COIL: a methodology for evaluating malarial complexity of infection using likelihood from single nucleotide polymorphism data

Malaria Journal

Methodology

Daniel E Neafsey¹  Mary Lynn Baniecki¹  Kevin Galinsky²  Clarissa Valim³  Dyann F Wirth⁴  Sarah K Volkman⁵  Aubrey Faust⁶  Daniel L Hartl⁷  Rachel F Daniels⁷  Pardis C Sabeti⁷  Daouda Ndiaye⁸  Lise Musset⁹  Arielle Salmier⁹  Eric Legrand⁹  Benoit de Thoisy⁹ 

[1] Broad Institute of MIT and Harvard, 02142, Cambridge, MA, USA;Department of Biostatistics, Harvard School of Public Health, 02115, Boston, MA, USA;Department of Immunology and Infectious Disease, Harvard School of Public Health, 02115, Boston, MA, USA;Department of Immunology and Infectious Disease, Harvard School of Public Health, 02115, Boston, MA, USA;Broad Institute of MIT and Harvard, 02142, Cambridge, MA, USA;Department of Immunology and Infectious Disease, Harvard School of Public Health, 02115, Boston, MA, USA;Broad Institute of MIT and Harvard, 02142, Cambridge, MA, USA;Simmons College School of Nursing and Health Sciences, 02115, Boston, MA, USA;Faculty of Arts and Sciences, Harvard University, 02138, Cambridge, MA, USA;Faculty of Arts and Sciences, Harvard University, 02138, Cambridge, MA, USA;Broad Institute of MIT and Harvard, 02142, Cambridge, MA, USA;Faculty of Medicine and Pharmacy, Cheikh Anta Diop University, Dakar, Senegal;Laboratoire de Parasitologie, National Reference Centre for Malaria, Institut Pasteur de la Guyane, Cayenne, French Guiana, France;

关键词: Malaria;    Plasmodium;    vivax;    falciparum;    Complexity of infection;    Multiplicity of infection;    SNP;    Barcode;    Genotype;    Likelihood;   

DOI  :  10.1186/1475-2875-14-4

 received in 2014-08-08, accepted in 2014-12-16,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundComplex malaria infections are defined as those containing more than one genetically distinct lineage of Plasmodium parasite. Complexity of infection (COI) is a useful parameter to estimate from patient blood samples because it is associated with clinical outcome, epidemiology and disease transmission rate. This manuscript describes a method for estimating COI using likelihood, called COIL, from a panel of bi-allelic genotyping assays.MethodsCOIL assumes that distinct parasite lineages in complex infections are unrelated and that genotyped loci do not exhibit significant linkage disequilibrium. Using the population minor allele frequency (MAF) of the genotyped loci, COIL uses the binomial distribution to estimate the likelihood of a COI level given the prevalence of observed monomorphic or polymorphic genotypes within each sample.ResultsCOIL reliably estimates COI up to a level of three or five with at least 24 or 96 unlinked genotyped loci, respectively, as determined by in silico simulation and empirical validation. Evaluation of COI levels greater than five in patient samples may require a very large collection of genotype data, making sequencing a more cost-effective approach for evaluating COI under conditions when disease transmission is extremely high. Performance of the method is positively correlated with the MAF of the genotyped loci. COI estimates from existing SNP genotype datasets create a more detailed portrait of disease than analyses based simply on the number of polymorphic genotypes observed within samples.ConclusionsThe capacity to reliably estimate COI from a genome-wide panel of SNP genotypes provides a potentially more accurate alternative to methods relying on PCR amplification of a small number of loci for estimating COI. This approach will also increase the number of applications of SNP genotype data, providing additional motivation to employ SNP barcodes for studies of disease epidemiology or control measure efficacy. The COIL program is available for download from GitHub, and users may also upload their SNP genotype data to a web interface for simple and efficient determination of sample COI.

【 授权许可】

Unknown   
© Galinsky et al.; licensee BioMed Central. 2015. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.