Next Article in Journal
Cyclic di-GMP Modulation of Quorum Sensing and Its Impact on Type VI Secretion System Function in Sinorhizobium fredii
Previous Article in Journal
Distribution and Potential Metabolic Functions of Soil Actinobacteria in Degraded Alpine Grassland on the Northern Tibetan Plateau
Previous Article in Special Issue
Population Dynamics of Plasmodium vivax in Mexico Determined by CSP, Pvs25, and SSU 18S rRNA S-Type Polymorphism Analyses
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Microorganisms
Volume 13
Issue 10
10.3390/microorganisms13102231
microorganisms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

A Multi-Approach for In Silico Detection of Chromosome Inversions in Mosquito Vectors

by
Marcus Vinicius Niz Alvarez
1,2,
Filipe Trindade Bozoni
1,2,
Diego Peres Alonso
1,2 and
Paulo Eduardo Martins Ribolla
1,2,*
1
Pangene Laboratory, Biotechnology Institute, São Paulo State University (UNESP), Botucatu 18618-689, Brazil
2
Genetics, Microbiology and Immunology Department, Bioscience Institute, São Paulo State University (UNESP), Botucatu 18618-689, Brazil
*
Author to whom correspondence should be addressed.
Microorganisms 2025, 13(10), 2231; https://doi.org/10.3390/microorganisms13102231
Submission received: 1 August 2025 / Revised: 17 September 2025 / Accepted: 17 September 2025 / Published: 24 September 2025
(This article belongs to the Special Issue Research on Mosquito-Borne Pathogens)
Browse Figures
Review Reports Versions Notes

Abstract

In Brazil, Nyssorhynchus darlingi stands out as the primary malaria vector. Chromosome inversions have long been recognized as critical evolutionary mechanisms in diverse organisms. In this study, we used biallelic SNPs to show that it is possible to detect chromosome inversions reliably with low coverage sequence data. We estimated chromosome inversions in an Amazon Basin sample of Ny. darlingi and compared them with Anopheles gambiae and Anopheles albimanus genomes in synteny analysis. The An. gambiae dataset benchmarked the inversion detection pipeline with known inversions. Genotyping by sequencing was performed using the LCSeqTools workflow for the lcWGS dataset with an average sequencing depth of 2x. A synteny analysis was performed for Ny. darlingi inversions regions with An. gambiae and An. albimanus genomes. The sliding window analysis of PCA components revealed 10 high-confidence candidate regions for chromosome inversions in Ny. darlingi genome and two known inversions for An. gambiae with possible identification of breakpoints and adjacent regions at lower resolution. We demonstrate that lcWGS is a cost-effective and accurate method for detecting chromosome inversions. We reliably detected chromosome inversions in Ny. darlingi from the Brazilian Amazon that does not share similar inversion arrangements in An. gambiae or An. albimanus genomes.

1. Introduction

Malaria is a disease caused by Plasmodium parasites and transmitted through the bites of infected Anophelinae mosquitoes. Despite ongoing efforts, it remains a major global health challenge, particularly in tropical and subtropical regions [1]. Several factors contribute to malaria transmission, including socio-ecological factors and changes in the human host and vector behavior. In 2022, there were an estimated 249 million malaria cases globally, exceeding the pre-pandemic level of 233 million in 2019 by 16 million cases [2]. Increased human population and land use/land cover change (LULC) influence the biological community, including Anophelinae mosquitoes, particularly those with some degree of synanthropy and competence to transmit Plasmodium sp. that circulate in the Amazon region [3]. This vast region is responsible for 99.5% of human malaria in Brazil, mainly Plasmodium vivax (>90% in 2019) [4].
Among the diverse Anophelinae mosquitoes, Nyssorhynchus darlingi is the primary vector responsible for malaria transmission in the Neotropical region, including the Amazon basin [5]. This vector demonstrates high anthropophilic behavior in many areas, sometimes combined with opportunistic zoophilic (non-human) feeding [6]. Ny. darlingi presents a chromosomal composition of 2n = 6, with two pairs of autosomes: the largest (III) is submetacentric and the smallest (II) is metacentric. For sex chromosomes, X is acrocentric and Y is punctiform [7]. The distribution of the species reaches from Southern Mexico to Northern Argentina and from the East of the Andean Mountains to the coast of the Atlantic Ocean. Current strategies of integrated vector management, including the widespread use of insecticide-treated bed nets and the regular application of indoor residual spraying, are important for malaria control; however, they may still be insufficient to completely eliminate transmission in all endemic regions, and therefore the development of new, complementary strategies remains important in this context. Understanding the genetic basis of vectorial capacity in Ny. darlingi is essential for developing effective control strategies to combat malaria transmission [8].
Chromosome inversions, structural rearrangements where a chromosome segment is reversed in orientation, have long been recognized as critical evolutionary mechanisms in diverse organisms [9]. In mosquitoes, chromosome inversions have been associated with ecological adaptation, population divergence and speciation. These inversions can suppress recombination, leading to the maintenance of coadapted gene complexes and potentially facilitating the rapid evolution of traits related to vectorial capacity, such as insecticide resistance and host preference [9]. In Anopheles gambiae, two inversions (2La and another in the 2R arm) have been associated with important physiological and transcriptional differences, depending on sex, climate and epistatic interactions, demonstrating the adaptive effect of these inversions [10]. The 2La inversion is directly linked to desiccation resistance, with strong implications for survival strategies in arid environments [11,12]. In Anopheles funestus, a study in Africa suggested that chromosomal inversions promote ecological differentiation even when neutral markers fail to detect significant population structure [13]. Previous studies have identified chromosomal inversions in Ny. darlingi populations, particularly in the Amazon region, lack a comprehensive understanding of their prevalence, distribution and functional significance [8].
The rapid evolution of technologies involved in whole-genome sequencing (WGS) has precipitated substantial reductions in the per-base sequencing cost. Nonetheless, large-scale projects that require the sequencing of large sample sizes remain financially burdensome, potentially presenting insurmountable obstacles within specific laboratory contexts. One economically viable strategy entails genotyping-by-sequencing for low-coverage WGS (lcWGS), concomitant with imputation procedures that furnish adequate genomic data for precise marker selection [14]. Although the fidelity of variant detection diminishes in genomes characterized by shallow coverage depth, thereby engendering a heightened false-positive rate, this limitation is ameliorated through amalgamating information across multiple samples, thus augmenting the discernment of common variants [14,15]. Notably, imputation-driven genotype inference has been empirically validated for both panel-based genotyping and sequencing genotyping modalities [16], facilitating the potential adoption of extremely low-coverage WGS (EXL-WGS) for identifying variants at a markedly diminished expenditure relative to conventional WGS methodologies [17,18]. Li and collaborators have demonstrated that detecting rare variants in LcWGS samples poses considerable challenges due to the intrinsic difficulty in distinguishing authentic rare alleles from sequencing artifacts [19]. Notably, the enumeration of variants positively correlates with the prevalence of polymorphisms among the sequenced individuals within the delineated population subset. Given the diverse methodological approaches available for EXL-WGS analysis, meticulous calibration of each method’s sensitivity is imperative, given that the attenuation in coverage inevitably amplifies the potential for erroneous identifications.
In the present study, we used lcWGS markers to investigate the population of Ny. darlingi collected in Mâncio Lima, Acre state, Brazil. We conducted a comprehensive genomic analysis of chromosome inversions in Ny. darlingi and performed a comparative genomics analysis with Anopheles albimanus and Anopheles gambiae genomes. This study examines closely related Anopheles species, providing insights into the evolutionary dynamics across different mosquito lineages. Our findings contribute to the broader understanding of mosquito genetics, evolution and vector biology, with implications for malaria control strategies and vector management programs.

