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Zhao S, Chi L, Fu M, Chen H. HaploSweep: Detecting and Distinguishing Recent Soft and Hard Selective Sweeps through Haplotype Structure. Mol Biol Evol 2024; 41:msae192. [PMID: 39288167 PMCID: PMC11452351 DOI: 10.1093/molbev/msae192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 07/29/2024] [Accepted: 09/03/2024] [Indexed: 09/19/2024] Open
Abstract
Identifying soft selective sweeps using genomic data is a challenging yet crucial task in population genetics. In this study, we present HaploSweep, a novel method for detecting and categorizing soft and hard selective sweeps based on haplotype structure. Through simulations spanning a broad range of selection intensities, softness levels, and demographic histories, we demonstrate that HaploSweep outperforms iHS, nSL, and H12 in detecting soft sweeps. HaploSweep achieves high classification accuracy-0.9247 for CHB, 0.9484 for CEU, and 0.9829 YRI-when applied to simulations in line with the human Out-of-Africa demographic model. We also observe that the classification accuracy remains consistently robust across different demographic models. Additionally, we introduce a refined method to accurately distinguish soft shoulders adjacent to hard sweeps from soft sweeps. Application of HaploSweep to genomic data of CHB, CEU, and YRI populations from the 1000 genomes project has led to the discovery of several new genes that bear strong evidence of population-specific soft sweeps (HRNR, AMBRA1, CBFA2T2, DYNC2H1, and RANBP2 etc.), with prevalent associations to immune functions and metabolic processes. The validated performance of HaploSweep, demonstrated through both simulated and real data, underscores its potential as a valuable tool for detecting and comprehending the role of soft sweeps in adaptive evolution.
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Affiliation(s)
- Shilei Zhao
- Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lianjiang Chi
- Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Mincong Fu
- Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hua Chen
- Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China
- CAS Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China
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2
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Moss S, Jones RT, Pretorius E, da Silva ET, Higgins M, Kristan M, Acford-Palmer H, Collins EL, Rodrigues A, Krishna S, Clark TG, Last A, Campino S. Phenotypic evidence of deltamethrin resistance and identification of selective sweeps in Anopheles mosquitoes on the Bijagós Archipelago, Guinea-Bissau. Sci Rep 2024; 14:22840. [PMID: 39354094 PMCID: PMC11445403 DOI: 10.1038/s41598-024-73996-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 09/23/2024] [Indexed: 10/03/2024] Open
Abstract
Vector control in the Bijagós Archipelago of Guinea-Bissau currently relies on pyrethroid insecticide-treated nets. However, data on insecticide resistance in Guinea-Bissau is limited. This study identified deltamethrin resistance in the Anopheles gambiae sensu lato complex on Bubaque island using WHO tube tests in November 2022. Whole genome sequencing of An. gambiae sensu stricto mosquitoes identified six single nucleotide polymorphisms (SNPs) previously associated with, or putatively associated with, insecticide resistance: T791M, L995F, N1570Y, A1746S and P1874L in the vgsc gene, and L119V in the gste2 gene. Twenty additional non-synonymous SNPs were identified in insecticide-resistance associated genes. Four of these SNPs were present at frequencies over 5% in the population: T154S, I126F and G26S in the vgsc gene and A65S in ace1. Genome wide selection scans using Garud's H12 statistic identified two selective sweeps: one in chromosome X and one in chromosome 2R. Both selective sweeps overlap with metabolic genes previously associated with insecticide resistance, including cyp9k1 and the cyp6aa/cyp6p gene cluster. This study presents the first phenotypic testing for deltamethrin resistance and the first whole genome sequence data for Anophelesgambiae mosquitoes from the Bijagós, contributing data of significance for vector control policy in this region.
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Affiliation(s)
- Sophie Moss
- Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK.
| | - Robert T Jones
- Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - Elizabeth Pretorius
- Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - Eunice Teixeira da Silva
- Projecto de Saúde Bandim, Bissau, Guinea-Bissau
- Ministério de Saúde Pública, Bissau, Guinea-Bissau
| | - Matthew Higgins
- Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - Mojca Kristan
- Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - Holly Acford-Palmer
- Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - Emma L Collins
- Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - Amabelia Rodrigues
- Projecto de Saúde Bandim, Bissau, Guinea-Bissau
- Ministério de Saúde Pública, Bissau, Guinea-Bissau
| | - Sanjeev Krishna
- Clinical Academic Group, Institute for Infection and Immunity, and St, George's University Hospitals NHS Foundation Trust, St. George's University of London, London, UK
- Centre de Recherches Médicales de Lambaréné (CERMEL), Lambaréné, Gabon
- Institut für Tropenmedizin Universitätsklinikum Tübingen, Tübingen, Germany
| | - Taane G Clark
- Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Anna Last
- Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - Susana Campino
- Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK
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3
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Temple SD, Waples RK, Browning SR. Modeling recent positive selection using identity-by-descent segments. Am J Hum Genet 2024:S0002-9297(24)00333-1. [PMID: 39362217 DOI: 10.1016/j.ajhg.2024.08.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 08/29/2024] [Accepted: 08/30/2024] [Indexed: 10/05/2024] Open
Abstract
Recent positive selection can result in an excess of long identity-by-descent (IBD) haplotype segments overlapping a locus. The statistical methods that we propose here address three major objectives in studying selective sweeps: scanning for regions of interest, identifying possible sweeping alleles, and estimating a selection coefficient s. First, we implement a selection scan to locate regions with excess IBD rates. Second, we estimate the allele frequency and location of an unknown sweeping allele by aggregating over variants that are more abundant in an inferred outgroup with excess IBD rate versus the rest of the sample. Third, we propose an estimator for the selection coefficient and quantify uncertainty using the parametric bootstrap. Comparing against state-of-the-art methods in extensive simulations, we show that our methods are more precise at estimating s when s≥0.015. We also show that our 95% confidence intervals contain s in nearly 95% of our simulations. We apply these methods to study positive selection in European ancestry samples from the Trans-Omics for Precision Medicine project. We analyze eight loci where IBD rates are more than four standard deviations above the genome-wide median, including LCT where the maximum IBD rate is 35 standard deviations above the genome-wide median. Overall, we present robust and accurate approaches to study recent adaptive evolution without knowing the identity of the causal allele or using time series data.
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Affiliation(s)
- Seth D Temple
- Department of Statistics, University of Washington, Seattle, WA, USA.
| | - Ryan K Waples
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Sharon R Browning
- Department of Biostatistics, University of Washington, Seattle, WA, USA.
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4
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Kientega M, Clarkson CS, Traoré N, Hui TYJ, O'Loughlin S, Millogo AA, Epopa PS, Yao FA, Belem AMG, Brenas J, Miles A, Burt A, Diabaté A. Whole-genome sequencing of major malaria vectors reveals the evolution of new insecticide resistance variants in a longitudinal study in Burkina Faso. Malar J 2024; 23:280. [PMID: 39285410 PMCID: PMC11406867 DOI: 10.1186/s12936-024-05106-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Accepted: 09/08/2024] [Indexed: 09/19/2024] Open
Abstract
BACKGROUND Intensive deployment of insecticide based malaria vector control tools resulted in the rapid evolution of phenotypes resistant to these chemicals. Understanding this process at the genomic level is important for the deployment of successful vector control interventions. Therefore, longitudinal sampling followed by whole genome sequencing (WGS) is necessary to understand how these evolutionary processes evolve over time. This study investigated the change in genetic structure and the evolution of the insecticide resistance variants in natural populations of Anopheles gambiae over time and space from 2012 to 2017 in Burkina Faso. METHODS New genomic data have been generated from An. gambiae mosquitoes collected from three villages in the western part of Burkina Faso between 2012 and 2017. The samples were whole-genome sequenced and the data used in the An. gambiae 1000 genomes (Ag1000G) project as part of the Vector Observatory. Genomic data were analysed using the analysis pipeline previously designed by the Ag1000G project. RESULTS The results showed similar and consistent nucleotide diversity and negative Tajima's D between An. gambiae sensu stricto (s.s.) and Anopheles coluzzii. Principal component analysis (PCA) and the fixation index (FST) showed a clear genetic structure in the An. gambiae sensu lato (s.l.) species. Genome-wide FST and H12 scans identified genomic regions under divergent selection that may have implications in the adaptation to ecological changes. Novel voltage-gated sodium channel pyrethroid resistance target-site alleles (V402L, I1527T) were identified at increasing frequencies alongside the established alleles (Vgsc-L995F, Vgsc-L995S and N1570Y) within the An. gambiae s.l. POPULATIONS Organophosphate metabolic resistance markers were also identified, at increasing frequencies, within the An. gambiae s.s. populations from 2012 to 2017, including the SNP Ace1-G280S and its associated duplication. Variants simultaneously identified in the same vector populations raise concerns about the long-term efficacy of new generation bed nets and the recently organophosphate pirimiphos-methyl indoor residual spraying in Burkina Faso. CONCLUSION These findings highlighted the benefit of genomic surveillance of malaria vectors for the detection of new insecticide resistance variants, the monitoring of the existing resistance variants, and also to get insights into the evolutionary processes driving insecticide resistance.
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Affiliation(s)
- Mahamadi Kientega
- Institut de Recherche en Sciences de la Santé (IRSS), 01 BP 545, Bobo-Dioulasso 01, Burkina Faso.
- Université Nazi Boni, 01 BP 1091, Bobo-Dioulasso, Burkina Faso.
| | - Chris S Clarkson
- Vector Surveillance Programme, Genomic Surveillance Unit, Wellcome Sanger Institute, Hinxton, Cambridge, UK
| | - Nouhoun Traoré
- Institut de Recherche en Sciences de la Santé (IRSS), 01 BP 545, Bobo-Dioulasso 01, Burkina Faso
- Université Nazi Boni, 01 BP 1091, Bobo-Dioulasso, Burkina Faso
| | - Tin-Yu J Hui
- Department of Life Sciences, Imperial College London, Silwood Park, Ascot, SL5 7PY, UK
| | - Samantha O'Loughlin
- Department of Life Sciences, Imperial College London, Silwood Park, Ascot, SL5 7PY, UK
| | - Abdoul-Azize Millogo
- Institut de Recherche en Sciences de la Santé (IRSS), 01 BP 545, Bobo-Dioulasso 01, Burkina Faso
- Institut des Sciences des Sociétés, 03 BP 7047, Ouagadougou 03, Burkina Faso
| | - Patric Stephane Epopa
- Institut de Recherche en Sciences de la Santé (IRSS), 01 BP 545, Bobo-Dioulasso 01, Burkina Faso
| | - Franck A Yao
- Institut de Recherche en Sciences de la Santé (IRSS), 01 BP 545, Bobo-Dioulasso 01, Burkina Faso
| | | | - Jon Brenas
- Vector Surveillance Programme, Genomic Surveillance Unit, Wellcome Sanger Institute, Hinxton, Cambridge, UK
| | - Alistair Miles
- Vector Surveillance Programme, Genomic Surveillance Unit, Wellcome Sanger Institute, Hinxton, Cambridge, UK
| | - Austin Burt
- Department of Life Sciences, Imperial College London, Silwood Park, Ascot, SL5 7PY, UK
| | - Abdoulaye Diabaté
- Institut de Recherche en Sciences de la Santé (IRSS), 01 BP 545, Bobo-Dioulasso 01, Burkina Faso.
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Caputo B, De Marco CM, Pichler V, Bottà G, Bennett KL, Amambua-Ngwa A, Assogba SB, Opondo KO, Clarkson CS, Tennessen JA, Weetman D, Miles A, Della Torre A. Population genomic evidence of a putative 'far-west' African cryptic taxon in the Anopheles gambiae complex. Commun Biol 2024; 7:1115. [PMID: 39256556 PMCID: PMC11387608 DOI: 10.1038/s42003-024-06809-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Accepted: 08/29/2024] [Indexed: 09/12/2024] Open
Abstract
The two main Afrotropical malaria vectors - Anopheles coluzzii and An. gambiae - are genetically distinct and reproductively isolated across West Africa. However, populations at the western extreme of their range are assigned as "intermediate" between the two species by whole genome sequence (WGS) data, and as hybrid forms by conventional molecular diagnostics. By exploiting WGS data from 1190 specimens collected across west Africa via the Anopheles gambiae 1000 Genomes network, we identified a putative taxon in the far-west (provisionally named Bissau molecular form), which did not arise by admixture but rather may have originated at the same time as the split between An. coluzzii and An. gambiae. Intriguingly, this taxon lacks insecticide resistance mechanisms commonly observed in the two main species. These findings lead to a change of perspective on malaria vector species in the far-west region with potential for epidemiological implications, and a new challenge for genetic-based mosquito control approaches.
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Affiliation(s)
- Beniamino Caputo
- Dipartimento di Sanità Pubblica e Malattie Infettive, Istituto Pasteur Italia-Fondazione Cenci-Bolognetti, Università di Roma "Sapienza", Rome, Italy
| | - Carlo M De Marco
- Dipartimento di Sanità Pubblica e Malattie Infettive, Istituto Pasteur Italia-Fondazione Cenci-Bolognetti, Università di Roma "Sapienza", Rome, Italy
| | - Verena Pichler
- Dipartimento di Sanità Pubblica e Malattie Infettive, Istituto Pasteur Italia-Fondazione Cenci-Bolognetti, Università di Roma "Sapienza", Rome, Italy
| | - Giordano Bottà
- Dipartimento di Sanità Pubblica e Malattie Infettive, Istituto Pasteur Italia-Fondazione Cenci-Bolognetti, Università di Roma "Sapienza", Rome, Italy
| | - Kelly L Bennett
- Wellcome Sanger Genomic Surveillance Unit, Wellcome Sanger Institute, Cambridge, UK
| | - Alfred Amambua-Ngwa
- Disease Control and Elimination Theme (DCE), Medical Research Council Unit, The Gambia at the London School of Hygiene and Tropical Medicine (MRCG-LSHTM), Banjul, The Gambia
| | - Sessinou B Assogba
- Disease Control and Elimination Theme (DCE), Medical Research Council Unit, The Gambia at the London School of Hygiene and Tropical Medicine (MRCG-LSHTM), Banjul, The Gambia
| | - Kevin O Opondo
- Disease Control and Elimination Theme (DCE), Medical Research Council Unit, The Gambia at the London School of Hygiene and Tropical Medicine (MRCG-LSHTM), Banjul, The Gambia
| | - Chris S Clarkson
- Wellcome Sanger Genomic Surveillance Unit, Wellcome Sanger Institute, Cambridge, UK
| | - Jacob A Tennessen
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Broad Institute, Cambridge, MA, USA
| | - David Weetman
- Liverpool School of Tropical Medicine, Liverpool, UK
| | - Alistair Miles
- Wellcome Sanger Genomic Surveillance Unit, Wellcome Sanger Institute, Cambridge, UK
| | - Alessandra Della Torre
- Dipartimento di Sanità Pubblica e Malattie Infettive, Istituto Pasteur Italia-Fondazione Cenci-Bolognetti, Università di Roma "Sapienza", Rome, Italy.
