1.1 Omics approaches to finding insecticide resistance genes¶
Theme: Theory
Mosquitoes around Lake Victoria survive pyrethroid-treated nets. Some of the reasons are well known, such as kdr mutations in the voltage-gated sodium channel. Many are not. This module covers how resistance works at the molecular level and the “omic” approaches used to find the genes and mutations involved. It finishes with the plan for this course: one study, taken from RNA-seq in Busia (Uganda) to field monitoring in Siaya (Kenya). If you have not read it yet, start with the study brief.
This is a lecture module: there is no analysis code, only a quiz at the end.
Learning objectives¶
At the end of this module you will be able to:
Explain in a few sentences what genomic surveillance adds to malaria vector control.
Describe the four main mechanisms of insecticide resistance and give an Anopheles example of each.
Compare the omic approaches used to discover resistance genes and mutations, and say what question each one answers.
Explain why no single approach is enough, and how the approaches in this course fit together.
Setup¶
Nothing to install. The only code in this notebook is the quiz at the end.
Genomic surveillance in vector control¶
You met this in the MalariaGEN–PAMCA course, so here is only a short reminder.
What. Genomic surveillance means sampling mosquito populations repeatedly and using DNA or RNA sequence data to track the genetic changes that matter for control: resistance mutations, species and population structure, and gene flow between populations.
Why. Insecticide-treated nets and indoor residual spraying caused most of the fall in malaria cases after 2000, and both depend on insecticides still killing mosquitoes. Bioassays (WHO tubes, CDC bottles) tell you whether a population is resistant, but not why. The mechanism matters for choosing a product. For example, resistance driven by cytochrome P450 enzymes can be partly overcome by PBO nets, because PBO inhibits P450s. PBO does nothing for target-site resistance. Genetic markers can also be screened in dead, stored mosquitoes, in large numbers and at low cost, so they can act as an early warning before control fails.
How. Collect mosquitoes → sequence (whole genomes, RNA, or targeted amplicons) → analyse
→ report to control programmes. The Anopheles gambiae 1000 Genomes Project (Ag1000G) [1, 2]
and the MalariaGEN Vector Observatory provide the whole-genome reference data that you used in
the PAMCA course through malariagen_data.
What this course adds. Whole-genome data tells you where variation is, but many resistance mechanisms act by changing how much of a gene is made. We therefore add RNA-seq (expression), the selection atlas (where selection is acting), assay design (how to test for a marker cheaply) and amplicon sequencing (how to monitor many markers in many mosquitoes).
Mechanisms of insecticide resistance¶
Resistance mechanisms fall into four broad groups (plus behavioural avoidance, which we do not cover). PAMCA Workshop 2, Module 2 covers metabolic resistance in depth; the table below is a summary.
| Mechanism | What happens | Anopheles examples | Main type of genetic change |
|---|---|---|---|
| Target-site | The protein the insecticide binds to changes shape, so the insecticide binds less well | Vgsc L995F and L995S (kdr) for pyrethroids and DDT [3, 4]; Ace1 G280S for organophosphates and carbamates; Rdl A296G for dieldrin | Amino acid substitutions; sometimes gene duplication (Ace1) |
| Metabolic | Enzymes break down or modify the insecticide before it reaches its target | P450s CYP6P3, CYP6M2, CYP6AA1, CYP9K1; glutathione S-transferase GSTE2; carboxylesterases COEAE1F/2F | More expression (cis-regulatory changes, copy number variants), and sometimes amino acid changes that alter enzyme activity |
| Cuticular | A thicker or altered cuticle slows the insecticide getting in | CYP4G16 and CYP4G17, which make cuticular hydrocarbons [5] | Mostly higher expression |
| Sequestration | A protein binds the insecticide and stops it acting | Sensory appendage protein SAP2, highly expressed in the legs, binds pyrethroids [6] | Higher expression |
Two points to take from this table:
Only target-site resistance is reliably caused by a single amino acid change. The other mechanisms usually work by changing how much protein is made. A DNA-only approach finds these less easily, which is one reason we need RNA-seq.