2. Materials and Methods

2.1. Sequencing Data Acquisition

This study used publicly available sequencing data from the National Center for Biotechnology Information (NCBI). Sequencing data types were selected based on their relevance to ensure comprehensive analysis. Sequencing data from lcWGS data of 321 Nyssorhynchus darlingi larvae and adults were obtained from the Bioproject PRJNA683015. The samples used in this study were collected in Mâncio Lima, Acre, Brazil.

2.2. Genome Reference

Ny. darlingi, An. gambiae and An. albimanus reference genomes used are publicly available at the NCBI database with respective accession numbers in Table 1. All reference genome assemblies are at chromosome level resolution, providing contiguous sequences suitable for downstream genomic analysis.

2.3. Genotyping by Sequencing and Variant Calling

The genotyping by sequencing pipeline was performed using LCSeqTools v0.1.0 [20]. Through the LCSeqTools, different steps are automatically applied optimized for lcWGS data, with customizable predefined parameters for filtering and quality control. Firstly, reads are trimmed using the Trimmomatic v0.39 software [21] step, with trimming parameters as follows: headcrop = 10, trailing = 20 and minlen = 100. Trimmed reads alignment was performed with the Burrows-Wheeler Aligner v0.7.17 software package [22], this step applies the bwa mem method with its default parameters for single-end mapping, followed by variant calling with the SamTools v1.10 software package [19] using the bcftools call-m method with its default calling penalties and weighting parameters. The LCSeqTools estimates missing data rates as the ratio of truly missing data, defined as missing genotype normalized probability (PL) fields due to zero depth, to non-missing data, defined as non-missing PL fields with at least one read depth. Given that, variant filtering parameters applied before genotype imputation were as follows: minor allele frequency (MAF) < 0.1, max missing data per sample and per variant < 0.5, genotype sequencing depth < 5 and genotype quality < 20. LCSeqTools uses depth threshold for genotype omission, so that GT fields are set as missing but PL are retained if non-missing. Using LCSeqTools, the last step applied genotype imputation with the BEAGLE v4.1 software package [23] using the PL method, with its imputation algorithm parameters as default and no reference panel. No post-imputation filtering through LCSeqTools was applied.
The genome coverage statistics was calculated using the bedtools v2.30.0 [24] software package with the genomecov method using the resulting BAM files from the read mapping step. A final filtering step was applied to the variant dataset with the SNP pruning algorithm from the PLINK v1.9 software package [25], using the indep-pairwise method, window size = 14 kbp, step size = 9 kbp and 0.05 as r2 threshold, as Alvarez and collaborators showed that the expected r2 value at a 12.57 kbp distance is approximately 0.1 in this Ny. darlingi population [20].

2.4. Data Processing and Statistical Analysis

All data manipulation, analysis and plotting were performed using RStudio Server (Ocean Storm version 2023.12.0) and R Language v4.2.1 [26], including packages provided by the tidyverse metapackage [27].

