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Predicting population gene frequency from sample data

Published online by Cambridge University Press:  18 August 2016

R. M. Lewis ,
B. Grundy  and
L. A. Kuehn
R. M. Lewis*
Affiliation:
Department of Animal and Poultry Sciences (0306), Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, USA Sustainable Livestock Systems Group, Scottish Agricultural College, West Mains Road, Edinburgh EH9 3JG, UK
B. Grundy
Affiliation:
Sustainable Livestock Systems Group, Scottish Agricultural College, West Mains Road, Edinburgh EH9 3JG, UK
L. A. Kuehn
Affiliation:
Department of Animal and Poultry Sciences (0306), Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, USA
*
E-mail:rmlewis@vt.edu
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Abstract

With an increase in the number of candidate genes for important traits in livestock, effective strategies for incorporating such genes into selection programmes are increasingly important. Those strategies in part depend on the frequency of a favoured allele in a population. Since comprehensive genotyping of a population is seldom possible, we investigate the consequences of sampling strategies on the reliability of the gene frequency estimate for a bi-allelic locus. Even within a subpopulation or line, often only a proportion of individuals will be genotype tested. However, through segregation analysis, probable genotypes can be assigned to individuals that themselves were not tested, using known genotypes on relatives and a starting (presumed) gene frequency. The value of these probable genotypes in estimation of gene frequency was considered. A subpopulation or line was stochastically simulated and sampled at random, over a cluster of years or by favouring a particular genotype. Line was simulated (replicated) 1000 times. The reliability of gene frequency estimates depended on the sampling strategy used. With random sampling, even when a small proportion of a line was genotyped (0·10), the gene frequency of the population was well estimated from the across-line mean. When information on probable genotypes on untested individuals was combined with known genotypes, the between-line variance in gene frequency was estimated well; including probable genotypes overcame problems of statistical sampling. When the sampling strategy favoured a particular genotype, unsurprisingly the estimate of gene frequency was biased towards the allele favoured. In using probable genotypes the bias was lessened but the estimate of gene frequency still reflected the sampling strategy rather than the true population frequency. When sampling was confined to a few clustered years, the estimation of gene frequency was biased for those generations preceding the sampling event, particularly when the presumed starting gene frequency differed from the true population gene frequency. The potential risks of basing inferences about a population from a potentially biased sample are discussed.

Keywords

gene frequencygenetic driftgenotype probabilitysampling
Type
Breeding and genetics
Information
Animal Science , Volume 78 , Issue 1 , February 2004 , pp. 03 - 11
Copyright
Copyright © British Society of Animal Science 2004

