EVENT : C3BI Seminars
Applying identity-by-descent mapping to malaria samples
Main speaker : Melanie Bahlo, from Professor, Co-Division Head, Population Health and Immunity Division The Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia Date : 09/06/2017 at 01:30 pm Location : Auditorium Francois Jacob – BIME (26) ,Institut Pasteur, Paris
Most of the human morbidity and mortality due to malaria is caused by two plasmodium species: falciparum and vivax.
Generation of whole genome sequencing data for malaria is now routine. It is generated by extracting the plasmodium DNA from human red blood cells. In countries with a high burden of malaria this can lead to samples with multiple clones, known as multiplicity of infection (MOI). We have developed two algorithms to help detect genetic relatedness, incorporating and leveraging MOI. These two algorithms implement hidden Markov models (HMMs) to identify relatednes, or identity-by-descnet (IBD), between clones within, and between, samples. Using these results we developed an algorithm to detect regions of high relatedness which we postulate as being due to selection pressures. The algorithms also give probability-based measures to distinguish local from imported infections. We demonstrate the algorithm on whole genome sequencing data from P. falciparum from Papua New Guinea and the MalariaGen Pf3K data. We can recapitulate all of the known selection signals as well as identifying some new ones.
Due to security policy in Institut Pasteur, please register before if you plan to come to this meeting