2.5. Chromosome Inversion Identification

Multiple approaches were combined to check the presence of chromosome inversion signals. Principal component analysis (PCA) for each chromosome was performed using PLINK, and the sliding window variance of SNP weight (eigenvector) estimates for each principal component were calculated within 10 kbp sliding windows with step size of 7.5 kbp, avoiding excessively low r2 SNPs in the window. Based on the absolute PCA SNP weights, the top 1% SNPs for each component were retained and the Identical by State (IBS) matrix was calculated using the respective SNP subset. The multidimensional scaling (MDS) technique was applied to the Euclidean IBS matrix with the cmdscale function from R base (k = 1 for genotype clustering and k = 2 for pairwise comparisons) and, subsequently, a clustering technique with the cmeans function from the e1071 package for R [21]. Only components clustered into three well-defined groups were retained, later identified as AA, AB and BB inversion genotypes using cmeans maximal membership probability.
A genome-wide association test was applied for each candidate chromosome inversion, using the linear model from Plink software, considering AA, AB and BB samples as quantitative 0, 1 and 2 phenotypes, respectively. A Bonferroni post hoc test was applied for multiple comparisons correction and SNPs were considered statistically significant when the adjusted p-value < 0.05.
A pairwise SNP linkage disequilibrium estimate analysis was performed for SNP pairs within ranges of 12 kbp of distance and the sliding window r2 median was calculated for each chromosome within sliding windows of 0.5% of the respective chromosome length. The Spearman correlation coefficient ρ was estimated for r2 and the mean SNP absolute weights using the cor.test function from R and was considered statistically significant when p-value < 0.05.

2.6. Chromosome Correlation Tests and Association Tests

All chromosome inversions were submitted to a pairwise Spearman correlation test using the estimated sample genotypes data and the p-values were adjusted for multiple comparisons using the Benjamini–Hochberg False Discovery Rate method (FDR). Correlations were considered statistically significant when FDR-adjusted p ≤ 0.05.

2.7. Pipeline Validation with Known Variants for Anopheles gambiae

High-coverage genome-wide variant data from the first phase of the “The Anopheles gambiae 1000 Genomes Consortium”, publicly available at EMBL-EBI, were used as reference (ERZ373588 with GCF_000005575.2 genome reference). An in silico lcWGS dataset was generated for chromosome identification pipeline validation, matching results with known An. gambiae structural variants (AgamP4 variant panel publicly available at Ensembl Metazoa). AgamP4 structural variants were localized in the GCF_000005575.2 genome reference using Minimap2 v2.25 [28]. A subset of 200 samples from the Republic of Cameroon group was selected from the full variant panel and lcWGS data were generated with the art_illumina program from the ART v2.5.8 software package [29] with approximately 2x sequencing depth per sample.

2.8. Comparative Genomics Analysis

An orthology inference was performed for An. gambiae and An. albimanus with Ny. darlingi as reference using BLASTp v2.12.0 [30] and the respective proteomes through the accession codes. The OrthoFinder v2.5.5 software [31] was also used as a second approach for ortholog inference and convergence check with BLASTp results. A genome synteny analysis was performed between the An. gambiae, An. albimanus and Ny. darlingi genomes using MCScanX v1.0.0 [32].

3. Results

3.1. Ny. Darlingi Chromosome Inversions Detection

The LCSeqTools pipeline generated a variant panel containing 4,241,254 SNPs for the Ny. darlingi sample. The sample-wise median proportion of genomic positions with zero coverage, representing missing data, was 36.89% (Q1 = 18.09%, Q3 = 57.94%, min = 2.73%, max = 76.04%). Also, at 5× depth (no GT omission threshold), the sample-wise observed median was 3.48% (Q1 = 1.11%, Q3 = 12.79%, min = 0.24%, max = 81.10%). The full sample-wise genome coverage statistics after properly read alignment are shown in Figure S1. After the SNP pruning, Plink retained 626,409 SNPs, around 3.4 per Kbp.
The PCA components analysis revealed continuous high-shifted regions from SNP weight sliding windows variance estimates, counting 10 candidate regions: 2, 4 and 4 shifted regions for chromosomes 2, 3 and X, respectively (Figure 1). Clustering analysis of each region revealed three well-defined genetic clusters with approximately the same 0.5 genetic distance between samples AA at the leftmost and AB in the middle and AB and BB at the rightmost (Figure 2). The chromosome inversion genotype membership probabilities of each sample are provided in the supplementary material. Genome-wide association test results for each remaining component showed statistically significant SNPs concentrated in converging chromosome regions of its respective PCA shifted variance windows (Figure 1). The chromosome inversion coordinate estimates are represented in Table 2.
The SNP pairwise linkage disequilibrium analysis showed that, despite the median r2 within a distance of 12 to 13 kbp being 0.08, 0.06 and 0.12 for chromosomes 2, 3 and X, respectively, spikes reaching almost double the median r2 were found in different regions along the chromosomes (Figure 1). Significant correlations were observed between r2 and mean SNP absolute weight from PCA components, as the ρ estimates were 0.62, 0.43 and 0.58 for chromosomes 2, 3 and X, respectively.

