Malaria life cycle intensifies both natural selection and random genetic drift

Citation:

Chang, HH, EL Moss, DJ Park, D Ndiaye, S Mboup, SK Volkman, PC Sabeti, DF Wirth, DE Neafsey, and DL Hartl. 2013. “Malaria life cycle intensifies both natural selection and random genetic drift.” Proc Natl Acad Sci U S A 110: 20129-34.

Date Published:

Dec 10

Abstract:

Analysis of genome sequences of 159 isolates of Plasmodium falciparum from Senegal yields an extraordinarily high proportion (26.85%) of protein-coding genes with the ratio of nonsynonymous to synonymous polymorphism greater than one. This proportion is much greater than observed in other organisms. Also unusual is that the site-frequency spectra of synonymous and nonsynonymous polymorphisms are virtually indistinguishable. We hypothesized that the complicated life cycle of malaria parasites might lead to qualitatively different population genetics from that predicted from the classical Wright-Fisher (WF) model, which assumes a single random-mating population with a finite and constant population size in an organism with nonoverlapping generations. This paper summarizes simulation studies of random genetic drift and selection in malaria parasites that take into account their unusual life history. Our results show that random genetic drift in the malaria life cycle is more pronounced than under the WF model. Paradoxically, the efficiency of purifying selection in the malaria life cycle is also greater than under WF, and the relative efficiency of positive selection varies according to conditions. Additionally, the site-frequency spectrum under neutrality is also more skewed toward low-frequency alleles than expected with WF. These results highlight the importance of considering the malaria life cycle when applying existing population genetic tools based on the WF model. The same caveat applies to other species with similarly complex life cycles.

Notes:

Chang, Hsiao-HanMoss, Eli LPark, Daniel JNdiaye, DaoudaMboup, SouleymaneVolkman, Sarah KSabeti, Pardis CWirth, Dyann FNeafsey, Daniel EHartl, Daniel LengAI099105/AI/NIAID NIH HHS/R01 AI099105/AI/NIAID NIH HHS/Research Support, N.I.H., ExtramuralResearch Support, Non-U.S. Gov't2013/11/22 06:00Proc Natl Acad Sci U S A. 2013 Dec 10;110(50):20129-34. doi: 10.1073/pnas.1319857110. Epub 2013 Nov 20.

Last updated on 05/12/2015