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6
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Chen XY, Zhou BF, Shi Y, Liu H, Liang YY, Ingvarsson PK, Wang B. Evolution of the Correlated Genomic Variation Landscape Across a Divergence Continuum in the Genus Castanopsis. Mol Biol Evol 2024; 41:msae191. [PMID: 39248185 PMCID: PMC11421576 DOI: 10.1093/molbev/msae191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2024] [Revised: 08/27/2024] [Accepted: 09/03/2024] [Indexed: 09/10/2024] Open
Abstract
The heterogeneous landscape of genomic variation has been well documented in population genomic studies. However, disentangling the intricate interplay of evolutionary forces influencing the genetic variation landscape over time remains challenging. In this study, we assembled a chromosome-level genome for Castanopsis eyrei and sequenced the whole genomes of 276 individuals from 12 Castanopsis species, spanning a broad divergence continuum. We found highly correlated genomic variation landscapes across these species. Furthermore, variations in genetic diversity and differentiation along the genome were strongly associated with recombination rates and gene density. These results suggest that long-term linked selection and conserved genomic features have contributed to the formation of a common genomic variation landscape. By examining how correlations between population summary statistics change throughout the species divergence continuum, we determined that background selection alone does not fully explain the observed patterns of genomic variation; the effects of recurrent selective sweeps must be considered. We further revealed that extensive gene flow has significantly influenced patterns of genomic variation in Castanopsis species. The estimated admixture proportion correlated positively with recombination rate and negatively with gene density, supporting a scenario of selection against gene flow. Additionally, putative introgression regions exhibited strong signals of positive selection, an enrichment of functional genes, and reduced genetic burdens, indicating that adaptive introgression has played a role in shaping the genomes of hybridizing species. This study provides insights into how different evolutionary forces have interacted in driving the evolution of the genomic variation landscape.
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Affiliation(s)
- Xue-Yan Chen
- Guangdong Provincial Key Laboratory of Applied Botany, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, China
- State Key Laboratory of Plant Diversity and Specialty Crops & Key Laboratory of National Forestry and Grassland Administration on Plant Conservation and Utilization in Southern China, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, China
- South China National Botanical Garden, Guangzhou, China
- University of the Chinese Academy of Sciences, Beijing, China
| | - Biao-Feng Zhou
- Guangdong Provincial Key Laboratory of Applied Botany, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, China
- State Key Laboratory of Plant Diversity and Specialty Crops & Key Laboratory of National Forestry and Grassland Administration on Plant Conservation and Utilization in Southern China, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, China
- South China National Botanical Garden, Guangzhou, China
| | - Yong Shi
- Guangdong Provincial Key Laboratory of Applied Botany, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, China
- State Key Laboratory of Plant Diversity and Specialty Crops & Key Laboratory of National Forestry and Grassland Administration on Plant Conservation and Utilization in Southern China, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, China
- South China National Botanical Garden, Guangzhou, China
| | - Hui Liu
- Guangdong Provincial Key Laboratory of Applied Botany, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, China
- State Key Laboratory of Plant Diversity and Specialty Crops & Key Laboratory of National Forestry and Grassland Administration on Plant Conservation and Utilization in Southern China, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, China
- South China National Botanical Garden, Guangzhou, China
| | - Yi-Ye Liang
- Guangdong Provincial Key Laboratory of Applied Botany, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, China
- State Key Laboratory of Plant Diversity and Specialty Crops & Key Laboratory of National Forestry and Grassland Administration on Plant Conservation and Utilization in Southern China, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, China
- South China National Botanical Garden, Guangzhou, China
| | - Pär K Ingvarsson
- Linnean Center for Plant Biology, Department of Plant Biology, Uppsala BioCenter, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Baosheng Wang
- Guangdong Provincial Key Laboratory of Applied Botany, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, China
- State Key Laboratory of Plant Diversity and Specialty Crops & Key Laboratory of National Forestry and Grassland Administration on Plant Conservation and Utilization in Southern China, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, China
- South China National Botanical Garden, Guangzhou, China
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Bolognini D, Halgren A, Lou RN, Raveane A, Rocha JL, Guarracino A, Soranzo N, Chin CS, Garrison E, Sudmant PH. Recurrent evolution and selection shape structural diversity at the amylase locus. Nature 2024:10.1038/s41586-024-07911-1. [PMID: 39232174 DOI: 10.1038/s41586-024-07911-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 08/06/2024] [Indexed: 09/06/2024]
Abstract
The adoption of agriculture triggered a rapid shift towards starch-rich diets in human populations1. Amylase genes facilitate starch digestion, and increased amylase copy number has been observed in some modern human populations with high-starch intake2, although evidence of recent selection is lacking3,4. Here, using 94 long-read haplotype-resolved assemblies and short-read data from approximately 5,600 contemporary and ancient humans, we resolve the diversity and evolutionary history of structural variation at the amylase locus. We find that amylase genes have higher copy numbers in agricultural populations than in fishing, hunting and pastoral populations. We identify 28 distinct amylase structural architectures and demonstrate that nearly identical structures have arisen recurrently on different haplotype backgrounds throughout recent human history. AMY1 and AMY2A genes each underwent multiple duplication/deletion events with mutation rates up to more than 10,000-fold the single-nucleotide polymorphism mutation rate, whereas AMY2B gene duplications share a single origin. Using a pangenome-based approach, we infer structural haplotypes across thousands of humans identifying extensively duplicated haplotypes at higher frequency in modern agricultural populations. Leveraging 533 ancient human genomes, we find that duplication-containing haplotypes (with more gene copies than the ancestral haplotype) have rapidly increased in frequency over the past 12,000 years in West Eurasians, suggestive of positive selection. Together, our study highlights the potential effects of the agricultural revolution on human genomes and the importance of structural variation in human adaptation.
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Affiliation(s)
| | - Alma Halgren
- Department of Integrative Biology, University of California Berkeley, Berkeley, CA, USA
| | - Runyang Nicolas Lou
- Department of Integrative Biology, University of California Berkeley, Berkeley, CA, USA
| | | | - Joana L Rocha
- Department of Integrative Biology, University of California Berkeley, Berkeley, CA, USA
| | - Andrea Guarracino
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Nicole Soranzo
- Human Technopole, Milan, Italy
- Wellcome Sanger Institute, Hinxton, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
- Department of Haematology, Cambridge Biomedical Campus, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
| | - Chen-Shan Chin
- Foundation for Biological Data Science, Belmont, CA, USA
| | - Erik Garrison
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA.
| | - Peter H Sudmant
- Department of Integrative Biology, University of California Berkeley, Berkeley, CA, USA.
- Center for Computational Biology, University of California Berkeley, Berkeley, CA, USA.
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8
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Wang Y, Allen SL, Reddiex AJ, Chenoweth SF. The impacts of positive selection on genomic variation in Drosophila serrata: Insights from a deep learning approach. Mol Ecol 2024:e17499. [PMID: 39188068 DOI: 10.1111/mec.17499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 07/22/2024] [Accepted: 08/07/2024] [Indexed: 08/28/2024]
Abstract
This study explores the impact of positive selection on the genetic composition of a Drosophila serrata population in eastern Australia through a comprehensive analysis of 110 whole genome sequences. Utilizing an advanced deep learning algorithm (partialS/HIC) and a range of inferred demographic histories, we identified that approximately 14% of the genome is directly affected by sweeps, with soft sweeps being more prevalent (10.6%) than hard sweeps (2.1%), and partial sweeps being uncommon (1.3%). The algorithm demonstrated robustness to demographic assumptions in classifying complete sweeps but faced challenges in distinguishing neutral regions from partial sweeps and linked regions under demographic misspecification. The findings reveal the indirect influence of sweeps on nearly two-thirds of the genome through linkage, with an over-representation of putatively deleterious variants suggesting that positive selection drags deleterious variants to higher frequency due to hitchhiking with beneficial loci. Gene ontology enrichment analysis further supported our confidence in the accuracy of sweep detection as several traits expected to be under positive selection due to evolutionary arms races (e.g. immunity) were detected in hard sweeps. This study provides valuable insights into the direct and indirect contributions of positive selection in shaping genomic variation in natural populations.
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Affiliation(s)
- Yiguan Wang
- School of Biological Sciences, The University of Queensland, St Lucia, Queensland, Australia
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | - Scott L Allen
- School of Biological Sciences, The University of Queensland, St Lucia, Queensland, Australia
| | - Adam J Reddiex
- School of Biological Sciences, The University of Queensland, St Lucia, Queensland, Australia
- Biological Data Science Institute, The Australian National University, Canberra, Australian Capital Territory, Australia
| | - Stephen F Chenoweth
- School of Biological Sciences, The University of Queensland, St Lucia, Queensland, Australia
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9
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Whitehouse LS, Ray D, Schrider DR. Tree sequences as a general-purpose tool for population genetic inference. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.20.581288. [PMID: 39185244 PMCID: PMC11343121 DOI: 10.1101/2024.02.20.581288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/27/2024]
Abstract
As population genetics data increases in size new methods have been developed to store genetic information in efficient ways, such as tree sequences. These data structures are computationally and storage efficient, but are not interchangeable with existing data structures used for many population genetic inference methodologies such as the use of convolutional neural networks (CNNs) applied to population genetic alignments. To better utilize these new data structures we propose and implement a graph convolutional network (GCN) to directly learn from tree sequence topology and node data, allowing for the use of neural network applications without an intermediate step of converting tree sequences to population genetic alignment format. We then compare our approach to standard CNN approaches on a set of previously defined benchmarking tasks including recombination rate estimation, positive selection detection, introgression detection, and demographic model parameter inference. We show that tree sequences can be directly learned from using a GCN approach and can be used to perform well on these common population genetics inference tasks with accuracies roughly matching or even exceeding that of a CNN-based method. As tree sequences become more widely used in population genetics research we foresee developments and optimizations of this work to provide a foundation for population genetics inference moving forward.
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Affiliation(s)
- Logan S. Whitehouse
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Dylan Ray
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Daniel R. Schrider
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, USA
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10
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Vaughn AH, Nielsen R. Fast and Accurate Estimation of Selection Coefficients and Allele Histories from Ancient and Modern DNA. Mol Biol Evol 2024; 41:msae156. [PMID: 39078618 PMCID: PMC11321360 DOI: 10.1093/molbev/msae156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 07/02/2024] [Accepted: 07/10/2024] [Indexed: 07/31/2024] Open
Abstract
We here present CLUES2, a full-likelihood method to infer natural selection from sequence data that is an extension of the method CLUES. We make several substantial improvements to the CLUES method that greatly increases both its applicability and its speed. We add the ability to use ancestral recombination graphs on ancient data as emissions to the underlying hidden Markov model, which enables CLUES2 to use both temporal and linkage information to make estimates of selection coefficients. We also fully implement the ability to estimate distinct selection coefficients in different epochs, which allows for the analysis of changes in selective pressures through time, as well as selection with dominance. In addition, we greatly increase the computational efficiency of CLUES2 over CLUES using several approximations to the forward-backward algorithms and develop a new way to reconstruct historic allele frequencies by integrating over the uncertainty in the estimation of the selection coefficients. We illustrate the accuracy of CLUES2 through extensive simulations and validate the importance sampling framework for integrating over the uncertainty in the inference of gene trees. We also show that CLUES2 is well-calibrated by showing that under the null hypothesis, the distribution of log-likelihood ratios follows a χ2 distribution with the appropriate degrees of freedom. We run CLUES2 on a set of recently published ancient human data from Western Eurasia and test for evidence of changing selection coefficients through time. We find significant evidence of changing selective pressures in several genes correlated with the introduction of agriculture to Europe and the ensuing dietary and demographic shifts of that time. In particular, our analysis supports previous hypotheses of strong selection on lactase persistence during periods of ancient famines and attenuated selection in more modern periods.
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Affiliation(s)
- Andrew H Vaughn
- Center for Computational Biology, University of California, Berkeley, CA 94720, USA
| | - Rasmus Nielsen
- Departments of Integrative Biology and Statistics, University of California, Berkeley, CA 94720, USA
- Center for GeoGenetics, University of Copenhagen, Copenhagen DK-1350, Denmark
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11
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Ndiaye YD, Wong W, Thwing J, Schaffner SF, Brenneman KV, Tine A, Diallo MA, Deme AB, Sy M, Bei AK, Thiaw AB, Daniels R, Ndiaye T, Gaye A, Ndiaye IM, Toure M, Gadiaga N, Sene A, Sow D, Garba MN, Yade MS, Dieye B, Diongue K, Zoumarou D, Ndiaye A, Gomis JF, Fall FB, Ndiop M, Diallo I, Sene D, Macinnis B, Seck MC, Ndiaye M, Ngom B, Diedhiou Y, Mbaye AM, Ndiaye L, Sy N, Badiane AS, Hartl DL, Wirth DF, Volkman SK, Ndiaye D. Two decades of molecular surveillance in Senegal reveal rapid changes in known drug resistance mutations over time. Malar J 2024; 23:205. [PMID: 38982475 PMCID: PMC11234717 DOI: 10.1186/s12936-024-05024-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Accepted: 06/25/2024] [Indexed: 07/11/2024] Open
Abstract
BACKGROUND Drug resistance in Plasmodium falciparum is a major threat to malaria control efforts. Pathogen genomic surveillance could be invaluable for monitoring current and emerging parasite drug resistance. METHODS Data from two decades (2000-2020) of continuous molecular surveillance of P. falciparum parasites from Senegal were retrospectively examined to assess historical changes in malaria drug resistance mutations. Several known drug resistance markers and their surrounding haplotypes were profiled using a combination of single nucleotide polymorphism (SNP) molecular surveillance and whole genome sequence based population genomics. RESULTS This dataset was used to track temporal changes in drug resistance markers whose timing correspond to historically significant events such as the withdrawal of chloroquine (CQ) and the introduction of sulfadoxine-pyrimethamine (SP) in 2003. Changes in the mutation frequency at Pfcrt K76T and Pfdhps A437G coinciding with the 2014 introduction of seasonal malaria chemoprevention (SMC) in Senegal were observed. In 2014, the frequency of Pfcrt K76T increased while the frequency of Pfdhps A437G declined. Haplotype-based analyses of Pfcrt K76T showed that this rapid increase was due to a recent selective sweep that started after 2014. DISCUSSION (CONCLUSION) The rapid increase in Pfcrt K76T is troubling and could be a sign of emerging amodiaquine (AQ) resistance in Senegal. Emerging AQ resistance may threaten the future clinical efficacy of artesunate-amodiaquine (ASAQ) and AQ-dependent SMC chemoprevention. These results highlight the potential of molecular surveillance for detecting rapid changes in parasite populations and stress the need to monitor the effectiveness of AQ as a partner drug for artemisinin-based combination therapy (ACT) and for chemoprevention.
