Hostname: page-component-76fb5796d-vvkck Total loading time: 0 Render date: 2024-04-25T19:16:00.980Z Has data issue: false hasContentIssue false

Inference of population structure of purebred dairy and beef cattle using high-density genotype data

Published online by Cambridge University Press:  22 June 2016

M. M. Kelleher
Affiliation:
Animal & Grassland Research and Innovation Centre, Teagasc, Moorepark, Co. Cork, Ireland School of Agriculture, Food Science, University College Dublin, Belfield, Dublin 4, Ireland
D. P. Berry*
Affiliation:
Animal & Grassland Research and Innovation Centre, Teagasc, Moorepark, Co. Cork, Ireland
J. F. Kearney
Affiliation:
Irish Cattle Breeding Federation, Bandon, Co. Cork, Ireland
S. McParland
Affiliation:
Animal & Grassland Research and Innovation Centre, Teagasc, Moorepark, Co. Cork, Ireland
F. Buckley
Affiliation:
Animal & Grassland Research and Innovation Centre, Teagasc, Moorepark, Co. Cork, Ireland
D. C. Purfield
Affiliation:
Animal & Grassland Research and Innovation Centre, Teagasc, Moorepark, Co. Cork, Ireland
Get access

Abstract

Information on the genetic diversity and population structure of cattle breeds is useful when deciding the most optimal, for example, crossbreeding strategies to improve phenotypic performance by exploiting heterosis. The present study investigated the genetic diversity and population structure of the most prominent dairy and beef breeds used in Ireland. Illumina high-density genotypes (777 962 single nucleotide polymorphisms; SNPs) were available on 4623 purebred bulls from nine breeds; Angus (n=430), Belgian Blue (n=298), Charolais (n=893), Hereford (n=327), Holstein-Friesian (n=1261), Jersey (n=75), Limousin (n=943), Montbéliarde (n=33) and Simmental (n=363). Principal component analysis revealed that Angus, Hereford, and Jersey formed non-overlapping clusters, representing distinct populations. In contrast, overlapping clusters suggested geographical proximity of origin and genetic similarity between Limousin, Simmental and Montbéliarde and to a lesser extent between Holstein, Friesian and Belgian Blue. The observed SNP heterozygosity averaged across all loci was 0.379. The Belgian Blue had the greatest mean observed heterozygosity (HO=0.389) among individuals within breed while the Holstein-Friesian and Jersey populations had the lowest mean heterozygosity (HO=0.370 and 0.376, respectively). The correlation between the genomic-based and pedigree-based inbreeding coefficients was weak (r=0.171; P<0.001). Mean genomic inbreeding estimates were greatest for Jersey (0.173) and least for Hereford (0.051). The pair-wise breed fixation index (Fst) ranged from 0.049 (Limousin and Charolais) to 0.165 (Hereford and Jersey). In conclusion, substantial genetic variation exists among breeds commercially used in Ireland. Thus custom-mating strategies would be successful in maximising the exploitation of heterosis in crossbreeding strategies.

