CLC number: Q78; TP31
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 0000-00-00
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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.
@article{title="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="AUVRAY Benoît, DODDS Ken G.",
journal="Journal of Zhejiang University Science B",
volume="8",
number="11",
pages="802-806",
year="2007",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2007.B0802"
}
%0 Journal Article
%T Use of stochastic simulations to investigate the power and design of a whole genome association study using single nucleotide polymorphism arrays in farm animals
%A AUVRAY Benoî
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%A DODDS Ken G.
%J Journal of Zhejiang University SCIENCE B
%V 8
%N 11
%P 802-806
%@ 1673-1581
%D 2007
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2007.B0802
TY - JOUR
T1 - Use of stochastic simulations to investigate the power and design of a whole genome association study using single nucleotide polymorphism arrays in farm animals
A1 - AUVRAY Benoî
A1 - t
A1 - DODDS Ken G.
J0 - Journal of Zhejiang University Science B
VL - 8
IS - 11
SP - 802
EP - 806
%@ 1673-1581
Y1 - 2007
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2007.B0802
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.
[1] Barendse, W., Reverter, A., Bunch, R.J., Harrison, B.E., Barris, W., Thomas, M.B., 2007. A validated whole-genome association study of efficient food conversion in cattle. Genetics, 176(3):1893-1905.
[2] Clark, A.G., Boerwinkle, E., Hixson, J., Sing, C.F., 2005. Determinants of the success of whole-genome association testing. Genome Res., 15(11):1463-1467.
[3] Guedj, M., Della-Chiesa, E., Picard, F., Nuel, G., 2007. Computing power in case-control association studies through the use of quadratic approximations: application to meta-statistics. Ann. Hum. Genet., 71(2):262-270.
[4] Henderson, C.R., 1984. Applications of Linear Models in Animal Breeding. University of Guelph, Guelph, Canada.
[5] Kang, S.J., Gordon, D., Finch, S.J., 2004. What SNP genotyping errors are most costly for genetic association studies? Genet. Epidemiol., 26(2):132-141.
[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.
[7] MacCluer, J.W., Vandeberg, J.L., Read, B., Ryder, O.A., 1986. Pedigree analysis by computer simulation. Zoo Biol., 5(2):147-160.
[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
[9] Shaffer, J.P., 1995. Multiple hypothesis testing. Ann. Rev. Psychol., 46:561-584.
[10] van Gestel, S., Houwing-Duistermaat, J.J., Adolfsson, R., van Duijn, C.M., van Broekhoven, C., 2000. Power of selective genotyping in genetic association analyses of quantitative traits. Behav. Genet., 30(2):141-146.
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