Hostname: page-component-848d4c4894-8kt4b Total loading time: 0 Render date: 2024-06-20T13:25:31.276Z Has data issue: false hasContentIssue false

Incorporation of genotype effects into animal model evaluations when only a small fraction of the population has been genotyped

Published online by Cambridge University Press:  01 January 2009

E. Baruch
Affiliation:
Faculty of Agriculture, Hebrew University of Jerusalem, Rehovot 76100, Israel
J. I. Weller*
Affiliation:
Institute of Animal Sciences, ARO, The Volcani Center, Bet Dagan 50250, Israel
Get access

Abstract

The method of Israel and Weller (Estimation of candidate gene effects in dairy cattle populations. Journal of Dairy Science 1998, 81, 1653–1662) to estimate quantitative trait locus (QTL) effects when only a small fraction of the population was genotyped was investigated by simulation. The QTL effect was underestimated in all cases, but bias was greater for extreme allelic frequencies, and increased with the number of generations included in the simulations. Apparently, as the fraction of animals with inferred genotypes increases, the genotype probabilities tend to ‘mimic’ the effect of relationships. Unbiased estimates of QTL effects were derived by a modified ‘cow model’ without the inclusion of the relationship matrix on simulated data, even though only a small fraction of the population was genotyped. This method yielded empirically unbiased estimates for the effects of the genes DGAT1 and ABCG2 on milk production traits in the Israeli Holstein population. Based on these results, an efficient algorithm for marker-assisted selection in dairy cattle was proposed. Quantitative trait loci effects are estimated and subtracted from the cows’ records. Genetic evaluations are then computed for the adjusted records. Animals are then selected based on the sum of their polygenic genetic evaluations and QTL effects. This scheme differs from a traditional dairy cattle breeding scheme in that all bull calves were considered candidates for selection. At year 10, total genetic gain was 20% greater by the proposed algorithm as compared to the selection based on a standard animal model for a locus with a substitution effect of 0.5 phenotypic standard deviations. The proposed method is easy to apply, and all required software are ‘on the shelf.’ It is only necessary to genotype breeding males, which are a very small fraction of the entire population. The method is flexible with respect to the model used for routine genetic evaluation. Any number of genetic markers can be easily incorporated into the algorithm, and the reduction in genetic gain due to incorrect QTL determination is minimal.

