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

Tests of the neutral evolution hypothesis are usually built on the standard model which assumes that mutations are neutral and the 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. Key ingredients are the first two moments of the site frequency spectrum. We show how these moments can be computed 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. Our results show that demography-adjusted test statistics facilitate the direct comparison between populations and that most of the differences among populations seen in the original unadjusted tests can be explained by their underlying demographies. Upon carrying out whole-genome screens for deviations from neutrality, we identify candidate regions of recent positive selection. We provide track files with values of the adjusted and unadjusted tests for upload to the UCSC genome browser. © 2014 Elsevier Inc.

Rafajlović, M., Klassmann, A., Eriksson, A., Wiehe, T., & Mehlig, B. (2014). Demography-adjusted tests of neutrality based on genome-wide SNP data. Theoretical Population Biology, 95, 1–12. doi:10.1016/j.tpb.2014.05.002

This work was financially supported by grants from Vetenskapsradet, from the Goran Gustafsson Foundation for Research in Natural Sciences and Medicine, through the Linnaeus Centre for Marine Evolutionary Biology (CeMEB, to BM, by a grant of the German Science Foundation (DFG-SFB680) to TW, and AE by the Leverhume Trust and the Biotechnology and Biological Sciences Research Council (Grant BB/H005854/1).

Elsevier BV

Theoretical Population Biology


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