A population phylogenetic view of mitochondrial heteroplasmy

Abstract

The mitochondrion has recently emerged as an active player in a myriad of cellular processes. Additionally, it was recently shown that more than 200 diseases are known to be linked to variants in mitochondrial DNA or in nuclear genes interacting with mitochondria. This has reinvigorated interest in its biology and population genetics. Mitochondrial heteroplasmy, or genotypic variation of mitochondria within an individual, is now understood to be common in humans and important in human health. However, it is still not possible to make quantitative predictions about the inheritance of heteroplasmy and its proliferation within the body, partly due to the lack of an appropriate model. Here, we present a population-genetic framework for modeling mitochondrial heteroplasmy as a process that occurs on an ontogenetic phylogeny, with genetic drift and mutation changing heteroplasmy frequencies during the various developmental processes represented in the phylogeny. Using this framework, we develop a Bayesian inference method for inferring rates of mitochondrial genetic drift and mutation at different stages of human life. Applying the method to previously published heteroplasmy frequency data, we demonstrate a severe effective germline bottleneck comprised of the cumulative genetic drift occurring between the divergence of germline and somatic cells in the mother and the separation of germ layers in the offspring. Additionally, we find that the two somatic tissues we analyze here undergo tissue-specific bottlenecks during embryogenesis, less severe than the effective germline bottleneck, and that these somatic tissues experience little additional genetic drift during adulthood. We conclude with a discussion of possible extensions of the ontogenetic phylogeny framework and its possible applications to other ontogenetic processes in addition to mitochondrial heteroplasmy.

Population-specific signatures of intra-individual mitochondrial DNA heteroplasmy and their potential evolutionary advantages

“…It is estimated that the mtDNA substitution rate can be 20 to 100 times higher than their nuclear counterparts 6 . Therefore, both at the cellular as well as the whole organism level, a state of homoplasmy (where all mtDNA copies are identical) is rarely found and heteroplasmy containing at least two or more variants is common 7 . In humans, heteroplasmy is common in healthy individuals 8 and can increase with age, as mutations during ones' lifetime accumulate 9 .…”

A genetic bottleneck of mitochondrial DNA during human lymphocyte development

“…The Wright-Fisher model assumes discrete generations and random sampling of individuals from the current generation without replacement by reproduction in the following generation. This model has been widely used to model the mtDNA population dynamics in both germline cells and somatic cells, including those that are neoplastic 21,52 . Because normal somatic cells typically contain 100-1,000 copies of mtDNA, we used n=500 as the baseline copy number in our model 6 .…”

An approximate full-likelihood method for inferring selection and allele frequency trajectories from DNA sequence data

“…To calculate allele frequency transition distributions for time spans and selection coefficients not contained in the grid of pre-computed values, we linearly interpolate between the nearest precomputed values. See [44] for details. Additionally, we condition the allele frequency process on the present-day frequency X 0 by using the following reweighting:…”

“…Allele frequency transition probabilities 205 Our likelihood calculations require allele frequency transition distributions for different selection coefficients, population sizes, and spans of time. Rather than employ the more common approach of numerically calculating allele frequency transition distributions using the Wright-Fisher diffusion process with drift and selection (e.g., [42,43]), we follow [44] and precompute allele frequency transition distributions on a grid of time spans (i.e., generations) and scaled selection coefficients (i.e., α = 2N s) using the Wright-Fisher model of reproduction in a finite population experiencing genetic drift and natural selection (see [42]). Specifically, for each value of α, we use simple matrix multiplication to produce allele frequency transition matrices for discrete frequencies in a haploid population of size N = 2000 at a number of generations spanning from g = 1 to g = g max (corresponding to scaled drift times of 1/2000 to g = g max /2000) with some spacing chosen a priori; in practice, we use linear spacing for recent history and/or periods of population growth.…”