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Affiliation(s)
- Yaye D Ndiaye
- International Research Training Center On Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, 16477, Dakar, Senegal
| | - Wesley Wong
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, 665 Huntington Ave, Boston, MA, 02115, USA
| | - Julie Thwing
- Malaria Branch, Division of Parasitic Diseases and Malaria, Global Health Center, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Stephen F Schaffner
- Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA, 02142, USA
| | - Katelyn Vendrely Brenneman
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, 665 Huntington Ave, Boston, MA, 02115, USA
| | - Abdoulaye Tine
- International Research Training Center On Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, 16477, Dakar, Senegal
| | - Mamadou A Diallo
- International Research Training Center On Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, 16477, Dakar, Senegal
| | - Awa B Deme
- International Research Training Center On Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, 16477, Dakar, Senegal
| | - Mouhamad Sy
- International Research Training Center On Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, 16477, Dakar, Senegal
| | - Amy K Bei
- Yale School of Public Health, 60 College St, New Haven, CT, 06510, USA
| | - Alphonse B Thiaw
- Department of Biochemistry and Functional Genomics, Sherbrooke University, 2500 Bd de L'Universite, Sherbrooke, QC, J1K 2R1, Canada
| | - Rachel Daniels
- RNA Therapeutics Institute, UMass Chan Medical School, 368 Plantation Street, Worcester, MA, 01605, USA
| | - Tolla Ndiaye
- International Research Training Center On Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, 16477, Dakar, Senegal
| | - Amy Gaye
- International Research Training Center On Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, 16477, Dakar, Senegal
| | - Ibrahima M Ndiaye
- International Research Training Center On Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, 16477, Dakar, Senegal
| | - Mariama Toure
- International Research Training Center On Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, 16477, Dakar, Senegal
| | - Nogaye Gadiaga
- International Research Training Center On Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, 16477, Dakar, Senegal
| | - Aita Sene
- International Research Training Center On Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, 16477, Dakar, Senegal
| | - Djiby Sow
- International Research Training Center On Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, 16477, Dakar, Senegal
| | - Mamane N Garba
- International Research Training Center On Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, 16477, Dakar, Senegal
| | - Mamadou S Yade
- International Research Training Center On Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, 16477, Dakar, Senegal
| | - Baba Dieye
- International Research Training Center On Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, 16477, Dakar, Senegal
| | - Khadim Diongue
- International Research Training Center On Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, 16477, Dakar, Senegal
| | - Daba Zoumarou
- International Research Training Center On Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, 16477, Dakar, Senegal
| | - Aliou Ndiaye
- International Research Training Center On Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, 16477, Dakar, Senegal
| | - Jules F Gomis
- International Research Training Center On Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, 16477, Dakar, Senegal
| | - Fatou B Fall
- National Malaria Control Programme (NMCP), 25270, Dakar, Senegal
| | - Medoune Ndiop
- National Malaria Control Programme (NMCP), 25270, Dakar, Senegal
| | - Ibrahima Diallo
- National Malaria Control Programme (NMCP), 25270, Dakar, Senegal
| | - Doudou Sene
- National Malaria Control Programme (NMCP), 25270, Dakar, Senegal
| | - Bronwyn Macinnis
- Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA, 02142, USA
| | - Mame C Seck
- International Research Training Center On Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, 16477, Dakar, Senegal
| | - Mouhamadou Ndiaye
- International Research Training Center On Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, 16477, Dakar, Senegal
| | - Bassirou Ngom
- International Research Training Center On Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, 16477, Dakar, Senegal
| | - Younouss Diedhiou
- International Research Training Center On Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, 16477, Dakar, Senegal
| | - Amadou M Mbaye
- International Research Training Center On Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, 16477, Dakar, Senegal
| | - Lamine Ndiaye
- International Research Training Center On Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, 16477, Dakar, Senegal
| | - Ngayo Sy
- Service de Lutte Antiparasitaire (SLAP), Thiès, Senegal
| | - Aida S Badiane
- International Research Training Center On Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, 16477, Dakar, Senegal
| | - Daniel L Hartl
- Department of Organismic and Evolutionary Biology, Harvard University, 16 Divinity Avenue, Cambridge, MA, 02138, USA
| | - Dyann F Wirth
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, 665 Huntington Ave, Boston, MA, 02115, USA
- Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA, 02142, USA
| | - Sarah K Volkman
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, 665 Huntington Ave, Boston, MA, 02115, USA.
- Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA, 02142, USA.
- Simmons University, 300 The Fenway, Boston, MA, 02115, USA.
| | - Daouda Ndiaye
- International Research Training Center On Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, 16477, Dakar, Senegal
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, 665 Huntington Ave, Boston, MA, 02115, USA
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12
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Nagi SC, Lucas ER, Egyir-Yawson A, Essandoh J, Dadzie S, Chabi J, Djogbénou LS, Medjigbodo AA, Edi CV, Ketoh GK, Koudou BG, Ashraf F, Clarkson CS, Miles A, Weetman D, Donnelly MJ. Parallel Evolution in Mosquito Vectors-A Duplicated Esterase Locus is Associated With Resistance to Pirimiphos-methyl in Anopheles gambiae. Mol Biol Evol 2024; 41:msae140. [PMID: 38985692 PMCID: PMC11267716 DOI: 10.1093/molbev/msae140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Revised: 05/02/2024] [Accepted: 05/29/2024] [Indexed: 07/12/2024] Open
Abstract
The primary control methods for the African malaria mosquito, Anopheles gambiae, are based on insecticidal interventions. Emerging resistance to these compounds is therefore of major concern to malaria control programs. The organophosphate (OP), pirimiphos-methyl, is a relatively new chemical in the vector control armory but is now widely used in indoor-residual spray campaigns. While generally effective, phenotypic resistance has developed in some areas in malaria vectors. Here, we used a population genomic approach to identify novel mechanisms of resistance to pirimiphos-methyl in A. gambiae s.l mosquitoes. In multiple populations, we found large and repeated signals of selection at a locus containing a cluster of detoxification enzymes, some of whose orthologs are known to confer resistance to OPs in Culex pipiens. Close examination revealed a pair of alpha-esterases, Coeae1f and Coeae2f, and a complex and diverse pattern of haplotypes under selection in A. gambiae, A. coluzzii and A. arabiensis. As in C. pipiens, copy number variants have arisen at this locus. We used diplotype clustering to examine whether these signals arise from parallel evolution or adaptive introgression. Using whole-genome sequenced phenotyped samples, we found that in West Africa, a copy number variant in A. gambiae is associated with resistance to pirimiphos-methyl. Overall, we demonstrate a striking example of contemporary parallel evolution which has important implications for malaria control programs.
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Affiliation(s)
- Sanjay C Nagi
- Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, L3 5QA, UK
| | - Eric R Lucas
- Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, L3 5QA, UK
| | | | - John Essandoh
- Department of Biomedical Sciences, University of Cape Coast, Cape Coast, Ghana
| | - Samuel Dadzie
- Department of Parasitology, Noguchi Memorial Institute for Medical Research, University of Ghana, Accra, Ghana
| | - Joseph Chabi
- Department of Parasitology, Noguchi Memorial Institute for Medical Research, University of Ghana, Accra, Ghana
| | - Luc S Djogbénou
- Laboratory of Infectious Vector Borne Diseases, Tropical Infectious Diseases Research Center (TIDRC), Université d’Abomey-Calavi (UAC), 01 B.P. 526 Cotonou, Benin
| | - Adandé A Medjigbodo
- Laboratory of Infectious Vector Borne Diseases, Tropical Infectious Diseases Research Center (TIDRC), Université d’Abomey-Calavi (UAC), 01 B.P. 526 Cotonou, Benin
| | - Constant V Edi
- Research and Development Department, Centre Suisse de Recherches Scientifiques en Côte d’Ivoire, 01 BP 1303 Abidjan, Côte d’Ivoire
| | - Guillaume K Ketoh
- Department of Zoology, Faculty of Sciences, Laboratory of Ecology and Ecotoxicology, Université de Lomé, 01 B.P. 1515 Lomé, Togo
| | - Benjamin G Koudou
- Research and Development Department, Centre Suisse de Recherches Scientifiques en Côte d’Ivoire, 01 BP 1303 Abidjan, Côte d’Ivoire
| | - Faisal Ashraf
- Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, L3 5QA, UK
| | - Chris S Clarkson
- Wellcome Sanger Genomic Surveillance Unit, Wellcome Sanger Institute, Hinxton, Cambridge, CB10 1RQ, UK
| | - Alistair Miles
- Wellcome Sanger Genomic Surveillance Unit, Wellcome Sanger Institute, Hinxton, Cambridge, CB10 1RQ, UK
| | - David Weetman
- Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, L3 5QA, UK
| | - Martin J Donnelly
- Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, L3 5QA, UK
- Wellcome Sanger Genomic Surveillance Unit, Wellcome Sanger Institute, Hinxton, Cambridge, CB10 1RQ, UK
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13
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Hernandes N, Qi XM, Bhide S, Brown C, Camm BJ, Baxter SW, Robin C. Acetylcholine esterase of Drosophila melanogaster: a laboratory model to explore insecticide susceptibility gene drives. PEST MANAGEMENT SCIENCE 2024; 80:2950-2964. [PMID: 38344908 DOI: 10.1002/ps.8003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 01/28/2024] [Accepted: 02/12/2024] [Indexed: 03/01/2024]
Abstract
BACKGROUND One of the proposed applications of gene drives has been to revert pesticide resistant mutations back to the ancestral susceptible state. Insecticides that have become ineffective because of the rise of resistance could have reinvigorated utility and be used to suppress pest populations again, perhaps at lower application doses. RESULTS We have created a laboratory model for susceptibility gene drives that replaces field-selected resistant variants of the acetylcholine esterase (Ace) locus of Drosophila melanogaster with ancestral susceptible variants. We constructed a CRISPR/Cas9 homing drive and found that homing occurred in many genetic backgrounds with varying efficiencies. While the drive itself could not be homozygous, it converted resistant alleles into susceptible ones and produced recessive lethal alleles that could suppress populations. Our studies provided evidence for two distinct classes of gene drive resistance (GDR): rather than being mediated by the conventional non-homologous end-joining (NHEJ) pathway, one seemed to involve short homologous repair and the other was defined by genetic background. Additionally, we used simulations to explore a distinct application of susceptibility drives; the use of chemicals to prevent the spread of synthetic gene drives into protected areas. CONCLUSIONS Insecticide susceptibility gene drives could be useful tools to control pest insects however problems with particularities of target loci and GDR will need to be overcome for them to be effective. Furthermore, realistic patterns of pest dispersal and high insecticide exposure rates would be required if susceptibility were to be useful as a 'safety-switch' to prevent the unwanted spread of gene drives. © 2024 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
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Affiliation(s)
- Natalia Hernandes
- The School of BioSciences, The University of Melbourne, Melbourne, Victoria, Australia
| | - Xiaomeng Mollyann Qi
- The School of BioSciences, The University of Melbourne, Melbourne, Victoria, Australia
| | - Soumitra Bhide
- The School of BioSciences, The University of Melbourne, Melbourne, Victoria, Australia
| | - Courtney Brown
- The School of BioSciences, The University of Melbourne, Melbourne, Victoria, Australia
| | - Benjamin J Camm
- The School of BioSciences, The University of Melbourne, Melbourne, Victoria, Australia
| | - Simon W Baxter
- The School of BioSciences, The University of Melbourne, Melbourne, Victoria, Australia
| | - Charles Robin
- The School of BioSciences, The University of Melbourne, Melbourne, Victoria, Australia
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14
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Daron J, Bouafou L, Tennessen JA, Rahola N, Makanga B, Akone-Ella O, Ngangue MF, Longo Pendy NM, Paupy C, Neafsey DE, Fontaine MC, Ayala D. Genomic Signatures of Microgeographic Adaptation in Anopheles coluzzii Along an Anthropogenic Gradient in Gabon. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.16.594472. [PMID: 38798379 PMCID: PMC11118577 DOI: 10.1101/2024.05.16.594472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Species distributed across heterogeneous environments often evolve locally adapted populations, but understanding how these persist in the presence of homogenizing gene flow remains puzzling. In Gabon, Anopheles coluzzii, a major African malaria mosquito is found along an ecological gradient, including a sylvatic population, away of any human presence. This study identifies into the genomic signatures of local adaptation in populations from distinct environments including the urban area of Libreville, and two proximate sites 10km apart in the La Lopé National Park (LLP), a village and its sylvatic neighborhood. Whole genome re-sequencing of 96 mosquitoes unveiled ∼ 5.7millions high-quality single nucleotide polymorphisms. Coalescent-based demographic analyses suggest an ∼ 8,000-year-old divergence between Libreville and La Lopé populations, followed by a secondary contact ( ∼ 4,000 ybp) resulting in asymmetric effective gene flow. The urban population displayed reduced effective size, evidence of inbreeding, and strong selection pressures for adaptation to urban settings, as suggested by the hard selective sweeps associated with genes involved in detoxification and insecticide resistance. In contrast, the two geographically proximate LLP populations showed larger effective sizes, and distinctive genomic differences in selective signals, notably soft-selective sweeps on the standing genetic variation. Although neutral loci and chromosomal inversions failed to discriminate between LLP populations, our findings support that microgeographic adaptation can swiftly emerge through selection on standing genetic variation despite high gene flow. This study contributes to the growing understanding of evolution of populations in heterogeneous environments amid ongoing gene flow and how major malaria mosquitoes adapt to human. Significance Anopheles coluzzii , a major African malaria vector, thrives from humid rainforests to dry savannahs and coastal areas. This ecological success is linked to its close association with domestic settings, with human playing significant roles in driving the recent urban evolution of this mosquito. Our research explores the assumption that these mosquitoes are strictly dependent on human habitats, by conducting whole-genome sequencing on An. coluzzii specimens from urban, rural, and sylvatic sites in Gabon. We found that urban mosquitoes show de novo genetic signatures of human-driven vector control, while rural and sylvatic mosquitoes exhibit distinctive genetic evidence of local adaptations derived from standing genetic variation. Understanding adaptation mechanisms of this mosquito is therefore crucial to predict evolution of vector control strategies.
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15
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Chen J, Liu C, Li W, Zhang W, Wang Y, Clark AG, Lu J. From sub-Saharan Africa to China: Evolutionary history and adaptation of Drosophila melanogaster revealed by population genomics. SCIENCE ADVANCES 2024; 10:eadh3425. [PMID: 38630810 PMCID: PMC11023512 DOI: 10.1126/sciadv.adh3425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Accepted: 03/13/2024] [Indexed: 04/19/2024]
Abstract
Drosophila melanogaster is a widely used model organism for studying environmental adaptation. However, the genetic diversity of populations in Asia is poorly understood, leaving a notable gap in our knowledge of the global evolution and adaptation of this species. We sequenced genomes of 292 D. melanogaster strains from various ecological settings in China and analyzed them along with previously published genome sequences. We have identified six global genetic ancestry groups, despite the presence of widespread genetic admixture. The strains from China represent a unique ancestry group, although detectable differentiation exists among populations within China. We deciphered the global migration and demography of D. melanogaster, and identified widespread signals of adaptation, including genetic changes in response to insecticides. We validated the effects of insecticide resistance variants using population cage trials and deep sequencing. This work highlights the importance of population genomics in understanding the genetic underpinnings of adaptation, an effort that is particularly relevant given the deterioration of ecosystems.
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Affiliation(s)
- Junhao Chen
- State Key Laboratory of Protein and Plant Gene Research, Center for Bioinformatics, School of Life Sciences, Peking University, Beijing 100871, China
| | - Chenlu Liu
- State Key Laboratory of Protein and Plant Gene Research, Center for Bioinformatics, School of Life Sciences, Peking University, Beijing 100871, China
| | - Weixuan Li
- State Key Laboratory of Protein and Plant Gene Research, Center for Bioinformatics, School of Life Sciences, Peking University, Beijing 100871, China
| | - Wenxia Zhang
- State Key Laboratory of Protein and Plant Gene Research, Center for Bioinformatics, School of Life Sciences, Peking University, Beijing 100871, China
| | - Yirong Wang
- College of Biology, Hunan University, Changsha 410082, China
| | - Andrew G. Clark
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, USA
| | - Jian Lu
- State Key Laboratory of Protein and Plant Gene Research, Center for Bioinformatics, School of Life Sciences, Peking University, Beijing 100871, China
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16
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Dennis TPW, Essandoh J, Mable BK, Viana MS, Yawson AE, Weetman D. Signatures of adaptation at key insecticide resistance loci in Anopheles gambiae in Southern Ghana revealed by reduced-coverage WGS. Sci Rep 2024; 14:8650. [PMID: 38622230 PMCID: PMC11018624 DOI: 10.1038/s41598-024-58906-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 04/04/2024] [Indexed: 04/17/2024] Open
Abstract
Resistance to insecticides and adaptation to a diverse range of environments present challenges to Anopheles gambiae s.l. mosquito control efforts in sub-Saharan Africa. Whole-genome-sequencing is often employed for identifying the genomic basis underlying adaptation in Anopheles, but remains expensive for large-scale surveys. Reduced coverage whole-genome-sequencing can identify regions of the genome involved in adaptation at a lower cost, but is currently untested in Anopheles mosquitoes. Here, we use reduced coverage WGS to investigate population genetic structure and identify signatures of local adaptation in Anopheles mosquitoes across southern Ghana. In contrast to previous analyses, we find no structuring by ecoregion, with Anopheles coluzzii and Anopheles gambiae populations largely displaying the hallmarks of large, unstructured populations. However, we find signatures of selection at insecticide resistance loci that appear ubiquitous across ecoregions in An. coluzzii, and strongest in forest ecoregions in An. gambiae. Our study highlights resistance candidate genes in this region, and validates reduced coverage WGS, potentially to very low coverage levels, for population genomics and exploratory surveys for adaptation in Anopheles taxa.
