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Warburton CL, Costilla R, Engle BN, Moore SS, Corbet NJ, Fordyce G, McGowan MR, Burns BM, Hayes BJ. Concurrently mapping quantitative trait loci associations from multiple subspecies within hybrid populations. Heredity (Edinb) 2023; 131:350-360. [PMID: 37798326 PMCID: PMC10673866 DOI: 10.1038/s41437-023-00651-4] [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: 10/14/2022] [Revised: 09/19/2023] [Accepted: 09/21/2023] [Indexed: 10/07/2023] Open
Abstract
Many of the world's agriculturally important plant and animal populations consist of hybrids of subspecies. Cattle in tropical and sub-tropical regions for example, originate from two subspecies, Bos taurus indicus (Bos indicus) and Bos taurus taurus (Bos taurus). Methods to derive the underlying genetic architecture for these two subspecies are essential to develop accurate genomic predictions in these hybrid populations. We propose a novel method to achieve this. First, we use haplotypes to assign SNP alleles to ancestral subspecies of origin in a multi-breed and multi-subspecies population. Then we use a BayesR framework to allow SNP alleles originating from the different subspecies differing effects. Applying this method in a composite population of B. indicus and B. taurus hybrids, our results show that there are underlying genomic differences between the two subspecies, and these effects are not identified in multi-breed genomic evaluations that do not account for subspecies of origin effects. The method slightly improved the accuracy of genomic prediction. More significantly, by allocating SNP alleles to ancestral subspecies of origin, we were able to identify four SNP with high posterior probabilities of inclusion that have not been previously associated with cattle fertility and were close to genes associated with fertility in other species. These results show that haplotypes can be used to trace subspecies of origin through the genome of this hybrid population and, in conjunction with our novel Bayesian analysis, subspecies SNP allele allocation can be used to increase the accuracy of QTL association mapping in genetically diverse populations.
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Affiliation(s)
- Christie L Warburton
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St. Lucia, QLD, Australia.
| | - Roy Costilla
- Agresearch Limited, Ruakura Research Centre, Hamilton, 3214, New Zealand
| | - Bailey N Engle
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St. Lucia, QLD, Australia
| | - Stephen S Moore
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St. Lucia, QLD, Australia
| | - Nicholas J Corbet
- Formerly Central Queensland University, School of Health, Medical and Applied Sciences, Rockhampton, QLD, Australia
| | - Geoffry Fordyce
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St. Lucia, QLD, Australia
| | - Michael R McGowan
- The University of Queensland, School of Veterinary Science, St Lucia, QLD, Australia
| | - Brian M Burns
- Formerly Department of Agriculture and Fisheries, Rockhampton, QLD, Australia
| | - Ben J Hayes
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St. Lucia, QLD, Australia
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Vahedi SM, Salek Ardetani S, Brito LF, Karimi K, Pahlavan Afshari K, Banabazi MH. Expanding the application of haplotype-based genomic predictions to the wild: A case of antibody response against Teladorsagia circumcincta in Soay sheep. BMC Genomics 2023; 24:335. [PMID: 37330501 DOI: 10.1186/s12864-023-09407-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 05/24/2023] [Indexed: 06/19/2023] Open
Abstract
BACKGROUND Genomic prediction of breeding values (GP) has been adopted in evolutionary genomic studies to uncover microevolutionary processes of wild populations or improve captive breeding strategies. While recent evolutionary studies applied GP with individual single nucleotide polymorphism (SNP), haplotype-based GP could outperform individual SNP predictions through better capturing the linkage disequilibrium (LD) between the SNP and quantitative trait loci (QTL). This study aimed to evaluate the accuracy and bias of haplotype-based GP of immunoglobulin (Ig) A (IgA), IgE, and IgG against Teladorsagia circumcincta in lambs of an unmanaged sheep population (Soay breed) based on Genomic Best Linear Unbiased Prediction (GBLUP) and five Bayesian [BayesA, BayesB, BayesCπ, Bayesian Lasso (BayesL), and