accepted: 08/24/2018] [Indexed: 01/27/2023]
Affiliation(s)
- Marjorie Chassier
- Unité Mixte de Recherche 1313 Génétique Animale et Biologie Intégrative; Département Sciences du Vivant; Institut National de la Recherche Agronomique; AgroParisTech; Université Paris Saclay; Jouy-en-Josas France
| | - Eric Barrey
- Unité Mixte de Recherche 1313 Génétique Animale et Biologie Intégrative; Département Sciences du Vivant; Institut National de la Recherche Agronomique; AgroParisTech; Université Paris Saclay; Jouy-en-Josas France
| | - Céline Robert
- Unité Mixte de Recherche 1313 Génétique Animale et Biologie Intégrative; Département Sciences du Vivant; Institut National de la Recherche Agronomique; AgroParisTech; Université Paris Saclay; Jouy-en-Josas France
- Ecole Nationale Vétérinaire d'Alfort; Maisons Alfort France
| | - Arnaud Duluard
- Département élevage et santé animale; Le Trot; Paris France
| | - Sophie Danvy
- Institut Français du Cheval et de l'Equitation; Pôle développement; Innovation et Recherche; Exmes France
| | - Anne Ricard
- Unité Mixte de Recherche 1313 Génétique Animale et Biologie Intégrative; Département Sciences du Vivant; Institut National de la Recherche Agronomique; AgroParisTech; Université Paris Saclay; Jouy-en-Josas France
- Institut Français du Cheval et de l'Equitation; Pôle développement; Innovation et Recherche; Exmes France
| |
Collapse
|
14
|
Jia C, Zhao F, Wang X, Han J, Zhao H, Liu G, Wang Z. Genomic Prediction for 25 Agronomic and Quality Traits in Alfalfa ( Medicago sativa). FRONTIERS IN PLANT SCIENCE 2018; 9:1220. [PMID: 30177947 PMCID: PMC6109793 DOI: 10.3389/fpls.2018.01220] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2018] [Accepted: 07/30/2018] [Indexed: 05/31/2023]
Abstract
Agronomic and quality traits in alfalfa are very important to forage industry. Genomic prediction (GP) based on genotyping-by-sequencing (GBS) data could shorten the breeding cycles and accelerate the genetic gains of these complex traits, if they display moderate to high prediction accuracies. The aim of this study was to investigate the predictive potentials of these traits in alfalfa. A total of 322 genotypes from 75 alfalfa accessions were used for GP of the agronomic and quality traits, which were related to yield and nutrition value, respectively, using BayesA, BayesB, and BayesCπ methods. Ten-fold cross validation was used to evaluate the accuracy of GP represented by the correlation between genomic estimated breeding value (GEBV) and estimated breeding value (EBV). The accuracies ranged from 0.0021 to 0.6485 for different traits. For each trait, three GP methods displayed similar prediction accuracies. Among 15 quality traits, mineral element Ca had a moderate and the highest prediction accuracy (0.34). NDF digestibility after 48 h (NDFD 48 h) and 30 h (NDFD 30 h) and mineral element Mg had prediction accuracies varying from 0.20 to 0.25. Other traits, for example, fat and crude protein, showed low prediction accuracies (0.05 to 0.19). Among 10 agronomic traits, however, some displayed relatively high prediction accuracies. Plant height (PH) in fall (FH) had the highest prediction accuracy (0.65), followed by flowering date (FD) and plant regrowth (PR) with accuracies at 0.52 and 0.51, respectively. Leaf to stem ratio (LS), plant branch (PB), and biomass yield (BY) reached to moderate prediction accuracies ranging from 0.25 to 0.32. Our results revealed that a few agronomic traits, such as FH, FD, and PR, had relatively high prediction accuracies, therefore it is feasible to apply genomic selection (GS) for these traits in alfalfa breeding programs. Because of the limitations of population size and density of SNP markers, several traits displayed low accuracies which could be improved by a bigger reference population, higher density of SNP markers, and more powerful statistic tools.
