---
title: "The malaria vector selection atlas"
shorttitle: "The malaria vector selection atlas"
slug: selection-atlas
date: 07/21/2025
thumbnail: '/thumbnails/atlas.png'
tag: malariagen_data, genetics
canonicalUrl: 'https://sanjaycnagi.com/blog/2025-07-21-selection-atlas/'
---

[![selection atlas signals](/blog/signals.png)](doi.org/10.1101/2025.07.16.664900)

Dual-active ingredient ITNs are now being deployed in sub-Saharan Africa. We know that these nets are not only highly effective but effective against pyrethroid-resistant malaria vectors, which should help in the fight against malaria. But given the evolutionary adaptability of malaria mosquitoes, we must rapidly find ways to detect resistance before it becomes widespread, in order employ insecticide resistance management practices and protect the lifespan of these critical tools. 

When a beneficial mutation arises — like one that helps a mosquito survive insecticide exposure — it begins to spread through a population, leaving a signature of reduced genetic diversity around it. We can exploit this signature to pinpoint regions of the genome that are under selection and threatening malaria vector control efforts. 

In a new pre-print on [bioRxiv](https://www.biorxiv.org/content/10.1101/2025.07.16.664900), we present a web resource, the [**Malaria Vector Selection Atlas**](https://anopheles-genomic-surveillance.github.io/selection-atlas/), a database of selection signals in wild Anopheles mosquitoes within the [Vector Observatory](https://www.malariagen.net/vobs/). We analysed whole-genome sequence data from over 4,300 mosquitoes collected across 21 African countries, using population genomic methods to scan the genomes of our cohorts for telltale signs of recent positive selection. The atlas reveals both familiar loci and new threats: we confirm selection at established resistance genes like the *Vgsc* (the target of DDT and pyrethroids) and metabolic enzyme clusters like the *Cyp6p* locus that break down insecticides. But we also discovered novel signals including a diacylglycerol kinase on the X chromosome that may represent a previously unknown resistance mechanism. The selection atlas is powered by an automated computational workflow that enables continuous updates as new data become available. As we integrate more recent data, we envisage that the resource will provide a crucial early warning system for tracking mosquito evolution in near real-time, helping to inform evidence-based decisions about where and how best to deploy our limited vector control tools.
 
---

*Explore the pre-print on [bioRxiv](https://www.biorxiv.org/content/10.1101/2025.07.16.664900v1) and the [selection-atlas web resource](https://anopheles-genomic-surveillance.github.io/selection-atlas/)*


The malaria vector selection atlas

---
title: Using diplotype clustering to discover mutations causing insecticide resistance in malaria mosquitoes
shorttitle: Diplotype clustering for genomic surveillance of mosquitoes
slug: diplotype-clustering
date: 08/15/2024
thumbnail: /thumbnails/dendro.png
tag: genetics 
canonicalUrl: https://sanjaycnagi.com/blog/2024-08-15-diplotype-clustering/
---

Vectors of disease evolve rapidly in response to the interventions we throw at them. By monitoring genetic changes in these populations over time, we can detect emerging insecticide resistance mechanisms and monitor the spread of known mechanisms, with the aim of informing vector control strategies. In a world of limited active ingredients, this surveillance will be crucial for maintaining the effectiveness of front-line interventions like long-lasting insecticide-treated bed nets (LLINs) and indoor residual spraying (IRS).

To facilitate this, we have been developing systems and tools for genomic surveillance. The MalariaGEN Vector Observatory have developed the Python package [`malariagen_data`](https://malariagen.github.io/malariagen-data-python/latest/), which provides tools for accessing and analysing genomic datasets from major malaria vectors. By developing innovative software, we can help to build capacity in genomic research, allowing more people to perform robust, complex genomic analyses that would otherwise be limited to a select few.

In this blog post, we wanted to share a new function that we have recently added to `malariagen_data`: [plot_diplotype_clustering_advanced()](https://malariagen.github.io/malariagen-data-python/latest/generated/malariagen_data.ag3.Ag3.plot_diplotype_clustering_advanced.html#malariagen_data.ag3.Ag3.plot_diplotype_clustering_advanced). This method allows us to rapidly zoom in on a genome region of interest and identify selective sweeps, assess their size, detect potential gene flow events between countries or species, and investigate whether sweeps are driven by copy number variants (CNVs), amino acid mutations or both.