BayesR] methods. RESULTS The accuracy and bias of GPs using SNP, haplotypic pseudo-SNP from blocks with different LD thresholds (0.15, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, and 1.00), or the combinations of pseudo-SNPs and non-LD clustered SNPs were obtained. Across methods and marker sets, higher ranges of genomic estimated breeding values (GEBV) accuracies were observed for IgA (0.20 to 0.49), followed by IgE (0.08 to 0.20) and IgG (0.05 to 0.14). Considering the methods evaluated, up to 8% gains in GP accuracy of IgG were achieved using pseudo-SNPs compared to SNPs. Up to 3% gain in GP accuracy for IgA was also obtained using the combinations of the pseudo-SNPs with non-clustered SNPs in comparison to fitting individual SNP. No improvement in GP accuracy of IgE was observed using haplotypic pseudo-SNPs or their combination with non-clustered SNPs compared to individual SNP. Bayesian methods outperformed GBLUP for all traits. Most scenarios yielded lower accuracies for all traits with an increased LD threshold. GP models using haplotypic pseudo-SNPs predicted less-biased GEBVs mainly for IgG. For this trait, lower bias was observed with higher LD thresholds, whereas no distinct trend was observed for other traits with changes in LD. CONCLUSIONS Haplotype information improves GP performance of anti-helminthic antibody traits of IgA and IgG compared to fitting individual SNP. The observed gains in the predictive performances indicate that haplotype-based methods could benefit GP of some traits in wild animal populations.
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Affiliation(s)
- Seyed Milad Vahedi
- Department of Animal Science and Aquaculture, Dalhousie University, Truro, NS, B2N5E3, Canada
| | | | - Luiz F Brito
- Department of Animal Sciences, Purdue University, West Lafayette, IN, 47907, USA
| | - Karim Karimi
- Molecular Diagnostics Program, Verspeeten Clinical Genome Centre, London Health Sciences Centre, London, ON, N6A 5W9, Canada
| | - Kian Pahlavan Afshari
- Department of Animal Sciences, Islamic Azad University, Varamin, Varamin-Pishva Branch3381774895, Iran
| | - Mohammad Hossein Banabazi
- Department of Animal Breeding and Genetics (HGEN), Centre for Veterinary Medicine and Animal Science (VHC), Swedish University of Agricultural Sciences (SLU), 75007, Uppsala, Sweden.
- Department of Biotechnology, Animal Science Research Institute of IRAN (ASRI), Agricultural Research, Education & Extension Organization (AREEO), Karaj, 3146618361, Iran.
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Araujo AC, Carneiro PLS, Oliveira HR, Lewis RM, Brito LF. SNP- and haplotype-based single-step genomic predictions for body weight, wool, and reproductive traits in North American Rambouillet sheep. J Anim Breed Genet 2023; 140:216-234. [PMID: 36408677 PMCID: PMC10099590 DOI: 10.1111/jbg.12748] [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: 05/01/2022] [Accepted: 10/23/2022] [Indexed: 11/22/2022]
Abstract
Rambouillet sheep are commonly raised in extensive grazing systems in the US, mainly for wool and meat production. Genomic evaluations in US sheep breeds, including Rambouillet, are still incipient. Therefore, we aimed to evaluate the feasibility of performing genomic prediction of breeding values for various traits in Rambouillet sheep based on single nucleotide polymorphisms (SNP) or haplotypes (fitted as pseudo-SNP) under a single-step GBLUP approach. A total of 28,834 records for birth weight (BWT), 23,306 for postweaning weight (PWT), 5,832 for yearling weight (YWT), 9,880 for yearling fibre diameter (YFD), 11,872 for yearling greasy fleece weight (YGFW), and 15,984 for number of lambs born (NLB) were used in this study. Seven hundred forty-one individuals were genotyped using a moderate (50 K; n = 677) or high (600 K; n = 64) density SNP panel, in which 32 K SNP in common between the two SNP panels (after genotypic quality control) were used for further analyses. Single-step genomic predictions using SNP (H-BLUP) or haplotypes (HAP-BLUP) from blocks with different linkage disequilibrium (LD) thresholds (0.15, 0.35, 0.50, 0.65, and 0.80) were evaluated. We also considered different blending parameters when constructing the genomic relationship matrix used to predict the genomic-enhanced estimated breeding values (GEBV), with alpha equal to 0.95 or 0.50. The GEBV were compared to the estimated breeding values (EBV) obtained from traditional pedigree-based