Breed-adjusted genomic relationship matrices as a method to account for population stratification in multibreed populations of tropically adapted beef heifers
Christie L. Warburton
A F , Roy Costilla A , Bailey N. Engle A , Nicholas J. Corbet B , Jack M. Allen C , Geoffry Fordyce A , Michael R. McGowan D , Brian M. Burns E and Ben J. Hayes A
A Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St Lucia, Qld 4067, Australia.
B Formerly Central Queensland University, School of Health, Medical and Applied Sciences, Rockhampton, Qld 4700, Australia.
C Agricultural Business Research Institute, University of New England, Armidale, NSW 2350, Australia.
D The University of Queensland, School of Veterinary Science, St Lucia, Qld 4067, Australia.
E Formerly Department of Agriculture and Fisheries, Rockhampton, Qld 4700, Australia.
F Corresponding author. Email: c.warburton@uq.edu.au
Animal Production Science - https://doi.org/10.1071/AN21057
Submitted: 9 February 2021 Accepted: 22 April 2021 Published online: 7 July 2021
Abstract
Context: Beef cattle breeds in Australia can broadly be broken up into two subspecies, namely, Bos indicus and Bos taurus. Due to the time since divergence between the subspecies, it is likely that mutations affecting quantitative traits have developed independently in each.
Aims: We hypothesise that this will affect the prediction accuracy of genomic selection of admixed and composite populations that include both ancestral subspecies. Our study investigates methods to quantify population stratification in a multibreed population of tropically adapted heifers, with the aim of improving prediction accuracy of genomic selection for reproductive maturity score.
Methods: We used genotypes and reproductive maturity phenotypes from 3695 tropically adapted heifers from three purebred populations, namely, Brahman, Santa Gertrudis and Droughtmaster. Two of these breeds, Santa Gertrudis and Droughtmaster, are stabilised composites of varying B. indicus × B. taurus ancestry, and the third breed, Brahman, has predominately B. indicus ancestry. Genotypes were imputed to three marker-panel densities and population stratification was accounted for in genomic relationship matrices by using breed-specific allele frequencies when calculating the genomic relationships among animals. Prediction accuracy and bias were determined using a five-fold cross validation of randomly selected multibreed cohorts.
Key Results: Our results showed that the use of breed-adjusted genomic relationship matrices did not improve either prediction accuracy or bias for a lowly heritable trait such as reproductive maturity score. However, using breed-adjusted genomic relationship matrices allowed the capture of a higher proportion of additive genetic effects when estimating variance components.
Conclusions: These findings suggest that, despite seeing no improvement in prediction accuracy, it may still be beneficial to use breed-adjusted genomic relationship matrices in multibreed populations to improve the estimation of variance components.
Implications: As such, genomic evaluations using breed-adjusted genomic relationship matrices may be beneficial in multibreed populations.
Keywords: adjusted genomic relationship matrix, allele frequency, genomic selection, admixed cattle population.
References
Achilli A, Olivieri A, Pellecchia M, Uboldi C, Colli L, Al-Zahery N, Accetturo M, Pala M, Kashani BH, Perego UA, Battaglia V, Fornarino S, Kalamati J, Houshmand M, Negrini R, Semino O, Richards M, Macaulay V, Ferretti L, Bandelt H-J, Ajmone-Marsan P, Torroni A (2008) Mitochondrial genomes of extinct aurochs survive in domestic cattle. Current Biology 18, R157–R158.| Mitochondrial genomes of extinct aurochs survive in domestic cattle.Crossref | GoogleScholarGoogle Scholar | 18302915PubMed |
Bezanson J, Edelman A, Karpinski S, Shah VB (2017) Julia: a Fresh Approach to Numerical Computing. SIAM Review 59, 65–98.
| Julia: a Fresh Approach to Numerical Computing.Crossref | GoogleScholarGoogle Scholar |
Bolormaa S, Hayes BJ, Hawken RJ, Zhang Y, Reverter A, Goddard ME (2011) Detection of chromosome segments of zebu and taurine origin and their effect on beef production and growth. Journal of Animal Science 89, 2050–2060.
| Detection of chromosome segments of zebu and taurine origin and their effect on beef production and growth.Crossref | GoogleScholarGoogle Scholar | 21297063PubMed |
Bolormaa S, Pryce JE, Kemper KE, Hayes BJ, Zhang Y, Tier B, Barendse W, Reverter A, Goddard ME (2013) Detection of quantitative trait loci in Bos indicus and Bos taurus cattle using genome-wide association studies. Genetics, Selection, Evolution 45, 43
| Detection of quantitative trait loci in Bos indicus and Bos taurus cattle using genome-wide association studies.Crossref | GoogleScholarGoogle Scholar | 24168700PubMed |
Burns BM, Corbet NJ, Allen JM, Laing A, Sullivan MT (2016) Next Gen Beef Breeding Strategies for the Northern Australian Beef Industry: Final Report. University of Queensland, Saint Lucia, Brisbane, Qld, Australia.
