Repository navigation
Statgen specific tutorial #240
Description
The last one of the subaudience tutorials to tick off is the one for statistical geneticists. This issue can collect some ideas.
I have been chatting to Arni Gunnarson, and he suggested that the following topics would be of interest to a statgen researcher looking into tskit (here biased towards the ARGneedle - type approach, obviously)
- fast GRM calculation (see tskit's `genetic_relatedness()` versus eGRM (Fan et al. 2022) tskit#2603 - this is not ready yet though)
- Luke O'Connor / Wilder's "LDGM" stuff, now in Nature Genetics(!): https://www.nature.com/articles/s41588-023-01487-8
- putting phenotypes onto individuals in a tree sequence (e.g. Daiki's tstrait)
- once phenotypes are associated with individuals in a tree sequence:
- fast calculation of heritability
- ARG-informed association analysis similar to that in the ARGneedle paper
Activity
hyanwong commentedon Mar 26, 2025
Once we get Hanbin's
tslmmpublished, this should give a great statgen angle. Also we should include running the Li & Stephens algorithm on a tree sequence, once that is documented (e.g. tskit-dev/tskit#452)hyanwong commentedon Jan 1, 2026
Also PCA would be good to include.
hyanwong commentedon May 2, 2026
We should also say that tree sequences can be efficient converted to GRGs and Linear ARGs, allowing the efficient statgen functions of those libraries to be used as alternatives to built-in Tskit 'site-mode' functions, which includes standard GWAS.
hyanwong commentedon May 3, 2026
Also GWAS via https://tskit.dev/tskit/docs/stable/stats.html#trait-correlations
gregorgorjanc commentedon May 4, 2026
We will be hiring on the BBSRC/NSF grant and that colleague could take a lead on this tutorial;)
hyanwong commentedon May 4, 2026
That would be excellent. Thanks @gregorgorjanc