evaluations (A-BLUP). The mean theoretical accuracy ranged from 0.499 (A-BLUP for PWT) to 0.795 (HAP-BLUP using haplotypes from blocks with LD threshold of 0.35 and alpha equal to 0.95 for YFD). The prediction accuracies ranged from 0.143 (A-BLUP for PWT) to 0.330 (A-BLUP for YGFW) while the prediction bias ranged from -0.104 (H-BLUP for PWT) to 0.087 (HAP-BLUP using haplotypes from blocks with LD threshold of 0.15 and alpha equal to 0.95 for YGFW). The GEBV dispersion ranged from 0.428 (A-BLUP for PWT) to 1.035 (A-BLUP for YGFW). Similar results were observed for H-BLUP or HAP-BLUP, independently of the LD threshold to create the haplotypes, alpha value, or trait analysed. Using genomic information (fitting individual SNP or haplotypes) provided similar or higher prediction and theoretical accuracies and reduced the dispersion of the GEBV for body weight, wool, and reproductive traits in Rambouillet sheep. However, there were no clear improvements in the prediction bias when compared to pedigree-based predictions. The next step will be to enlarge the training populations for this breed to increase the benefits of genomic predictions.
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Affiliation(s)
- Andre C. Araujo
- Graduate Program in Animal SciencesState University of Southwestern BahiaItapetingaBahiaBrazil
- Department of Animal SciencesPurdue UniversityWest LafayetteIndianaUSA
| | | | | | - Ronald M. Lewis
- Department of Animal SciencesUniversity of Nebraska‐LincolnLincolnNebraskaUSA
| | - Luiz F. Brito
- Department of Animal SciencesPurdue UniversityWest LafayetteIndianaUSA
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Ren D, Cai X, Lin Q, Ye H, Teng J, Li J, Ding X, Zhang Z. Impact of linkage disequilibrium heterogeneity along the genome on genomic prediction and heritability estimation. Genet Sel Evol 2022; 54:47. [PMID: 35761182 PMCID: PMC9235212 DOI: 10.1186/s12711-022-00737-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 06/15/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Compared to medium-density single nucleotide polymorphism (SNP) data, high-density SNP data contain abundant genetic variants and provide more information for the genetic evaluation of livestock, but it has been shown that they do not confer any advantage for genomic prediction and heritability estimation. One possible reason is the uneven distribution of the linkage disequilibrium (LD) along the genome, i.e., LD heterogeneity among regions. The aim of this study was to effectively use genome-wide SNP data for genomic prediction and heritability estimation by using models that control LD heterogeneity among regions. METHODS The LD-adjusted kinship (LDAK) and LD-stratified multicomponent (LDS) models were used to control LD heterogeneity among regions and were compared with the classical model that has no such control. Simulated and real traits of 2000 dairy cattle individuals with imputed high-density (770K) SNP data were used. Five types of phenotypes were simulated, which were controlled by very strongly, strongly, moderately, weakly and very weakly tagged causal variants, respectively. The performances of the models with high- and medium-density (50K) panels were compared to verify that the models that controlled LD heterogeneity among regions were more effective with high-density data. RESULTS Compared to the medium-density panel, the use of the high-density panel did not improve and even decreased prediction accuracies and heritability estimates from the classical model for both simulated and real traits. Compared to the classical model, LDS effectively improved the accuracy of genomic predictions and unbiasedness of heritability estimates, regardless of the genetic architecture of the trait. LDAK applies only to traits that are mainly controlled by weakly tagged causal variants, but is still less effective than LDS for this type of trait. Compared with the classical model, LDS improved prediction accuracy by about 13% for simulated phenotypes and by 0.3 to ~ 10.7% for real traits with the high-density panel, and by ~ 1% for simulated phenotypes and by - 0.1 to ~ 6.9% for real traits with the medium-density panel. CONCLUSIONS Grouping SNPs based on regional LD to construct the LD-stratified multicomponent model can effectively eliminate the adverse effects of LD heterogeneity among regions, and greatly improve the efficiency of high-density SNP data for genomic prediction and heritability estimation.
