Jul 15, 2015

Inference of super-exponential human population growth via efficient computation of the site frequency spectrum for generalized models

BioRxiv : the Preprint Server for Biology
Feng Gao, Alon Keinan


The site frequency spectrum (SFS) and other genetic summary statistics are at the heart of many population genetics studies. Previous studies have shown that human populations had undergone a recent epoch of fast growth in effective population size. These studies assumed that growth is exponential, and the ensuing models leave unexplained excess amount of extremely rare variants. This suggests that human populations might have experienced a recent growth with speed faster than exponential. Recent studies have introduced a generalized growth model where the growth speed can be faster or slower than exponential. However, only simulation approaches were available for obtaining summary statistics under such models. In this study, we provide expressions to accurately and efficiently evaluate the SFS and other summary statistics under generalized models, which we further implement in a publicly available software. Investigating the power to infer deviation of growth from being exponential, we observed that decent sample sizes facilitate accurate inference, e.g. a sample of 3000 individuals with the amount of data expected from exome sequencing allows observing and accurately estimating growth with speed deviating by 10% or more from ...Continue Reading

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Computer Software
Animal Cancer Model
Whole Exome Sequencing

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