Type
Research Article
Copyright
© The Animal Consortium 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Albrechtsen, A, Nielsen, FC and Nielsen, R 2010. Ascertainment biases in SNP chips affect measures of population divergence. Molecular Biology and Evolution 27, 25342547.CrossRefGoogle ScholarPubMed
Alexander, DH, Novembre, J and Lange, K 2009. Fast model-based estimation of ancestry in unrelated individuals. Genome Research 19, 16551664.Google Scholar
Bamshad, M and Wooding, SP 2003. Signatures of natural selection in the human genome. Nature Reviews Genetics 4, 99111.Google Scholar
Barbato, M, Orozco-terWengel, P, Tapio, M and Bruford, MW 2015. SNeP: a tool to estimate trends in recent effective population size trajectories using genome-wide SNP data. Frontiers in Genetics 6, 112.CrossRefGoogle ScholarPubMed
Beja-Pereira, A, Alexandrino, P, Bessa, I, Carretero, Y, Dunner, S, Ferrand, N, Jordana, J, Laloe, D, Moazami-Goudarzi, K, Sanchez, A and Canon, J 2003. Genetic characterization of southwestern European bovine breeds: a historical and biogeographical reassessment with a set of 16 microsatellites. Journal of Heredity 94, 243250.Google Scholar
Blott, SC, Williams, JL and Haley, CS 1998. Genetic relationships among European cattle breeds. Animal Genetics 29, 273282.Google Scholar
Buckley, F, Lopez-Villalobos, N and Heins, BJ 2014. Crossbreeding: implications for dairy cow fertility and survival. Animal 8, 122133.Google Scholar
Calus, MP 2010. Genomic breeding value prediction: methods and procedures. Animal 4, 157164.Google Scholar
Canon, J, Alexandrino, P, Bessa, I, Carleos, C, Carretero, Y, Dunner, S, Ferran, N, Garcia, D, Jordana, J, Laloe, D, Pereira, A, Sanchez, A and Moazami-Goudarzi, K 2001. Genetic diversity measures of local European beef cattle breeds for conservation purposes. Genetic Selection Evolution 33, 311332.Google Scholar
Chikhi, L, Goossens, B, Treanor, A and Bruford, MW 2004. Population genetic structure of and inbreeding in an insular cattle breed, the Jersey, and its implications for genetic resource management. Heredity 92, 396401.CrossRefGoogle Scholar
Decker, JE, McKay, SD, Rolf, MM, Kim, J, Molina Alcalá, A, Sonstegard, TS, Hanotte, O, Götherström, A, Seabury, CM, Praharani, L, Babar, ME, Correia de Almeida Regitano, L, Yildiz, MA, Heaton, MP, Liu, WS, Lei, CZ, Reecy, JM, Saif-Ur-Rehman, M, Schnabel, RD and Taylor, JF 2014. Worldwide patterns of ancestry, divergence, and admixture in domesticated cattle. PLoS Genetics 10, e1004254.Google Scholar
De Roos, APW, Hayes, BJ, Spelman, RJ and Goddard, ME 2008. Linkage disequilibrium and persitence of phase in Holsetin-Friesian, Jersey and Anjus cattle. Genetics 179, 15031512.Google Scholar
Del Bo, L, Polli, M, Longeri, M, Ceriotti, G, Looft, C, Barre-Dirie, A, Dolf, G and Zanotti, M 2001. Genetic diversity among some cattle breeds in the Alpine area. Journal of Animal Breeding and Genetics 118, 317325.CrossRefGoogle Scholar
Diamond, J 2002. Evolution, consequences and future of plant and animal domestication. Nature 418, 700707.Google Scholar
Edea, Z, Bhuiyan, MS, Dessie, T, Rothschild, MF, Dadi, H and Kim, KS 2015. Genome-wide genetic diversity, population structure and admixture analysis in African and Asian cattle breeds. Animal 9, 218226.Google Scholar
Ehiobu, NG, Goddard, ME and Taylor, JF 1990. Prediction of heterosis in crosses between inbred lines of Drosophila melanogaster . Theoretical and Applied Genetics 80, 321325.Google Scholar
Falconer, DS and Mackay, TFC 1996. Introduction to quantitative genetics, 4th edition. Pearson Education LTD, Essex, UK.Google Scholar
Felius, M 2007. Cattle breeds: an encyclopedia. Trafalgar Square Books, North Pomfret, United States.Google Scholar