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Affiliation(s)
- Duanyang Ren
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Xiaodian Cai
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Qing Lin
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Haoqiang Ye
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Jinyan Teng
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Jiaqi Li
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China
| | - Xiangdong Ding
- Key Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture and Rural Affairs, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Zhe Zhang
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou, 510642, China.
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Mota RR, Brito LF, Berry DP. Editorial: Beef on Dairy: The Use of a Simple Tool to Improve Both Cattle Production Systems. Front Genet 2022; 13:813949. [PMID: 35559015 PMCID: PMC9086432 DOI: 10.3389/fgene.2022.813949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 04/06/2022] [Indexed: 11/25/2022] Open
Affiliation(s)
- Rodrigo R Mota
- Council on Dairy Cattle Breeding (CDCB), Bowie, MD, United States
| | - Luiz F Brito
- Department of Animal Sciences, Purdue University, West Lafayette, IN, United States
| | - Donagh P Berry
- Teagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Ireland
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Araujo AC, Carneiro PLS, Alvarenga AB, Oliveira HR, Miller SP, Retallick K, Brito LF. Haplotype-Based Single-Step GWAS for Yearling Temperament in American Angus Cattle. Genes (Basel) 2021; 13:17. [PMID: 35052358 PMCID: PMC8775055 DOI: 10.3390/genes13010017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 12/14/2021] [Accepted: 12/18/2021] [Indexed: 01/23/2023] Open
Abstract
Behavior is a complex trait and, therefore, understanding its genetic architecture is paramount for the development of effective breeding strategies. The objective of this study was to perform traditional and weighted single-step genome-wide association studies (ssGWAS and WssGWAS, respectively) for yearling temperament (YT) in North American Angus cattle using haplotypes. Approximately 266 K YT records and 70 K animals genotyped using a 50 K single nucleotide polymorphisms (SNP) panel were used. Linkage disequilibrium thresholds (LD) of 0.15, 0.50, and 0.80 were used to create the haploblocks, and the inclusion of non-LD-clustered SNPs (NCSNP) with the haplotypes in the genomic models was also evaluated. WssGWAS did not perform better than ssGWAS. Cattle YT was found to be a highly polygenic trait, with genes and quantitative trait loci (QTL) broadly distributed across the whole genome. Association studies using LD-based haplotypes should include NCSNPs and different LD thresholds to increase the likelihood of finding the relevant genomic regions affecting the trait of interest. The main candidate genes identified, i.e., ATXN10, ADAM10, VAX2, ATP6V1B1, CRISPLD1, CAPRIN1, FA2H, SPEF2, PLXNA1, and CACNA2D3, are involved in important biological processes and metabolic pathways related to behavioral traits, social interactions, and aggressiveness in cattle. Future studies should further investigate the role of these candidate genes.
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Affiliation(s)
- Andre C. Araujo
- Graduate Program in Animal Sciences, State University of Southwestern Bahia, Itapetinga 45700-000, Brazil;
- Department of Animal Science, Purdue University, West Lafayette, IN 47907, USA; (A.B.A.); (H.R.O.)
| | - Paulo L. S. Carneiro
- Department of Biology, State University of Southwest Bahia, Jequié 45205-490, Brazil;
| | - Amanda B. Alvarenga
- Department of Animal Science, Purdue University, West Lafayette, IN 47907, USA; (A.B.A.); (H.R.O.)
| | - Hinayah R. Oliveira
- Department of Animal Science, Purdue University, West Lafayette, IN 47907, USA; (A.B.A.); (H.R.O.)
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G2W1, Canada
| | - Stephen P. Miller
- American Angus Association, Angus Genetics Inc., 3201 Frederick Ave, St. Joseph, MO 64506, USA; (S.P.M.); (K.R.)
| | - Kelli Retallick
- American Angus Association, Angus Genetics Inc., 3201 Frederick Ave, St. Joseph, MO 64506, USA; (S.P.M.); (K.R.)
| | - Luiz F. Brito
- Department of Animal Science, Purdue University, West Lafayette, IN 47907, USA; (A.B.A.); (H.R.O.)
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