Population growth of human Y chromosomes: a study of Y chromosome microsatellites

Abstract

We use variation at a set of eight human Y chromosome microsatellite loci to investigate the demographic history of the Y chromosome. Instead of assuming a population of constant size, as in most of the previous work on the Y chromosome, we consider a model which permits a period of recent population growth. We show that for most of the populations in our sample this model fits the data far better than a model with no growth. We estimate the demographic parameters of this model for each population and also the time to the most recent common ancestor. Since there is some uncertainty about the details of the microsatellite mutation process, we consider several plausible mutation schemes and estimate the variance in mutation size simultaneously with the demographic parameters of interest. Our finding of a recent common ancestor (probably in the last 120,000 years), coupled with a strong signal of demographic expansion in all populations, suggests either a recent human expansion from a small ancestral population, or natural selection acting on the Y chromosome.

MUTYH-associated polyposis (MAP): evidence for the origin of the common European mutations p.Tyr179Cys and p.Gly396Asp by founder events

“…On the other hand, these results have to be interpreted with some caution, as they are sensitive to changes in the parameters used, namely g and gen r. For instance, use of lower gen r values, such as 0.0075, corresponding to the average gen r estimated for Europe in the last 20 000 years, 57 would date the mutations back to around 30 000 years ago, toward the end of the last Pleistocene glaciation in the Paleolithic. However, this result is less compatible with the absence or very low frequency of these mutations in non European populations, as other genetic variants, such as factor V Leiden, that likely date back to that period are widely spread in other ethnic groups.…”

“…56 The calculations were repeated using the gen r value estimated for the last 20 000 years, corresponding to 0.0075. 57 The proportion of MB chromosomes sampled (fc) was first calculated separately for Italy and Germany considering the following parameters: current Italian (60 742 397 inhabitants, http://demo.istat.it/) and German population size (82 438 000 inhabitants, www.destatis.de/); p.Tyr179Cys and p.Gly396Asp allele frequencies in Europeans: 0.3 and 0.7, respectively; 35,42 and the number of chromosomes investigated in the study. Mutation age was calculated both separately for the two countries and on combined Italian and German data using the average fc value estimated for the two populations (0.00014 for p.Tyr179Cys and 0.000043 for p.Gly396Asp).…”

Effects of Linked Selective Sweeps on Demographic Inference and Model Selection

“…Thus, if natural selection has a substantial impact on genome-wide patterns of variation, then many demographic parameter estimates could be biased (Hahn 2008;Gazave et al 2014). Indeed this has been shown to be the case for at least some scenarios of background selection (Ewing and Jensen 2016), where purifying selection reduces levels of neutral polymorphism at linked sites.Here, we examine the potential impact of linked positive selection on three of the most widely used methods for demographic inference: ABC (Pritchard et al 1999;Beaumont et al 2002), @a@i (Gutenkunst et al 2009, and pairwise sequentially Markovian coalescent (PSMC); Li and Durbin 2011). We demonstrate that selection can substantially bias parameter estimates, often leading to overestimates of the severity of population bottlenecks and/or the rate of population growth.…”

“…Here, we examine the potential impact of linked positive selection on three of the most widely used methods for demographic inference: ABC (Pritchard et al 1999;Beaumont et al 2002), @a@i (Gutenkunst et al 2009, and pairwise sequentially Markovian coalescent (PSMC); Li and Durbin 2011). We demonstrate that selection can substantially bias parameter estimates, often leading to overestimates of the severity of population bottlenecks and/or the rate of population growth.…”

A tutorial introduction to Bayesian inference for stochastic epidemic models using Approximate Bayesian Computation

“…For continuous distributions, or discrete ones in which the acceptance probability in step 3 is unacceptably low, Pritchard et al (1999) suggested the following algorithm:…”