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Affiliation(s)
- Tristan P W Dennis
- Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, UK.
- School of Biodiversity, One Health, and Veterinary Medicine, University of Glasgow, Glasgow, UK.
| | - John Essandoh
- Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, UK
- Department of Conservation Biology and Entomology, School of Biological Sciences, University of Cape Coast, Cape Coast, Ghana
| | - Barbara K Mable
- School of Biodiversity, One Health, and Veterinary Medicine, University of Glasgow, Glasgow, UK
| | - Mafalda S Viana
- School of Biodiversity, One Health, and Veterinary Medicine, University of Glasgow, Glasgow, UK
| | - Alexander E Yawson
- Department of Biomedical Sciences, School of Allied Health Sciences, University of Cape Coast, Cape Coast, Ghana
| | - David Weetman
- Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, UK
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17
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Harris M, Kim BY, Garud N. Enrichment of hard sweeps on the X chromosome compared to autosomes in six Drosophila species. Genetics 2024; 226:iyae019. [PMID: 38366786 PMCID: PMC10990427 DOI: 10.1093/genetics/iyae019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 01/17/2024] [Accepted: 01/18/2024] [Indexed: 02/18/2024] Open
Abstract
The X chromosome, being hemizygous in males, is exposed one-third of the time increasing the visibility of new mutations to natural selection, potentially leading to different evolutionary dynamics than autosomes. Recently, we found an enrichment of hard selective sweeps over soft selective sweeps on the X chromosome relative to the autosomes in a North American population of Drosophila melanogaster. To understand whether this enrichment is a universal feature of evolution on the X chromosome, we analyze diversity patterns across 6 commonly studied Drosophila species. We find an increased proportion of regions with steep reductions in diversity and elevated homozygosity on the X chromosome compared to autosomes. To assess if these signatures are consistent with positive selection, we simulate a wide variety of evolutionary scenarios spanning variations in demography, mutation rate, recombination rate, background selection, hard sweeps, and soft sweeps and find that the diversity patterns observed on the X are most consistent with hard sweeps. Our findings highlight the importance of sex chromosomes in driving evolutionary processes and suggest that hard sweeps have played a significant role in shaping diversity patterns on the X chromosome across multiple Drosophila species.
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Affiliation(s)
- Mariana Harris
- Department of Computational Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Bernard Y Kim
- Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Nandita Garud
- Department of Ecology and Evolutionary Biology, University of California Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, University of California Los Angeles, Los Angeles, CA 90095, USA
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18
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Song H, Chu J, Li W, Li X, Fang L, Han J, Zhao S, Ma Y. A Novel Approach Utilizing Domain Adversarial Neural Networks for the Detection and Classification of Selective Sweeps. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2304842. [PMID: 38308186 PMCID: PMC11005742 DOI: 10.1002/advs.202304842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 01/10/2024] [Indexed: 02/04/2024]
Abstract
The identification and classification of selective sweeps are of great significance for improving the understanding of biological evolution and exploring opportunities for precision medicine and genetic improvement. Here, a domain adaptation sweep detection and classification (DASDC) method is presented to balance the alignment of two domains and the classification performance through a domain-adversarial neural network and its adversarial learning modules. DASDC effectively addresses the issue of mismatch between training data and real genomic data in deep learning models, leading to a significant improvement in its generalization capability, prediction robustness, and accuracy. The DASDC method demonstrates improved identification performance compared to existing methods and excels in classification performance, particularly in scenarios where there is a mismatch between application data and training data. The successful implementation of DASDC in real data of three distinct species highlights its potential as a useful tool for identifying crucial functional genes and investigating adaptive evolutionary mechanisms, particularly with the increasing availability of genomic data.
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Affiliation(s)
- Hui Song
- Key Laboratory of Agricultural Animal GeneticsBreeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of AgricultureHuazhong Agricultural UniversityWuhan430070China
| | - Jinyu Chu
- Key Laboratory of Agricultural Animal GeneticsBreeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of AgricultureHuazhong Agricultural UniversityWuhan430070China
| | - Wangjiao Li
- Key Laboratory of Agricultural Animal GeneticsBreeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of AgricultureHuazhong Agricultural UniversityWuhan430070China
| | - Xinyun Li
- Key Laboratory of Agricultural Animal GeneticsBreeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of AgricultureHuazhong Agricultural UniversityWuhan430070China
- Hubei Hongshan LaboratoryWuhan430070China
| | - Lingzhao Fang
- Center for Quantitative Genetics and GenomicsAarhus UniversityAarhus8000Denmark
| | - Jianlin Han
- Key Laboratory of Agricultural Animal GeneticsBreeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of AgricultureHuazhong Agricultural UniversityWuhan430070China
- CAAS‐ILRI Joint Laboratory on Livestock and Forage Genetic ResourcesInstitute of Animal ScienceChinese Academy of Agricultural Sciences (CAAS)Beijing100193China
- Livestock Genetics ProgramInternational Livestock Research Institute (ILRI)Nairobi00100Kenya
| | - Shuhong Zhao
- Key Laboratory of Agricultural Animal GeneticsBreeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of AgricultureHuazhong Agricultural UniversityWuhan430070China
- Hubei Hongshan LaboratoryWuhan430070China
- Lingnan Modern Agricultural Science and Technology Guangdong LaboratoryGuangzhou510642China
| | - Yunlong Ma
- Key Laboratory of Agricultural Animal GeneticsBreeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of AgricultureHuazhong Agricultural UniversityWuhan430070China
- Hubei Hongshan LaboratoryWuhan430070China
- Lingnan Modern Agricultural Science and Technology Guangdong LaboratoryGuangzhou510642China
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19
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Lyulina AS, Liu Z, Good BH. Linkage equilibrium between rare mutations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.28.587282. [PMID: 38617331 PMCID: PMC11014483 DOI: 10.1101/2024.03.28.587282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
Recombination breaks down genetic linkage by reshuffling existing variants onto new genetic backgrounds. These dynamics are traditionally quantified by examining the correlations between alleles, and how they decay as a function of the recombination rate. However, the magnitudes of these correlations are strongly influenced by other evolutionary forces like natural selection and genetic drift, making it difficult to tease out the effects of recombination. Here we introduce a theoretical framework for analyzing an alternative family of statistics that measure the homoplasy produced by recombination. We derive analytical expressions that predict how these statistics depend on the rates of recombination and recurrent mutation, the strength of negative selection and genetic drift, and the present-day frequencies of the mutant alleles. We find that the degree of homoplasy can strongly depend on this frequency scale, which reflects the underlying timescales over which these mutations occurred. We show how these scaling properties can be used to isolate the effects of recombination, and discuss their implications for the rates of horizontal gene transfer in bacteria.
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Affiliation(s)
- Anastasia S Lyulina
- Department of Biology, Stanford University, Stanford, CA 94305, USA
- Department of Applied Physics, Stanford University, Stanford, CA 94305, USA
| | - Zhiru Liu
- Department of Applied Physics, Stanford University, Stanford, CA 94305, USA
| | - Benjamin H Good
- Department of Biology, Stanford University, Stanford, CA 94305, USA
- Department of Applied Physics, Stanford University, Stanford, CA 94305, USA
- Chan Zuckerberg Biohub - San Francisco, San Francisco, CA 94158, USA
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20
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Caputo B, De Marco C, Pichler V, Bottà G, Bennett K, Clarkson C, Tennessen J, Weetman D, Miles A, Torre AD. Speciation within the Anopheles gambiae complex: high-throughput whole genome sequencing reveals evidence of a putative new cryptic taxon in 'far-west' Africa. RESEARCH SQUARE 2024:rs.3.rs-3914444. [PMID: 38562903 PMCID: PMC10984024 DOI: 10.21203/rs.3.rs-3914444/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
The two main Afrotropical malaria vectors - Anopheles coluzzii and An. gambiae - are genetically distinct and reproductively isolated across West Africa. However, populations at the western extreme of their range are assigned as "intermediate" between the two species by whole genome sequence (WGS) data, and as hybrid forms by conventional molecular diagnostics. By exploiting WGS data from 1,190 specimens collected across west Africa via the Anopheles gambiae 1000 Genomes network, we identify a novel putative taxon in the far-west (provisionally named Bissau molecular form), which did not arise by admixture but rather originated at the same time as the split between An. coluzzii and An. gambiae. Intriguingly, these populations lack insecticide resistance mechanisms commonly observed in the two main species. These findings lead to a change of perspective on malaria vector species in the far-west region with potential for epidemiological implications, and a new challenge for genetic-based mosquito control approaches.
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Affiliation(s)
- B. Caputo
- Dipartimento di Sanità Pubblica e Malattie Infettive, Istituto Pasteur Italia-Fondazione Cenci-Bolognetti, Università di Roma “Sapienza”, Rome Italy
| | - C.M. De Marco
- Dipartimento di Sanità Pubblica e Malattie Infettive, Istituto Pasteur Italia-Fondazione Cenci-Bolognetti, Università di Roma “Sapienza”, Rome Italy
| | - V. Pichler
- Dipartimento di Sanità Pubblica e Malattie Infettive, Istituto Pasteur Italia-Fondazione Cenci-Bolognetti, Università di Roma “Sapienza”, Rome Italy
| | - G. Bottà
- Dipartimento di Sanità Pubblica e Malattie Infettive, Istituto Pasteur Italia-Fondazione Cenci-Bolognetti, Università di Roma “Sapienza”, Rome Italy
| | - K.L. Bennett
- Wellcome Sanger Genomic Surveillance Unit, Wellcome Sanger Institute, Hinxton, Cambridge, UK
| | - C.S. Clarkson
- Wellcome Sanger Genomic Surveillance Unit, Wellcome Sanger Institute, Hinxton, Cambridge, UK
| | - J.A. Tennessen
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Broad Institute, Cambridge, MA, USA
| | - D. Weetman
- Liverpool School of Tropical Medicine, Liverpool, UK
| | - A. Miles
- Wellcome Sanger Genomic Surveillance Unit, Wellcome Sanger Institute, Hinxton, Cambridge, UK
| | - A. della Torre
- Dipartimento di Sanità Pubblica e Malattie Infettive, Istituto Pasteur Italia-Fondazione Cenci-Bolognetti, Università di Roma “Sapienza”, Rome Italy
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21
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Lucas ER, Nagi SC, Kabula B, Batengana B, Kisinza W, Egyir-Yawson A, Essandoh J, Dadzie S, Chabi J, Van't Hof AE, Rippon EJ, Pipini D, Harding NJ, Dyer NA, Clarkson CS, Miles A, Weetman D, Donnelly MJ. Copy number variants underlie the major selective sweeps in insecticide resistance genes in Anopheles arabiensis from Tanzania. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.11.583874. [PMID: 38559088 PMCID: PMC10979859 DOI: 10.1101/2024.03.11.583874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
To keep ahead of the evolution of resistance to insecticides in mosquitoes, national malaria control programmes must make use of a range of insecticides, both old and new, while monitoring resistance mechanisms. Knowledge of the mechanisms of resistance remains limited in Anopheles arabiensis, which in many parts of Africa is of increasing importance because it is apparently less susceptible to many indoor control interventions. Furthermore, comparatively little is known in general about resistance to non-pyrethroid insecticides such as pirimiphos-methyl (PM), which are crucial for effective control in the context of resistance to pyrethroids. We performed a genome-wide association study to determine the molecular mechanisms of resistance to deltamethrin (commonly used in bednets) and PM, in An. arabiensis from two regions in Tanzania. Genomic regions of positive selection in these populations were largely driven by copy number variants (CNVs) in gene families involved in resistance to these two insecticides. We found evidence of a new gene cluster involved in resistance to PM, identifying a strong selective sweep tied to a CNV in the Coeae2g-Coeae6g cluster of carboxylesterase genes. Using complementary data from An. coluzzii in Ghana, we show that copy number at this locus is significantly associated with PM resistance. Similarly, for deltamethrin, resistance was strongly associated with a novel CNV allele in the Cyp6aa / Cyp6p cluster. Against this background of metabolic resistance, target site resistance was very rare or absent for both insecticides. Mutations in the pyrethroid target site Vgsc were at very low frequency in Tanzania, yet combining these samples with three An. arabiensis individuals from West Africa revealed a startling diversity of evolutionary origins of target site resistance, with up to 5 independent origins of Vgsc-995 mutations found within just 8 haplotypes. Thus, despite having been first recorded over 10 years ago, Vgsc resistance mutations in Tanzanian An. arabiensis have remained at stable low frequencies. Overall, our results provide a new copy number marker for monitoring resistance to PM in malaria mosquitoes, and reveal the complex picture of resistance patterns in An. arabiensis.
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Affiliation(s)
- Eric R Lucas
- Department of Vector Biology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool L3 5QA, UK
| | - Sanjay C Nagi
- Department of Vector Biology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool L3 5QA, UK
| | - Bilali Kabula
- National Institute for Medical Research, Amani Research Centre, P.O. Box 81, Muheza, Tanzania
| | - Bernard Batengana
- National Institute for Medical Research, Amani Research Centre, P.O. Box 81, Muheza, Tanzania
| | - William Kisinza
- National Institute for Medical Research, Amani Research Centre, P.O. Box 81, Muheza, Tanzania
| | | | - John Essandoh
- Department of Biomedical Sciences, University of Cape Coast, Cape Coast, Ghana
| | - Sam Dadzie
- Department of Parasitology, Noguchi Memorial Institute for Medical Research, University of Ghana, Accra, Ghana
| | - Joseph Chabi
- Department of Parasitology, Noguchi Memorial Institute for Medical Research, University of Ghana, Accra, Ghana
| | - Arjen E Van't Hof
- Department of Vector Biology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool L3 5QA, UK
- Biology Centre of the Czech Academy of Sciences, Institute of Entomology, Branišovská 31, 370 05 České Budějovice, Czech Republic
| | - Emily J Rippon
- Department of Vector Biology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool L3 5QA, UK
| | - Dimitra Pipini
- Department of Vector Biology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool L3 5QA, UK
| | - Nicholas J Harding
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Naomi A Dyer
- Department of Vector Biology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool L3 5QA, UK
| | - Chris S Clarkson
- Wellcome Sanger Institute, Hinxton, Cambridge CB10 1SA, United Kingdom
| | - Alistair Miles
- Wellcome Sanger Institute, Hinxton, Cambridge CB10 1SA, United Kingdom
| | - David Weetman
- Department of Vector Biology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool L3 5QA, UK
| | - Martin J Donnelly
- Department of Vector Biology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool L3 5QA, UK
- Wellcome Sanger Institute, Hinxton, Cambridge CB10 1SA, United Kingdom
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22
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Liu S, Luo H, Zhang P, Li Y, Hao D, Zhang S, Song T, Xu T, He S. Adaptive Selection of Cis-regulatory Elements in the Han Chinese. Mol Biol Evol 2024; 41:msae034. [PMID: 38377343 PMCID: PMC10917166 DOI: 10.1093/molbev/msae034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 01/18/2024] [Accepted: 02/05/2024] [Indexed: 02/22/2024] Open
Abstract
Cis-regulatory elements have an important role in human adaptation to the living environment. However, the lag in population genomic cohort studies and epigenomic studies, hinders the research in the adaptive analysis of cis-regulatory elements in human populations. In this study, we collected 4,013 unrelated individuals and performed a comprehensive analysis of adaptive selection of genome-wide cis-regulatory elements in the Han Chinese. In total, 12.34% of genomic regions are under the influence of adaptive selection, where 1.00% of enhancers and 2.06% of promoters are under positive selection, and 0.06% of enhancers and 0.02% of promoters are under balancing selection. Gene ontology enrichment analysis of these cis-regulatory elements under adaptive selection reveals that many positive selections in the Han Chinese occur in pathways involved in cell-cell adhesion processes, and many balancing selections are related to immune processes. Two classes of adaptive cis-regulatory elements related to cell adhesion were in-depth analyzed, one is the adaptive enhancers derived from neanderthal introgression, leads to lower hyaluronidase level in skin, and brings better performance on UV-radiation resistance to the Han Chinese. Another one is the cis-regulatory elements regulating wound healing, and the results suggest the positive selection inhibits coagulation and promotes angiogenesis and wound healing in the Han Chinese. Finally, we found that many pathogenic alleles, such as risky alleles of type 2 diabetes or schizophrenia, remain in the population due to the hitchhiking effect of positive selections. Our findings will help deepen our understanding of the adaptive evolution of genome regulation in the Han Chinese.
