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Research article - Peer-reviewed, 2023

Expanding the application of haplotype-based genomic predictions to the wild: A case of antibody response against Teladorsagia circumcincta in Soay sheep

Vahedi, Seyed Milad; Salek Ardetani, Siavash; Brito, Luiz F.; Karimi, Karim; Afshari, Kian Pahlavan; Banabazi, Mohammad Hossein

Abstract

BackgroundGenomic 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, BayesC pi, Bayesian Lasso (BayesL), and BayesR] methods.ResultsThe 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.ConclusionsHaplotype 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.

Keywords

Genomic prediction; Haplotype-based models; Pseudo-SNP; Individual SNP models; GBLUP; Bayesian; Soay sheep

Published in

BMC Genomics
2023, Volume: 24, number: 1, article number: 335
Publisher: BMC

    UKÄ Subject classification

    Animal and Dairy Science

    Publication identifier

    DOI: https://doi.org/10.1186/s12864-023-09407-0

    Permanent link to this page (URI)

    https://res.slu.se/id/publ/122783