How Good Are Indirect Tests at Detecting Recombination in Human mtDNA?

Empirical proof of human mitochondrial DNA (mtDNA) recombination in somatic tissues was obtained in 2004; however, a lack of irrefutable evidence exists for recombination in human mtDNA at the population level. Our inability to demonstrate convincingly a signal of recombination in population data sets of human mtDNA sequence may be due, in part, to the ineffectiveness of current indirect tests. Previously, we tested some well-established indirect tests of recombination (linkage disequilibrium vs. distance using D′ and r(2), Homoplasy Test, Pairwise Homoplasy Index, Neighborhood Similarity Score, and Max χ(2)) on sequence data derived from the only empirically confirmed case of human mtDNA recombination thus far and demonstrated that some methods were unable to detect recombination. Here, we assess the performance of these six well-established tests and explore what characteristics specific to human mtDNA sequence may affect their efficacy by simulating sequence under various parameters with levels of recombination (ρ) that vary around an empirically derived estimate for human mtDNA (population parameter ρ = 5.492). No test performed infallibly under any of our scenarios, and error rates varied across tests, whereas detection rates increased substantially with ρ values > 5.492. Under a model of evolution that incorporates parameters specific to human mtDNA, including rate heterogeneity, population expansion, and ρ = 5.492, successful detection rates are limited to a range of 7−70% across tests with an acceptable level of false-positive results: the neighborhood similarity score incompatibility test performed best overall under these parameters. Population growth seems to have the greatest impact on recombination detection probabilities across all models tested, likely due to its impact on sequence diversity. The implications of our findings on our current understanding of mtDNA recombination in humans are discussed.