3.2. In Silico Validation of the Pipeline

The An. gambiae lcWGS simulated data resulted in a variant panel containing 3,825,409 SNPs. The simulated genotypes accuracy after genotyping by sequencing pipeline and imputation showed 84.52% and 92.02% for average genotype concordance and average allelic concordance, respectively (Table 3). Also, the total dosage R2 was 0.7465. Plink retained 658,413 SNPs after the pruning step, resulting in 2.36 SNPs per Kbp. Two meaningful components were detected with the sliding window PCA weight analysis approach and, subsequently, a genome-wide association test revealed statistically significant SNPs concentrated within two regions, as coordinate estimates were [20,466,374, 42,223,401] and [19,275,187, 26,282,153] for 2L and 2R (Figure 3). Both inversion estimates greatly coincided with the well-known An. gambiae inversions: 2La [20,524,057, 42,165,532] and 2Rb [19,023,924, 26,758,676]. The observed range offsets were −57,683 and 57,869 for the start and end coordinates of 2La, and 251,263 and −476,523 for 2Rb. Significant correlations were observed between r2 and mean SNP absolute weight from PCA components, as the ρ estimates were 0.88 and 0.49 for 2L and 2R, respectively.

3.3. Genome Synteny Analysis

Comparative genomics analysis revealed similar arrangements between Ny. darlingi and An. albimanus genomes and a significant rearrangement when comparing chromosomes 2 and 3 from An. gambiae and Ny. darlingi (Figure 4). Synteny analysis for each chromosome inversion region showed no similarity between Ny. darlingi detected inversions and An. gambiae known chromosome inversions (Figure 5). No data was available comparing An. albimanus inversions.

4. Discussion

This study shows that lcWGS provides a powerful and cost-effective approach for detecting chromosome inversions in large sample sizes. The method offers a practical alternative to high-coverage sequencing, substantially reducing the overall study budget while resulting in a lower resolution but reliable process of inversion presence detection in the population. However, it is important to note that the sensitivity of this approach was not estimated because no chromosome inversion genotyping metadata is available for both datasets samples to be used as reference, so further tests should be designed to estimate the performance on lcWGS data. The in silico experiment reinforces that, besides the higher genotyping error probability of lcWGS, high-confidence inversion signal detection is possible because multiple independent SNPs are reduced into a single genotype data with the PCA technique, reducing any lcWGS genotyping error and false-positive SNP bias. The genotype association analysis could extract an approximation of the chromosome inversion coordinates using the p-value threshold, but with unpredictable offsets from the true chromosome inversion ranges.
Nowling and collaborators used a similar approach to traditional WGS data for An. gambiae dataset and found a comparable performance for PCA-based analysis on chromosome inversion detection [33]. Although there are various pipelines for detecting chromosome inversion using SNPs or different genetic parameters as they summarized, a simple sliding window analysis is enough to detect inversion footprints if an in-depth eigenvectors analysis is performed. The An. gambiae inversion range estimates from lcWGS data in this study were consistent and close to the known ranges [34]. Even on genome drafts in which chromosome data is not well assembled, it is possible to use PCA clustering to detect inversion presence because eigenvectors are estimated essentially with a set of independent SNPs, so contiguity is not needed for detection. This is an essential feature for non-model organism studies, but the more shattered the assembly is, the more difficult it will be to estimate the inversion coordinate range.
A significant correlation is observed between linkage disequilibrium and the absolute values of PCA eigenvectors, although PCA-based methods provide a more precise approach for detecting structural variants [35]. The observed linkage disequilibrium oscillation towards the inversion breakpoints is a known effect caused by the change in the recombination rate [36]. Therefore, our main goal with the proposed approach is to identify the most informative SNPs for chromosome inversions, which are mainly composed of non-recombinant sites and almost-fixed alleles. The methodology is capable of detecting those fixed SNPs associated with inversions. The exact location of breakpoints is not relevant. By focusing on the most informative, high-confidence SNPs, this approach enables robust and clear interpretability of chromosome inversion presence, providing a practical tool for studies of genomic structural variation and population genomics.
The genome synteny analysis revealed an interesting rearrangement between the arms of chromosomes 2 and 3 for Ny. darlingi and An. albimanus when compared to An. gambiae genome. This phenomenon is evidenced by other comparative genomics studies focusing on Anophelinae, which have consistently reported analogous chromosomal rearrangements [37,38]. The ten chromosome inversions detected in Ny. darlingi genome did not show similar ranges or arrangements for known An. gambiae inversions in Figure 5. However, An. albimanus revealed some interesting equivalent syntenic regions for the chromosome inversions. As far as we know, there is no data available on An. albimanus chromosome inversions. Unfortunately, during this study, there was no publicly accessible sequencing data for An. albimanus that would facilitate a comprehensive analysis of inversion footprints using the methodology employed in this study.
Soboleva and collaborators also identified two large nested X chromosome inversions using physical genome mapping approach with fluorescence in situ hybridization of gene markers in polytene chromosome to locate the breakpoints for Anopheles atroparvus and Anopheles messeae. Thus, our approach is not directly comparable to their method, both in data type or resolution, the reported ranges seem slightly different from the observed in this study [39]. Their findings provide complementary evidence on X chromosome inversions for Anophelines.

5. Conclusions

Chromosomal inversions are important biological markers for genome evolution, environmental adaptation and speciation. Our study showed that lcWGS is particularly promising for large-scale genomic studies, offering a cost-effective alternative strategy to detect chromosome inversions. On the other hand, a 2× sequencing depth will increase genotyping errors, resulting in a limited precision for the exact location of the breakpoints.
The sliding window analysis of PCA eigenvectors to detect inversions ensures accurate inversion detection and range estimation with a simple, standalone approach. Ten chromosome inversions were found using lcWGS data from Ny. darlingi samples collected in one municipality located in the Brazilian Amazon basin. The synteny analysis revealed that Ny. darlingi do not share similar chromosomal inversion arrangements with An. gambiae. This finding emphasizes the 100 MYA separation of Ny. darlingi and An. gambiae represented by the Pangea breakup. The lack of data on An. albimanus chromosome inversions impairs any comparisons with Ny. darlingi.
The methodology presented in this paper facilitates the detection of chromosomal inversions based on a low coverage data and enables population genetics studies to be performed, mitigating the influence of chromosomal inversions.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/microorganisms13102231/s1. Figure S1: Heatmap of sequencing coverage across samples.