The same gene can be affected in several ways at once. Around Lake Victoria, the Cyp6aa/Cyp6p cluster on chromosome 2R carries gene duplications, amino acid changes (e.g. Cyp6p4 I236M) and regulatory changes on the same haplotypes. The Ag-vampIR panel you analyse on Day 4 tags several of these.
Resistance genes do not act alone. Most resistant populations carry several mechanisms, and their effects add up (or multiply). That is why surveillance targets panels of markers rather than one mutation.
Omic approaches to finding resistance genes¶
Candidate gene studies¶
The oldest approach: pick genes you already suspect (the insecticide’s target, detoxification enzymes) and sequence or measure them. This is how kdr was found [3]. It is cheap and the results are easy to interpret, but you only find what you already expected.
Microarrays, then RNA-seq¶
Transcriptomics measures the expression of many genes at once in resistant and susceptible mosquitoes. Early studies used microarrays, first a “detox chip” of about 250 detoxification genes [7], then whole-genome arrays. These identified CYP6P3 as overexpressed in pyrethroid-resistant field mosquitoes, and showed that the enzyme metabolises pyrethroids [8].
RNA-seq replaced microarrays. It measures all genes, including unannotated ones, with a wider dynamic range. It also produces sequence, so the same data can be used to call SNPs, estimate allele frequencies and compare populations. RNA-Seq-Pop [9] was built to use both parts of the data, and it is the workflow you run on the Busia data. Because single RNA-seq experiments often disagree, a meta-analysis across many studies (AnoExpress [10]) helps separate consistent resistance genes from noise.
Limits: expression is not causation. A gene can be overexpressed because it sits next to the causal gene on a swept haplotype, because it is induced by stress, or because the resistant and susceptible strains differ in many other ways. The susceptible comparator in many studies, including Busia, is the Kisumu lab strain, which has been in colony for decades.
Genome-wide association studies (GWAS)¶
GWAS compares genotypes of mosquitoes that survived or died after insecticide exposure. It links genotype directly to phenotype, which the other approaches do not. A candidate-gene association study in Ugandan An. gambiae found reproducible markers in the Cyp6aa/Cyp6p cluster [11].
Limits: bioassay phenotypes are noisy, resistance is polygenic, and a selective sweep makes long blocks of the genome travel together, so many SNPs are associated and the causal one is hard to separate. Large sample sizes are needed.
Selection scans¶
Strong selection by insecticides leaves footprints in the genome: a stretch of reduced diversity and long, shared haplotypes around the favoured mutation. Genome-wide selection scans (H12, G123, iHS; see PAMCA Workshop 6, Module 3) find these loci without any phenotype data. The malaria vector selection atlas [12] catalogues signals across thousands of genomes from Ag1000G and the An. funestus 1000 Genomes Project.
Limits: a signal points to a region, often with several genes in it, and says nothing about which insecticide (if any) was the selective pressure.
Copy number variant (CNV) discovery¶
Gene duplications are a common way to make more enzyme. Whole-genome sequencing reads can be used to detect CNVs from changes in coverage and from discordant or split reads. Using Ag1000G data, CNVs were found to cluster at a few resistance loci (Cyp6aa/Cyp6p, Cyp9k1, the Gste cluster, Cyp6m/Cyp6z), often as many independent duplications [13]. A CNV is a strong explanation for why a gene is overexpressed.
Amplicon sequencing for monitoring¶
Once markers are known, you do not need whole genomes to track them. Amplicon sequencing amplifies tens to hundreds of short targets in one multiplex PCR and sequences them in thousands of mosquitoes cheaply. Ag-vampIR [14] is a panel of this kind, covering resistance markers, tagging SNPs for the major sweeps, and ancestry-informative markers for species identification. It can also be used for association studies, as you will do with the Siaya data.
Limits: it only sees the markers on the panel, so it monitors rather than discovers.
Functional validation¶
None of the approaches above proves that a gene causes resistance. That needs experiments: expressing the enzyme in cell lines and testing whether it metabolises the insecticide, knocking the gene down with RNAi, or overexpressing it in transgenic mosquitoes. SAP2 is a good example where all of these were done [6]. This is outside the scope of the course, but your dossier verdict should say whether it has been done for your gene.