Calus MPL, Huang H, Wientjes YCJ, ten Napel J, Bastiaansen JWM, Price MD, Veerkamp RF, Vereijken A, Windig JJ (2014) (A)cross-breed genomic prediction. In ‘Proceedings of the 10th Congress of Genetics Applied to Livestock Production’. (Vancouver, Canada) Available at https://edepot.wur.nl/320500
Davis GP (1993) Genetic parameters for tropical beef cattle in northern Australia: a review. Australian Journal of Agricultural Research 44, 179–198.
| Genetic parameters for tropical beef cattle in northern Australia: a review.Crossref | GoogleScholarGoogle Scholar |
Duenk P, Calus MPL, Wientjes YCJ, Breen VP, Henshall JM, Hawken R, Bijma P (2019) Validation of genomic predictions for body weight in broilers using crossbred information and considering breed-of-origin of alleles. Genetics, Selection, Evolution 51, 38
| Validation of genomic predictions for body weight in broilers using crossbred information and considering breed-of-origin of alleles.Crossref | GoogleScholarGoogle Scholar | 31286857PubMed |
Engle BN, Corbet NJ, Allen JM, Laing AR, Fordyce G, McGowan MR, Burns BM, Lyons RE, Hayes BJ (2019) Multivariate genomic predictions for age at puberty in tropically adapted beef heifers. Journal of Animal Science 97, 90–100.
| Multivariate genomic predictions for age at puberty in tropically adapted beef heifers.Crossref | GoogleScholarGoogle Scholar | 30481306PubMed |
Gurman PM, Bunter KL, Boerner V, Swan AA, Brown DJ (2019) Adjusting the genomic relationship matrix for breed differences in single step genomic BLUP analyses. In ‘Proceedings of the 23rd Association for the Advancement of Animal Breeding and Genetics’. pp. 254–257. (Armidale, NSW, Australia)
Hayes BJ, Corbet NJ, Allen JM, Laing AR, Fordyce G, Lyons R, McGowan MR, Burns BM (2019) Towards multi-breed genomic evaluations for female fertility of tropical beef cattle. Journal of Animal Science 97, 55–62.
| Towards multi-breed genomic evaluations for female fertility of tropical beef cattle.Crossref | GoogleScholarGoogle Scholar | 30371787PubMed |
Hiendleder S, Lewalski H, Janke A (2008) Complete mitochondrial genomes of Bos taurus and Bos indicus provide new insights into intra-species variation, taxonomy and domestication. Cytogenetic and Genome Research 120, 150–156.
| Complete mitochondrial genomes of Bos taurus and Bos indicus provide new insights into intra-species variation, taxonomy and domestication.Crossref | GoogleScholarGoogle Scholar | 18467841PubMed |
Hofer A (1998) Variance component estimation in animal breeding: a review. Journal of Animal Breeding and Genetics 115, 247–265.
| Variance component estimation in animal breeding: a review.Crossref | GoogleScholarGoogle Scholar |
Ibánẽz-Escriche N, Fernando RL, Toosi A, Dekkers JCM (2009) Genomic selection of purebreds for crossbred performance. Genetics, Selection, Evolution 41, 12
| Genomic selection of purebreds for crossbred performance.Crossref | GoogleScholarGoogle Scholar | 19284703PubMed |
Kemper KE, Hayes BJ, Daetwyler HD, Goddard ME (2015) How old are quantitative trait loci and how widely do they segregate? Journal of Animal Breeding and Genetics 132, 121–134.
| How old are quantitative trait loci and how widely do they segregate?Crossref | GoogleScholarGoogle Scholar | 25823838PubMed |
Koufariotis L, Hayes BJ, Kelly M, Burns BM, Lyons R, Stothard P, Chamberlain AJ, Moore S (2018) Sequencing the mosaic genome of Brahman cattle identifies historic and recent introgression including polled. Scientific Reports 8, 17761
| Sequencing the mosaic genome of Brahman cattle identifies historic and recent introgression including polled.Crossref | GoogleScholarGoogle Scholar | 30531891PubMed |
Lopes MS, Bovenhuis H, Hidalgo AM, Van Arendonk JA, Knol EF, Bastiaansen JW (2017) Genomic selection for crossbred performance accounting for breed-specific effects. Genetics, Selection, Evolution 49, 51
| Genomic selection for crossbred performance accounting for breed-specific effects.Crossref | GoogleScholarGoogle Scholar | 28651536PubMed |
Lourenco DAL, Tsuruta S, Fragomeni BO, Chen CY, Herring WO, Misztal I (2016) Crossbreed evaluations in single-step genomic best linear unbiased predictor using adjusted realized relationship matrices. Journal of Animal Science 94, 909–919.
