---
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: Karyotyping chromosomal inversions in malariagen-data
shorttitle: Karyotyping chromosomal inversions in malariagen-data
slug: karyotyping
date: 11/28/2024
thumbnail: '/thumbnails/chrom.png'
tag: malariagen_data
canonicalUrl: 'https://sanjaycnagi.com/blog/2024-11-28-karyotyping/'
---

Chromosomal inversions, where a region of a chromosome is flipped, play an important role in the evolution of malaria vectors, influencing their ability to exploit ecological niches and potentially affecting their ability to transmit parasites and tolerate insecticide exposure.

The Vector Observatory's [malariagen_data package](https://github.com/malariagen/malariagen-data-python) itself continues to evolve, providing researchers with powerful tools for accessing and analyzing genomic datasets from major malaria vectors. A recent addition to this toolkit is a karyotyping method that allows users to determine the karyotype of specimens in the Vector Observatory by genotyping inversion tagging SNPs. 

Here I plot karyotype frequencies of the 2La inversion in samples from phase 3 of the ag1000g resource, including additional cohorts in VObs we've been working on at the Liverpool School of Tropical Medicine. 

<HTMLPlot
  title="2La frequencies in Ag3"
  pathname="/blog/2La.html"
  height_before_scale={550}
  width_before_scale={600}
/>

You can see that the 2La karyotype (in green) is at higher frequencies as you move towards the more arid Sahel. The Colab notebook to generate this plot is [here](https://colab.research.google.com/drive/1qlhygvn1N9XFn5oQgWIoQOMIzMQkSGoE?usp=sharing).

#### Usage of karyotyping in malariagen_data

The functionality uses tagging SNPs from [compkaryo](https://doi.org/10.1534/g3.119.400445). Previously, the tool only reported the positions of the tagging SNPs, which would cause issues at sites which are multi-allelic - we would not know which alternate allele is the inversion tagging SNP. I've now found the exact alternate alleles, making the estimates more robust.

Here's an example of how to use the new karyotyping function in malariagen_data:

```python
df_karyo = ag3.karyotype(
    inversion="2La",  # Inversions to analyze
    sample_sets="1244-VO-GH-YAWSON-VMF00149",  # The sample set
    sample_query=None
)

df_karyo.head()
```

I hope this is a useful addition for users of malariagen_data! Any questions or feedback, please get in [touch](mailto:sanjay.c.nagi@gmail.com).

*Thank you to Anastasia Hernandez-Koutoucheva and Alistair Miles for the code to plot pie charts with Bokeh!* 


🔥 2 Posts filtered by tag "malariagen_data"

The malaria vector selection atlas

Karyotyping chromosomal inversions in malariagen-data