But what exactly are diplotypes, and why are they useful? A diplotype, sometimes referred to as a multi-locus genotype, is essentially the combination of two haplotypes from a single mosquito - one from each chromosome - at a particular genomic region. By analysing diplotypes rather than haplotypes, we can better capture the full genetic variation present in an individual, including complex structural variants like CNVs that can be difficult to phase onto haplotypes. Often, CNVs and multiallelic SNPs are ignored when analysing haplotype data. The more mosquitoes we sequence, the worse this problem gets - *An. gambiae s.l* is so genetically diverse, eventually, a significant proportion of all SNPs become multiallelic. 

![diplotype](/blog/diplotype.png)
*Figure 1. Illustration of the relationship between diplotypes and haplotypes*

The new diplotype clustering functionality in `malariagen_data` performs hierarchical clustering on diplotypes from a specified genomic region. It then visualises the results, displaying:  

&nbsp;&nbsp;&nbsp; 1. The clustering dendrogram  
&nbsp;&nbsp;&nbsp; 2. Sample metadata (e.g., species, collection location)  
&nbsp;&nbsp;&nbsp; 3. Heterozygosity of each sample (within this genomic region)  
&nbsp;&nbsp;&nbsp; 4. Copy number at genes of interest  
&nbsp;&nbsp;&nbsp; 5. Amino acid variants in a specified transcript  

#### A case study

To illustrate the power of this approach, let's look at a case study of the *Gste2* gene from some recent whole-genome data of *An. gambiae s.l* from Obuasi, central Ghana (Figure 2). *Anopheles* mosquitoes from this area are highly resistant to multiple classes of insecticides [[1](https://bmcinfectdis.biomedcentral.com/articles/10.1186/s12879-022-07795-4)]. The *Gste2* gene is known to be involved in resistance to DDT (and potentially other insecticides), through either copy number variation [[2](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6673711/)], amino acid mutations [[3](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3968025/)], or both. *Gste2*-I114T and *Gste2*-L119V are the major amino acid mutations at this locus known to confer resistance.

Here is an example of how to use the [plot_diplotype_clustering_advanced()](https://malariagen.github.io/malariagen-data-python/latest/generated/malariagen_data.ag3.Ag3.plot_diplotype_clustering_advanced.html#malariagen_data.ag3.Ag3.plot_diplotype_clustering_advanced) function:  


``` python
ag3.plot_diplotype_clustering_advanced(
    region="3R:28,597,000-28,600,000",         # The genomic region for clustering
    cnv_region="3R:28,594,000-28,605,000",     # The genomic region for CNV data
    snp_transcript="AGAP009194-RA",            # The transcript for amino acid variants
    sample_sets="1244-VO-GH-YAWSON-VMF00149",  # The sample set
    sample_query=None,                         # A query to filter samples
    site_mask="gamb_colu",                     # The site mask to use
    linkage_method="complete",                 # The linkage method to use
    color="taxon",                             # The metadata column to determine color
    )
```  

<br></br>
<br></br>
<br></br>
<br></br>

There are many more optional parameters for the user to configure - see the [API docs](https://malariagen.github.io/malariagen-data-python/latest/generated/malariagen_data.ag3.Ag3.plot_diplotype_clustering_advanced.html#malariagen_data.ag3.Ag3.plot_diplotype_clustering_advanced) for more information. Here is the figure it produces:


![dipclust](/blog/dipclust-gste2.png)
*Figure 2. Diplotype clustering at Gste2.*

We've annotated this figure with some diplotype clusters which are particularly interesting. For example, cluster B contains a large number of diplotypes which are all genetically identical and have very low heterozygosity - this is what you expect when a selective sweep has occurred, and you find many individuals that are homozygous for the haplotype under selection. All individuals in cluster B also carry the *Gste2*-I114T substitution. If we did not already know something about this mutation, this figure would give us a clue that the mutation is a potential driver of selection and insecticide resistance. In fact, we already know this mutation causes insecticide resistance, so it is no surprise to find it linked to a selective sweep in this dataset.

We can also see another large cluster (cluster C) which does not harbour either I114T or L119V, but instead, the *Gste2*-F120L mutation. This cluster is homozygous for F120L and shows low heterozygosity, again indicative of diplotypes which have two copies of the same swept haplotype. Two things about the *Gste2*-F120L mutation are convincing as a potential driver of resistance. Firstly, it is in very close physical proximity to known resistance mutations in codons 114 and 119. According to Riveron et al., the 120 codon is located at the active site of the enzyme and is therefore likely to interact with the insecticide [[4](https://genomebiology.biomedcentral.com/articles/10.1186/gb-2014-15-2-r27)]. Secondly, there are no CNVs associated with this sweep, and no other amino acid variants except N3K, which is less likely to be causative due to its physical location away from the active site. 

