Use of stochastic simulations to investigate the power and design of a whole genome association study using single nucleotide polymorphism arrays in farm animals.
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.
Auvray
Benoît
B
Applied Biotechnologies Group, AgResearch Limited, Invermay Research Centre, Private Bag 50034, Mosgiel, New Zealand. benoit.auvray@agresearch.co.nz
Dodds
Ken G
KG
D000818:Animals; D000829:Animals, Domestic / Q000235:genetics*; D003198:Computer Simulation; D005787:Gene Frequency; D005838:Genotype; D015810:Linkage Disequilibrium; D020641:Polymorphism, Single Nucleotide; D040641:Quantitative Trait Loci; D013269:Stochastic Processes