Gautier, M, Laloë, D and Moazami-Goudarzi, K 2010. Insights into the genetic history of French cattle from dense SNP data on 47 worldwide breeds. PLoS One 5, e13038.Google Scholar
Gibbs, RA, Taylor, JF, van Tassell, CP, Barendse, W, Eversole, KA, Gill, CA, Green, RD, Hamernik, DL, Kappes, SM, Lien, S, Matukumalli, LK, McEwan, JC, Nazareth, LV, Schnabel, RD, Weinstock, GM, Wheeler, DA, Ajmone-Marsan, P, Boettcher, PJ, Caetano, AR, Garcia, JF, Hanotte, O, Mariani, P, Skow, LC, Sonstegard, TS, Williams, JL, Diallo, B, Hailemariam, L, Martinez, ML, Morris, CA, Silva, LO, Spelman, RJ, Mulatu, W, Zhao, K, Abbey, CA, Agaba, M, Araujo, FR, Bunch, RJ, Burton, J, Gorni, C, Olivier, H, Harrison, BE, Luff, B, Machado, MA, Mwakaya, J, Plastow, G, Sim, W, Smith, T, Thomas, MB, Valentini, A, Williams, P, Womack, J, Woolliams, JA, Liu, Y, Qin, X, Worley, KC, Gao, C, Jiang, H, Moore, SS, Ren, Y, Song, XZ, Bustamante, CD, Hernandez, RD, Muzny, DM, Patil, S, San Lucas, A, Fu, Q, Kent, MP, Vega, R, Matukumalli, A, McWilliam, S, Sclep, G, Bryc, K, Choi, J, Gao, H, Grefenstette, JJ, Murdoch, B, Stella, A, Villa-Angulo, R, Wright, M, Aerts, J, Jann, O, Negrini, R, Goddard, ME, Hayes, BJ, Bradley, DG, Barbosa da Silva, M, Lau, LP, Liu, GE, Lynn, DJ, Panzitta, F and Dodds, KG 2009. Genome-wide survey of SNP variation uncovers the genetic structure of cattle breeds. Science 324, 528532.Google Scholar
Harris, BL and Kolver, ES 2001. Review of Holsteinization of intensive pastoral dairy farming in New Zealand. Journal of Dairy Science 84 (suppl.), E56E61.Google Scholar
Harris, BL, Winkelman, AM and Johnson, DE 2014. Across-breed genomic prediction in dairy cattle. Proceedings of the 10th World Congress of Genetics Applied to Livestock Production. Livestock Improvement Corporation, Hamilton, New Zealand.Google Scholar
Hawksworth, DL 1995. Biodiversity: measurement and estimation. Chapman and Hall, London, UK.Google Scholar
Hayes, BJ, Bowman, PJ, Chamberlain, AJ and Goddard, ME 2009. Invited review: genomic selection in dairy cattle: progress and challenges. Journal of Dairy Science 92, 433443.Google Scholar
Hayes, BJ, Visscher, PM, McPartlan, HC and Goddard, ME 2003. Novel multilocus measure of linkage disequilibrium to estimate past effective population size. Genome Research 13, 635643.CrossRefGoogle ScholarPubMed
Horan, B, Dillon, P, Faverdin, P, Delaby, L, Buckley, F and Rath, M 2005. The interaction of strain of Holstein-Friesian cows and pasture-based feed systems on milk yield, body weight and body condition score. Journal of Diary Science 88, 12311243.Google Scholar
Jolliffe, IT 2002. Principal component analysis Springer. Springer-Verlag, New York.Google Scholar
Kahn, L and Cottle, D 2014. Beef cattle production and trade. CSIRO Publishing, Victoria, Australia.Google Scholar
Kantanen, J, Olsaker, I, Holm, L-E, Lien, S, Vilkki, J, Brusgaard, K, Eythorsdottir, E, Danell, B and Adalsteinsson, S 2000. Genetic diversity and population structure of 20 north European cattle breeds. Journal of Heredity 91, 446457.Google Scholar
Kijas, JW, Lenstra, JA, Hayes, B, Boitard, S, Porto Neto, LR, San Cristobal, M, Servin, B, McCulloch, R, Whan, V, Gietzen, K, Paiva, S, Barendse, W, Ciani, E, Raadsma, H, McEwan, J and Dalrymple, B, other members of the International Sheep Genomics C 2012. Genome-wide analysis of the world’s sheep breeds reveals high levels of historic mixture and strong recent selection. PLoS Biology 10, e1001258.Google Scholar
Kim, E and Rothschild, MF 2014. Genomic adaptation of admixed dairy cattle in East Africa. Frontiers in Genetics 5, 443453.Google Scholar
