Imputation-based population genetics analysis of Plasmodium falciparum malaria parasites.
Whole-genome sequencing technologies are being increasingly applied to Plasmodium falciparum clinical isolates to identify genetic determinants of malaria pathogenesis. However, genome-wide discovery methods, such as haplotype scans for signatures of natural selection, are hindered by missing genotypes in sequence data. Poor correlation between single nucleotide polymorphisms (SNPs) in the P. falciparum genome complicates efforts to apply established missing-genotype imputation methods that leverage off patterns of linkage disequilibrium (LD). The accuracy of state-of-the-art, LD-based imputation methods (IMPUTE, Beagle) was assessed by measuring allelic r2 for 459 P. falciparum samples from malaria patients in 4 countries: Thailand, Cambodia, Gambia, and Malawi. In restricting our analysis to 86 k high-quality SNPs across the populations, we found that the complete-case analysis was restricted to 21k SNPs (24.5%), despite no single SNP having more than 10% missing genotypes. The accuracy of Beagle in filling in missing genotypes was consistently high across all populations (allelic r2, 0.87-0.96), but the performance of IMPUTE was mixed (allelic r2, 0.34-0.99) depending on reference haplotypes and population. Positive selection analysis using Beagle-imputed haplotypes identified loci involved in resistance to chloroquine (crt) in Thailand, Cambodia, and Gambia, sulfadoxine-pyrimethamine (dhfr, dhps) in Cambodia, and artemisinin (kelch13) in Cambodia. Tajima's D-based analysis identified genes under balancing selection that encode well-characterized vaccine candidates: apical merozoite antigen 1 (ama1) and merozoite surface protein 1 (msp1). In contrast, the complete-case analysis failed to identify any well-validated drug resistance or candidate vaccine loci, except kelch13. In a setting of low LD and modest levels of missing genotypes, using Beagle to impute P. falciparum genotypes is a viable strategy for conducting accurate large-scale population genetics and association analyses, and supporting global surveillance for drug resistance markers and candidate vaccine antigens.
Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom.
Hanif
H
Samad
Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom.
Francesc
F
Coll
Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom.
Mark D
MD
Preston
Malawi-Liverpool-Wellcome Trust Clinical Research Programme, Blantyre, Malawi and Liverpool School of Tropical Medicine, Liverpool, United Kingdom.
Harold
H
Ocholla
Laboratory of Malaria and Vector Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America.
Rick M
RM
Fairhurst
Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom; Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom.
Taane G
TG
Clark
D000483:Alleles; D000818:Animals; D002160:Cambodia; D005260:Female; D005714:Gambia; D005828:Genetics, Population; D005838:Genotype; D006239:Haplotypes; D006801:Humans; D016778:Malaria, Falciparum / Q000235:genetics* / Q000469:parasitology; D008295:Malawi; D008297:Male; D010963:Plasmodium falciparum / Q000235:genetics* / Q000472:pathogenicity; D020641:Polymorphism, Single Nucleotide; D012641:Selection, Genetic / Q000235:genetics*; D013785:Thailand