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Affiliation(s)
- Shuai Liu
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Huaxia Luo
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Peng Zhang
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Yanyan Li
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Di Hao
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Sijia Zhang
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tingrui Song
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Tao Xu
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan 250117, Shandong, China
| | - Shunmin He
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
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23
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Ingham V, Nagi S. Genomic Profiling of Insecticide Resistance in Malaria Vectors: Insights into Molecular Mechanisms. RESEARCH SQUARE 2024:rs.3.rs-3910702. [PMID: 38410472 PMCID: PMC10896400 DOI: 10.21203/rs.3.rs-3910702/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
Malaria control faces challenges from widespread insecticide resistance in major Anopheles species. This study, employing a cross-species approach, integrates RNA-Sequencing, whole-genome sequencing, and microarray data to elucidate drivers of insecticide resistance in Anopheles gambiae complex and An. funestus. Findings show an inverse relationship between genetic diversity and gene expression, with highly expressed genes experiencing stronger purifying selection. These genes cluster physically in the genome, revealing potential coordinated regulation. We identified known and novel candidate insecticide resistance genes, enriched in metabolic, cuticular, and behavioural functions. We also present AnoExpress, a Python package, and an online interface for user-friendly exploration of resistance candidate expression. Despite millions of years of speciation, convergent gene expression responses to insecticidal selection pressures are observed across Anopheles species, providing crucial insights for malaria vector control. This study culminates in a rich dataset that allows us to understand molecular mechanisms, better enabling us to combat insecticide resistance effectively.
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24
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Nunez JCB, Lenhart BA, Bangerter A, Murray CS, Mazzeo GR, Yu Y, Nystrom TL, Tern C, Erickson PA, Bergland AO. A cosmopolitan inversion facilitates seasonal adaptation in overwintering Drosophila. Genetics 2024; 226:iyad207. [PMID: 38051996 PMCID: PMC10847723 DOI: 10.1093/genetics/iyad207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Accepted: 11/28/2023] [Indexed: 12/07/2023] Open
Abstract
Fluctuations in the strength and direction of natural selection through time are a ubiquitous feature of life on Earth. One evolutionary outcome of such fluctuations is adaptive tracking, wherein populations rapidly adapt from standing genetic variation. In certain circumstances, adaptive tracking can lead to the long-term maintenance of functional polymorphism despite allele frequency change due to selection. Although adaptive tracking is likely a common process, we still have a limited understanding of aspects of its genetic architecture and its strength relative to other evolutionary forces such as drift. Drosophila melanogaster living in temperate regions evolve to track seasonal fluctuations and are an excellent system to tackle these gaps in knowledge. By sequencing orchard populations collected across multiple years, we characterized the genomic signal of seasonal demography and identified that the cosmopolitan inversion In(2L)t facilitates seasonal adaptive tracking and shows molecular footprints of selection. A meta-analysis of phenotypic studies shows that seasonal loci within In(2L)t are associated with behavior, life history, physiology, and morphological traits. We identify candidate loci and experimentally link them to phenotype. Our work contributes to our general understanding of fluctuating selection and highlights the evolutionary outcome and dynamics of contemporary selection on inversions.
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Affiliation(s)
- Joaquin C B Nunez
- Department of Biology, University of Virginia, 90 Geldard Drive, Charlottesville, VA 22901, USA
- Department of Biology, University of Vermont, 109 Carrigan Drive, Burlington, VT 05405, USA
| | - Benedict A Lenhart
- Department of Biology, University of Virginia, 90 Geldard Drive, Charlottesville, VA 22901, USA
| | - Alyssa Bangerter
- Department of Biology, University of Virginia, 90 Geldard Drive, Charlottesville, VA 22901, USA
| | - Connor S Murray
- Department of Biology, University of Virginia, 90 Geldard Drive, Charlottesville, VA 22901, USA
| | - Giovanni R Mazzeo
- Department of Biology, University of Virginia, 90 Geldard Drive, Charlottesville, VA 22901, USA
| | - Yang Yu
- Department of Biology, University of Virginia, 90 Geldard Drive, Charlottesville, VA 22901, USA
| | - Taylor L Nystrom
- Department of Biology, University of Virginia, 90 Geldard Drive, Charlottesville, VA 22901, USA
| | - Courtney Tern
- Department of Biology, University of Virginia, 90 Geldard Drive, Charlottesville, VA 22901, USA
| | - Priscilla A Erickson
- Department of Biology, University of Virginia, 90 Geldard Drive, Charlottesville, VA 22901, USA
- Department of Biology, University of Richmond, 138 UR Drive, Richmond, VA 23173, USA
| | - Alan O Bergland
- Department of Biology, University of Virginia, 90 Geldard Drive, Charlottesville, VA 22901, USA
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25
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Champer SE, Chae B, Haller BC, Champer J, Messer PW. Resource-explicit interactions in spatial population models. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.13.575512. [PMID: 38293045 PMCID: PMC10827080 DOI: 10.1101/2024.01.13.575512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Continuous-space population models can yield significantly different results from their panmictic counterparts when assessing evolutionary, ecological, or population-genetic processes. However, the computational burden of spatial models is typically much greater than that of panmictic models due to the overhead of determining which individuals interact with one another and how strongly they interact. Though these calculations are necessary to model local competition that regulates the population density, they can lead to prohibitively long runtimes. Here, we present a novel modeling method in which the resources available to a population are abstractly represented as an additional layer of the simulation. Instead of interacting directly with one another, individuals interact indirectly via this resource layer. We find that this method closely matches other spatial models, yet can dramatically increase the speed of the model, allowing the simulation of much larger populations. Additionally, models structured in this manner exhibit other desirable characteristics, including more realistic spatial dynamics near the edge of the simulated area, and an efficient route for modeling more complex heterogeneous landscapes.
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Affiliation(s)
- Samuel E. Champer
- Department of Computational Biology, Cornell University, Ithaca, NY 14853
| | - Bryan Chae
- Department of Computational Biology, Cornell University, Ithaca, NY 14853
| | - Benjamin C. Haller
- Department of Computational Biology, Cornell University, Ithaca, NY 14853
| | - Jackson Champer
- Center for Bioinformatics, School of Life Sciences, Center for Life Sciences, Peking University, Beijing, China 100871
| | - Philipp W. Messer
- Department of Computational Biology, Cornell University, Ithaca, NY 14853
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26
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Wolff R, Garud NR. Pervasive selective sweeps across human gut microbiomes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.22.573162. [PMID: 38187688 PMCID: PMC10769429 DOI: 10.1101/2023.12.22.573162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
The human gut microbiome is composed of a highly diverse consortia of species which are continually evolving within and across hosts. The ability to identify adaptations common to many host gut microbiomes would not only reveal shared selection pressures across hosts, but also key drivers of functional differentiation of the microbiome that may affect community structure and host traits. However, to date there has not been a systematic scan for adaptations that have spread across host microbiomes. Here, we develop a novel selection scan statistic, named the integrated linkage disequilibrium score (iLDS), that can detect the spread of adaptive haplotypes across host microbiomes via migration and horizontal gene transfer. Specifically, iLDS leverages signals of hitchhiking of deleterious variants with the beneficial variant, a common feature of adaptive evolution. We find that iLDS is capable of detecting simulated and known cases of selection, and moreover is robust to potential confounders that can also elevate LD. Application of the statistic to ~20 common commensal gut species from a large cohort of healthy, Western adults reveals pervasive spread of selected alleles across human microbiomes mediated by horizontal gene transfer. Among the candidate selective sweeps recovered by iLDS is an enrichment for genes involved in the metabolism of maltodextrin, a synthetic starch that has recently become a widespread component of Western diets. In summary, we demonstrate that selective sweeps across host microbiomes are a common feature of the evolution of the human gut microbiome.
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Affiliation(s)
- Richard Wolff
- Department of Ecology and Evolutionary Biology, UCLA
| | - Nandita R. Garud
- Department of Ecology and Evolutionary Biology, UCLA
- Department of Human Genetics, UCLA
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27
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Dyer NA, Lucas ER, Nagi SC, McDermott DP, Brenas JH, Miles A, Clarkson CS, Mawejje HD, Wilding CS, Halfon MS, Asma H, Heinz E, Donnelly MJ. Mechanisms of transcriptional regulation in Anopheles gambiae revealed by allele specific expression. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.22.568226. [PMID: 38045426 PMCID: PMC10690255 DOI: 10.1101/2023.11.22.568226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Malaria control relies on insecticides targeting the mosquito vector, but this is increasingly compromised by insecticide resistance, which can be achieved by elevated expression of detoxifying enzymes that metabolize the insecticide. In diploid organisms, gene expression is regulated both in cis, by regulatory sequences on the same chromosome, and by trans acting factors, affecting both alleles equally. Differing levels of transcription can be caused by mutations in cis-regulatory modules (CRM), but few of these have been identified in mosquitoes. We crossed bendiocarb resistant and susceptible Anopheles gambiae strains to identify cis-regulated genes that might be responsible for the resistant phenotype using RNAseq, and cis-regulatory module sequences controlling gene expression in insecticide resistance relevant tissues were predicted using machine learning. We found 115 genes showing allele specific expression in hybrids of insecticide susceptible and resistant strains, suggesting cis regulation is an important mechanism of gene expression regulation in Anopheles gambiae. The genes showing allele specific expression included a higher proportion of Anopheles specific genes on average younger than genes those with balanced allelic expression.
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Affiliation(s)
- Naomi A Dyer
- Department of Vector Biology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA, UK
| | - Eric R Lucas
- Department of Vector Biology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA, UK
| | - Sanjay C Nagi
- Department of Vector Biology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA, UK
| | - Daniel P McDermott
- Department of Vector Biology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA, UK
| | - Jon H Brenas
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Alistair Miles
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Chris S Clarkson
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Henry D Mawejje
- Infectious Diseases Research Collaboration (IDRC), Plot 2C Nakasero Hill Road, P.O.Box 7475, Kampala, Uganda
| | - Craig S Wilding
- School of Biological and Environmental Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK
| | - Marc S Halfon
- Department of Biochemistry, Jacobs School of Medicine & Biomedical Sciences, University at Buffalo-State University of New York, 955 Main Street, Buffalo, New York 14203, USA
| | - Hasiba Asma
- Department of Biochemistry, Jacobs School of Medicine & Biomedical Sciences, University at Buffalo-State University of New York, 955 Main Street, Buffalo, New York 14203, USA
| | - Eva Heinz
- Department of Vector Biology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA, UK
- Department of Clinical Sciences, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA, UK
| | - Martin J Donnelly
- Department of Vector Biology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA, UK
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28
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Schrider DR. Allelic gene conversion softens selective sweeps. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.05.570141. [PMID: 38106127 PMCID: PMC10723294 DOI: 10.1101/2023.12.05.570141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
The prominence of positive selection, in which beneficial mutations are favored by natural selection and rapidly increase in frequency, is a subject of intense debate. Positive selection can result in selective sweeps, in which the haplotype(s) bearing the adaptive allele "sweep" through the population, thereby removing much of the genetic diversity from the region surrounding the target of selection. Two models of selective sweeps have been proposed: classical sweeps, or "hard sweeps", in which a single copy of the adaptive allele sweeps to fixation, and "soft sweeps", in which multiple distinct copies of the adaptive allele leave descendants after the sweep. Soft sweeps can be the outcome of recurrent mutation to the adaptive allele, or the presence of standing genetic variation consisting of multiple copies of the adaptive allele prior to the onset of selection. Importantly, soft sweeps will be common when populations can rapidly adapt to novel selective pressures, either because of a high mutation rate or because adaptive alleles are already present. The prevalence of soft sweeps is especially controversial, and it has been noted that selection on standing variation or recurrent mutations may not always produce soft sweeps. Here, we show that the inverse is true: selection on single-origin de novo mutations may often result in an outcome that is indistinguishable from a soft sweep. This is made possible by allelic gene conversion, which "softens" hard sweeps by copying the adaptive allele onto multiple genetic backgrounds, a process we refer to as a "pseudo-soft" sweep. We carried out a simulation study examining the impact of gene conversion on sweeps from a single de novo variant in models of human, Drosophila, and Arabidopsis populations. The fraction of simulations in which gene conversion had produced multiple haplotypes with the adaptive allele upon fixation was appreciable. Indeed, under realistic demographic histories and gene conversion rates, even if selection always acts on a single-origin mutation, sweeps involving multiple haplotypes are more likely than hard sweeps in large populations, especially when selection is not extremely strong. Thus, even when the mutation rate is low or there is no standing variation, hard sweeps are expected to be the exception rather than the rule in large populations. These results also imply that the presence of signatures of soft sweeps does not necessarily mean that adaptation has been especially rapid or is not mutation limited.
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Affiliation(s)
- Daniel R Schrider
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599
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29
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Panigrahi M, Rajawat D, Nayak SS, Ghildiyal K, Sharma A, Jain K, Lei C, Bhushan B, Mishra BP, Dutt T. Landmarks in the history of selective sweeps. Anim Genet 2023; 54:667-688. [PMID: 37710403 DOI: 10.1111/age.13355] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 08/28/2023] [Indexed: 09/16/2023]
Abstract
Half a century ago, a seminal article on the hitchhiking effect by Smith and Haigh inaugurated the concept of the selection signature. Selective sweeps are characterised by the rapid spread of an advantageous genetic variant through a population and hence play an important role in shaping evolution and research on genetic diversity. The process by which a beneficial allele arises and becomes fixed in a population, leading to a increase in the frequency of other linked alleles, is known as genetic hitchhiking or genetic draft. Kimura's neutral theory and hitchhiking theory are complementary, with Kimura's neutral evolution as the 'null model' and positive selection as the 'signal'. Both are widely accepted in evolution, especially with genomics enabling precise measurements. Significant advances in genomic technologies, such as next-generation sequencing, high-density SNP arrays and powerful bioinformatics tools, have made it possible to systematically investigate selection signatures in a variety of species. Although the history of selection signatures is relatively recent, progress has been made in the last two decades, owing to the increasing availability of large-scale genomic data and the development of computational methods. In this review, we embark on a journey through the history of research on selective sweeps, ranging from early theoretical work to recent empirical studies that utilise genomic data.
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Affiliation(s)
- Manjit Panigrahi
- Division of Animal Genetics, Indian Veterinary Research Institute, Bareilly, India
| | - Divya Rajawat
- Division of Animal Genetics, Indian Veterinary Research Institute, Bareilly, India
| | | | - Kanika Ghildiyal
- Division of Animal Genetics, Indian Veterinary Research Institute, Bareilly, India
| | - Anurodh Sharma
- Division of Animal Genetics, Indian Veterinary Research Institute, Bareilly, India
| | - Karan Jain
- Division of Animal Genetics, Indian Veterinary Research Institute, Bareilly, India
| | - Chuzhao Lei
- College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, China
| | - Bharat Bhushan
- Division of Animal Genetics, Indian Veterinary Research Institute, Bareilly, India
| | - Bishnu Prasad Mishra
- Division of Animal Biotechnology, ICAR-National Bureau of Animal Genetic Resources, Karnal, India
| | - Triveni Dutt
- Livestock Production and Management Section, Indian Veterinary Research Institute, Bareilly, India
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30
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Unckless RL. Meiotic drive, postzygotic isolation, and the Snowball Effect. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.14.567107. [PMID: 38014228 PMCID: PMC10680770 DOI: 10.1101/2023.11.14.567107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
As populations diverge, they accumulate incompatibilities which reduce gene flow and facilitate the formation of new species. Simple models suggest that the genes that cause Dobzhansky-Muller incompatibilities should accumulate at least as fast as the square of the number of substitutions between taxa, the so-called snowball effect. We show, however, that in the special- but possibly common- case in which hybrid sterility is due primarily to cryptic meiotic (gametic) drive, the number of genes that cause postzygotic isolation may increase nearly linearly with the number of substitutions between species.
