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Genome-wide associations for fertility traits in Holstein–Friesian dairy cows using data from experimental research herds in four European countries

Published online by Cambridge University Press:  30 January 2012

D. P. Berry*
Affiliation:
Animal & Grassland Research and Innovation Centre, Teagasc, Moorepark, Co. Cork, Ireland
J. W. M. Bastiaansen
Affiliation:
Animal Breeding and Genomics Centre, Wageningen University, 6708 WD Wageningen, The Netherlands
R. F. Veerkamp
Affiliation:
Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, 8200 AB Lelystad, The Netherlands
S. Wijga
Affiliation:
Animal Breeding and Genomics Centre, Wageningen University, 6708 WD Wageningen, The Netherlands
E. Wall
Affiliation:
Sustainable Livestock Systems Group, Scottish Agricultural College, EH25 9RG Midlothian, United Kingdom
B. Berglund
Affiliation:
Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, PO Box 7023, S-75007 Uppsala, Sweden
M. P. L. Calus
Affiliation:
Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, 8200 AB Lelystad, The Netherlands
*
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Abstract

Genome-wide association studies for difficult-to-measure traits are generally limited by the sample population size with accurate phenotypic data. The objective of this study was to utilise data on primiparous Holstein–Friesian cows from experimental farms in Ireland, the United Kingdom, the Netherlands and Sweden to identify genomic regions associated with traditional measures of fertility, as well as a fertility phenotype derived from milk progesterone profiles. Traditional fertility measures investigated were days to first heat, days to first service, pregnancy rate to first service, number of services and calving interval (CI); post-partum interval to the commencement of luteal activity (CLA) was derived using routine milk progesterone assays. Phenotypic and genotypic data on 37 590 single nucleotide polymorphisms (SNPs) were available for up to 1570 primiparous cows. Genetic parameters were estimated using linear animal models, and univariate and bivariate genome-wide association analyses were undertaken using Bayesian stochastic search variable selection performed using Gibbs sampling. Heritability estimates of the traditional fertility traits varied from 0.03 to 0.16; the heritability for CLA was 0.13. The posterior quantitative trait locus (QTL) probabilities, across the genome, for the traditional fertility measures were all <0.021. Posterior QTL probabilities of 0.060 and 0.045 were observed for CLA on SNPs each on chromosome 2 and chromosome 21, respectively, in the univariate analyses; these probabilities increased when CLA was included in the bivariate analyses with the traditional fertility traits. For example, in the bivariate analysis with CI, the posterior QTL probability of the two aforementioned SNPs were 0.662 and 0.123. Candidate genes in the vicinity of these SNPs are discussed. The results from this study suggest that the power of genome-wide association studies in cattle may be increased by sharing of data and also possibly by using physiological measures of the trait under investigation.