Collapse
Affiliation(s)
- Congjun Jia
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Fuping Zhao
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xuemin Wang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Jianlin Han
- CAAS-ILRI Joint Laboratory on Livestock and Forage Genetic Resources, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
- International Livestock Research Institute (ILRI), Nairobi, Kenya
| | - Haiming Zhao
- Institute of Dryland Farming, Hebei Academy of Agriculture and Forestry Sciences, Hengshui, China
| | - Guibo Liu
- Institute of Dryland Farming, Hebei Academy of Agriculture and Forestry Sciences, Hengshui, China
| | - Zan Wang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| |
Collapse
|
15
|
Friedrich J, Antolín R, Edwards SM, Sánchez‐Molano E, Haskell MJ, Hickey JM, Wiener P. Accuracy of genotype imputation in Labrador Retrievers. Anim Genet 2018; 49:303-311. [PMID: 29974966 PMCID: PMC6055857 DOI: 10.1111/age.12677] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/05/2018] [Indexed: 12/12/2022]
Abstract
The dog is a valuable model species for the genetic analysis of complex traits, and the use of genotype imputation in dogs will be an important tool for future studies. It is of particular interest to analyse the effect of factors like single nucleotide polymorphism (SNP) density of genotyping arrays and relatedness between dogs on imputation accuracy due to the acknowledged genetic and pedigree structure of dog breeds. In this study, we simulated different genotyping strategies based on data from 1179 Labrador Retriever dogs. The study involved 5826 SNPs on chromosome 1 representing the high density (HighD) array; the low-density (LowD) array was simulated by masking different proportions of SNPs on the HighD array. The correlations between true and imputed genotypes for a realistic masking level of 87.5% ranged from 0.92 to 0.97, depending on the scenario used. A correlation of 0.92 was found for a likely scenario (10% of dogs genotyped using HighD, 87.5% of HighD SNPs masked in the LowD array), which indicates that genotype imputation in Labrador Retrievers can be a valuable tool to reduce experimental costs while increasing sample size. Furthermore, we show that genotype imputation can be performed successfully even without pedigree information and with low relatedness between dogs in the reference and validation sets. Based on these results, the impact of genotype imputation was evaluated in a genome-wide association analysis and genomic prediction in Labrador Retrievers.
Collapse
Affiliation(s)
- J. Friedrich
- Division of Genetics and GenomicsThe Roslin Institute and Royal (Dick) School of Veterinary StudiesUniversity of EdinburghMidlothianEH25 9RGUK
| | - R. Antolín
- Division of Genetics and GenomicsThe Roslin Institute and Royal (Dick) School of Veterinary StudiesUniversity of EdinburghMidlothianEH25 9RGUK
| | - S. M. Edwards
- Division of Genetics and GenomicsThe Roslin Institute and Royal (Dick) School of Veterinary StudiesUniversity of EdinburghMidlothianEH25 9RGUK
| | - E. Sánchez‐Molano
- Division of Genetics and GenomicsThe Roslin Institute and Royal (Dick) School of Veterinary StudiesUniversity of EdinburghMidlothianEH25 9RGUK
| | - M. J. Haskell
- Animal and Veterinary Sciences GroupScotland's Rural CollegeEdinburghEH9 3JGUK
| | - J. M. Hickey
- Division of Genetics and GenomicsThe Roslin Institute and Royal (Dick) School of Veterinary StudiesUniversity of EdinburghMidlothianEH25 9RGUK
| | - P. Wiener
- Division of Genetics and GenomicsThe Roslin Institute and Royal (Dick) School of Veterinary StudiesUniversity of EdinburghMidlothianEH25 9RGUK
| |
Collapse
|
16
|
Genomic prediction of the polled and horned phenotypes in Merino sheep. Genet Sel Evol 2018; 50:28. [PMID: 29788905 PMCID: PMC5964914 DOI: 10.1186/s12711-018-0398-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2017] [Accepted: 05/15/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In horned sheep breeds, breeding for polledness has been of interest for decades. The objective of this study was to improve prediction of the horned and polled phenotypes using horn scores classified as polled, scurs, knobs or horns. Derived phenotypes polled/non-polled (P/NP) and horned/non-horned (H/NH) were used to test four different strategies for prediction in 4001 purebred Merino sheep. These strategies include the use of single 'single nucleotide polymorphism' (SNP) genotypes, multiple-SNP haplotypes, genome-wide and chromosome-wide genomic best linear unbiased prediction and information from imputed sequence variants from the region including the RXFP2 gene. Low-density genotypes of these animals were imputed to the Illumina Ovine high-density (600k) chip and the 1.78-kb insertion polymorphism in RXFP2 was included in the imputation process to whole-genome sequence. We evaluated the mode of inheritance and validated models by a fivefold cross-validation and across- and between-family prediction. RESULTS The most significant SNPs for prediction of P/NP and H/NH were OAR10_29546872.1 and OAR10_29458450, respectively, located on chromosome 10 close to the 1.78-kb insertion at 29.5 Mb. The mode of inheritance included an additive effect and a sex-dependent effect for dominance for P/NP and a sex-dependent additive and dominance effect for H/NH. Models with the highest prediction accuracies for H/NH used either single SNPs or 3-SNP haplotypes and included a polygenic effect estimated based on traditional pedigree relationships. Prediction accuracies for H/NH were 0.323 for females and 0.725 for males. For predicting P/NP, the best models were the same as for H/NH but included a genomic relationship matrix with accuracies of 0.713 for females and 0.620 for males. CONCLUSIONS Our results show that prediction accuracy is high using a single SNP, but does not reach 1 since the causative mutation is not genotyped. Incomplete penetrance or allelic heterogeneity, which can influence expression of the phenotype, may explain why prediction accuracy did not approach 1 with any of the genetic models tested here. Nevertheless, a breeding program to eradicate horns from Merino sheep can be effective by selecting genotypes GG of SNP OAR10_29458450 or TT of SNP OAR10_29546872.1 since all sheep with these genotypes will be non-horned.