Author Contributions

M.V.N.A. and P.E.M.R. worked on bioinformatics; M.V.N.A., F.T.B., D.P.A. and P.E.M.R. analyzed the data. All authors have read and agreed to the published version of the manuscript.

Funding

M.V.N.A. was supported by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) 88887.817610/2023-00. P.E.M.R. is a CNPq Productivity Fellow (process number: 305252/2024-0).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original data presented in the study are openly available in Zenodo, record 13755768 (https://doi.org/10.5281/ZENODO.13755768) and Zenodo, record 17064546 (https://doi.org/10.5281/zenodo.17064546).

Acknowledgments

The authors have reviewed and edited the output and take full responsibility for the content of this publication.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. World Health Organization. World Malaria Report 2018; World Health Organization: Geneva, Switzerland, 2019; ISBN 9789241565653.
  2. Venkatesan, P. The 2023 WHO World Malaria Report. Lancet Microbe 2024, 5, e214. [Google Scholar] [CrossRef] [PubMed]
  3. Pimenta, P.F.P.; Orfano, A.S.; Bahia, A.C.; Duarte, A.P.M.; Ríos-Velásquez, C.M.; Melo, F.F.; Pessoa, F.A.C.; Oliveira, G.A.; Campos, K.M.M.; Villegas, L.M.; et al. An Overview of Malaria Transmission from the Perspective of Amazon Anopheles Vectors. Mem. Inst. Oswaldo Cruz 2015, 110, 23–47. [Google Scholar] [CrossRef] [PubMed]
  4. De Aguiar, G.M.S.C.; da Silva Picoli, M.E.F.; Ananias, F. Comportamento Epidemiológico Da Malária No Período Entre 2010 E 2020, No Brasil. Braz. J. Dev. 2022, 8, 73299–73315. [Google Scholar] [CrossRef]
  5. Nagaki, S.S.; Chaves, L.S.M.; López, R.V.M.; Bergo, E.S.; Laporta, G.Z.; Conn, J.E.; Sallum, M.A.M. Host Feeding Patterns of Nyssorhynchus darlingi (Diptera: Culicidae) in the Brazilian Amazon. Acta Trop. 2021, 213, 105751. [Google Scholar] [CrossRef]
  6. Barbosa, L.M.C.; Souto, R.N.P.; Dos Anjos Ferreira, R.M.; Scarpassa, V.M. Behavioral Patterns, Parity Rate and Natural Infection Analysis in Anopheline Species Involved in the Transmission of Malaria in the Northeastern Brazilian Amazon Region. Acta Trop. 2016, 164, 216–225. [Google Scholar] [CrossRef]
  7. Rafael, M.S.; Tadei, W.P. Metaphase Karyotypes of Anopheles (Nyssorhynchus) darlingi Root and A. (N.) nuneztovari Gabaldón (Diptera; Culicidae). Genet. Mol. Biol. 1998, 21, 351–354. [Google Scholar] [CrossRef]
  8. Tadei, W.P.; dos Santos, J.M.M.; Rabbani, M.G. Biologia de Anofelinos Amazônicos. V. Polimorfismo Cromossômico de Anopheles Darlingi Root (Diptera, Culicidae). Acta Amazon. 1982, 12, 353–369. [Google Scholar] [CrossRef][Green Version]
  9. Cornel, A.J.; Brisco, K.K.; Tadei, W.P.; Secundino, N.F.; Rafael, M.S.; Galardo, A.K.; Medeiros, J.F.; Pessoa, F.A.; Ríos-Velásquez, C.M.; Lee, Y.; et al. Anopheles darlingi Polytene Chromosomes: Revised Maps Including Newly Described Inversions and Evidence for Population Structure in Manaus. Mem. Inst. Oswaldo Cruz 2016, 111, 335–346. [Google Scholar] [CrossRef]
  10. Cheng, C.; Tan, J.C.; Hahn, M.W.; Besansky, N.J. Systems Genetic Analysis of Inversion Polymorphisms in the Malaria Mosquito Anopheles gambiae. Proc. Natl. Acad. Sci. USA 2018, 115, E7005–E7014. [Google Scholar] [CrossRef]
  11. Fouet, C.; Gray, E.; Besansky, N.J.; Costantini, C. Adaptation to Aridity in the Malaria Mosquito Anopheles gambiae: Chromosomal Inversion Polymorphism and Body Size Influence Resistance to Desiccation. PLoS ONE 2012, 7, e34841. [Google Scholar] [CrossRef]
  12. Gray, E.M.; Rocca, K.A.C.; Costantini, C.; Besansky, N.J. Inversion 2La Is Associated with Enhanced Desiccation Resistance in Anopheles gambiae. Malar. J. 2009, 8, 215. [Google Scholar] [CrossRef]
  13. Ayala, D.; Fontaine, M.C.; Cohuet, A.; Fontenille, D.; Vitalis, R.; Simard, F. Chromosomal Inversions, Natural Selection and Adaptation in the Malaria Vector Anopheles funestus. Mol. Biol. Evol. 2011, 28, 745–758. [Google Scholar] [CrossRef] [PubMed]
  14. Gorjanc, G.; Dumasy, J.-F.; Gonen, S.; Gaynor, R.C.; Antolin, R.; Hickey, J.M. Potential of Low-Coverage Genotyping-by-Sequencing and Imputation for Cost-Effective Genomic Selection in Biparental Segregating Populations. Crop Sci. 2017, 57, 1404–1420. [Google Scholar] [CrossRef]
  15. Sims, D.; Sudbery, I.; Ilott, N.E.; Heger, A.; Ponting, C.P. Sequencing Depth and Coverage: Key Considerations in Genomic Analyses. Nat. Rev. Genet. 2014, 15, 121–132. [Google Scholar] [CrossRef] [PubMed]
