An open dataset of Plasmodium falciparum genome variation in 7,000 worldwide samples

Abstract

MalariaGEN is a data-sharing network that enables groups around the world to work together on the genomic epidemiology of malaria. Here we describe a new release of curated genome variation data on 7,000 Plasmodium falciparum samples from MalariaGEN partner studies in 28 malaria-endemic countries. High-quality genotype calls on 3 million single nucleotide polymorphisms (SNPs) and short indels were produced using a standardised analysis pipeline. Copy number variants associated with drug resistance and structural variants that cause failure of rapid diagnostic tests were also analysed. Almost all samples showed genetic evidence of resistance to at least one antimalarial drug, and some samples from Southeast Asia carried markers of resistance to six commonly-used drugs. Genes expressed during the mosquito stage of the parasite life-cycle are prominent among loci that show strong geographic differentiation. By continuing to enlarge this open data resource we aim to facilitate research into the evolutionary processes affecting malaria control and to accelerate development of the surveillance toolkit required for malaria elimination.

The protective effect of sickle cell haemoglobin against severe malaria depends on parasite genotype

“…In brief, we sequenced the P. falciparum genome using the Illumina × Ten platform using two approaches based on sequencing whole DNA and selective whole genome amplification 7 . We used an established pipeline 8 to identify and call genotypes at over 2 million single nucleotide polymorphisms (SNPs) and short insertion/deletion variants across the Pf genome in these samples ( Methods ). The following analysis is based on 4,171 samples that had high quality data for both parasite and human genotypes and were not closely related, of which a subset of 3,346 had human genome-wide genotyping available.…”

“…The Pfsa1 +, Pfsa2 + and Pfsa3 + alleles had similar frequencies in Kenya (approximately 10-20%) whereas in Gambia Pfsa2 + had a much lower allele frequency than Pfsa1 + or Pfsa3 + (< 3% in all years studied, versus 25-60% for the Pfsa1 + or Pfsa3 + alleles; Figure 3a and Supplementary Figure 9 ). To explore the population genetic features of these loci in more detail, we analysed the MalariaGEN Pf6 open resource which gives P. falciparum genome variation data for 7,000 worldwide samples 8 ( Figure 3b ). This showed considerable variation in the frequency of these alleles across Africa, the maximum observed value being 61% for > Pfsa3 + in the Democratic Republic of Congo, and indicated that these alleles are rare outside Africa.…”

“…Details of allele frequencies and sample counts across years of ascertainment can be found in Supplementary Figure 9 . b) bars show the estimated frequency (y axis, as in panel a) of each Pfsa + allele in worldwide populations from the MalariaGEN Pf6 resource, which contains samples collected in the period 2008-2015 8 . Only countries with at least 50 samples are shown (this excludes Columbia, Peru, Benin, Nigeria, Ethiopia, Madagascar, Uganda, and Bangladesh).…”

Malaria in the ‘Omics Era’

“…For an excellent review of malaria population genomics, see [46]. The large-scale data-sharing Malaria Genomic Epidemiology Network (MalariaGEN) has made available thousands of parasite genomes-7000 P. falciparum genomes from 28 countries at the time of writing-to the research community [47,48]. The data provide an unprecedented snapshot of P. falciparum population genetics and the asso-ciated advances in sample preparation, sequencing technology, analysis methods and data sharing lay the foundations to more fully integrate genomics into disease epidemiology, though technical challenges remain [49,50].…”

“…For example, extensive outcrossing occurs in many parts of sub-Saharan differing treatment regimes (the pill). Below the map is a schematic population structure tree based on genome-wide variation data, after [48]. (Regional populations are indicated at the tips of the tree: SAm = South America; WA = West Africa; CA = Central Africa; EA = East Africa; SAs = South Asia; SEA,W = South East Asia, West; SEA,W = South East Asia, East; O = Oceania).…”