Collapse
|
17
|
Ye S, Yuan X, Lin X, Gao N, Luo Y, Chen Z, Li J, Zhang X, Zhang Z. Imputation from SNP chip to sequence: a case study in a Chinese indigenous chicken population. J Anim Sci Biotechnol 2018; 9:30. [PMID: 29581880 PMCID: PMC5861640 DOI: 10.1186/s40104-018-0241-5] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2017] [Accepted: 01/26/2018] [Indexed: 11/24/2022] Open
Abstract
Background Genome-wide association studies and genomic predictions are thought to be optimized by using whole-genome sequence (WGS) data. However, sequencing thousands of individuals of interest is expensive. Imputation from SNP panels to WGS data is an attractive and less expensive approach to obtain WGS data. The aims of this study were to investigate the accuracy of imputation and to provide insight into the design and execution of genotype imputation. Results We genotyped 450 chickens with a 600 K SNP array, and sequenced 24 key individuals by whole genome re-sequencing. Accuracy of imputation from putative 60 K and 600 K array data to WGS data was 0.620 and 0.812 for Beagle, and 0.810 and 0.914 for FImpute, respectively. By increasing the sequencing cost from 24X to 144X, the imputation accuracy increased from 0.525 to 0.698 for Beagle and from 0.654 to 0.823 for FImpute. With fixed sequence depth (12X), increasing the number of sequenced animals from 1 to 24, improved accuracy from 0.421 to 0.897 for FImpute and from 0.396 to 0.777 for Beagle. Using optimally selected key individuals resulted in a higher imputation accuracy compared with using randomly selected individuals as a reference population for re-sequencing. With fixed reference population size (24), imputation accuracy increased from 0.654 to 0.875 for FImpute and from 0.512 to 0.762 for Beagle as the sequencing depth increased from 1X to 12X. With a given total cost of genotyping, accuracy increased with the size of the reference population for FImpute, but the pattern was not valid for Beagle, which showed the highest accuracy at six fold coverage for the scenarios used in this study. Conclusions In conclusion, we comprehensively investigated the impacts of several key factors on genotype imputation. Generally, increasing sequencing cost gave a higher imputation accuracy. But with a fixed sequencing cost, the optimal imputation enhance the performance of WGP and GWAS. An optimal imputation strategy should take size of reference population, imputation algorithms, marker density, and population structure of the target population and methods to select key individuals into consideration comprehensively. This work sheds additional light on how to design and execute genotype imputation for livestock populations. Electronic supplementary material The online version of this article (10.1186/s40104-018-0241-5) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Shaopan Ye
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, National Engineering Research Centre for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong China
| | - Xiaolong Yuan
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, National Engineering Research Centre for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong China
| | - Xiran Lin
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, National Engineering Research Centre for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong China
| | - Ning Gao
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, National Engineering Research Centre for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong China
| | - Yuanyu Luo
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, National Engineering Research Centre for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong China
| | - Zanmou Chen
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, National Engineering Research Centre for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong China
| | - Jiaqi Li
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, National Engineering Research Centre for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong China
| | - Xiquan Zhang
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, National Engineering Research Centre for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong China
| | - Zhe Zhang
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, National Engineering Research Centre for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou, Guangdong China
| |
Collapse
|
18
|
Raoul J, Swan AA, Elsen JM. Using a very low-density SNP panel for genomic selection in a breeding program for sheep. Genet Sel Evol 2017; 49:76. [PMID: 29065868 PMCID: PMC5655911 DOI: 10.1186/s12711-017-0351-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2017] [Accepted: 10/17/2017] [Indexed: 01/11/2023] Open
