Using deep learning to identify recent positive selection in malaria parasite sequence data

Wouter Deelder, Ernest Diez Benavente, Jody Phelan, Emilia Manko, Susana Campino, Luigi Palla, Taane G Clark

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Background: Malaria, caused by Plasmodium parasites, is a major global public health problem. To assist an understanding of malaria pathogenesis, including drug resistance, there is a need for the timely detection of underlying genetic mutations and their spread. With the increasing use of whole-genome sequencing (WGS) of Plasmodium DNA, the potential of deep learning models to detect loci under recent positive selection, historically signals of drug resistance, was evaluated. Methods: A deep learning-based approach (called “DeepSweep”) was developed, which can be trained on haplotypic images from genetic regions with known sweeps, to identify loci under positive selection. DeepSweep software is available from https://github.com/WDee/Deepsweep. Results: Using simulated genomic data, DeepSweep could detect recent sweeps with high predictive accuracy (areas under ROC curve > 0.95). DeepSweep was applied to Plasmodium falciparum (n = 1125; genome size 23 Mbp) and Plasmodium vivax (n = 368; genome size 29 Mbp) WGS data, and the genes identified overlapped with two established extended haplotype homozygosity methods (within-population iHS, across-population Rsb) (~ 60–75% overlap of hits at P < 0.0001). DeepSweep hits included regions proximal to known drug resistance loci for both P. falciparum (e.g. pfcrt, pfdhps and pfmdr1) and P. vivax (e.g. pvmrp1). Conclusion: The deep learning approach can detect positive selection signatures in malaria parasite WGS data. Further, as the approach is generalizable, it may be trained to detect other types of selection. With the ability to rapidly generate WGS data at low cost, machine learning approaches (e.g. DeepSweep) have the potential to assist parasite genome-based surveillance and inform malaria control decision-making.

Original languageEnglish
Article number270
Pages (from-to)270
JournalMalaria Journal
Volume20
Issue number1
DOIs
Publication statusPublished - 14 Jun 2021
Externally publishedYes

Keywords

  • Deep Learning/statistics & numerical data
  • Genome Size
  • Genome, Protozoan
  • Plasmodium falciparum/genetics
  • Plasmodium vivax/genetics
  • Selection, Genetic
  • Sequence Analysis, DNA

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