First genome-wide association study of non-severe malaria in two birth cohorts in Benin

Abstract

Recent research efforts to identify genes involved in malaria susceptibility using genome-wide approaches have focused on severe malaria. Here we present the first GWAS on non-severe malaria designed to identify genetic variants involved in innate immunity or innate resistance mechanisms.Our study was performed on two cohorts of infants from southern Benin (525 and 250 individuals respectively) closely followed from birth to 18-24 months of age, with an assessment of a space-and time-dependent environmental risk of exposure. Both the recurrence of mild malaria attacks and the recurrence of malaria infections as a whole (symptomatic and asymptomatic) were considered. Our study highlights a role of PTPRT, a tyrosine phosphatase receptor involved in STAT3 pathway and several other genes whose biological functions are relevant in malaria infection. Results shows that GWAS on non-severe malaria can successfully identify new candidate genes and inform physiological mechanisms underlying natural protection against malaria.

Three hallmarks of malaria-induced selection in human genomes

“…We observed an enrichment for malaria-associated chromosomal regions from three large GWAS (Timmann et al 2012; Malaria Genomic Epidemiology Network 2019; Milet et al 2019), especially among outliers (Supp Table 2). Two particularly promising candidates are PTPRM and MYLK4 , which are among the very strongest candidates for recurrence of mild malaria attacks in infants (Milet et al 2019).…”

“…We observed an enrichment for malaria-associated chromosomal regions from three large GWAS (Timmann et al 2012; Malaria Genomic Epidemiology Network 2019; Milet et al 2019), especially among outliers (Supp Table 2). Two particularly promising candidates are PTPRM and MYLK4 , which are among the very strongest candidates for recurrence of mild malaria attacks in infants (Milet et al 2019). In particular, PTPRM was the top prioritized gene in the GWAS based on functional consequences (p = 3.8e-08; Milet et al 2019), and the top disease-associated variant in the gene occurs in between the two variants that define the Repeated Shifts signal , just upstream of an alternate transcript start.…”

Mixed Logistic Regression in Genome-Wide Association Studies

“…We retrieved similar results in an analysis of a simulated phenotype with large differences of prevalence among strata, based on genotype data from two geographically close cohorts (ca. 20 km apart) from South Benin (Milet et al, 2019), but with different self-reported ethnicities.…”

“…Benin (Milet et al, 2019). The participants were ascertained in two sites distant of 20 km from each other, in three different health centers for the first cohort and two for the second one.…”

“…We evaluate the capacity of logistic regression, MLM, and MLR (using either the score test or the methods mentioned above) to properly take into account population structure associated to heterogeneous prevalence. While Chen et al were interested in geographically distinct populations, part of our work focuses on the context of two geographically very close populations in West Africa, using real genotype data from a recent GWAS (Milet et al, 2019). We also use data simulated with a coalescent model (Hudson, 2002), reproducing the simulations presented in Chen et al (2016).…”