Insilico Functional Analysis of Genome-Wide Dataset From 17,000 Individuals Identifies Candidate Malaria Resistance Genes Enriched in Malaria Pathogenic Pathways

Recent genome-wide association studies (GWASs) of severe malaria have identified several association variants. However, much about the underlying biological functions are yet to be discovered. Here, we systematically predicted plausible candidate genes and pathways from functional analysis of severe...

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Autores principales:	Delesa Damena, Francis E. Agamah, Peter O. Kimathi, Ntumba E. Kabongo, Hundaol Girma, Wonderful T. Choga, Lemu Golassa, Emile R. Chimusa
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	functional analysis genome-wide association study severe malaria genes pathways Genetics QH426-470
Acceso en línea:	https://doaj.org/article/02590ec393d4460aa190cd892a6a06bd
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spelling	oai:doaj.org-article:02590ec393d4460aa190cd892a6a06bd2021-11-18T08:44:41ZInsilico Functional Analysis of Genome-Wide Dataset From 17,000 Individuals Identifies Candidate Malaria Resistance Genes Enriched in Malaria Pathogenic Pathways1664-802110.3389/fgene.2021.676960https://doaj.org/article/02590ec393d4460aa190cd892a6a06bd2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fgene.2021.676960/fullhttps://doaj.org/toc/1664-8021Recent genome-wide association studies (GWASs) of severe malaria have identified several association variants. However, much about the underlying biological functions are yet to be discovered. Here, we systematically predicted plausible candidate genes and pathways from functional analysis of severe malaria resistance GWAS summary statistics (N = 17,000) meta-analysed across 11 populations in malaria endemic regions. We applied positional mapping, expression quantitative trait locus (eQTL), chromatin interaction mapping, and gene-based association analyses to identify candidate severe malaria resistance genes. We further applied rare variant analysis to raw GWAS datasets (N = 11,000) of three malaria endemic populations including Kenya, Malawi, and Gambia and performed various population genetic structures of the identified genes in the three populations and global populations. We performed network and pathway analyses to investigate their shared biological functions. Our functional mapping analysis identified 57 genes located in the known malaria genomic loci, while our gene-based GWAS analysis identified additional 125 genes across the genome. The identified genes were significantly enriched in malaria pathogenic pathways including multiple overlapping pathways in erythrocyte-related functions, blood coagulations, ion channels, adhesion molecules, membrane signalling elements, and neuronal systems. Our population genetic analysis revealed that the minor allele frequencies (MAF) of the single nucleotide polymorphisms (SNPs) residing in the identified genes are generally higher in the three malaria endemic populations compared to global populations. Overall, our results suggest that severe malaria resistance trait is attributed to multiple genes, highlighting the possibility of harnessing new malaria therapeutics that can simultaneously target multiple malaria protective host molecular pathways.Delesa DamenaFrancis E. AgamahPeter O. KimathiNtumba E. KabongoHundaol GirmaWonderful T. ChogaLemu GolassaEmile R. ChimusaEmile R. ChimusaFrontiers Media S.A.articlefunctional analysisgenome-wide association studysevere malariagenespathwaysGeneticsQH426-470ENFrontiers in Genetics, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	functional analysis genome-wide association study severe malaria genes pathways Genetics QH426-470
spellingShingle	functional analysis genome-wide association study severe malaria genes pathways Genetics QH426-470 Delesa Damena Francis E. Agamah Peter O. Kimathi Ntumba E. Kabongo Hundaol Girma Wonderful T. Choga Lemu Golassa Emile R. Chimusa Emile R. Chimusa Insilico Functional Analysis of Genome-Wide Dataset From 17,000 Individuals Identifies Candidate Malaria Resistance Genes Enriched in Malaria Pathogenic Pathways
description	Recent genome-wide association studies (GWASs) of severe malaria have identified several association variants. However, much about the underlying biological functions are yet to be discovered. Here, we systematically predicted plausible candidate genes and pathways from functional analysis of severe malaria resistance GWAS summary statistics (N = 17,000) meta-analysed across 11 populations in malaria endemic regions. We applied positional mapping, expression quantitative trait locus (eQTL), chromatin interaction mapping, and gene-based association analyses to identify candidate severe malaria resistance genes. We further applied rare variant analysis to raw GWAS datasets (N = 11,000) of three malaria endemic populations including Kenya, Malawi, and Gambia and performed various population genetic structures of the identified genes in the three populations and global populations. We performed network and pathway analyses to investigate their shared biological functions. Our functional mapping analysis identified 57 genes located in the known malaria genomic loci, while our gene-based GWAS analysis identified additional 125 genes across the genome. The identified genes were significantly enriched in malaria pathogenic pathways including multiple overlapping pathways in erythrocyte-related functions, blood coagulations, ion channels, adhesion molecules, membrane signalling elements, and neuronal systems. Our population genetic analysis revealed that the minor allele frequencies (MAF) of the single nucleotide polymorphisms (SNPs) residing in the identified genes are generally higher in the three malaria endemic populations compared to global populations. Overall, our results suggest that severe malaria resistance trait is attributed to multiple genes, highlighting the possibility of harnessing new malaria therapeutics that can simultaneously target multiple malaria protective host molecular pathways.
format	article
author	Delesa Damena Francis E. Agamah Peter O. Kimathi Ntumba E. Kabongo Hundaol Girma Wonderful T. Choga Lemu Golassa Emile R. Chimusa Emile R. Chimusa
author_facet	Delesa Damena Francis E. Agamah Peter O. Kimathi Ntumba E. Kabongo Hundaol Girma Wonderful T. Choga Lemu Golassa Emile R. Chimusa Emile R. Chimusa
author_sort	Delesa Damena
title	Insilico Functional Analysis of Genome-Wide Dataset From 17,000 Individuals Identifies Candidate Malaria Resistance Genes Enriched in Malaria Pathogenic Pathways
title_short	Insilico Functional Analysis of Genome-Wide Dataset From 17,000 Individuals Identifies Candidate Malaria Resistance Genes Enriched in Malaria Pathogenic Pathways
title_full	Insilico Functional Analysis of Genome-Wide Dataset From 17,000 Individuals Identifies Candidate Malaria Resistance Genes Enriched in Malaria Pathogenic Pathways
title_fullStr	Insilico Functional Analysis of Genome-Wide Dataset From 17,000 Individuals Identifies Candidate Malaria Resistance Genes Enriched in Malaria Pathogenic Pathways
title_full_unstemmed	Insilico Functional Analysis of Genome-Wide Dataset From 17,000 Individuals Identifies Candidate Malaria Resistance Genes Enriched in Malaria Pathogenic Pathways
title_sort	insilico functional analysis of genome-wide dataset from 17,000 individuals identifies candidate malaria resistance genes enriched in malaria pathogenic pathways
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/02590ec393d4460aa190cd892a6a06bd
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Insilico Functional Analysis of Genome-Wide Dataset From 17,000 Individuals Identifies Candidate Malaria Resistance Genes Enriched in Malaria Pathogenic Pathways

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