Hostname: page-component-77f85d65b8-g98kq Total loading time: 0 Render date: 2026-04-18T02:36:21.098Z Has data issue: false hasContentIssue false
  • English
  • Français

SNP-based heritability estimation using a Bayesian approach

Published online by Cambridge University Press:  23 November 2012

K. Krag ,
L. L. Janss ,
M. M. Shariati ,
P. Berg  and
A. J. Buitenhuis
K. Krag*
Affiliation:
Department of Molecular Biology and Genetics, Faculty of Science and Technology, Aarhus University, PO Box 50, DK-8830 Tjele, Denmark
L. L. Janss
Affiliation:
Department of Molecular Biology and Genetics, Faculty of Science and Technology, Aarhus University, PO Box 50, DK-8830 Tjele, Denmark
M. M. Shariati
Affiliation:
Department of Molecular Biology and Genetics, Faculty of Science and Technology, Aarhus University, PO Box 50, DK-8830 Tjele, Denmark
P. Berg
Affiliation:
Department of Molecular Biology and Genetics, Faculty of Science and Technology, Aarhus University, PO Box 50, DK-8830 Tjele, Denmark
A. J. Buitenhuis
Affiliation:
Department of Molecular Biology and Genetics, Faculty of Science and Technology, Aarhus University, PO Box 50, DK-8830 Tjele, Denmark
*
†E-mail: Kristian.Krag@agrsci.dk
Get access

Abstract

Heritability is a central element in quantitative genetics. New molecular markers to assess genetic variance and heritability are continually under development. The availability of molecular single nucleotide polymorphism (SNP) markers can be applied for estimation of variance components and heritability on population, where relationship information is unknown. In this study, we evaluated the capabilities of two Bayesian genomic models to estimate heritability in simulated populations. The populations comprised different family structures of either no or a limited number of relatives, a single quantitative trait, and with one of two densities of SNP markers. All individuals were both genotyped and phenotyped. Results illustrated that the two models were capable of estimating heritability, when true heritability was 0.15 or higher and populations had a sample size of 400 or higher. For heritabilities of 0.05, all models had difficulties in estimating the true heritability. The two Bayesian models were compared with a restricted maximum likelihood (REML) approach using a genomic relationship matrix. The comparison showed that the Bayesian approaches performed equally well as the REML approach. Differences in family structure were in general not found to influence the estimation of the heritability. For the sample sizes used in this study, a 10-fold increase of SNP density did not improve precision estimates compared with set-ups with a less dense distribution of SNPs. The methods used in this study showed that it was possible to estimate heritabilities on the basis of SNPs in animals with direct measurements. This conclusion is valuable in cases when quantitative traits are either difficult or expensive to measure.

Keywords

Bayesian predictionheritabilitySNP markerssimulation study

Information

Type
Breeding and genetics
Information
animal , Volume 7 , Issue 4 , April 2013 , pp. 531 - 539
Copyright
Copyright © The Animal Consortium 2012

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.)

