Global demographic history of human populations inferred from whole mitochondrial genomes

Abstract

The Neolithic transition has led to marked increases in census population sizes across the world, as recorded by a rich archaeological record. However, previous attempts to detect such changes using genetic markers, especially mitochondrial DNA (mtDNA), have mostly been unsuccessful. We use complete mtDNA genomes from over 1700 individuals, from the 1000 Genomes Project Phase 3, to explore changes in populations sizes in five populations for each of four major geographical regions, using a sophisticated coalescent-based Bayesian method (extended Bayesian skyline plots) and mutation rates calibrated with ancient DNA. Despite the power and sophistication of our analysis, we fail to find size changes that correspond to the Neolithic transitions of the study populations. However, we do detect a number of size changes, which tend to be replicated in most populations within each region. These changes are mostly much older than the Neolithic transition and could reflect either population expansion or changes in population structure. Given the amount of migration and population mixing that occurred after these ancient signals were generated, we caution that modern populations will often carry ghost signals of demographic events that occurred far away from their current location.

Reef development and Sea level changes driveAcanthasterPopulation Expansion in the Indo-Pacific region

“…We used simulated populations to validate the LGM calibration by visual comparison of the inferred histories after calibration to a simulated scenario. We then compared the simulated RSL scenario in conjunction with a reef development curve (Hoareau and Pretorius in prep) to the newly inferred demographic histories of the populations of Acanthaster species using a newly developed Similarity Index which was inspired by the recent Dissimilarity Index used to compare BSP curves (Miller and Amos 2018). To account for the differences in the scale of expansion, we standardized the range of Ne τ from 0 to 1 which allows the comparison of the distances between curves across a selection of points.…”

Origin of tuberculosis in the Paleolithic predicts unprecedented population growth and female resistance

“…This means that the sustainability of the group was not simply dependent on the growth in birth rate and also that the infection spread as a result of contact between tribes, thus resulting in the persistence of MtbC and the subsequent disappearance of these groups. A "Neolithic-like" growth by 43,000 BCE similar to that proposed by Miller et al 23 allows us to understand how "modern" lineages and modern humans were able to persist until modern times.…”

“…Thus, the trade-off for "ancient" MtbC lineages is the higher probability of progression to active TB at a cost of being less able to disseminate through the population explaining why "ancient" lineages can only be found in certain geographical locations 21 . The fact that the appearance of "modern" lineages (46,000 BCE) coincided with a significant increase in the population (from 10 4 to 10 6 individuals) in Asia, while remaining constant in Africa (around 10 6 people) 22,23 , indicates that some sort of population explosion occurred in Asia before the Neolithic, as proposed by several authors [24][25][26] , giving support to its dissemination.…”

“…(i.e., the compartment with TB-sick with the two strains). The evolution of all variables is described by Eqs (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)…”

mtDNA-based reconstructions of change in effective population sizes of Holarctic birds do not agree with their reconstructed range sizes based on paleoclimates

“…Given the positive relationship generally observed between 65 range extent and mean local population density (Connor et al 2000), Ne would also be expected to 66 increase by a greater proportion than range extent under climatic amelioration. Another plausible 67 cause of discordance between changes in Ne and range size is that, without substantial gene flow, 68 skyline plots will mostly reconstruct the population dynamics of the sampled locations rather than 69 the whole species (Miller et al 2018). Pooling samples from multiple locations can help, but it will 70 not fully resolve the problem (Heller et al 2013).…”