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 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.

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.

The functionality uses tagging SNPs from compkaryo. 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:

1df_karyo = ag3.karyotype(
2    inversion="2La",  # Inversions to analyze
3    sample_sets="1244-VO-GH-YAWSON-VMF00149",  # The sample set
4    sample_query=None
5)
6
7df_karyo.head()

I hope this is a useful addition for users of malariagen_data! Any questions or feedback, please get in touch.

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