We also observe a small cluster of individuals (cluster A) which harbour a copy number variant (CNV) spanning *Gste2, Gste1, Gste3* and *Gste7*. CNVs could be driving insecticide resistance by increasing the expression of the genes they encompass, allowing the mosquito to detoxify more of the insecticide as a result. This CNV, an amplification, seems to exist at a variable copy number. In total, we can see six or seven distinct CNVs in these samples.

This case study demonstrates how diplotype clustering can provide insights into the mutations causing insecticide resistance; in a single snapshot, we can explore amino acid and CNV data and really understand the nature of selection at a genomic region. 

We hope that others will find it useful for their own research. If you use diplotype clustering in your own research, please cite [this paper where the method was first published](https://doi.org/10.1093/molbev/msae140). Please feel free to [get in touch](mailto:sanjay.c.nagi@gmail.com?subject=diplotype-clustering) if you have any questions or feedback :)

[Sanjay C Nagi](https://www.sanjaycnagi.com/) & [Alistair Miles](https://alimanfoo.github.io/)


Diplotype clustering for genomic surveillance of mosquitoes

Using diplotype clustering to discover mutations causing insecticide resistance in malaria mosquitoes

---
title: Parallelising freebayes with snakemake
shorttitle: Parallelising freebayes with snakemake
slug: parallelising-freebayes-with-snakemake
date: 11/01/2021
thumbnail: '/thumbnails/snakemake.png'
tag: genetics
canonicalUrl: 'https://sanjaycnagi.com/blog/2021-01-11-freebayes-parallel/'
---

[`freebayes`](https://github.com/freebayes/freebayes) is a bayesian haplotype-based variant caller, used widely in genomics. As with many variant callers, it is not readily parallelised, but can be done so by splitting the genome into smaller chunks, calling them separately, and subsequently combining the chunks together.

A wrapper for freebayes, [`freebayes-parallel`](https://github.com/freebayes/freebayes/blob/master/scripts/freebayes-parallel), does exactly this, making use of `gnu-parallel`. However, this approach has a major limitation:

* When a chunk is completed, that cpu core will not move onto the next region until all cores have completed their respective chunk. This is particularly problematic in regions of variable coverage, and so one can attempt to split the genome into regions of roughly equal coverage. Unfortunately, this still results in many cores being unused for substantial periods of time.

I was implementing a `freebayes` variant calling step in a snakemake RNA-Sequencing pipeline I was writing (more on this later), and wanted to parallelise freebayes, without the above limitation. 

To do so, we can write a snakemake rule (below) which runs an [R script](https://github.com/sanjaynagi/rna-seq-ir/blob/master/workflow/scripts/GenerateFreebayesParams.R) to read in the genome index (.fai) file, and output multiple bed files, breaking the genome into chunks of equal size. By using an extra snakemake wildcard, the index of each genome chunk, we can produce, and supply freebayes with different bed files. Finally, after concatenating the vcfs with `bcftools concat` it is also important to stream the output through `vcfuniq`, to ensure there are no duplicate calls at the region overlaps. 

The benefit of this, is that snakemake will automatically run the next job when each chunk is complete, reducing overall computation time as compared with `freebayes-parallel`. Importantly, it also allows us to perform joint, multi-sample calling, which is one of the main benefits of using freebayes in the first place. 


```python
# Read in the desired number of genome chunks from the config.yaml, and arange a sequence 1-n. 
chunks = np.arange(1, config['chunks'])

# Note - in this case the script also produces some other files for Freebayes
rule GenerateFreebayesParams:
    input:
        ref = config['ref']['genome'],
        index = config['ref']['genome'] + ".fai",
        bams = expand("resources/alignments/{sample}.bam", sample=samples)
    output:
        bamlist = "resources/bam.list",
        pops = "resources/populations.tsv",
        regions = expand("resources/regions/genome.{chrom}.region.{i}.bed", chrom=config['chroms'], i = chunks) # bed files 
    log:
        "logs/GenerateFreebayesParams.log"
    params:
        metadata = config['samples'],
        chroms = config['chroms'],
        chunks = config['chunks']
    conda:
        "../envs/diffsnps.yaml"
    script:
        "../scripts/GenerateFreebayesParams.R"
```

*Update - 09/03/2021 - In the beautiful nature of open source software development, I have now written a parallelisation section in the [freebayes documentation](https://github.com/freebayes/freebayes) :)*

I'll leave you with a song I've been enjoying recently. Happy variant calling.

<iframe width="560" height="315" src="https://www.youtube.com/embed/1fBEEANitDY" frameBorder="0" allow="autoplay; encrypted-media" allowFullScreen></iframe>



🔥 3 Posts filtered by tag "genetics"

The malaria vector selection atlas

Diplotype clustering for genomic surveillance of mosquitoes

Parallelising freebayes with snakemake