  16. Zheng, C.; Boer, M.P.; van Eeuwijk, F.A. Accurate Genotype Imputation in Multiparental Populations from Low-Coverage Sequence. Genetics 2018, 210, 71–82. [Google Scholar] [CrossRef] [PubMed]
  17. Pasaniuc, B.; Rohland, N.; McLaren, P.J.; Garimella, K.; Zaitlen, N.; Li, H.; Gupta, N.; Neale, B.M.; Daly, M.J.; Sklar, P.; et al. Extremely Low-Coverage Sequencing and Imputation Increases Power for Genome-Wide Association Studies. Nat. Genet. 2012, 44, 631–635. [Google Scholar] [CrossRef] [PubMed]
  18. Rustagi, N.; Zhou, A.; Watkins, W.S.; Gedvilaite, E.; Wang, S.; Ramesh, N.; Muzny, D.; Gibbs, R.A.; Jorde, L.B.; Yu, F.; et al. Extremely Low-Coverage Whole Genome Sequencing in South Asians Captures Population Genomics Information. BMC Genom. 2017, 18, 396. [Google Scholar] [CrossRef]
  19. Li, Y.; Sidore, C.; Kang, H.M.; Boehnke, M.; Abecasis, G.R. Low-Coverage Sequencing: Implications for Design of Complex Trait Association Studies. Genome Res. 2011, 21, 940–951. [Google Scholar] [CrossRef] [PubMed]
  20. Alvarez, M.V.N.; Alonso, D.P.; Kadri, S.M.; Rufalco-Moutinho, P.; Bernardes, I.A.F.; de Mello, A.C.F.; Souto, A.C.; Carrasco-Escobar, G.; Moreno, M.; Gamboa, D.; et al. Nyssorhynchus darlingi Genome-Wide Studies Related to Microgeographic Dispersion and Blood-Seeking Behavior. Parasit. Vectors 2022, 15, 106. [Google Scholar] [CrossRef] [PubMed]
  21. Bolger, A.M.; Lohse, M.; Usadel, B. Trimmomatic: A Flexible Trimmer for Illumina Sequence Data. Bioinformatics 2014, 30, 2114–2120. [Google Scholar] [CrossRef] [PubMed]
  22. Li, H.; Durbin, R. Fast and Accurate Short Read Alignment with Burrows–Wheeler Transform. Bioinformatics 2009, 25, 1754–1760. [Google Scholar] [CrossRef]
  23. Browning, B.L.; Browning, S.R. Genotype Imputation with Millions of Reference Samples. Am. J. Hum. Genet. 2016, 98, 116–126. [Google Scholar] [CrossRef] [PubMed]
  24. Quinlan, A.R.; Hall, I.M. BEDTools: A Flexible Suite of Utilities for Comparing Genomic Features. Bioinformatics 2010, 26, 841–842. [Google Scholar] [CrossRef] [PubMed]
  25. Weeks, J.P. Plink: AnRPackage for Linking Mixed-Format Tests Using IRT-Based Methods. J. Stat. Softw. 2010, 35, 1–33. [Google Scholar] [CrossRef]
  26. Araz, Ö.M.; Olson, D.L. R Programming Language and RStudio. In Risk and Predictive Analytics in Business with R; Chapman and Hall/CRC: Boca Raton, FL, USA, 2025; pp. 9–14. ISBN 9781003562399. [Google Scholar]
  27. Irizarry, R.A. The Tidyverse. In Introduction to Data Science; Chapman and Hall/CRC: Boca Raton, FL, USA, 2024; pp. 52–71. ISBN 9781003220923. [Google Scholar]
  28. Li, H. Minimap2: Pairwise Alignment for Nucleotide Sequences. Bioinformatics 2018, 34, 3094–3100. [Google Scholar] [CrossRef] [PubMed]
  29. Huang, W.; Li, L.; Myers, J.R.; Marth, G.T. ART: A next-Generation Sequencing Read Simulator. Bioinformatics 2012, 28, 593–594. [Google Scholar] [CrossRef]
  30. Camacho, C.; Coulouris, G.; Avagyan, V.; Ma, N.; Papadopoulos, J.; Bealer, K.; Madden, T.L. BLAST+: Architecture and Applications. BMC Bioinform. 2009, 10, 421. [Google Scholar] [CrossRef]
  31. Emms, D.M.; Kelly, S. OrthoFinder: Phylogenetic Orthology Inference for Comparative Genomics. Genome Biol. 2019, 20, 238. [Google Scholar] [CrossRef]
  32. Wang, Y.; Tang, H.; Debarry, J.D.; Tan, X.; Li, J.; Wang, X.; Lee, T.-H.; Jin, H.; Marler, B.; Guo, H.; et al. MCScanX: A Toolkit for Detection and Evolutionary Analysis of Gene Synteny and Collinearity. Nucleic Acids Res. 2012, 40, e49. [Google Scholar] [CrossRef]
  33. Nowling, R.J.; Manke, K.R.; Emrich, S.J. Detecting Inversions with PCA in the Presence of Population Structure. PLoS ONE 2020, 15, e0240429. [Google Scholar] [CrossRef]
  34. Nowling, R.J.; Fallas-Moya, F.; Sadovnik, A.; Emrich, S.; Aleck, M.; Leskiewicz, D.; Peters, J.G. Fast, Low-Memory Detection and Localization of Large, Polymorphic Inversions from SNPs. PeerJ 2022, 10, e12831. [Google Scholar] [CrossRef]
  35. Cáceres, A.; González, J.R. Following the Footprints of Polymorphic Inversions on SNP Data: From Detection to Association Tests. Nucleic Acids Res. 2015, 43, e53. [Google Scholar] [CrossRef]
  36. Seich Al Basatena, N.-K.; Hoggart, C.J.; Coin, L.J.; O’Reilly, P.F. The Effect of Genomic Inversions on Estimation of Population Genetic Parameters from SNP Data. Genetics 2013, 193, 243–253. [Google Scholar] [CrossRef]