Abstract
Background Building an efficient reference population for genomic selection is an issue when the recorded population is small and phenotypes are poorly informed, which is often the case in sheep breeding programs. Using stochastic simulation, we evaluated a genomic design based on a reference population with medium-density genotypes [around 45 K single nucleotide polymorphisms (SNPs)] of dams that were imputed from very low-density genotypes (≤ 1000 SNPs). Methods A population under selection for a maternal trait was simulated using real genotypes. Genetic gains realized from classical selection and genomic selection designs were compared. Genomic selection scenarios that differed in reference population structure (whether or not dams were included in the reference) and genotype quality (medium-density or imputed to medium-density from very low-density) were evaluated. Results The genomic design increased genetic gain by 26% when the reference population was based on sire medium-density genotypes and by 54% when the reference population included both sire and dam medium-density genotypes. When medium-density genotypes of male candidates and dams were replaced by imputed genotypes from very low-density SNP genotypes (1000 SNPs), the increase in gain was 22% for the sire reference population and 42% for the sire and dam reference population. The rate of increase in inbreeding was lower (from − 20 to − 34%) for the genomic design than for the classical design regardless of the genomic scenario. Conclusions We show that very low-density genotypes of male candidates and dams combined with an imputation process result in a substantial increase in genetic gain for small sheep breeding programs.
Collapse
Affiliation(s)
- Jérôme Raoul
- Institut de l'Elevage, Castanet-Tolosan, France. .,GenPhySE, INRA, Castanet-Tolosan, France.
| | - Andrew A Swan
- Animal Genetics and Breeding Unit, University of New England, Armidale, Australia
| | | |
Collapse
|
19
|
Oliveira Júnior GA, Chud TCS, Ventura RV, Garrick DJ, Cole JB, Munari DP, Ferraz JBS, Mullart E, DeNise S, Smith S, da Silva MVGB. Genotype imputation in a tropical crossbred dairy cattle population. J Dairy Sci 2017; 100:9623-9634. [PMID: 28987572 DOI: 10.3168/jds.2017-12732] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2017] [Accepted: 08/16/2017] [Indexed: 11/19/2022]
Abstract
The objective of this study was to investigate different strategies for genotype imputation in a population of crossbred Girolando (Gyr × Holstein) dairy cattle. The data set consisted of 478 Girolando, 583 Gyr, and 1,198 Holstein sires genotyped at high density with the Illumina BovineHD (Illumina, San Diego, CA) panel, which includes ∼777K markers. The accuracy of imputation from low (20K) and medium densities (50K and 70K) to the HD panel density and from low to 50K density were investigated. Seven scenarios using different reference populations (RPop) considering Girolando, Gyr, and Holstein breeds separately or combinations of animals of these breeds were tested for imputing genotypes of 166 randomly chosen Girolando animals. The population genotype imputation were performed using FImpute. Imputation accuracy was measured as the correlation between observed and imputed genotypes (CORR) and also as the proportion of genotypes that were imputed correctly (CR). This is the first paper on imputation accuracy in a Girolando population. The sample-specific imputation accuracies ranged from 0.38 to 0.97 (CORR) and from 0.49 to 0.96 (CR) imputing from low and medium densities to HD, and 0.41 to 0.95 (CORR) and from 0.50 to 0.94 (CR) for imputation from 20K to 50K. The CORRanim exceeded 0.96 (for 50K and 70K panels) when only Girolando animals were included in RPop (S1). We found smaller CORRanim when Gyr (S2) was used instead of Holstein (S3) as RPop. The same behavior was observed between S4 (Gyr + Girolando) and S5 (Holstein + Girolando) because the target animals were more related to the Holstein population than to the Gyr population. The highest imputation accuracies were observed for scenarios including Girolando animals in the reference population, whereas using only Gyr animals resulted in low imputation accuracies, suggesting that the haplotypes segregating in the Girolando population had a greater effect on accuracy than the purebred haplotypes. All chromosomes had similar imputation accuracies (CORRsnp) within each scenario. Crossbred animals (Girolando) must be included in the reference population to provide the best imputation accuracies.