Article purchase

Temporarily unavailable

References

George, EI, McCulloch, RE 1993. Variable selection via Gibbs sampling. Journal of the American Statistical Association 88, 881–889.CrossRefGoogle Scholar
Goddard, ME, Hayes, BJ 2009. Mapping genes for complex traits in domestic animals and their use in breeding programmes. Nature reviews. Genetics 10, 381–391.Google Scholar
Goddard, ME, Hayes, BJ, Meuwissen, THE 2010. Genomics selection in farm animal species – lessons learnt and future perspectives. In Proceedings of the 9th World Congress on Genetics Applied to Livestock Production, Leipzig, Germany, CD-Rom Communication 0701.Google Scholar
Hayes, BJ, Chamberlain, AJ, Goddard, ME 2006. Use of markers in linkage disequilibrium with QTL in breeding programs. In Proceedings of the 8th World Congress on Genetics Applied to Livestock Production, Belo Horizonte, MG, Brazil, pp. 30–06.Google Scholar
Ihara, N, Takasuga, A, Mizoshita, K, Takeda, H, Sugimoto, M, Mizoguchi, Y, Hirano, T, Itoh, T, Watanabe, T, Reed, KM, Snelling, WM, Kappes, SM, Beattie, CW, Bennett, GL, Sugimoto, Y 2004. A comprehensive genetic map of the cattle genome based on 3802 microsatellites. Genome Research 14, 1987–1998.Google Scholar
Janss, LLG 2010. iBay. Bayesian Solutions, Leiden, The Netherlands.Google Scholar
Krag, K, Janss, L, Shariati, MM, Buitenhuis, AJ 2010. Heritability estimation based on small sample size using SNP markers. In Proceedings of the 9th World Congress on Genetics Applied to Livestock Production, Leipzig, Germany, CD-Rom Communication 0654.Google Scholar
Madsen, P, Jensen, J 2007. An User's guide to DMU. A package for Analysing Multivariate Mixed Models. Version 6, release 4.7. Aarhus University, Denmark.Google Scholar
Makowsky, R, Pajewski, NM, Klimentidis, YC, Vazquez, AI, Duarte, CW, Allison, DB, de los Campos, G 2011. Beyond missing heritability: prediction of complex traits. PLoS Genetics 7(4): e1002051.Google Scholar
Meuwissen, TH, Hayes, BJ, Goddard, ME 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829.Google Scholar
Mrode, RA, Thompson, R 2005. Linear models for the prediction of animal breeding values. CABI Publishing, Wallingford.CrossRefGoogle Scholar
National Center for Biotechnology Information (NCBI) 2005. MGI Genetic Map of the Mouse Genome (Mus musculus). Retrieved February 1, 2005, from http://www.ncbi.nlm.nih.gov/Genomes/.Google Scholar
Pribyl, J, Rehout, V, Citek, J, Pribylova, J 2010. Genetic evaluation of dairy cattle using a simple heritable genetic ground. Journal of the Science of Food and Agriculture 90, 1765–1773.Google ScholarPubMed
R Development Core Team 2011. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.Google Scholar
Rutten, MJM, Bovenhuis, H, Hettinga, KA, van Valenberg, HJF, van Arendonk, JAM 2009. Predicting bovine milk fat composition using infrared spectroscopy based on milk samples collected in winter and summer. Journal of Dairy Science 92, 6202–6209.Google Scholar
Sargolzaei, M, Schenkel, FS 2009. QMSim: a large-scale genome simulator for livestock. Bioinformatics 25, 680–681.Google Scholar
Schwartz, MK, Luikart, G, Waples, RS 2007. Genetic monitoring as a promising tool for conservation and management. Trends in Ecology & Evolution 22, 25–33.Google Scholar
Sved, JA 1971. Linkage disequilibrium and homozygosity of chromosome segments in finite populations. Theoretical Population Biology 2, 125–141.Google Scholar
Thomas, SC, Pemberton, JM, Hill, WG 2000. Estimating variance components in natural populations using inferred relationships. Heredity 84, 427–436.Google Scholar
VanRaden, PM 2007. Genomic measures of relationship and inbreeding. In Proceedings of the Interbull Meeting, Dublin, Ireland, pp. 33–36.Google Scholar
Veerkamp, RF, Mulder, HA, Thompson, R, Calus, MP 2011. Genomic and pedigree-based genetic parameters for scarcely recorded traits when some animals are genotyped. Journal of Dairy Science 94, 4189–4197.Google Scholar
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, 307–311.Google Scholar
Villumsen, TM, Janss, L, Lund, MS 2009. The importance of haplotype length and heritability using genomic selection in dairy cattle. Journal of Animal Breeding and Genetics 126, 3–13.Google Scholar
Visscher, PM, Hill, WG, Wray, NR 2008. Heritability in the genomics era – concepts and misconceptions. Nature Reviews. Genetics 9, 255–266.Google Scholar
Visscher, PM, Medland, SE, Ferreira, MA, Morley, KI, Zhu, G, Cornes, BK, Montgomery, GW, Martin, NG 2006. Assumption-free estimation of heritability from genome-wide identity-by-descent sharing between full siblings. PLoS Genetics 2, 316–325.Google Scholar
Wenne, R, Boudry, P, Hemmer-Hansen, J, Lubieniecki, KP, Was, A, Kause, A 2007. What role for genomics in fisheries management and aquaculture? Aquatic Living Resources 20, 241–255.Google Scholar
Supplementary material: File

Krag Supplementary Material

Appendix

Download Krag Supplementary Material(File)
File 334 KB