Unravelling population structure heterogeneity within the genome of the malaria vector Anopheles gambiae
File(s)s12864-021-07722-y.pdf (5.11 MB)
Published version
Author(s)
Campos, Melina
Willis, Katie
Rona, Luisa DP
Christophides, George
Maccallum, Robert
Type
Journal Article
Abstract
Background:
Whole genome re-sequencing provides powerful data for population genomic studies, allowing robust inferences of population structure, gene flow and evolutionary history. For the major malaria vector in Africa, Anopheles gambiae, other genetic aspects such as selection and adaptation are also important. In the present study, we explore population genetic variation from genome-wide sequencing of 765 An. gambiae and An. coluzzii specimens collected from across Africa. We used t-SNE, a recently popularized dimensionality reduction method, to create a 2D-map of An. gambiae and An. coluzzii genes that reflect their population structure similarities.
Results:
The map allows intuitive navigation among genes distributed throughout the so-called “mainland” and numerous surrounding “island-like” gene clusters. These gene clusters of various sizes correspond predominantly to low recombination genomic regions such as inversions and centromeres, and also to recent selective sweeps. Because this mosquito species complex has been studied extensively, we were able to support our interpretations with previously published findings. Several novel observations and hypotheses are also made, including selective sweeps and a multi-locus selection event in Guinea-Bissau, a known intense hybridization zone between An. gambiae and An. coluzzii.
Conclusions:
Our results present a rich dataset that could be utilized in functional investigations aiming to shed light onto An. gambiae s.l genome evolution and eventual speciation. In addition, the methodology presented here can be used to further characterize other species not so well studied as An. gambiae, shortening the time required to progress from field sampling to the identification of genes and genomic regions under unique evolutionary processes.
Whole genome re-sequencing provides powerful data for population genomic studies, allowing robust inferences of population structure, gene flow and evolutionary history. For the major malaria vector in Africa, Anopheles gambiae, other genetic aspects such as selection and adaptation are also important. In the present study, we explore population genetic variation from genome-wide sequencing of 765 An. gambiae and An. coluzzii specimens collected from across Africa. We used t-SNE, a recently popularized dimensionality reduction method, to create a 2D-map of An. gambiae and An. coluzzii genes that reflect their population structure similarities.
Results:
The map allows intuitive navigation among genes distributed throughout the so-called “mainland” and numerous surrounding “island-like” gene clusters. These gene clusters of various sizes correspond predominantly to low recombination genomic regions such as inversions and centromeres, and also to recent selective sweeps. Because this mosquito species complex has been studied extensively, we were able to support our interpretations with previously published findings. Several novel observations and hypotheses are also made, including selective sweeps and a multi-locus selection event in Guinea-Bissau, a known intense hybridization zone between An. gambiae and An. coluzzii.
Conclusions:
Our results present a rich dataset that could be utilized in functional investigations aiming to shed light onto An. gambiae s.l genome evolution and eventual speciation. In addition, the methodology presented here can be used to further characterize other species not so well studied as An. gambiae, shortening the time required to progress from field sampling to the identification of genes and genomic regions under unique evolutionary processes.
Date Issued
2021-06-08
Date Acceptance
2021-05-18
Citation
BMC Genomics, 2021, 22
ISSN
1471-2164
Publisher
BioMed Central
Journal / Book Title
BMC Genomics
Volume
22
License URL
Sponsor
Wellcome Trust
National Institutes of Health
The Royal Society
Grant Number
107983/Z/15/Z
NIH-NIAID-DMID-AI02013169
NF161472
Subjects
Chromosomal inversions
Malaria
Population genetics
T-SNE
Visualization method
Whole-genome analysis
Africa
Animals
Anopheles
Guinea-Bissau
Islands
Malaria
Mosquito Vectors
Animals
Anopheles
Malaria
Africa
Guinea-Bissau
Islands
Mosquito Vectors
Bioinformatics
06 Biological Sciences
08 Information and Computing Sciences
11 Medical and Health Sciences
Publication Status
Published
Article Number
ARTN 422