  37. Wei, Y.; Cheng, B.; Zhu, G.; Shen, D.; Liang, J.; Wang, C.; Wang, J.; Tang, J.; Cao, J.; Sharakhov, I.V.; et al. Comparative Physical Genome Mapping of Malaria Vectors Anopheles sinensis and Anopheles gambiae. Malar. J. 2017, 16, 235. [Google Scholar] [CrossRef] [PubMed]
  38. Jiang, X.; Peery, A.; Hall, A.B.; Sharma, A.; Chen, X.-G.; Waterhouse, R.M.; Komissarov, A.; Riehle, M.M.; Shouche, Y.; Sharakhova, M.V.; et al. Genome Analysis of a Major Urban Malaria Vector Mosquito, Anopheles stephensi. Genome Biol. 2014, 15, 459. [Google Scholar] [CrossRef] [PubMed]
  39. Soboleva, E.S.; Kirilenko, K.M.; Fedorova, V.S.; Kokhanenko, A.A.; Artemov, G.N.; Sharakhov, I.V. Two Nested Inversions in the X Chromosome Differentiate the Dominant Malaria Vectors in Europe, Anopheles atroparvus and Anopheles messeae. Insects 2024, 15, 312. [Google Scholar] [CrossRef] [PubMed]
Microorganisms 13 02231 g001
Figure 1. Chromosome inversion detection analysis for Ny. darlingi dataset. PCA Weights Var: Sliding window of SNPs weights variance estimate. ρ: Spearman correlation coefficient for r2 and mean absolute SNP weights. *: Correlation test p value < 0.05. Legend stands for Chromosome: Principal Component. Horizontal colored segments represent the inversion coordinate estimates described in Table 2. Horizontal scale in Mbp.
Figure 1. Chromosome inversion detection analysis for Ny. darlingi dataset. PCA Weights Var: Sliding window of SNPs weights variance estimate. ρ: Spearman correlation coefficient for r2 and mean absolute SNP weights. *: Correlation test p value < 0.05. Legend stands for Chromosome: Principal Component. Horizontal colored segments represent the inversion coordinate estimates described in Table 2. Horizontal scale in Mbp.
Microorganisms 13 02231 g001
Microorganisms 13 02231 g002
Figure 2. Inversion genotyping analysis. Top biplots are displayed in two dimensions (MDS with k = 2), showing the relative distances between samples based on genetic similarity from the top 1% most representative SNPs for each respective inversion. χ2: Chi-square test between genotypes from two respective inversions. ρ: Spearman correlation coefficient for genotypes states of both inversions. Bottom line plots represent in one dimension the density of samples grouping for each genetic cluster for each inversion. Numbers under cluster points represent cluster counts. MAF: Minor inversion frequency. F: Inversion fixation index. *: FDR adjusted p values < 0.05.
Figure 2. Inversion genotyping analysis. Top biplots are displayed in two dimensions (MDS with k = 2), showing the relative distances between samples based on genetic similarity from the top 1% most representative SNPs for each respective inversion. χ2: Chi-square test between genotypes from two respective inversions. ρ: Spearman correlation coefficient for genotypes states of both inversions. Bottom line plots represent in one dimension the density of samples grouping for each genetic cluster for each inversion. Numbers under cluster points represent cluster counts. MAF: Minor inversion frequency. F: Inversion fixation index. *: FDR adjusted p values < 0.05.
Microorganisms 13 02231 g002
Microorganisms 13 02231 g003
Figure 3. Chromosome inversion detection analysis for An. gambiae dataset. PCA Weights Var: Sliding window of SNPs weights variance estimate. ρ: Spearman correlation coefficient for r2 and mean absolute SNP weights. *: Correlation test p value < 0.05. Legend stands for Chromosome: Principal Component. Horizontal scale in Mbp.
Figure 3. Chromosome inversion detection analysis for An. gambiae dataset. PCA Weights Var: Sliding window of SNPs weights variance estimate. ρ: Spearman correlation coefficient for r2 and mean absolute SNP weights. *: Correlation test p value < 0.05. Legend stands for Chromosome: Principal Component. Horizontal scale in Mbp.
Microorganisms 13 02231 g003
Microorganisms 13 02231 g004
Figure 4. Genome synteny analysis for Ny. darlingi with An. gambiae and An. albimanus. Black arrow inside each segment represents the original reference orientation.
Figure 4. Genome synteny analysis for Ny. darlingi with An. gambiae and An. albimanus. Black arrow inside each segment represents the original reference orientation.
Microorganisms 13 02231 g004
Microorganisms 13 02231 g005
