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

Proc Natl Acad Sci U S A
Authors
Keywords
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.

Year of Publication
2012
Journal
Proc Natl Acad Sci U S A
Volume
109
Issue
32
Pages
13052-7
Date Published
2012 Aug 07
ISSN
1091-6490
URL
DOI
10.1073/pnas.1210585109
PubMed ID
22826220
PubMed Central ID
PMC3420184
Links
Grant list
R01 AI075080 / AI / NIAID NIH HHS / United States
T32 AI007638 / AI / NIAID NIH HHS / United States
1R01AI075080-01A1 / AI / NIAID NIH HHS / United States