The power of association studies to detect the contribution of candidate genetic loci to variation in complex traits.
The statistical power of five association study test statistics (two haplotype-based tests, two marker-based tests, and the Transmission Disequilibrium Test-Q5) to detect single nucleotide polymorphism (SNP)/phenotype associations in a linkage-disequilibrium-based candidate gene scan employing a number of SNPs is examined. Power is estimated as a function of realistic parameters expected to affect the likelihood of detecting a significant association: the number of SNPs examined, the scaled recombination size of the region examined, the proportion of variance in the trait attributable to a hidden causative polymorphism within the region, and the number of individuals or families examined. For the different combinations of parameter values, power is estimated from a large number of realizations of a simulated coalescent describing a single random mating population with mutation, random genetic drift, and recombination. This explicit population genetics model results in a distribution of DNA marker heterozygosities and linkage disequilibria that are likely to resemble those expected in actual population samples. The study concludes that (1) marker-based permutation tests are more powerful than simple haplotype-based tests, (2) there is sufficient power to detect the presence of causative polymorphisms of small effect if on the order of 500 individuals are sampled, (3) greater power is achieved by increasing the sample size than by increasing the number of polymorphisms, (4) association studies are generally more powerful than transmission disequilibrium-based tests, and (5) for the range of parameters considered association studies have a low repeatability unless sample sizes are on the order of 500 individuals. Estimates of 4Nc for a number of gene regions and human populations will be of use in determining the density of SNPs that are likely to be required for successful association studies.
Long
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Department of Ecology and Evolutionary Biology, University of California at Irvine, Irvine, California 92697-2525, USA. tdlong@uci.edu
Langley
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CH
D019295:Computational Biology / Q000379:methods / Q000706:statistics & numerical data; D005819:Genetic Markers / Q000235:genetics; D014644:Genetic Variation / Q000235:genetics*; D005838:Genotype; D006801:Humans; D015810:Linkage Disequilibrium / Q000235:genetics; D009711:Nucleotides / Q000235:genetics; D010641:Phenotype; D011110:Polymorphism, Genetic / Q000235:genetics; D019655:Quantitative Trait, Heritable
D005819:Genetic Markers; D009711:Nucleotides