Figure 5. Chromosome inversion synteny analysis. Green color: Ny. darlingi. Blue color: An. gambiae. Red color: An. albimanus. Upper left label indicates the chromosome and the i-th principal component in which chromosome inversion was detected for Ny. darlingi. Known An. gambiae inversions for chromosome 2 are represented with the black line segments outside the circles. Black arrow inside each segment represents the original reference orientation. Only chromosomes with at least one link were retained in each plot.
Figure 5. Chromosome inversion synteny analysis. Green color: Ny. darlingi. Blue color: An. gambiae. Red color: An. albimanus. Upper left label indicates the chromosome and the i-th principal component in which chromosome inversion was detected for Ny. darlingi. Known An. gambiae inversions for chromosome 2 are represented with the black line segments outside the circles. Black arrow inside each segment represents the original reference orientation. Only chromosomes with at least one link were retained in each plot.
Microorganisms 13 02231 g005
Table 1. Summary of reference genomes used in the study.
Table 1. Summary of reference genomes used in the study.
SpecieReference
Accession		Number of
Chromosomes		Genome
Size		Scaffold
N50 (L50)
Nyssorhynchus darlingiGCF_943734745.13181.6 Mb95 Mb (1)
Anopheles gambiaeGCF_943734735.23264.5 Mb99 Mb (2)
Anopheles albimanusGCF_013758885.13172.6 Mb89 Mb (1)
Accession codes can be used to retrieve the corresponding records through the NCBI.
Table 2. Ny. darlingi chromosome inversion coordinate estimates based on genome-wide association test.
Table 2. Ny. darlingi chromosome inversion coordinate estimates based on genome-wide association test.
ChromosomeAccession CodeComponentStartEndLength
2NC_064874.1C171,998,91984,371,38612,372,467
2NC_064874.1C332,875,09339,167,0946,292,001
2NC_064874.1C419,048,04622,354,4383,306,392
2NC_064874.1C538,207,55145,614,6427,407,091
3NC_064875.1C15,786,82122,248,01316,461,192
3NC_064875.1C360,944,93467,169,6636,224,729
3NC_064875.1C447,375,56154,594,9567,219,395
3NC_064875.1C521,879,30526,791,3514,912,046
XNC_064873.1C1169,21912,292,79612,123,577
XNC_064873.1C210,405,32912,856,5942,451,265
Component: Principal component from PCA.
Table 3. Post LCSeqTools imputation performance for Simulated lcWGS data.
Table 3. Post LCSeqTools imputation performance for Simulated lcWGS data.
MAFGenotype ConcordanceAllelic ConcordanceDosage R2NN FPFP Rate
(0, 0.1]92.48%96.19%0.848214,082--
(0.1, 0.2]90.80%95.28%0.82271,097,63285570.7736%
(0.2, 0.3]85.27%92.40%0.75371,161,7041410.0121%
(0.3, 0.4]80.67%90.02%0.6898830,1221580.0190%
(0.4, 0.5]77.89%88.64%0.6379712,6713420.0480%
MAF: Minor allele frequency range based on reference data. FP: False-positive SNPs; Genotype Concordance: Average proportion of genotypes that are exactly identical between imputed low-coverage simulated data and reference data; Allelic Concordance: Average proportion of shared alleles between genotypes from imputed low-coverage simulated data and reference data. Dosage R2: Squared Pearson correlation of alleles dosages. No false positives were identified with MAF (0, 0.1] because simulated data is filtered for MAF < 0.1 and true positives in that range stand for inflated frequencies (simulated MAF > 0.1) compared to the reference frequencies.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Alvarez, M.V.N.; Bozoni, F.T.; Alonso, D.P.; Ribolla, P.E.M. A Multi-Approach for In Silico Detection of Chromosome Inversions in Mosquito Vectors. Microorganisms 2025, 13, 2231. https://doi.org/10.3390/microorganisms13102231

AMA Style

Alvarez MVN, Bozoni FT, Alonso DP, Ribolla PEM. A Multi-Approach for In Silico Detection of Chromosome Inversions in Mosquito Vectors. Microorganisms. 2025; 13(10):2231. https://doi.org/10.3390/microorganisms13102231

Chicago/Turabian Style

Alvarez, Marcus Vinicius Niz, Filipe Trindade Bozoni, Diego Peres Alonso, and Paulo Eduardo Martins Ribolla. 2025. "A Multi-Approach for In Silico Detection of Chromosome Inversions in Mosquito Vectors" Microorganisms 13, no. 10: 2231. https://doi.org/10.3390/microorganisms13102231

APA Style

Alvarez, M. V. N., Bozoni, F. T., Alonso, D. P., & Ribolla, P. E. M. (2025). A Multi-Approach for In Silico Detection of Chromosome Inversions in Mosquito Vectors. Microorganisms, 13(10), 2231. https://doi.org/10.3390/microorganisms13102231

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop