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

Abstract

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

  • References
  • Citations

References

  • We're still populating references for this paper, please check back later.
  • References
  • Citations

Citations

  • This paper may not have been cited yet.

Mentioned in this Paper

Computer Software
Study
Size
Animal Cancer Model
Epoch
Whole Exome Sequencing
Site
Sequencing
Heart
Simulation

About this Paper

Related Feeds

BioRxiv & MedRxiv Preprints

BioRxiv and MedRxiv are the preprint servers for biology and health sciences respectively, operated by Cold Spring Harbor Laboratory. Here are the latest preprint articles (which are not peer-reviewed) from BioRxiv and MedRxiv.

Related Papers

Journal of Computational and Graphical Statistics : a Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
John A KammYun S Song
BioRxiv : the Preprint Server for Biology
F. De LorenzoMerja H Voutilainen
© 2020 Meta ULC. All rights reserved