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.

Family-based mitochondrial association study of traits related to type 2 diabetes and the metabolic syndrome in adolescents

“…It has been previously shown by us [33] and others [21] that the tagging SNPs genotyped here can be used to tag all the major European haplogroups. Using a linear discriminant function analysis method described by us previously [33], each haplotype was assigned to a haplogroup. Because of missing genotypes, a small number of haplotypes (<1%) could not unambiguously be assigned to haplogroups.…”

“…In spite of the loss of optimal power because of relatedness, our study still has at least equivalent power to a study of just one individual per family. The overall analysis contains data from 908 families, so our study has almost 100% power to detect a hypothetical mitochondrial variant with a frequency of 1%, which explains 5% of the variance of a quantitative trait, and~50% power to detect a variant explaining 1% of the variance [33].…”

“…Genotyping The details of the genotyping have been described elsewhere [33,34], but briefly, Saxena et al [21] identified 64 mtSNPs that tag all mitochondrial variants of >1% frequency in the European population with an r 2 of 0.8. For this study, 61 of the 64 tagSNPs were genotyped as well as an additional nine SNPs, which tag the three nontyped tagSNPs from Saxena et al as well as further variation at r 2 >0.8, that otherwise required the use of multi-SNP haplotypes using a method described in a previous study [33].…”

“…For this study, 61 of the 64 tagSNPs were genotyped as well as an additional nine SNPs, which tag the three nontyped tagSNPs from Saxena et al as well as further variation at r 2 >0.8, that otherwise required the use of multi-SNP haplotypes using a method described in a previous study [33]. A common D-loop variant, mt16189, which has previously been implicated as being associated with type 2 diabetes was also genotyped.…”

The use of common mitochondrial variants to detect and characterise population structure in the Australian population: implications for genome-wide association studies

“…Haplogroups were assigned according to a previously published method. 17 To ensure greater accuracy in the assignment of haplogroups, haplotypes that contained missing data were discarded for the haplogroup analyses. A total of 1314 haplotypes had no missing data.…”