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References

Arendonk, J. A. M. van, Smith, C. and Kennedy, B. W. 1989. Method to estimate genotype probabilities at individual loci in farm livestock. Theoretical and Applied Genetics 78: 735–740.CrossRefGoogle ScholarPubMed
Dawson, M., Hoinville, L. J., Hosie, B. D. and Hunter, N. 1998. Guidance on the use of PrP genotyping as an aid to the control of clinical scrapie. Veterinary Record 142: 623–625.Google Scholar
Dekkers, J. C. M. and Arendonk, J. A. M.van. 1998. Optimizing selection for quantitative traits with information on an identified locus in outbred populations Genetical Research, Cambridge 71: 257–275.Google Scholar
Department for Environment, Food and Rural Affairs. 2001. National scrapie plan for Great Britain. Available at: http://www.defra.gov.uk/animalh/bse/bse-science/scrapie/nsp/brochure.pdf.Google Scholar
Falconer, D. S. 1989. Introduction to quantitative genetics. Longman Scientific and Technical, England.Google Scholar
Fernando, R. L., Stickler, C. and Elston, R. C. 1993. An efficient algorithm to compute the posterior genotypic distribution for every member of a pedigree without loops. Theoretical and Applied Genetics 87: 89–93.CrossRefGoogle ScholarPubMed
Fujii, J., Otsu, K., Zorzato, F., De Leon, S., Khanna, V. K., Weiler, J. E., O’Brien, P. J. and McLennan, D. H. 1991. Identification of a mutation in porcine ryanondine receptor associated with malignant hyperthermia. Science 253: 448–451.CrossRefGoogle ScholarPubMed
Goldmann, W., Hunter, N., Foster, J. D., Salbaum, J. M., Beyreuther, K. and Hope, J. 1990. Two alleles of a neural protein gene linked to scrapie in sheep. Proceedings of the National Academy of Sciences of the United States of America 87: 2476–2480.CrossRefGoogle ScholarPubMed
Hunter, N., Foster, J. D., Goldmann, W., Stear, M. J., Hope, J. and Bostock, C. 1991. Restriction fragment length polymorphisms of the scrapie-associated fibril protein (PrP) gene and their association with susceptibility to natural scrapie in British sheep. Journal of General Virology 72: 1287–1292.CrossRefGoogle ScholarPubMed
Hunter, N., Goldmann, W., Benson, G., Foster, J. D. and Hope, J. 1993. Swaledale sheep affected by natural scrapie differ significantly in PrP genotype frequencies from healthy sheep and those selected for reduced incidence of scrapie. Journal of General Virology 74: 1025–1031.Google Scholar
Hunter, N., Moore, L., Hosie, B. D., Dingwall, W. S. and Greig, A. 1997. Association between natural scrapie and PrP genotype in a flock of Suffolk sheep in Scotland. Veterinary Record 140: 59–63.CrossRefGoogle Scholar
Kerr, R. J. and Kinghorn, B. P. 1996. An efficient algorithm for segregation analysis in large populations. Journal of Animal Breeding and Genetics 113: 457–469.CrossRefGoogle Scholar
Kinghorn, B. P. 1997. An index of information content for genotype probabilities derived from segregation analysis. Genetics 145: 479–483.Google Scholar
Luo, Z. W., Thompson, R. and Woolliams, J. A. 1997. A population genetics model of marker-assisted selection. Genetics 146: 1173–1183.Google Scholar
Shuster, D. E., Kehrli, M. E. Jr, Ackermann, M. R. and Gilbert, R. O. 1992. Identification and prevalence of a genetic defect that causes leukocyte adhesion deficiency in Holstein cattle. Proceedings of the National Academy of Sciences of the United States of America 89: 9225–9229.Google Scholar
Villanueva, B., Pong-Wong, R. and Woolliams, J. A. 2002. Marker assisted selection with optimised contributions of the candidates to selection. Genetics, Selection, Evolution 34: 679–703.Google Scholar
Weir, B. S. 1996. Genetic data analysis II. Sinauer Associates Inc., Sunderland, MA.Google Scholar
Weller, J. I. 2001. Quantitative trait loci analysis in animals. CABI Publishing, Oxford.CrossRefGoogle Scholar
Wilson, T., Wu, X.-Y., Juengel, J. L., Ross, I. K., Lumsden, J. M., Lord, E. A., Dodds, K. G., Walling, G. A., McEwan, J. C., O’Connell, A.R., McNatty, K. P. and Montgomery, G. W. 2001. Highly prolific Booroola sheep have a mutation in the intracellular kinase domain of bone morphologenetic protein IB receptor (ALK-6) that is expressed in both oocytes and granulosa cells. Biology of Reproduction 64: 1225–1235.Google Scholar
Woolliams, J. A., Pong-Wong, R. and Villanueva, B. 2002. Strategic optimisation of short- and long-term gain and inbreeding in MAS and non-MAS schemes. Proceedings of the seventh world congress on genetics applied to livestock production, Montpellier. CD-ROM communication no. 23–02.Google Scholar
Wright, S. 1931. Evolution in Mendelian populations. Genetics 16: 97–159.CrossRefGoogle ScholarPubMed