Power and SNP tagging in whole mitochondrial genome association studies

Abstract

The application of genetic association studies to detect mitochondrial variants responsible for phenotypic variation has recently been demonstrated. However, the only power estimates currently available are based on the use of mitochondrial haplogroups, which can only tag a small fraction of the common variation in the mitochondrial genome. Here, power estimates are derived for a SNP-based study design for both disease (case-control) and quantitative trait mapping studies. Power is estimated using simulations based on a collection of publicly available mitochondrial sequences of European origin. The power when testing all common mitochondrial SNPs is shown to be equivalent to that when testing only tagging SNPs, despite the relatively high ratio of tagging SNPs to total SNPs resulting from the tagging of all SNPs with a minor allele frequency greater than 1%. The sample size requirements of mitochondrial genome association studies are compared with that of nuclear whole-genome studies. Remarkably, the trade off between the number of tests being performed and the proportion of phenotypic variance explained for a fixed effect size results in approximately equal sample sizes required for both study types, although the per individual cost for the mitochondrial association study is much less. To test the representation of the sequences used in the power simulations, a sample of 3839 individuals from 1037 Australian families was genotyped for 69 tagging SNPs. The strong concordance in allele frequencies and linkage disequilibrium between the European sequences and the Australian sample indicates that the results presented here are transferable across populations of European descent.