Summary of approaches¶
| Approach | Question it answers | Needs phenotype? | Finds new genes? | Where in this course |
|---|---|---|---|---|
| Candidate gene | Is this suspected gene changed? | Optional | No | Background |
| RNA-seq | Which genes are expressed differently? | Yes (resistant vs susceptible groups) | Yes | Days 1–2 (RNA-Seq-Pop) |
| Meta-analysis | Is the difference consistent across studies? | Yes (in each study) | Yes | Day 2 (AnoExpress) |
| CNV discovery | Is the gene duplicated? | No | Yes | Day 3 (malariagen_data) |
| Selection scan | Is the region under recent selection? | No | Yes | Day 3 (selection atlas) |
| GWAS | Is the variant associated with surviving? | Yes (individual dead/alive) | Yes | Day 4 (Siaya, targeted) |
| Amplicon sequencing | How common is the marker, where and when? | Optional | No | Day 4 (AmpSeeker) |
| Functional validation | Does the gene cause resistance? | – | No | Not covered |
How the approaches fit together: the course study¶
Each approach has a blind spot, and each one covers a blind spot of another. RNA-seq finds candidates but cannot separate cause from passenger. Selection scans show that a region matters in the field but not which gene or why. CNVs and SNPs explain how expression goes up. Amplicon data links markers to survival in the field. Put together, they make a much stronger case than any one of them.
This course follows that chain for one public study of pyrethroid resistance in An. gambiae around Lake Victoria:
The loop closes because the Busia selection-atlas cohort has signals at four loci (Cyp6aa/Cyp6p on 2R, a region near 34 Mb on 2L, the Gste cluster on 3R and Cyp9k1 on X), and Ag-vampIR carries tagging SNPs for all four. On Day 4 you can test whether a locus found by RNA-seq and selection scans in Busia predicts survival on a deltamethrin net 50 km away in Siaya.
On Day 2 you are given two or three candidate genes and you follow them along this chain, building a gene dossier as you go.
Exercises¶
These are discussion exercises. Write short notes in the empty cell (as a comment or in a text cell), then compare with the answer.
Exercise 1¶
A national malaria control programme wants to know, every year, the frequencies of 30 known resistance markers in about 2,000 mosquitoes from 10 districts. Which approach would you recommend, and why not whole-genome sequencing?
Show answer
Amplicon sequencing. The markers are already known, so there is nothing to discover, and a multiplex panel can genotype thousands of mosquitoes for a small fraction of the cost of whole genomes. The data are small and quick to analyse, which makes yearly reporting realistic. Whole-genome sequencing would still be useful for a subset of samples, to look for new signals the panel cannot see.
Exercise 2¶
One of your candidate genes is five times more highly expressed in the deltamethrin-selected Busia line than in Kisumu. Give three reasons, other than “the gene causes resistance”, why this could happen.
Show answer
Any three of:
Different genetic backgrounds. Kisumu is an old lab colony from a different place. Many genes will differ between the strains for reasons unrelated to insecticides.
Hitchhiking. The gene may sit on the same swept haplotype, or inside the same CNV, as the gene that really causes resistance, so it is overexpressed as a side effect.
Induction. If the mosquitoes were exposed to insecticide (or other stress) shortly before sampling, detoxification genes can be switched on temporarily.
Drift in the colony. Small lab populations change by chance.
Tissue or developmental differences, such as differences in age or body size between groups.
This is why the course compares selected vs parental Busia lines (same background) as well as Busia vs Kisumu, and why you check replication in AnoExpress.
Exercise 3¶
The Busia 2016 cohort has a selection signal over the Cyp6aa/Cyp6p cluster on 2R, which contains several P450 genes. What extra evidence would convince you that one particular gene in the cluster is the target of selection?
Show answer
Useful lines of evidence include:
That gene, and not its neighbours, is consistently overexpressed in resistant populations (RNA-seq, AnoExpress).
A CNV or amino acid change in that gene is at high frequency on the swept haplotype (Ag3 data).
