Demography-adjusted tests of neutrality based on genome-wide SNP data

TL;DR

This paper develops a framework for adjusting neutrality tests based on the site frequency spectrum to account for population size changes, enabling more accurate detection of selection across diverse human populations.

Contribution

It extends existing neutrality tests to populations with varying size, providing demography-adjusted statistics and demonstrating their application to human genome data.

Findings

Most differences in neutrality tests among populations are due to demography.

Demography-adjusted tests improve the detection of regions under positive selection.

The method allows direct comparison of neutrality across populations.

Abstract

Tests of the neutral evolution hypothesis are usually built on the standard null model which assumes that mutations are neutral and population size remains constant over time. However, it is unclear how such tests are affected if the last assumption is dropped. Here, we extend the unifying framework for tests based on the site frequency spectrum, introduced by Achaz and Ferretti, to populations of varying size. A key ingredient is to specify the first two moments of the frequency spectrum. We show that these moments can be determined analytically if a population has experienced two instantaneous size changes in the past. We apply our method to data from ten human populations gathered in the 1000 genomes project, estimate their demographies and define demography-adjusted versions of Tajima's $D$, Fay & Wu's $H$, and Zeng's $E$. The adjusted test statistics facilitate the direct comparison between populations and they show that most of the differences among populations seen in the original tests can be explained by demography. We carried out whole genome screens for deviation from neutrality and identified candidate regions of recent positive selection. We provide track files with values of the adjusted and original tests for upload to the UCSC genome browser.