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Affiliation(s)
- Robert L Unckless
- Department of Molecular Biosciences, University of Kansas, Lawrence, KS 66045
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31
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Bakoev SY, Korobeinikova AV, Mishina AI, Kabieva SS, Mitrofanov SI, Ivashechkin AA, Akinshina AI, Snigir EA, Yudin SM, Yudin VS, Getmantseva LV, Anderzhanova EA. Genomic Signatures of Positive Selection in Human Populations of the OXT, OXTR, AVP, AVPR1A and AVR1B Gene Variants Related to the Regulation of Psychoemotional Response. Genes (Basel) 2023; 14:2053. [PMID: 38002996 PMCID: PMC10670988 DOI: 10.3390/genes14112053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 11/03/2023] [Accepted: 11/04/2023] [Indexed: 11/26/2023] Open
Abstract
The neurobiological systems of maintenance and control of behavioral responses result from natural selection. We have analyzed the selection signatures for single nucleotide variants (SNV) of the genes of oxytocin (OXT, OXTR) and vasopressin (AVP, AVPR1A, AVPR1B) systems, which are associated with the regulation of social and emotional behavior in distinct populations. The analysis was performed using original WGS (whole genome sequencing) data on Eastern Slavs (SlEast), as well as publicly available data from the 1000 Genomes Project on GBR, FIN, IBR, PUR, BEB, CHB, and ACB populations (the latter were taken as reference). To identify selection signatures, we rated the integrated haplotype scores (iHS), the numbers of segregating sites by length (nSl), and the integrated haplotype homozygosity pooled (iHH12) measures; the fixation index Fst was implemented to assess genetic differentiation between populations. We revealed that the strongest genetic differentiation of populations was found with respect to the AVPR1B gene, with the greatest differentiation observed in GRB (Fst = 0.316) and CHB (Fst = 0.325) in comparison to ACB. Also, high Fst values were found for SNVs of the AVPR1B gene rs28499431, rs33940624, rs28477649, rs3883899, and rs28452187 in most of the populations. Selection signatures have also been identified in the AVP, AVPR1A, OXT, and OXTR genes. Our analysis shows that the OXT, OXTR, AVP, AVPR1A, and AVPR1B genes were subject to positive selection in a population-specific process, which was likely contributing to the diversity of adaptive emotional response types and social function realizations.
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Affiliation(s)
- Siroj Yu. Bakoev
- Federal State Budgetary Institution “Centre for Strategic Planning and Management of Biomedical Health Risks” of the Federal Medical Biological Agency (Centre for Strategic Planning of FMBA of Russia), Pogodinskaya Street, 10, Bld. 1, 119121 Moscow, Russia; (A.V.K.); (A.I.M.); (S.S.K.); (S.I.M.); (A.A.I.); (A.I.A.); (E.A.S.); (S.M.Y.); (V.S.Y.); (L.V.G.); (E.A.A.)
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32
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Horton JS, Taylor TB. Mutation bias and adaptation in bacteria. MICROBIOLOGY (READING, ENGLAND) 2023; 169. [PMID: 37943288 DOI: 10.1099/mic.0.001404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2023]
Abstract
Genetic mutation, which provides the raw material for evolutionary adaptation, is largely a stochastic force. However, there is ample evidence showing that mutations can also exhibit strong biases, with some mutation types and certain genomic positions mutating more often than others. It is becoming increasingly clear that mutational bias can play a role in determining adaptive outcomes in bacteria in both the laboratory and the clinic. As such, understanding the causes and consequences of mutation bias can help microbiologists to anticipate and predict adaptive outcomes. In this review, we provide an overview of the mechanisms and features of the bacterial genome that cause mutational biases to occur. We then describe the environmental triggers that drive these mechanisms to be more potent and outline the adaptive scenarios where mutation bias can synergize with natural selection to define evolutionary outcomes. We conclude by describing how understanding mutagenic genomic features can help microbiologists predict areas sensitive to mutational bias, and finish by outlining future work that will help us achieve more accurate evolutionary forecasts.
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Affiliation(s)
- James S Horton
- Milner Centre for Evolution, Department of Life Sciences, University of Bath, BA2 7AY, UK
| | - Tiffany B Taylor
- Milner Centre for Evolution, Department of Life Sciences, University of Bath, BA2 7AY, UK
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33
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Salazar-Tortosa DF, Huang YF, Enard D. Assessing the Presence of Recent Adaptation in the Human Genome With Mixture Density Regression. Genome Biol Evol 2023; 15:evad170. [PMID: 37713622 PMCID: PMC10563788 DOI: 10.1093/gbe/evad170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Revised: 08/30/2023] [Accepted: 09/04/2023] [Indexed: 09/17/2023] Open
Abstract
How much genome differences between species reflect neutral or adaptive evolution is a central question in evolutionary genomics. In humans and other mammals, the presence of adaptive versus neutral genomic evolution has proven particularly difficult to quantify. The difficulty notably stems from the highly heterogeneous organization of mammalian genomes at multiple levels (functional sequence density, recombination, etc.) which complicates the interpretation and distinction of adaptive versus neutral evolution signals. In this study, we introduce mixture density regressions (MDRs) for the study of the determinants of recent adaptation in the human genome. MDRs provide a flexible regression model based on multiple Gaussian distributions. We use MDRs to model the association between recent selection signals and multiple genomic factors likely to affect the occurrence/detection of positive selection, if the latter was present in the first place to generate these associations. We find that an MDR model with two Gaussian distributions provides an excellent fit to the genome-wide distribution of a common sweep summary statistic (integrated haplotype score), with one of the two distributions likely enriched in positive selection. We further find several factors associated with signals of recent adaptation, including the recombination rate, the density of regulatory elements in immune cells, GC content, gene expression in immune cells, the density of mammal-wide conserved elements, and the distance to the nearest virus-interacting gene. These results support the presence of strong positive selection in recent human evolution and highlight MDRs as a powerful tool to make sense of signals of recent genomic adaptation.
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Affiliation(s)
- Diego F Salazar-Tortosa
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, Arizona, USA
- Department of Ecology, University of Granada, Granada, Spain
| | - Yi-Fei Huang
- Department of Biology, Pennsylvania State University, University Park, State College, Pennsylvania, PA 16801, USA
- Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, State College, Pennsylvania, PA 16801, USA
| | - David Enard
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, Arizona, USA
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34
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Amin MR, Hasan M, Arnab SP, DeGiorgio M. Tensor Decomposition-based Feature Extraction and Classification to Detect Natural Selection from Genomic Data. Mol Biol Evol 2023; 40:msad216. [PMID: 37772983 PMCID: PMC10581699 DOI: 10.1093/molbev/msad216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 08/10/2023] [Accepted: 09/14/2023] [Indexed: 09/30/2023] Open
Abstract
Inferences of adaptive events are important for learning about traits, such as human digestion of lactose after infancy and the rapid spread of viral variants. Early efforts toward identifying footprints of natural selection from genomic data involved development of summary statistic and likelihood methods. However, such techniques are grounded in simple patterns or theoretical models that limit the complexity of settings they can explore. Due to the renaissance in artificial intelligence, machine learning methods have taken center stage in recent efforts to detect natural selection, with strategies such as convolutional neural networks applied to images of haplotypes. Yet, limitations of such techniques include estimation of large numbers of model parameters under nonconvex settings and feature identification without regard to location within an image. An alternative approach is to use tensor decomposition to extract features from multidimensional data although preserving the latent structure of the data, and to feed these features to machine learning models. Here, we adopt this framework and present a novel approach termed T-REx, which extracts features from images of haplotypes across sampled individuals using tensor decomposition, and then makes predictions from these features using classical machine learning methods. As a proof of concept, we explore the performance of T-REx on simulated neutral and selective sweep scenarios and find that it has high power and accuracy to discriminate sweeps from neutrality, robustness to common technical hurdles, and easy visualization of feature importance. Therefore, T-REx is a powerful addition to the toolkit for detecting adaptive processes from genomic data.
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Affiliation(s)
- Md Ruhul Amin
- Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA
| | - Mahmudul Hasan
- Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA
| | - Sandipan Paul Arnab
- Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA
| | - Michael DeGiorgio
- Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA
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35
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Soni V, Johri P, Jensen JD. Evaluating power to detect recurrent selective sweeps under increasingly realistic evolutionary null models. Evolution 2023; 77:2113-2127. [PMID: 37395482 PMCID: PMC10547124 DOI: 10.1093/evolut/qpad120] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 06/15/2023] [Accepted: 06/30/2023] [Indexed: 07/04/2023]
Abstract
The detection of selective sweeps from population genomic data often relies on the premise that the beneficial mutations in question have fixed very near the sampling time. As it has been previously shown that the power to detect a selective sweep is strongly dependent on the time since fixation as well as the strength of selection, it is naturally the case that strong, recent sweeps leave the strongest signatures. However, the biological reality is that beneficial mutations enter populations at a rate, one that partially determines the mean wait time between sweep events and hence their age distribution. An important question thus remains about the power to detect recurrent selective sweeps when they are modeled by a realistic mutation rate and as part of a realistic distribution of fitness effects, as opposed to a single, recent, isolated event on a purely neutral background as is more commonly modeled. Here we use forward-in-time simulations to study the performance of commonly used sweep statistics, within the context of more realistic evolutionary baseline models incorporating purifying and background selection, population size change, and mutation and recombination rate heterogeneity. Results demonstrate the important interplay of these processes, necessitating caution when interpreting selection scans; specifically, false-positive rates are in excess of true-positive across much of the evaluated parameter space, and selective sweeps are often undetectable unless the strength of selection is exceptionally strong.
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Affiliation(s)
- Vivak Soni
- School of Life Sciences, Arizona State University, Tempe, AZ, United States
| | - Parul Johri
- School of Life Sciences, Arizona State University, Tempe, AZ, United States
| | - Jeffrey D Jensen
- School of Life Sciences, Arizona State University, Tempe, AZ, United States
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36
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Tanaka T, Hayakawa T, Teshima KM. Power of neutrality tests for detecting natural selection. G3 (BETHESDA, MD.) 2023; 13:jkad161. [PMID: 37481468 PMCID: PMC10542275 DOI: 10.1093/g3journal/jkad161] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 06/09/2023] [Accepted: 07/19/2023] [Indexed: 07/24/2023]
Abstract
Detection of natural selection is one of the main interests in population genetics. Thus, many tests have been developed for detecting natural selection using genomic data. Although it is recognized that the utility of tests depends on several evolutionary factors, such as the timing of selection, strength of selection, frequency of selected alleles, demographic events, and initial frequency of selected allele when selection started acting (softness of selection), the relationships between such evolutionary factors and the power of tests are not yet entirely clear. In this study, we investigated the power of 4 tests: Tajiama's D, Fay and Wu's H, relative extended haplotype homozygosity (rEHH), and integrated haplotype score (iHS), under ranges of evolutionary parameters and demographic models to quantitatively expand the understanding of approaches for detecting selection. The results show that each test detects selection within a limited parameter range, and there are still wide ranges of parameters for which none of these tests work effectively. In addition, the parameter space in which each test shows the highest power overlaps the empirical results of previous research. These results indicate that our present perspective of adaptation is limited to only a part of actual adaptation.
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Affiliation(s)
- Tomotaka Tanaka
- Graduate School of System Life Science, Kyushu University, Fukuoka 819-0395, Japan
| | - Toshiyuki Hayakawa
- Graduate School of System Life Science, Kyushu University, Fukuoka 819-0395, Japan
- Faculty of Arts and Science, Kyushu University, Fukuoka 819-0395, Japan
| | - Kosuke M Teshima
- Department of Biology, Faculty of Science, Kyushu University, Fukuoka 819-0395, Japan
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37
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Lucas ER, Nagi SC, Egyir-Yawson A, Essandoh J, Dadzie S, Chabi J, Djogbénou LS, Medjigbodo AA, Edi CV, Kétoh GK, Koudou BG, Van't Hof AE, Rippon EJ, Pipini D, Harding NJ, Dyer NA, Cerdeira LT, Clarkson CS, Kwiatkowski DP, Miles A, Donnelly MJ, Weetman D. Genome-wide association studies reveal novel loci associated with pyrethroid and organophosphate resistance in Anopheles gambiae and Anopheles coluzzii. Nat Commun 2023; 14:4946. [PMID: 37587104 PMCID: PMC10432508 DOI: 10.1038/s41467-023-40693-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 08/03/2023] [Indexed: 08/18/2023] Open
Abstract
Resistance to insecticides in Anopheles mosquitoes threatens the effectiveness of malaria control, but the genetics of resistance are only partially understood. We performed a large scale multi-country genome-wide association study of resistance to two widely used insecticides: deltamethrin and pirimiphos-methyl, using sequencing data from An. gambiae and An. coluzzii from ten locations in West Africa. Resistance was highly multi-genic, multi-allelic and variable between populations. While the strongest and most consistent association with deltamethrin resistance came from Cyp6aa1, this was based on several independent copy number variants (CNVs) in An. coluzzii, and on a non-CNV haplotype in An. gambiae. For pirimiphos-methyl, signals included Ace1, cytochrome P450s, glutathione S-transferases and the nAChR target site of neonicotinoid insecticides. The regions around Cyp9k1 and the Tep family of immune genes showed evidence of cross-resistance to both insecticides. These locally-varying, multi-allelic patterns highlight the challenges involved in genomic monitoring of resistance, and may form the basis for improved surveillance methods.
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Affiliation(s)
- Eric R Lucas
- Department of Vector Biology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA, UK.
| | - Sanjay C Nagi
- Department of Vector Biology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA, UK
| | | | - John Essandoh
- Department of Biomedical Sciences, University of Cape Coast, Cape Coast, Ghana
| | - Samuel Dadzie
- Department of Parasitology, Noguchi Memorial Institute for Medical Research, University of Ghana, Accra, Ghana
| | - Joseph Chabi
- Department of Parasitology, Noguchi Memorial Institute for Medical Research, University of Ghana, Accra, Ghana
| | - Luc S Djogbénou
- Tropical Infectious Diseases Research Centre (TIDRC), Université d'Abomey-Calavi (UAC), 01 B.P. 526, Cotonou, Benin
| | - Adandé A Medjigbodo
- Tropical Infectious Diseases Research Centre (TIDRC), Université d'Abomey-Calavi (UAC), 01 B.P. 526, Cotonou, Benin
| | - Constant V Edi
- Centre Suisse de Recherches Scientifiques en Côte d'Ivoire, 01 BP 1303, Abidjan, Côte d'Ivoire
| | - Guillaume K Kétoh
- Laboratory of Ecology and Ecotoxicology, Department of Zoology, Faculty of Sciences, Université de Lomé, 01 B.P. 1515, Lomé, Togo
| | - Benjamin G Koudou
- Centre Suisse de Recherches Scientifiques en Côte d'Ivoire, 01 BP 1303, Abidjan, Côte d'Ivoire
| | - Arjen E Van't Hof
- Department of Vector Biology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA, UK
- Biology Centre of the Czech Academy of Sciences, Institute of Entomology, Branišovská 31, 370 05, České Budějovice, Czech Republic
| | - Emily J Rippon
- Department of Vector Biology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA, UK
| | - Dimitra Pipini
- Department of Vector Biology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA, UK
| | - Nicholas J Harding
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Naomi A Dyer
- Department of Vector Biology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA, UK
| | - Louise T Cerdeira
- Department of Vector Biology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA, UK
| | | | | | - Alistair Miles
- Wellcome Sanger Institute, Hinxton, Cambridge, CB10 1SA, UK
| | - Martin J Donnelly
- Department of Vector Biology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA, UK.