The signal’s focus lies on or next to that gene (selection atlas intervals).
Markers on the swept haplotype are associated with surviving insecticide exposure (Siaya amplicon data).
Functional work shows the enzyme metabolises pyrethroids, or knockdown restores susceptibility.
In practice, for gene clusters the answer is often “more than one gene”, since duplications can include several genes.
Quiz¶
Check your understanding. The quiz runs in Colab and in the course book.
%pip install -q jupyterquiz==2.9.6.4
from jupyterquiz import display_quiz
display_quiz("https://raw.githubusercontent.com/sanjaynagi/anopheles-omics-training/main/quizzes/1-1-omics-approaches.json")Summary¶
Genomic surveillance explains why populations are resistant, which guides product choice and gives early warning.
Resistance is target-site, metabolic, cuticular or by sequestration. Apart from target-site resistance, most mechanisms work by changing how much protein is made.
Candidate genes, transcriptomics, GWAS, selection scans, CNV discovery and amplicon sequencing each answer a different question and each has blind spots.
The course follows one chain of evidence: Busia RNA-seq → AnoExpress → Ag3 CNVs and SNPs → selection atlas → AnoPrimer → panel design → Siaya amplicons.
Well done!¶
In the next module you set up the habits and tools (project layout, environments and git) that you will use for the rest of the week.
References¶
The Anopheles gambiae 1000 Genomes Consortium (2017). Genetic diversity of the African malaria vector Anopheles gambiae. Nature. https://
doi .org /10 .1038 /nature24995 The Anopheles gambiae 1000 Genomes Consortium (2020). Genome variation and population structure among 1142 mosquitoes of the African malaria vector species Anopheles gambiae and Anopheles coluzzii. Genome Research. Cold Spring Harbor Laboratory (2020)
Martinez-Torres D et al. (1998). Molecular characterization of pyrethroid knockdown resistance (kdr) in the major malaria vector Anopheles gambiae s.s. Insect Molecular Biology. Martinez‐Torres et al. (1998)
Clarkson CS et al. (2021). The genetic architecture of target-site resistance to pyrethroid insecticides in the African malaria vectors Anopheles gambiae and Anopheles coluzzii. Molecular Ecology. Clarkson et al. (2021)
Balabanidou V et al. (2016). Cytochrome P450 associated with insecticide resistance catalyzes cuticular hydrocarbon production in Anopheles gambiae. PNAS. Balabanidou et al. (2016)
Ingham VA et al. (2020). A sensory appendage protein protects malaria vectors from pyrethroids. Nature. Ingham et al. (2019)
David J-P et al. (2005). The Anopheles gambiae detoxification chip: a highly specific microarray to study metabolic-based insecticide resistance in malaria vectors. PNAS. David et al. (2005)
Müller P et al. (2008). Field-caught permethrin-resistant Anopheles gambiae overexpress CYP6P3, a P450 that metabolises pyrethroids. PLoS Genetics. Müller et al. (2008)
Nagi SC et al. (2023). RNA-Seq-Pop: exploiting the sequence in RNA sequencing — a Snakemake workflow reveals patterns of insecticide resistance in the malaria vector Anopheles gambiae. Molecular Ecology Resources. Nagi et al. (2023)
Nagi SC, Ingham VA (2025). A multi-omic meta-analysis reveals novel mechanisms of insecticide resistance in malaria vectors. Communications Biology. Nagi et al. (2025)
Weetman D et al. (2018). Candidate-gene based GWAS identifies reproducible DNA markers for metabolic pyrethroid resistance from standing genetic variation in East African Anopheles gambiae. Scientific Reports. Weetman et al. (2018)
Nagi SC et al. (2025). An atlas of positive selection in the genomes of major malaria vectors. bioRxiv. Nagi et al. (2025)
Lucas ER et al. (2019). Whole-genome sequencing reveals high complexity of copy number variation at insecticide resistance loci in malaria mosquitoes. Genome Research. Lucas et al. (2019)
Nagi SC et al. (2025). Targeted genomic surveillance of insecticide resistance in African malaria vectors. bioRxiv. Nagi et al. (2025)
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