Collapse
Affiliation(s)
- Gerson A Oliveira Júnior
- Departamento de Medicina Veterinária, Universidade de São Paulo (USP), Faculdade de Zootecnia e Engenharia de Alimentos, Pirassununga, SP, 13635-900, Brazil
| | - Tatiane C S Chud
- Departamento de Ciências Exatas, Universidade Estadual Paulista (Unesp), Faculdade de Ciências Agrárias e Veterinárias, Jaboticabal, SP, 14884-900, Brazil
| | - Ricardo V Ventura
- Beef Improvement Opportunities, Guelph, ON N1K1E5, Canada; Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, ON N1G2W1, Canada
| | - Dorian J Garrick
- Department of Animal Science, Iowa State University, Ames 50011-3150
| | - John B Cole
- Animal Genomics and Improvement Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD, 20705-2350
| | - Danísio P Munari
- Departamento de Ciências Exatas, Universidade Estadual Paulista (Unesp), Faculdade de Ciências Agrárias e Veterinárias, Jaboticabal, SP, 14884-900, Brazil
| | - José B S Ferraz
- Departamento de Medicina Veterinária, Universidade de São Paulo (USP), Faculdade de Zootecnia e Engenharia de Alimentos, Pirassununga, SP, 13635-900, Brazil
| | | | | | | | | |
Collapse
|
20
|
Genotype Imputation To Improve the Cost-Efficiency of Genomic Selection in Farmed Atlantic Salmon. G3-GENES GENOMES GENETICS 2017; 7:1377-1383. [PMID: 28250015 PMCID: PMC5386885 DOI: 10.1534/g3.117.040717] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Genomic selection uses genome-wide marker information to predict breeding values for traits of economic interest, and is more accurate than pedigree-based methods. The development of high density SNP arrays for Atlantic salmon has enabled genomic selection in selective breeding programs, alongside high-resolution association mapping of the genetic basis of complex traits. However, in sibling testing schemes typical of salmon breeding programs, trait records are available on many thousands of fish with close relationships to the selection candidates. Therefore, routine high density SNP genotyping may be prohibitively expensive. One means to reducing genotyping cost is the use of genotype imputation, where selected key animals (e.g., breeding program parents) are genotyped at high density, and the majority of individuals (e.g., performance tested fish and selection candidates) are genotyped at much lower density, followed by imputation to high density. The main objectives of the current study were to assess the feasibility and accuracy of genotype imputation in the context of a salmon breeding program. The specific aims were: (i) to measure the accuracy of genotype imputation using medium (25 K) and high (78 K) density mapped SNP panels, by masking varying proportions of the genotypes and assessing the correlation between the imputed genotypes and the true genotypes; and (ii) to assess the efficacy of imputed genotype data in genomic prediction of key performance traits (sea lice resistance and body weight). Imputation accuracies of up to 0.90 were observed using the simple two-generation pedigree dataset, and moderately high accuracy (0.83) was possible even with very low density SNP data (∼250 SNPs). The performance of genomic prediction using imputed genotype data was comparable to using true genotype data, and both were superior to pedigree-based prediction. These results demonstrate that the genotype imputation approach used in this study can provide a cost-effective method for generating robust genome-wide SNP data for genomic prediction in Atlantic salmon. Genotype imputation approaches are likely to form a critical component of cost-efficient genomic selection programs to improve economically important traits in aquaculture.
Collapse
|
21
|
Ventura RV, Miller SP, Dodds KG, Auvray B, Lee M, Bixley M, Clarke SM, McEwan JC. Assessing accuracy of imputation using different SNP panel densities in a multi-breed sheep population. Genet Sel Evol 2016; 48:71. [PMID: 27663120 PMCID: PMC5035503 DOI: 10.1186/s12711-016-0244-7] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2015] [Accepted: 08/31/2016] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Genotype imputation is a key element of the implementation of genomic selection within the New Zealand sheep industry, but many factors can influence imputation accuracy. Our objective was to provide practical directions on the implementation of imputation strategies in a multi-breed sheep population genotyped with three single nucleotide polymorphism (SNP) panels: 5K, 50K and HD (600K SNPs). RESULTS Imputation from 5K to HD was slightly better (0.6 %) than imputation from 5K to 50K. Two-step imputation from 5K to 50K and then from 50K to HD outperformed direct imputation from 5K to HD. A slight loss in imputation accuracy was observed when a large fixed reference population was used compared to a smaller within-breed reference (including all 50K genotypes on animals from different breeds excluding those in the validation set i.e. to be imputed), but only for a few animals across all imputation scenarios from 5K to 50K. However, a major gain in imputation accuracy for a large proportion of animals (purebred and crossbred), justified the use of a fixed and large reference dataset for all situations. This study also investigated the loss in imputation accuracy specifically for SNPs located at the ends of each chromosome, and showed that only chromosome 26 had an overall imputation (5K to 50K) accuracy for 100 SNPs at each end higher than 60 % (r2). Most of the chromosomes displayed reduced imputation accuracy at least at one of their ends. Prediction of imputation accuracy based on the relatedness of low-density genotypes to those of the reference dataset, before imputation (without running an imputation software) was also investigated. FIMPUTE V2.2 outperformed BEAGLE 3.3.2 across all imputation scenarios. CONCLUSIONS Imputation accuracy in sheep breeds can be improved by following a set of recommendations on SNP panels, software, strategies of imputation (one- or two-step imputation), and choice of the animals to be genotyped using both high- and low-density SNP panels. We present a method that predicts imputation accuracy for individual animals at the low-density level, before running imputation, which can be used to restrict genomic prediction only to the animals that can be imputed with sufficient accuracy.