| Crossbreed evaluations in single-step genomic best linear unbiased predictor using adjusted realized relationship matrices.Crossref | GoogleScholarGoogle Scholar |
Makgahlela ML, Strandén I, Nielsen US, Sillanpää MJ, Mäntysaari EA (2013) The estimation of genomic relationships using breedwise allele frequencies among animals in multibreed populations. Journal of Dairy Science 96, 5364–5375.
| The estimation of genomic relationships using breedwise allele frequencies among animals in multibreed populations.Crossref | GoogleScholarGoogle Scholar | 23769355PubMed |
Moghaddar N, Swan AA, van Der Werf JH (2014) Comparing genomic prediction accuracy from purebred, crossbred and combined purebred and crossbred reference populations in sheep. Genetics, Selection, Evolution 46, 58
| Comparing genomic prediction accuracy from purebred, crossbred and combined purebred and crossbred reference populations in sheep.Crossref | GoogleScholarGoogle Scholar | 25927315PubMed |
Porto-Neto LR, Sonstegard TS, Liu GE, Bickhart DM, Da Silva MVB, Machado MA, Utsunomiya YT, Garcia JF, Gondro C, Van Tassell CP (2013) Genomic divergence of zebu and taurine cattle identified through high-density SNP genotyping. BMC Genomics 14, 876
| Genomic divergence of zebu and taurine cattle identified through high-density SNP genotyping.Crossref | GoogleScholarGoogle Scholar | 24330634PubMed |
R Core Team (2020) R: a language and environment for statistical computing. (R Foundation for Statistical Computing: Vienna, Austria) Available at https://www.R-project.org/
Sargolzaei M, Chesnais J, Schenkel F (2014) A new approach for efficient genotype imputation using information from relatives. BMC Genomics 15, 478
| A new approach for efficient genotype imputation using information from relatives.Crossref | GoogleScholarGoogle Scholar | 24935670PubMed |
Sevillano CA, Bovenhuis H, Calus MPL (2019) Genomic evaluation for a crossbreeding system implementing breed-of-origin for targeted markers. Frontiers in Genetics Livestock Genomics 10, 418
| Genomic evaluation for a crossbreeding system implementing breed-of-origin for targeted markers.Crossref | GoogleScholarGoogle Scholar |
VanRaden PM (2008) Efficient methods to compute genomic predictions. Journal of Dairy Science 91, 4414–4423.
| Efficient methods to compute genomic predictions.Crossref | GoogleScholarGoogle Scholar | 18946147PubMed |
Warburton CL, Engle BN, Ross EM, Costilla R, Moore SS, Corbet NJ, Allen JM, Laing AR, Fordyce G, Lyons RE, McGowan MR, Burns BM, Hayes BJ (2020) Use of whole-genome sequence data and novel genomic selection strategies to improve selection for age at puberty in tropically-adapted beef heifers. Genetics, Selection, Evolution 52, 28
| Use of whole-genome sequence data and novel genomic selection strategies to improve selection for age at puberty in tropically-adapted beef heifers.Crossref | GoogleScholarGoogle Scholar | 32460805PubMed |
Wickham H (2016) ‘ggplot2: Elegant Graphics for Data Analysis.’ (Springer-Verlag: New York, NY, USA) Available at https://ggplot2.tidyverse.org
Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyholt DR, Madden PA, Heath AC, Martin NG, Montgomery GW, Goddard ME, Visscher PM (2010) Common SNPs explain a large proportion of the heritability for human height. Nature Genetics 42, 565–569.
| Common SNPs explain a large proportion of the heritability for human height.Crossref | GoogleScholarGoogle Scholar | 20562875PubMed |
Yang J, Lee SH, Goddard ME, Visscher PM (2011) GCTA: a Tool for Genome-wide Complex Trait Analysis. American Journal of Human Genetics 88, 76–82.
| GCTA: a Tool for Genome-wide Complex Trait Analysis.Crossref | GoogleScholarGoogle Scholar | 21167468PubMed |