Type
Full Paper
Copyright
Copyright © The Animal Consortium 2008

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

Baruch, E, Weller, JI 2008. Incorporation of discrete genotype effects for multiple genes into animal model evaluations when only a small fraction of the population has been genotyped. Journal of Dairy Science 91 (in press).CrossRefGoogle Scholar
Bennewitz J, Reinsch N, Thomsen H, Szyda J, Reinhart F, Lui Z, Kuhn Ch, Tuchscherer A, Schwerin M, Weimann C, Erhardt G and Kalm E 2003. Marker assisted selection in German Holstein dairy cattle breeding: outline of the program and marker assisted breeding value estimation. In the fifty-fourth annual meeting of the European Association of Animal Production. Session G1.9, Rome, Italy.Google Scholar
Bennewitz, J, Reinsch, N, Reinhardt, F, Liu, Z, Kalm, E 2004. Top down preselection using marker assisted estimates of breeding values in dairy cattle. Journal of Animal Breeding and Genetics 121, 307318.CrossRefGoogle Scholar
Brascamp, EW, van Arendonk, JAM, Groen, AF 1993. Economic appraisal of the utilization of genetic markers in dairy cattle breeding. Journal of Dairy Science 76, 12041213.Google Scholar
Cohen-Zinder, M, Seroussi, E, Larkin, DM, Loor, JJ, Everts-van der Wind, A, Lee, JH, Drackley, JK, Band, MR, Hernandez, AG, Shani, M, Lewin, HA, Weller, JI, Ron, M 2005. Identification of a missense mutation in the bovine ABCG2 gene with a major effect on the QTL on chromosome 6 affecting milk yield and composition in Holstein cattle. Genome Research 15, 936944.Google Scholar
de Koning, GJ, Weller, JI 1994. Efficiency of direct selection on quantitative trait loci for a two-trait breeding objective. Theoretical and Applied Genetics 88, 669677.CrossRefGoogle ScholarPubMed
Fernando, RL, Grossman, M 1989. Marker assisted selection using best linear unbiased prediction. Genetics Selection Evolution 21, 467477.CrossRefGoogle Scholar
Gibson JP 1994. Short-term gain at the expense of long-term response with selection on identified loci. In Proceedings of the Fifth World Congress for Genetics Applied to Livestock Production, vol. 21, pp. 201–204. Guelph, ON, Canada.Google Scholar
Goddard, ME, Hayes, BJ 2007. Genomic selection. Journal of Animal Breeding and Genetic 124, 323330.CrossRefGoogle ScholarPubMed
Grisart, B, Coppieters, W, Farnir, F, Karim, L, Ford, C, Berzi, P, Cambisano, N, Mni, M, Reid, S, Simon, P, Spelman, R, Georges, M, Snell, R 2002. Positional candidate cloning of a QTL in dairy cattle: identification of a missense mutation in the bovine DGAT1 gene with major effect on milk yield and composition. Genome Research 12, 222231.CrossRefGoogle ScholarPubMed
Guillaume, F, Fritz, S, Boichard, D, Druet, T 2008. Estimation by simulation of the efficiency of the French marker-assisted selection program in dairy cattle. Genetics Selection Evolution 40, 91102.Google ScholarPubMed
Israel, C, Weller, JI 1998. Estimation of candidate gene effects in dairy cattle populations. Journal of Dairy Science 81, 16531662.Google Scholar
Israel, C, Weller, JI 2002. Estimation of quantitative trait loci effects in dairy cattle populations. Journal of Dairy Science 85, 12851297.Google Scholar
Kashi, Y, Hallerman, E, Soller, M 1990. Marker-assisted selection of candidate bulls for progeny testing programmes. Animal Production 51, 6374.Google Scholar
Kerr, RJ, Kinghorn, BP 1996. An efficient algorithm for segregation analysis in large populations. Journal of Animal Breeding and Genetics 113, 457469.CrossRefGoogle Scholar
Mackinnon, MJ, Georges, MAJ 1998. Marker-assisted preselection of young dairy sires prior to progeny-testing. Livestock Production Science 54, 229250.Google Scholar
MacLeod IM, Hayes BJ and Goddard ME 2006. Efficiency of dense bovine single-nucleotide polymorphisms to detect and position quantitative trait loci. In Proceedings of the Eighth World Congress for Genetics Applied to Livestock Production, 20-04. Belo Horizonte, MG, Brazil.Google Scholar
Meuwissen, THE, Goddard, ME 1999. Marker assisted estimation of breeding values when marker information is missing on many animals. Genetics Selection and Evolution 31, 375394.Google Scholar
Meuwissen, THE, Van Arendonk, JAM 1992. Potential improvements in rate of genetic gain from marker assisted selection in dairy cattle breeding schemes. Journal of Dairy Science 75, 16511659.Google Scholar
Nicholas, FW, Smith, C 1983. Increased rates of genetic change in dairy cattle by embryo transfer and splitting. Animal Production 36, 341353.Google Scholar
Schnabel RD, Van Tassell CP, Matukumalli LK, Sonstegard TS, Smith TP, Moore SS, Lawley CT, and Taylor JF 2008. Application of the BovineSNP50 assay for QTL mapping and prediction of genetic merit in Holstein cattle. Plant and Animal Genomes XVI Conference, p. 521. San Diego, CA (http://www.intl-pag.org/16/abstracts/PAG16_P05k_521.html).Google Scholar
Spelman, RJ, Garrick, DJ, van Arendonk, JAM 1999. Utilization of genetic variation by marker assisted selection in commercial diary cattle populations. Livestock Production Science 59, 5160.CrossRefGoogle Scholar
VanRaden PM, Wiggans G, Van Tassell C, Sonstegard T and Leigh Walton L 2008. Genomic prediction. http://aipl.arsusda.gov/reference/changes/eval0804.htmlGoogle Scholar
Weller, JI 1994. Economic aspects of animal breeding. Chapman & Hall, London, UK.Google Scholar
Weller, JI 2007. Marker assisted selection in dairy cattle. In Marker-assisted selection, current status and future perspectives in crops, livestock, forestry and fish (ed. E Guimarães, J Ruane, BD Scherf, A Sonnino and JD Dargie), pp. 199–228. Food and Agriculture Organization of the United Nations, Rome, Italy.Google Scholar
Weller, JI, Golik, M, Seroussi, E, Ezra, E, Ron, M 2003. Population-wide analysis of a QTL affecting milk-fat production in the Israeli Holstein population. Journal of Dairy Science 86, 22192227.CrossRefGoogle ScholarPubMed
Weller, JI, Shlezinger, M, Ron, M 2005. Correcting for bias in estimation of quantitative trait loci effects. Genetics Selection Evolution 37, 501522.Google Scholar
Whittaker, JC, Thompson, R, Visscher, PM 1996. On the mapping of QTL by regression of phenotype on marker-type. Heredity 77, 2332.CrossRefGoogle Scholar