Collapse
Affiliation(s)
- Ricardo V Ventura
- Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, ON, N1G2W1, Canada.,Beef Improvement Opportunities, Guelph, ON, N1K1E5, Canada
| | - Stephen P Miller
- Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, ON, N1G2W1, Canada. .,Invermay Agricultural Centre, AgResearch Limited, Mosgiel, 9053, New Zealand.
| | - Ken G Dodds
- Invermay Agricultural Centre, AgResearch Limited, Mosgiel, 9053, New Zealand
| | - Benoit Auvray
- Department of Mathematics and Statistics, University of Otago, Dunedin, 9016, New Zealand
| | - Michael Lee
- Department of Mathematics and Statistics, University of Otago, Dunedin, 9016, New Zealand
| | - Matthew Bixley
- Invermay Agricultural Centre, AgResearch Limited, Mosgiel, 9053, New Zealand
| | - Shannon M Clarke
- Invermay Agricultural Centre, AgResearch Limited, Mosgiel, 9053, New Zealand
| | - John C McEwan
- Invermay Agricultural Centre, AgResearch Limited, Mosgiel, 9053, New Zealand
| |
Collapse
|
22
|
Lu D, Akanno EC, Crowley JJ, Schenkel F, Li H, De Pauw M, Moore SS, Wang Z, Li C, Stothard P, Plastow G, Miller SP, Basarab JA. Accuracy of genomic predictions for feed efficiency traits of beef cattle using 50K and imputed HD genotypes1. J Anim Sci 2016; 94:1342-53. [DOI: 10.2527/jas.2015-0126] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- D. Lu
- Livestock Gentec, Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, Canada
- AgResearch, Invermay Agricultural Centre, Post Box 50034, Mosgiel 9053, New Zealand
| | - E. C. Akanno
- Livestock Gentec, Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, Canada
| | - J. J. Crowley
- Livestock Gentec, Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, Canada
- Canadian Beef Breeds Council, Calgary, AB T2E 7H7, Canada
| | - F. Schenkel
- Centre for Genetic Improvement of Livestock, Department of Animal and Poultry Sciences, University of Guelph, ON, Canada
| | - H. Li
- Centre for Genetic Improvement of Livestock, Department of Animal and Poultry Sciences, University of Guelph, ON, Canada
| | - M. De Pauw
- Livestock Gentec, Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, Canada
| | - S. S. Moore
- Livestock Gentec, Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, Canada
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St Lucia, Queensland, Australia
| | - Z. Wang
- Livestock Gentec, Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, Canada
| | - C. Li
- Livestock Gentec, Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, Canada
- Centre for Genetic Improvement of Livestock, Department of Animal and Poultry Sciences, University of Guelph, ON, Canada
- Lacombe Research Centre, Agriculture and Agri-Food Canada, 6000 C & E Trail, Lacombe, AB, Canada
| | - P. Stothard
- Livestock Gentec, Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, Canada
| | - G. Plastow
- Livestock Gentec, Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, Canada
| | - S. P. Miller
- Livestock Gentec, Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, Canada
- AgResearch, Invermay Agricultural Centre, Post Box 50034, Mosgiel 9053, New Zealand
- Centre for Genetic Improvement of Livestock, Department of Animal and Poultry Sciences, University of Guelph, ON, Canada
| | - J. A. Basarab
- Livestock Gentec, Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, Canada
- Lacombe Research Centre, Alberta Agriculture and Forestry, 6000 C & E Trail, Lacombe, AB, Canada
| |
Collapse
|