- Wellcome Sanger Institute, Hinxton, Cambridge, CB10 1SA, UK.
| | - David Weetman
- Department of Vector Biology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA, UK.
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38
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Tavares H, Readshaw A, Kania U, de Jong M, Pasam RK, McCulloch H, Ward S, Shenhav L, Forsyth E, Leyser O. Artificial selection reveals complex genetic architecture of shoot branching and its response to nitrate supply in Arabidopsis. PLoS Genet 2023; 19:e1010863. [PMID: 37616321 PMCID: PMC10482290 DOI: 10.1371/journal.pgen.1010863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 09/06/2023] [Accepted: 07/08/2023] [Indexed: 08/26/2023] Open
Abstract
Quantitative traits may be controlled by many loci, many alleles at each locus, and subject to genotype-by-environment interactions, making them difficult to map. One example of such a complex trait is shoot branching in the model plant Arabidopsis, and its plasticity in response to nitrate. Here, we use artificial selection under contrasting nitrate supplies to dissect the genetic architecture of this complex trait, where loci identified by association mapping failed to explain heritability estimates. We found a consistent response to selection for high branching, with correlated responses in other traits such as plasticity and flowering time. Genome-wide scans for selection and simulations suggest that at least tens of loci control this trait, with a distinct genetic architecture between low and high nitrate treatments. While signals of selection could be detected in the populations selected for high branching on low nitrate, there was very little overlap in the regions selected in three independent populations. Thus the regulatory network controlling shoot branching can be tuned in different ways to give similar phenotypes.
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Affiliation(s)
- Hugo Tavares
- Sainsbury Laboratory, University of Cambridge, Cambridge, United Kingdom
| | - Anne Readshaw
- Department of Biology, University of York, York, United Kingdom
| | - Urszula Kania
- Sainsbury Laboratory, University of Cambridge, Cambridge, United Kingdom
| | - Maaike de Jong
- Sainsbury Laboratory, University of Cambridge, Cambridge, United Kingdom
| | - Raj K. Pasam
- Sainsbury Laboratory, University of Cambridge, Cambridge, United Kingdom
| | - Hayley McCulloch
- Sainsbury Laboratory, University of Cambridge, Cambridge, United Kingdom
| | - Sally Ward
- Sainsbury Laboratory, University of Cambridge, Cambridge, United Kingdom
- Department of Biology, University of York, York, United Kingdom
| | - Liron Shenhav
- Sainsbury Laboratory, University of Cambridge, Cambridge, United Kingdom
| | - Elizabeth Forsyth
- Sainsbury Laboratory, University of Cambridge, Cambridge, United Kingdom
| | - Ottoline Leyser
- Sainsbury Laboratory, University of Cambridge, Cambridge, United Kingdom
- Department of Biology, University of York, York, United Kingdom
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39
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Wang Z, Zhang X, Lei W, Zhu H, Wu S, Liu B, Ru D. Chromosome-level genome assembly and population genomics of Robinia pseudoacacia reveal the genetic basis for its wide cultivation. Commun Biol 2023; 6:797. [PMID: 37524773 PMCID: PMC10390555 DOI: 10.1038/s42003-023-05158-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 07/19/2023] [Indexed: 08/02/2023] Open
Abstract
Urban greening provides important ecosystem services and ideal places for urban recreation and is a serious consideration for municipal decision-makers. Among the tree species cultivated in urban green spaces, Robinia pseudoacacia stands out due to its attractive flowers, fragrances, high trunks, wide adaptability, and essential ecosystem services. However, the genomic basis and consequences of its wide-planting in urban green spaces remains unknown. Here, we report the chromosome-level genome assembly of R. pseudoacacia, revealing a genome size of 682.4 Mb and 33,187 protein-coding genes. More than 99.3% of the assembly is anchored to 11 chromosomes with an N50 of 59.9 Mb. Comparative genomic analyses among 17 species reveal that gene families related to traits favoured by urbanites, such as wood formation, biosynthesis, and drought tolerance, are notably expanded in R. pseudoacacia. Our population genomic analyses further recover 11 genes that are under recent selection. Ultimately, these genes play important roles in the biological processes related to flower development, water retention, and immunization. Altogether, our results reveal the evolutionary forces that shape R. pseudoacacia cultivated for urban greening. These findings also present a valuable foundation for the future development of agronomic traits and molecular breeding strategies for R. pseudoacacia.
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Affiliation(s)
- Zefu Wang
- State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystem, College of Ecology, Lanzhou University, Lanzhou, 730000, China
- Tianjin Key Laboratory of Conservation and Utilization of Animal Diversity, College of Life Sciences, Tianjin Normal University, Tianjin, 300387, China
- Co-Innovation Center for Sustainable Forestry in Southern China, College of Biology and the Environment, Nanjing Forestry University, Nanjing, 210037, China
| | - Xiao Zhang
- Tianjin Key Laboratory of Conservation and Utilization of Animal Diversity, College of Life Sciences, Tianjin Normal University, Tianjin, 300387, China.
| | - Weixiao Lei
- State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystem, College of Ecology, Lanzhou University, Lanzhou, 730000, China
| | - Hui Zhu
- State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystem, College of Ecology, Lanzhou University, Lanzhou, 730000, China
| | - Shengdan Wu
- State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystem, College of Ecology, Lanzhou University, Lanzhou, 730000, China.
| | - Bingbing Liu
- Institute of Loess Plateau, Shanxi University, Taiyuan, 030006, China.
| | - Dafu Ru
- State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystem, College of Ecology, Lanzhou University, Lanzhou, 730000, China.
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40
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Whitehouse LS, Schrider DR. Timesweeper: accurately identifying selective sweeps using population genomic time series. Genetics 2023; 224:iyad084. [PMID: 37157914 PMCID: PMC10324941 DOI: 10.1093/genetics/iyad084] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 07/25/2022] [Accepted: 04/25/2023] [Indexed: 05/10/2023] Open
Abstract
Despite decades of research, identifying selective sweeps, the genomic footprints of positive selection, remains a core problem in population genetics. Of the myriad methods that have been developed to tackle this task, few are designed to leverage the potential of genomic time-series data. This is because in most population genetic studies of natural populations, only a single period of time can be sampled. Recent advancements in sequencing technology, including improvements in extracting and sequencing ancient DNA, have made repeated samplings of a population possible, allowing for more direct analysis of recent evolutionary dynamics. Serial sampling of organisms with shorter generation times has also become more feasible due to improvements in the cost and throughput of sequencing. With these advances in mind, here we present Timesweeper, a fast and accurate convolutional neural network-based tool for identifying selective sweeps in data consisting of multiple genomic samplings of a population over time. Timesweeper analyzes population genomic time-series data by first simulating training data under a demographic model appropriate for the data of interest, training a one-dimensional convolutional neural network on said simulations, and inferring which polymorphisms in this serialized data set were the direct target of a completed or ongoing selective sweep. We show that Timesweeper is accurate under multiple simulated demographic and sampling scenarios, identifies selected variants with high resolution, and estimates selection coefficients more accurately than existing methods. In sum, we show that more accurate inferences about natural selection are possible when genomic time-series data are available; such data will continue to proliferate in coming years due to both the sequencing of ancient samples and repeated samplings of extant populations with faster generation times, as well as experimentally evolved populations where time-series data are often generated. Methodological advances such as Timesweeper thus have the potential to help resolve the controversy over the role of positive selection in the genome. We provide Timesweeper as a Python package for use by the community.
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Affiliation(s)
- Logan S Whitehouse
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27514, USA
| | - Daniel R Schrider
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27514, USA
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41
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Soni V, Johri P, Jensen JD. Evaluating power to detect recurrent selective sweeps under increasingly realistic evolutionary null models. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.15.545166. [PMID: 37398347 PMCID: PMC10312679 DOI: 10.1101/2023.06.15.545166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
The detection of selective sweeps from population genomic data often relies on the premise that the beneficial mutations in question have fixed very near the sampling time. As it has been previously shown that the power to detect a selective sweep is strongly dependent on the time since fixation as well as the strength of selection, it is naturally the case that strong, recent sweeps leave the strongest signatures. However, the biological reality is that beneficial mutations enter populations at a rate, one that partially determines the mean wait time between sweep events and hence their age distribution. An important question thus remains about the power to detect recurrent selective sweeps when they are modelled by a realistic mutation rate and as part of a realistic distribution of fitness effects (DFE), as opposed to a single, recent, isolated event on a purely neutral background as is more commonly modelled. Here we use forward-in-time simulations to study the performance of commonly used sweep statistics, within the context of more realistic evolutionary baseline models incorporating purifying and background selection, population size change, and mutation and recombination rate heterogeneity. Results demonstrate the important interplay of these processes, necessitating caution when interpreting selection scans; specifically, false positive rates are in excess of true positive across much of the evaluated parameter space, and selective sweeps are often undetectable unless the strength of selection is exceptionally strong. Teaser Text Outlier-based genomic scans have proven a popular approach for identifying loci that have potentially experienced recent positive selection. However, it has previously been shown that an evolutionarily appropriate baseline model that incorporates non-equilibrium population histories, purifying and background selection, and variation in mutation and recombination rates is necessary to reduce often extreme false positive rates when performing genomic scans. Here we evaluate the power to detect recurrent selective sweeps using common SFS-based and haplotype-based methods under these increasingly realistic models. We find that while these appropriate evolutionary baselines are essential to reduce false positive rates, the power to accurately detect recurrent selective sweeps is generally low across much of the biologically relevant parameter space.
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Affiliation(s)
- Vivak Soni
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
| | - Parul Johri
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
- Present address: Department of Biology, Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
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42
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Ahlquist KD, Sugden LA, Ramachandran S. Enabling interpretable machine learning for biological data with reliability scores. PLoS Comput Biol 2023; 19:e1011175. [PMID: 37235578 PMCID: PMC10249903 DOI: 10.1371/journal.pcbi.1011175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 06/08/2023] [Accepted: 05/10/2023] [Indexed: 05/28/2023] Open
Abstract
Machine learning tools have proven useful across biological disciplines, allowing researchers to draw conclusions from large datasets, and opening up new opportunities for interpreting complex and heterogeneous biological data. Alongside the rapid growth of machine learning, there have also been growing pains: some models that appear to perform well have later been revealed to rely on features of the data that are artifactual or biased; this feeds into the general criticism that machine learning models are designed to optimize model performance over the creation of new biological insights. A natural question arises: how do we develop machine learning models that are inherently interpretable or explainable? In this manuscript, we describe the SWIF(r) reliability score (SRS), a method building on the SWIF(r) generative framework that reflects the trustworthiness of the classification of a specific instance. The concept of the reliability score has the potential to generalize to other machine learning methods. We demonstrate the utility of the SRS when faced with common challenges in machine learning including: 1) an unknown class present in testing data that was not present in training data, 2) systemic mismatch between training and testing data, and 3) instances of testing data that have missing values for some attributes. We explore these applications of the SRS using a range of biological datasets, from agricultural data on seed morphology, to 22 quantitative traits in the UK Biobank, and population genetic simulations and 1000 Genomes Project data. With each of these examples, we demonstrate how the SRS can allow researchers to interrogate their data and training approach thoroughly, and to pair their domain-specific knowledge with powerful machine-learning frameworks. We also compare the SRS to related tools for outlier and novelty detection, and find that it has comparable performance, with the advantage of being able to operate when some data are missing. The SRS, and the broader discussion of interpretable scientific machine learning, will aid researchers in the biological machine learning space as they seek to harness the power of machine learning without sacrificing rigor and biological insight.
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Affiliation(s)
- K. D. Ahlquist
- Center for Computational Molecular Biology, Brown University, Providence, Rhode Island, United States of America
- Department of Molecular Biology, Cell Biology, and Biochemistry, Brown University, Providence, Rhode Island, United States of America
| | - Lauren A. Sugden
- Department of Mathematics and Computer Science, Duquesne University, Pittsburgh, Pennsylvania, United States of America
| | - Sohini Ramachandran
- Center for Computational Molecular Biology, Brown University, Providence, Rhode Island, United States of America
- Department of Ecology, Evolution and Organismal Biology, Brown University, Providence, Rhode Island, United States of America
- Data Science Initiative, Brown University, Providence, Rhode Island, United States of America
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43
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Nagi SC, Oruni A, Weetman D, Donnelly MJ. RNA-Seq-Pop: Exploiting the sequence in RNA sequencing-A Snakemake workflow reveals patterns of insecticide resistance in the malaria vector Anopheles gambiae. Mol Ecol Resour 2023; 23:946-961. [PMID: 36695302 PMCID: PMC10568660 DOI: 10.1111/1755-0998.13759] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 11/12/2022] [Accepted: 01/06/2023] [Indexed: 01/26/2023]
Abstract
We provide a reproducible and scalable Snakemake workflow, called RNA-Seq-Pop, which provides end-to-end analysis of RNA sequencing data sets. The workflow allows the user to perform quality control, perform differential expression analyses and call genomic variants. Additional options include the calculation of allele frequencies of variants of interest, summaries of genetic variation and population structure, and genome-wide selection scans, together with clear visualizations. RNA-Seq-Pop is applicable to any organism, and we demonstrate the utility of the workflow by investigating pyrethroid resistance in selected strains of the major malaria mosquito, Anopheles gambiae. The workflow provides additional modules specifically for An. gambiae, including estimating recent ancestry and determining the karyotype of common chromosomal inversions. The Busia laboratory colony used for selections was collected in Busia, Uganda, in November 2018. We performed a comparative analysis of three groups: a parental G24 Busia strain; its deltamethrin-selected G28 offspring; and the susceptible reference strain Kisumu. Measures of genetic diversity reveal patterns consistent with that of laboratory colonization and selection, with the parental Busia strain exhibiting the highest nucleotide diversity, followed by the selected Busia offspring, and finally, Kisumu. Differential expression and variant analyses reveal that the selected Busia colony exhibits a number of distinct mechanisms of pyrethroid resistance, including the Vgsc-995S target-site mutation, upregulation of SAP genes, P450s and a cluster of carboxylesterases. During deltamethrin selections, the 2La chromosomal inversion rose in frequency (from 33% to 86%), supporting a previous link with pyrethroid resistance. RNA-Seq-Pop is hosted at: github.com/sanjaynagi/rna-seq-pop. We anticipate that the workflow will provide a useful tool to facilitate reproducible, transcriptomic studies in An. gambiae and other taxa.
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Affiliation(s)
- Sanjay C. Nagi
- Department of Vector BiologyLiverpool School of Tropical MedicineLiverpoolUK
| | | | - David Weetman
- Department of Vector BiologyLiverpool School of Tropical MedicineLiverpoolUK
| | - Martin J. Donnelly
- Department of Vector BiologyLiverpool School of Tropical MedicineLiverpoolUK
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44
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Gupta S, Harkess A, Soble A, Van Etten M, Leebens-Mack J, Baucom RS. Interchromosomal linkage disequilibrium and linked fitness cost loci associated with selection for herbicide resistance. THE NEW PHYTOLOGIST 2023; 238:1263-1277. [PMID: 36721257 DOI: 10.1111/nph.18782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 01/20/2023] [Indexed: 06/18/2023]
Abstract
The adaptation of weeds to herbicide is both a significant problem in agriculture and a model of rapid adaptation. However, significant gaps remain in our knowledge of resistance controlled by many loci and the evolutionary factors that influence the maintenance of resistance. Here, using herbicide-resistant populations of the common morning glory (Ipomoea purpurea), we perform a multilevel analysis of the genome and transcriptome to uncover putative loci involved in nontarget-site herbicide resistance (NTSR) and to examine evolutionary forces underlying the maintenance of resistance in natural populations. We found loci involved in herbicide detoxification and stress sensing to be under selection and confirmed that detoxification is responsible for glyphosate (RoundUp) resistance using a functional assay. We identified interchromosomal linkage disequilibrium (ILD) among loci under selection reflecting either historical processes or additive effects leading to the resistance phenotype. We further identified potential fitness cost loci that were strongly linked to resistance alleles, indicating the role of genetic hitchhiking in maintaining the cost. Overall, our work suggests that NTSR glyphosate resistance in I. purpurea is conferred by multiple genes which are potentially maintained through generations via ILD, and that the fitness cost associated with resistance in this species is likely a by-product of genetic hitchhiking.