Kristensen, TN, Hoffmann, AA, Pertoldi, C and Stronen, AV 2015. What can livestock breeders learn from conservation genetics and vice versa? Frontiers in Genetics 6, 112.CrossRefGoogle ScholarPubMed
Kuehn, LA, Keele, JW, Bennett, GL, McDaneld, TG, Smith, TP, Snelling, WM, Sonstegard, TS and Thallman, RM 2011. Predicting breed composition using breed frequencies of 50 000 markers from the US Meat Animal Research Center 2000 Bull Project. Journal of Animal Science 89, 17421750.CrossRefGoogle Scholar
LIC 2015. Livestock Improvement Corporation. Retrieved on 17 April 2015 from www.lic.co.nz Google Scholar
Lewis, J, Abas, Z, Dadousis, C, Lykidis, D, Paschou, P and Drineas, P 2011. Tracing cattle breeds with principal components analysis ancestry informative SNPs. PLoS One 6, e18007.Google Scholar
MacHugh, DE, Loftus, RT, Bradley, DG, Sharp, PM and Cunningham, P 1994. Microsatellite DNA variation within and among European cattle breeds. Proceedings of the Royal Society: Biological Sciences 256, 2531.Google Scholar
MacHugh, DE, Loftus, RT, Cunningham, P and Bradley, DG 1998. Genetic structure of seven European cattle breeds assessed using 20 microsatellite markers. Animal Genetics 29, 333340.Google Scholar
MacHugh, DE, Shriver, MD, Loftus, RT, Cunningham, P and Bradley, DG 1997. Microsatellite DNA variation and the evolution, domestication and phylogeography of taurine and zebu cattle (Bos taurus and Bos indicus). Genetics 146, 10711086.Google Scholar
Matukumalli, LK, Lawley, CT, Schnabel, RD, Taylor, JF, Allan, MF, Heaton, MP, O’Connell, J, Moore, SS, Smith, TPL, Sonstegard, TS and Van Tassell, CP 2009. Development and characterization of a high density SNP genotyping assay for cattle. PLoS One 4, e5350.Google Scholar
McKay, SD, Schnabel, RD, Murdoch, BM, Matukumalli, LK, Aerts, J, Coppieters, W, Crews, D, Dias Neto, E, Gill, CA, Gao, C, Mannen, H, Wang, Z, Van Tassell, CP, Williams, JL, Taylor, JF and Moore, SS 2008. An assessment of population structure in eight breeds of cattle using a whole genome SNP panel. BMC Genetics 9, 37.Google Scholar
McParland, S, Kearney, JF, Rath, M and Berry, DP 2007a. Inbreeding effects on milk production, calving performance, fertility, and conformation in Irish Holstein-Friesians. Journal of Dairy Science 90, 44114419.Google Scholar
McParland, S, Kearney, JF, Rath, M and Berry, DP 2007b. Inbreeding trends and pedigree analysis of Irish dairy and beef cattle populations. Journal of Animal Science 85, 322331.Google Scholar
Melka, MG and Schenkel, FS 2012. Analysis of genetic diversity in Brown Swiss, Jersey and Holstein populations using genome-wide single nucleotide polymorphism markers. BMC Research Notes 5, 161.CrossRefGoogle ScholarPubMed
Meuwissen, THE and Luo, Z 1992. Computing inbreeding coefficients in large populations. Genetic Selection Evolution 24, 305313.Google Scholar
Miglior, F, Muir, BL and Van Doormaal, BJ 2005. Selection indices in Holstein cattle of various countries. Journal of Dairy Science 88, 12551263.Google Scholar
Nei, M 1978. Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics 89, 583590.Google Scholar
Ohta, T and Kimura, M 1971. Linkage disequilibrium between two segregating nucleotide sites under the steady flux of mutations in a finite population. Genetics 68, 571580.Google Scholar
Paradis, E, Claude, J and Strimmer, K 2004. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289290.Google Scholar
Patterson, N, Price, AL and Reich, D 2006. Population structure and eigenanalysis. PLoS Genetics 2, 20742093.Google Scholar
Penasa, M, López-Villalobos, N, Evans, RD, Cromie, AR, Dal Zotto, R and Cassandro, M 2010. Crossbreeding effects on milk yield traits and calving interval in spring-calving dairy cows. Journal of Animal Breeding and Genetics 127, 300307.Google Scholar