Type
Full Paper
Copyright
Copyright © The Animal Consortium 2012

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References

Berry, DP, Buckley, F, Dillon, P, Evans, RD, Rath, M, Veerkamp, RF 2003. Genetic relationships among body condition score, body weight, milk yield and fertility in dairy cows. Journal of Dairy Science 86, 21932204.CrossRefGoogle ScholarPubMed
Berry, DP, Evans, RD, Mc Parland, S 2011. Evaluation of bull fertility in dairy and beef cattle using cow field data. Theriogenology 75, 172181.CrossRefGoogle ScholarPubMed
Calus, MPL, Meuwissen, THE, de Roos, APW, Veerkamp, RF 2008. Accuracy of genomic selection using different methods to define haplotypes. Genetics 178, 553561.CrossRefGoogle ScholarPubMed
Calus, MPL, Mulder, HA, Veerkamp, RF 2011a. Estimating genomic breeding values and detecting QTL using univariate and bivariate models. BMC Proceedings 5 (suppl. 3), S5.CrossRefGoogle ScholarPubMed
Calus, MPL, Mulder, HA, Bastiaansen, JWM 2011b. Identification of Mendelian inconsistencies between SNP and pedigree information of sibs. Genetics Selection Evolution 43, 34.CrossRefGoogle ScholarPubMed
Calus, MPL, Veerkamp, RF 2011. Accuracy of multi-trait genomic selection using different methods. Genetics, Selection, Evolution 43, 26.CrossRefGoogle ScholarPubMed
Daetwyler, H, Villanueva, B, Woolliams, JA 2008. Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLoS One 3, 18.CrossRefGoogle ScholarPubMed
Felty, Q, Porther, N 2008. Estrogen-induced redox sensitive Id3 signaling controls the growth of vascular cells. Atherosclerosis 198, 1221.CrossRefGoogle ScholarPubMed
Gilmour, AR, Cullis, BR, Welham, SJ, Thompson, R 2009. ASREML reference manual. New South Wales Agriculture, Orange Agricultural Institute, Orange, NSW, Australia.Google Scholar
Höglund, JK, Buitenhuis, AJ, Guldbrandtsen, B, Su, G, Thomsen, B, Lund, MS 2009. Overlapping chromosomal regions for fertility traits and production traits in the Danish Holstein population. Journal of Dairy Science 92, 57125719.CrossRefGoogle ScholarPubMed
Holmberg, M, Andersson-Eklund, L 2006. Quantitative trait loci affecting fertility and calving traits in Swedish dairy cattle. Journal of Dairy Science 89, 36643671.CrossRefGoogle ScholarPubMed
Horan, B, Mee, JF, O'Connor, P, Rath, M, Dillon, P 2005. The effect of strain of Holstein–Friesian cow and feeding system on postpartum ovarian function, animal production and conception rate to first service. Theriogenology 63, 950961.CrossRefGoogle ScholarPubMed
Huang, W, Kirkpatrick, BW, Rosa, GJM, Khatib, K 2010. A genome-wide association study using selective DNA pooling identifies candidate markers for fertility in Holstein cattle. Animal Genetics 41, 570578.CrossRefGoogle ScholarPubMed
Jeffreys, H 1961. The theory of probability, 3rd edition. Oxford University Press, United Kindom.Google Scholar
Kass, RE, Raftery, AE 1995. Bayes factors. Journal of American Statistics Association 90, 773795.CrossRefGoogle Scholar
McKay, SD, Schnabel, RD, Murdoch, BM, Matukumalli, LK, Aerts, J, Coppieters, W, Crews, D, Dias, E, Neto, K, Gill, CA, Gao, C, Mannen, H, Stothard, P, Wang, Z, Van Tassell, CP, Williams, JL, Taylor, JF, Moore, SS 2007. Whole genome linkage disequilibrium maps in cattle. BMC Genetics 8, 74.CrossRefGoogle ScholarPubMed
Meuwissen, THE, Goddard, ME 2004. Mapping multiple QTL using linkage disequilibrium and linkage analysis information and multitrait data. Genetics, Selection, Evolution 36, 261279.CrossRefGoogle ScholarPubMed
Miglior, F, Muir, BL, Van Doormaal, BJ 2005. Selection indices in Holstein cattle of various countries. Journal of Dairy Science 88, 12551263.CrossRefGoogle ScholarPubMed
Petersson, K-J, Strandberg, E, Gustafsson, H, Berglund, B 2006. Environmental effects on progesterone profile measures of dairy cow fertility. Animal Reproduction Science 91, 201214.CrossRefGoogle ScholarPubMed
Petersson, K-J, Berglund, B, Strandberg, E, Gustafsson, H, Flint, APF, Wooliams, JA, Royal, MD 2007. Genetic analysis of postpartum measures of luteal activity in dairy cows. Journal of Dairy Science 90, 427434.CrossRefGoogle ScholarPubMed
Pollot, GE, Coffey, MP 2008. The effect of genetic merit and production system on dairy cow fertility, measured using progesterone profiles and on-farm recording. Journal of Dairy Science 91, 36493660.CrossRefGoogle Scholar
Pryce, JE, Veerkamp, RF 2001. The incorporation of fertility indices in genetic improvement programmes. In Fertility in the high-producing dairy cow (ed. M.G. Diskin), 223236. British Society of Animal Science Occasional Publication No. 26, Edinburgh, Scotland.Google Scholar
Pryce, JE, Bolormaa, S, Chamberlain, AJ, Bowman, PJ, Savin, K, Goddard, ME, Hayes, BJ 2010. A validation genome-wide association study in 2 dairy cattle breeds for milk production and fertility traits using variable length haplotypes. Journal of Dairy Science 93, 33313345.CrossRefGoogle ScholarPubMed
Royal, MD, Darwash, AO, Flint, APF, Webb, R, Woolliams, JA, Lamming, GE 2000. Declining fertility in dairy cattle: changes in traditional and endocrine parameters of fertility. Animal Science 70, 487501.CrossRefGoogle Scholar
Royal, MD, Flint, APF, Woolliams, JA 2002. Genetic and phenotypic relationships among endocrine and traditional fertility traits and production traits in Holstein–Friesian dairy cows. Journal of Dairy Science 85, 958967.CrossRefGoogle ScholarPubMed
Sahana, G, Guldbrandtsen, B, Bendixen, C, Lund, MS 2010. Genomic-wide association mapping for female fertility traits in Danish and Swedish Holstein cattle. Animal Genetics 41, 579588.CrossRefGoogle Scholar
Schulman, NF, Sahana, G, Iso-Touro, T, McKay, SD, Schnabel, RD, Lund, MS, Taylor, JF, Virta, J, Vikki, JH 2011. Mapping of fertility traits in finnish Ayrshire by genomie-wide association analysis. Animal Genetics, doi:10.1111/j.1365-2052.2010.02149.x.CrossRefGoogle ScholarPubMed
Veerkamp, RF, Oldenbroek, JK, Van Der Gaast, HJ, Van Der Werf, JHJ 2000. Genetic correlations between days until start of luteal activity and milk yield, energy balance, and live weights. Journal of Dairy Science 83, 577583.CrossRefGoogle ScholarPubMed
Venditti, JJ, Donigan, KA, Bean, BS 2007. Crypticity and functional distribution of the membrane associated alpha-l-fucosidase of human sperm. Molecular Reproduction and Development 74, 758766.CrossRefGoogle ScholarPubMed
Verbyla, KL, Hayes, BJ, Bowman, PJ, Goddard, ME 2009. Accuracy of genomic selection using stochastic search variable selection in Australian Holstein Friesian dairy cattle. Genetics Research 91, 307311.CrossRefGoogle ScholarPubMed
Wall, E, Brotherstone, S, Woolliams, JA, Banos, G, Coffey, MP 2003. Genetic evaluation of fertility using direct and correlated traits. Journal of Dairy Science 86, 40934102.CrossRefGoogle ScholarPubMed
Weller, JI, Kashi, Y, Soller, M 1990. Power of daughter and granddaughter designs for determining linkage between marker loci and quantitative trait loci in dairy-cattle. Journal of Dairy Science 73, 25252537.CrossRefGoogle ScholarPubMed