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Affiliation(s)
- Sonal Gupta
- Ecology and Evolutionary Biology Department, University of Michigan, 4034 Biological Sciences Building, Ann Arbor, MI, 48109, USA
- Center for Genomics and Systems Biology, New York University, New York, NY, 10003, USA
| | - Alex Harkess
- Department of Crop, Soil and Environmental Sciences, Auburn University, Auburn, AL, 36849, USA
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, 35806, USA
| | - Anah Soble
- Ecology and Evolutionary Biology Department, University of Michigan, 4034 Biological Sciences Building, Ann Arbor, MI, 48109, USA
| | - Megan Van Etten
- Biology Department, Pennsylvania State University, Dunmore, PA, 18512, USA
| | - James Leebens-Mack
- Department of Plant Biology, University of Georgia, Athens, GA, 30602, USA
| | - Regina S Baucom
- Ecology and Evolutionary Biology Department, University of Michigan, 4034 Biological Sciences Building, Ann Arbor, MI, 48109, USA
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45
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Ndiaye YD, Wong W, Thwing J, Schaffner SS, Tine A, Diallo MA, Deme A, Sy M, Bei AK, Thiaw AB, Daniels R, Ndiaye T, Gaye A, Ndiaye IM, Toure M, Gadiaga N, Sene A, Sow D, Garba MN, Yade MS, Dieye B, Diongue K, Zoumarou D, Ndiaye A, Gomis J, Fall FB, Ndiop M, Diallo I, Sene D, Macinnis B, Seck MC, Ndiaye M, Badiane AS, Hartl DL, Volkman SK, Wirth DF, Ndiaye D. Two decades of molecular surveillance in Senegal reveal changes in known drug resistance mutations associated with historical drug use and seasonal malaria chemoprevention. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.24.23288820. [PMID: 37163114 PMCID: PMC10168519 DOI: 10.1101/2023.04.24.23288820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Drug resistance in Plasmodium falciparum is a major threat to malaria control efforts. We analyzed data from two decades (2000-2020) of continuous molecular surveillance of P. falciparum parasite strains in Senegal to determine how historical changes in drug administration policy may have affected parasite evolution. We profiled several known drug resistance markers and their surrounding haplotypes using a combination of single nucleotide polymorphism (SNP) molecular surveillance and whole-genome sequence (WGS) based population genomics. We observed rapid changes in drug resistance markers associated with the withdrawal of chloroquine and introduction of sulfadoxine-pyrimethamine in 2003. We also observed a rapid increase in Pfcrt K76T and decline in Pfdhps A437G starting in 2014, which we hypothesize may reflect changes in resistance or fitness caused by seasonal malaria chemoprevention (SMC). Parasite populations evolve rapidly in response to drug use, and SMC preventive efficacy should be closely monitored.
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Affiliation(s)
- Yaye Die Ndiaye
- International Research Training Center on Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, Dakar, 16477, Senegal
| | - Wesley Wong
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, 665 Huntington Ave, Boston, MA, 02115, USA
| | - Julie Thwing
- Centers for Disease Control and Prevention, 1600 Clifton Road, Atlanta, GA ,30329, USA
| | - Stephen S Schaffner
- Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA, 02142, USA
| | - Abdoulaye Tine
- International Research Training Center on Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, Dakar, 16477, Senegal
| | - Mamadou Alpha Diallo
- International Research Training Center on Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, Dakar, 16477, Senegal
| | - Awa Deme
- International Research Training Center on Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, Dakar, 16477, Senegal
| | - Mouhammad Sy
- International Research Training Center on Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, Dakar, 16477, Senegal
| | - Amy K Bei
- Yale School of Public Health, 60 College St, New Haven, CT 06510
| | - Alphonse B Thiaw
- Department of biochemistry and Functional Genomics, Sherbrooke University, 2500 Bd de l'Universite, Sherbrooke, QC J1K 2R1, Canada
| | - Rachel Daniels
- RNA Therapeutics Institute, UMass Chan Medical School, 368 Plantation Street, Worcester MA 01605
| | - Tolla Ndiaye
- International Research Training Center on Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, Dakar, 16477, Senegal
| | - Amy Gaye
- International Research Training Center on Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, Dakar, 16477, Senegal
| | - Ibrahima Mbaye Ndiaye
- International Research Training Center on Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, Dakar, 16477, Senegal
| | - Mariama Toure
- International Research Training Center on Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, Dakar, 16477, Senegal
| | - Nogaye Gadiaga
- International Research Training Center on Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, Dakar, 16477, Senegal
| | - Aita Sene
- International Research Training Center on Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, Dakar, 16477, Senegal
| | - Djiby Sow
- International Research Training Center on Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, Dakar, 16477, Senegal
| | - Mamane N Garba
- International Research Training Center on Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, Dakar, 16477, Senegal
| | - Mamadou Samba Yade
- International Research Training Center on Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, Dakar, 16477, Senegal
| | - Baba Dieye
- International Research Training Center on Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, Dakar, 16477, Senegal
| | - Khadim Diongue
- International Research Training Center on Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, Dakar, 16477, Senegal
| | - Daba Zoumarou
- International Research Training Center on Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, Dakar, 16477, Senegal
| | - Aliou Ndiaye
- International Research Training Center on Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, Dakar, 16477, Senegal
| | - Jules Gomis
- International Research Training Center on Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, Dakar, 16477, Senegal
| | - Fatou Ba Fall
- Yale School of Public Health, 60 College St, New Haven, CT 06510
| | - Medoune Ndiop
- National Malaria Control Program (NMCP), Rue FN 20, Dakar 25270, Senegal
| | - Ibrahima Diallo
- National Malaria Control Program (NMCP), Rue FN 20, Dakar 25270, Senegal
| | - Doudou Sene
- National Malaria Control Program (NMCP), Rue FN 20, Dakar 25270, Senegal
| | - Bronwyn Macinnis
- Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA, 02142, USA
| | - Mame Cheikh Seck
- International Research Training Center on Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, Dakar, 16477, Senegal
| | - Mouhamadou Ndiaye
- International Research Training Center on Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, Dakar, 16477, Senegal
| | - Aida S Badiane
- International Research Training Center on Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, Dakar, 16477, Senegal
| | - Daniel L Hartl
- Department of Organismic and Evolutionary Biology, Harvard University, 16 Divinity Avenue, Cambridge, MA, 02138 USA
| | - Sarah K Volkman
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, 665 Huntington Ave, Boston, MA, 02115, USA
- Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA, 02142, USA
- Simmons University, 300 The Fenway, Boston, MA, 02115, USA
| | - Dyann F Wirth
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, 665 Huntington Ave, Boston, MA, 02115, USA
- Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA, 02142, USA
| | - Daouda Ndiaye
- International Research Training Center on Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University, Dakar, 16477, Senegal
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, 665 Huntington Ave, Boston, MA, 02115, USA
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46
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Pandey D, Harris M, Garud NR, Narasimhan VM. Understanding natural selection in Holocene Europe using multi-locus genotype identity scans. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.24.538113. [PMID: 37163039 PMCID: PMC10168228 DOI: 10.1101/2023.04.24.538113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Ancient DNA (aDNA) has been a revolutionary technology in understanding human history but has not been used extensively to study natural selection as large sample sizes to study allele frequency changes over time have thus far not been available. Here, we examined a time transect of 708 published samples over the past 7,000 years of European history using multi-locus genotype-based selection scans. As aDNA data is affected by high missingness, ascertainment bias, DNA damage, random allele calling, and is unphased, we first validated our selection scan, G 12 a n c i e n t , on simulated data resembling aDNA under a demographic model that captures broad features of the allele frequency spectrum of European genomes as well as positive controls that have been previously identified and functionally validated in modern European datasets on data from ancient individuals from time periods very close to the present time. We then applied our statistic to the aDNA time transect to detect and resolve the timing of natural selection occurring genome wide and found several candidates of selection across the different time periods that had not been picked up by selection scans using single SNP allele frequency approaches. In addition, enrichment analysis discovered multiple categories of complex traits that might be under adaptation across these periods. Our results demonstrate the utility of applying different types of selection scans to aDNA to uncover putative selection signals at loci in the ancient past that might have been masked in modern samples.
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Affiliation(s)
- Devansh Pandey
- Department of Integrative Biology, The University of Texas at Austin
| | - Mariana Harris
- Department of Computational Medicine, University of California, Los Angeles
| | - Nandita R Garud
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles
- Department of Human Genetics, University of California, Los Angeles
| | - Vagheesh M Narasimhan
- Department of Integrative Biology, The University of Texas at Austin
- Department of Statistics and Data Science, The University of Texas at Austin
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47
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Love RR, Sikder JR, Vivero RJ, Matute DR, Schrider DR. Strong Positive Selection in Aedes aegypti and the Rapid Evolution of Insecticide Resistance. Mol Biol Evol 2023; 40:msad072. [PMID: 36971242 PMCID: PMC10118305 DOI: 10.1093/molbev/msad072] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 02/13/2023] [Accepted: 03/23/2023] [Indexed: 03/29/2023] Open
Abstract
Aedes aegypti vectors the pathogens that cause dengue, yellow fever, Zika virus, and chikungunya and is a serious threat to public health in tropical regions. Decades of work has illuminated many aspects of Ae. aegypti's biology and global population structure and has identified insecticide resistance genes; however, the size and repetitive nature of the Ae. aegypti genome have limited our ability to detect positive selection in this mosquito. Combining new whole genome sequences from Colombia with publicly available data from Africa and the Americas, we identify multiple strong candidate selective sweeps in Ae. aegypti, many of which overlap genes linked to or implicated in insecticide resistance. We examine the voltage-gated sodium channel gene in three American cohorts and find evidence for successive selective sweeps in Colombia. The most recent sweep encompasses an intermediate-frequency haplotype containing four candidate insecticide resistance mutations that are in near-perfect linkage disequilibrium with one another in the Colombian sample. We hypothesize that this haplotype may continue to rapidly increase in frequency and perhaps spread geographically in the coming years. These results extend our knowledge of how insecticide resistance has evolved in this species and add to a growing body of evidence suggesting that Ae. aegypti has an extensive genomic capacity to rapidly adapt to insecticide-based vector control.
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Affiliation(s)
- R Rebecca Love
- Department of Genetics, School of Medicine, University of North Carolina, Chapel Hill, NCUSA
| | - Josh R Sikder
- Department of Genetics, School of Medicine, University of North Carolina, Chapel Hill, NCUSA
| | - Rafael J Vivero
- Programa de Estudio y Control de Enfermedades Tropicales, PECET, Universidad de Antioquia, Chapel Hill, NCColombia
| | - Daniel R Matute
- Department of Biology, College of Arts and Sciences, University of North Carolina, Chapel Hill, NC, USA
| | - Daniel R Schrider
- Department of Genetics, School of Medicine, University of North Carolina, Chapel Hill, NCUSA
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48
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Amin MR, Hasan M, Arnab SP, DeGiorgio M. Tensor decomposition based feature extraction and classification to detect natural selection from genomic data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.27.527731. [PMID: 37034767 PMCID: PMC10081272 DOI: 10.1101/2023.03.27.527731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Inferences of adaptive events are important for learning about traits, such as human digestion of lactose after infancy and the rapid spread of viral variants. Early efforts toward identifying footprints of natural selection from genomic data involved development of summary statistic and likelihood methods. However, such techniques are grounded in simple patterns or theoretical models that limit the complexity of settings they can explore. Due to the renaissance in artificial intelligence, machine learning methods have taken center stage in recent efforts to detect natural selection, with strategies such as convolutional neural networks applied to images of haplotypes. Yet, limitations of such techniques include estimation of large numbers of model parameters under non-convex settings and feature identification without regard to location within an image. An alternative approach is to use tensor decomposition to extract features from multidimensional data while preserving the latent structure of the data, and to feed these features to machine learning models. Here, we adopt this framework and present a novel approach termed T-REx , which extracts features from images of haplotypes across sampled individuals using tensor decomposition, and then makes predictions from these features using classical machine learning methods. As a proof of concept, we explore the performance of T-REx on simulated neutral and selective sweep scenarios and find that it has high power and accuracy to discriminate sweeps from neutrality, robustness to common technical hurdles, and easy visualization of feature importance. Therefore, T-REx is a powerful addition to the toolkit for detecting adaptive processes from genomic data.
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49
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Fluctuating selection and the determinants of genetic variation. Trends Genet 2023; 39:491-504. [PMID: 36890036 DOI: 10.1016/j.tig.2023.02.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 02/01/2023] [Accepted: 02/07/2023] [Indexed: 03/08/2023]
Abstract
Recent studies of cosmopolitan Drosophila populations have found hundreds to thousands of genetic loci with seasonally fluctuating allele frequencies, bringing temporally fluctuating selection to the forefront of the historical debate surrounding the maintenance of genetic variation in natural populations. Numerous mechanisms have been explored in this longstanding area of research, but these exciting empirical findings have prompted several recent theoretical and experimental studies that seek to better understand the drivers, dynamics, and genome-wide influence of fluctuating selection. In this review, we evaluate the latest evidence for multilocus fluctuating selection in Drosophila and other taxa, highlighting the role of potential genetic and ecological mechanisms in maintaining these loci and their impacts on neutral genetic variation.
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Ghildiyal K, Panigrahi M, Kumar H, Rajawat D, Nayak SS, Lei C, Bhushan B, Dutt T. Selection signatures for fiber production in commercial species: A review. Anim Genet 2023; 54:3-23. [PMID: 36352515 DOI: 10.1111/age.13272] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 10/11/2022] [Accepted: 10/11/2022] [Indexed: 11/11/2022]
Abstract
Natural fibers derived from diverse animal species have gained increased attention in recent years due to their favorable environmental effects, long-term sustainability benefits, and remarkable physical and mechanical properties that make them valuable raw materials used for textile and non-textile production. Domestication and selective breeding for the economically significant fiber traits play an imperative role in shaping the genomes and, thus, positively impact the overall productivity of the various fiber-producing species. These selection pressures leave unique footprints on the genome due to alteration in the allelic frequencies at specific loci, characterizing selective sweeps. Recent advances in genomics have enabled the discovery of selection signatures across the genome using a variety of methods. The increased demand for 'green products' manufactured from natural fibers necessitates a detailed investigation of the genomes of the various fiber-producing plant and animal species to identify the candidate genes associated with important fiber attributes such as fiber diameter/fineness, color, length, and strength, among others. The objective of this review is to present a comprehensive overview of the concept of selection signature and selective sweeps, discuss the main methods used for its detection, and address the selection signature studies conducted so far in the diverse fiber-producing animal species.
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Affiliation(s)
- Kanika Ghildiyal
- Division of Animal Genetics, Indian Veterinary Research Institute, Bareilly, India
| | - Manjit Panigrahi
- Division of Animal Genetics, Indian Veterinary Research Institute, Bareilly, India
| | - Harshit Kumar
- Division of Animal Genetics, Indian Veterinary Research Institute, Bareilly, India
| | - Divya Rajawat
- Division of Animal Genetics, Indian Veterinary Research Institute, Bareilly, India
| | | | - Chuzhao Lei
- College of Animal Science and Technology, Northwest A&F University, Yangling, China
| | - Bharat Bhushan
- Division of Animal Genetics, Indian Veterinary Research Institute, Bareilly, India
| | - Triveni Dutt
- Livestock Production and Management Section, Indian Veterinary Research Institute, Bareilly, India
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