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CLC number: Q78; TP31

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Received: 2007-09-19

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Journal of Zhejiang University SCIENCE B 2007 Vol.8 No.11 P.802-806

http://doi.org/10.1631/jzus.2007.B0802


Use of stochastic simulations to investigate the power and design of a whole genome association study using single nucleotide polymorphism arrays in farm animals


Author(s):  AUVRAY Benoî,t, DODDS Ken G.

Affiliation(s):  Applied Biotechnologies Group, AgResearch Limited, Invermay Research Centre, Private Bag 50034, Mosgiel 9053, New Zealand

Corresponding email(s):   benoit.auvray@agresearch.co.nz

Key Words:  Simulation, Association study, Single nucleotide polymorphism (SNP), Power, Quantitative trait loci (QTL)


AUVRAY Benoît, DODDS Ken G.. Use of stochastic simulations to investigate the power and design of a whole genome association study using single nucleotide polymorphism arrays in farm animals[J]. Journal of Zhejiang University Science B, 2007, 8(11): 802-806.

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Abstract: 
This paper presents a quick, easy to implement and versatile way of using stochastic simulations to investigate the power and design of using single nucleotide polymorphism (SNP) arrays for genome-wide association studies in farm animals. It illustrates the methodology by discussing a small example where 6 experimental designs are considered to analyse the same resource consisting of 6 006 animals with pedigree and phenotypic records: (1) genotyping the 30 most widely used sires in the population and all of their progeny (515 animals in total), (2) genotyping the 100 most widely used sires in the population and all of their progeny (1 102 animals in total), genotyping respectively (3) 515 and (4) 1 102 animals selected randomly or genotyping respectively (5) 515 and (6) 1 102 animals from the tails of the phenotypic distribution. Given the resource at hand, designs where the extreme animals are genotyped perform the best, followed by designs selecting animals at random. Designs where sires and their progeny are genotyped perform the worst, as even genotyping the 100 most widely used sires and their progeny is not as powerful of genotyping 515 extreme animals.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

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[6] Lawrence, R.W., Evans, D.M., Cardon, L.R., 2005. Prospects and pitfalls in whole genome association studies. Philosophical Transactions of the Royal Society B Biological Sciences, 360(1460):1589-1595.

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[8] R Development Core Team, 2007. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Vienna, Austria, Http://www.R-project.org

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