Porter, V 1991. Cattle – a handbook to the breeds of the world. Facts on File Inc, New York, USA.Google Scholar
Price, AL, Patterson, NJ, Plenge, RM, Weinblatt, ME, Shadick, NA and Reich, D 2006. Principal components analysis corrects for stratification in genome-wide association studies. Nature Genetics 38, 904909.Google Scholar
Pruett, CL and Winker, K 2008. The effects of sample size on population genetic diversity estimates in song sparrows Melospiza melodia . Journal of Avian Biology 39, 252256.Google Scholar
Purcell, S, Neale, B, Todd-Brown, K, Thomas, L, Ferreira, MA, Bender, D, Maller, J, Sklar, P, de Bakker, PI, Daly, MJ and Sham, PC 2007. PLINK: a tool set for whole-genome association and population-based linkage analyses. American Journal of Human Genetics 81, 559575.CrossRefGoogle ScholarPubMed
Pryce, JE, Haile-Mariam, M, Goddard, ME and Hayes, BJ 2014. Identification of genomic regions associated with inbreeding depression in Holstein and Jersey dairy cattle. Genetics Selection Evolution 46, 71.Google Scholar
Purfield, DC, Berry, DP, McParland, S and Bradley, DG 2012. Runs of homozygosity and population history in cattle. BMC Genetics 13, 7080.Google Scholar
Sørensen, MK, Norberg, E, Pedersen, J and Christensen, LG 2008. Invited review: crossbreeding in dairy cattle: a Danish perspective. Journal of Dairy Science 91, 41164128.Google Scholar
Stachowicz, K, Sargolzaei, M, Miglior, F and Schenkel, FS 2011. Rates of inbreeding and genetic diversity in Canadian Holstein and Jersey cattle. Journal of Dairy Science 94, 51605175.Google Scholar
Thompson, JR, Everett, RW and Hammerschmidt, NL 2000. Effects of inbreeding on production and survival in Holsteins. Journal of Dairy Science 83, 18561864.Google Scholar
Tracy, CA and Widom, H 2009. The distributions of random matrix theory and their applications. In New trends in mathematical physics (ed. V Sidoravicius), pp. 753765. Springer, The Netherlands.Google Scholar
Wall, E, Brotherstone, S, Kearney, JF, Woolliams, JA and Coffey, MP 2005. Impact of nonadditive genetic effects in the estimation of breeding values for fertility and correlated traits. Journal of Dairy Science 88, 376385.Google Scholar
Weigel, KA 2001. Controlling inbreeding in modern breeding programs. Journal of Dairy Science 84, E177E184.Google Scholar
Weir, BS and Cockerham, CC 1984. Esti mating F-statistics for the analysis of population structure. Evolution 38, 13581370.Google Scholar
Weir, BS and Hill, WG 2002. Estimating F-statistics. Annual Review of Genetics 36, 721750.Google Scholar
Williams, JL, Aguilar, I, Rekaya, R and Bertrand, JK 2010. Estimation of breed and heterosis effects for growth and carcass traits in cattle using published crossbreeding studies. Journal of Animal Science 88, 460466.Google Scholar
Xu, L, Bickhart, DM, Cole, JB, Schroeder, SG, Song, J, Tassell, CPV, Sonstegard, TS and Liu, GE 2015. Genomic signatures reveal new evidences for selection of important traits in domestic cattle. Molecular Biology and Evolution 32, 711725.Google Scholar
Zhao, F, McParland, S, Kearney, JF, Du, L and Berry, DP 2015. Detection of selection signatures in dairy and beef cattle using high-density genomic information. Genetics Selection Evolution 47, 49.Google Scholar
Supplementary material: PDF

Kelleher supplementary material

Figure S1

Download Kelleher supplementary material(PDF)
PDF 103.2 KB
Supplementary material: PDF

Kelleher supplementary material

Figure S2

Download Kelleher supplementary material(PDF)
PDF 212.5 KB
Supplementary material: PDF

Kelleher supplementary material

Figure S3

Download Kelleher supplementary material(PDF)
PDF 41.2 KB