Sequence-based association and selection scans identify drug resistance loci in the Plasmodium falciparum malaria parasite

Citation:

Park, DJ, AK Lukens, DE Neafsey, SF Schaffner, HH Chang, C Valim, U Ribacke, et al. 2012. “Sequence-based association and selection scans identify drug resistance loci in the Plasmodium falciparum malaria parasite.” Proc Natl Acad Sci U S A 109: 13052-7.

Date Published:

Aug 7

Abstract:

Through rapid genetic adaptation and natural selection, the Plasmodium falciparum parasite--the deadliest of those that cause malaria--is able to develop resistance to antimalarial drugs, thwarting present efforts to control it. Genome-wide association studies (GWAS) provide a critical hypothesis-generating tool for understanding how this occurs. However, in P. falciparum, the limited amount of linkage disequilibrium hinders the power of traditional array-based GWAS. Here, we demonstrate the feasibility and power improvements gained by using whole-genome sequencing for association studies. We analyzed data from 45 Senegalese parasites and identified genetic changes associated with the parasites' in vitro response to 12 different antimalarials. To further increase statistical power, we adapted a common test for natural selection, XP-EHH (cross-population extended haplotype homozygosity), and used it to identify genomic regions associated with resistance to drugs. Using this sequence-based approach and the combination of association and selection-based tests, we detected several loci associated with drug resistance. These loci included the previously known signals at pfcrt, dhfr, and pfmdr1, as well as many genes not previously implicated in drug-resistance roles, including genes in the ubiquitination pathway. Based on the success of the analysis presented in this study, and on the demonstrated shortcomings of array-based approaches, we argue for a complete transition to sequence-based GWAS for small, low linkage-disequilibrium genomes like that of P. falciparum.

Notes:

Park, Daniel JLukens, Amanda KNeafsey, Daniel ESchaffner, Stephen FChang, Hsiao-HanValim, ClarissaRibacke, UlfVan Tyne, DariaGalinsky, KevinGalligan, MeghanBecker, Justin SNdiaye, DaoudaMboup, SouleymaneWiegand, Roger CHartl, Daniel LSabeti, Pardis CWirth, Dyann FVolkman, Sarah Keng1R01AI075080-01A1/AI/NIAID NIH HHS/T32 AI007638/AI/NIAID NIH HHS/Research Support, N.I.H., ExtramuralResearch Support, Non-U.S. Gov'tResearch Support, U.S. Gov't, Non-P.H.S.2012/07/25 06:00Proc Natl Acad Sci U S A. 2012 Aug 7;109(32):13052-7. doi: 10.1073/pnas.1210585109. Epub 2012 Jul 23.

Last updated on 05/12/2015