(iii) As the authors note, discrete genetic ancestry groups are made-up things. There are a bazillion ways to define clusters of participants by projecting their genotypes into some abstract mathematical space—it’s not clear which (if any) adequately captures variation in genetic effect. 10/n

We just compared pooled versus stratified analysis of GWAS for locus discovery (pubmed.ncbi.nlm.nih.gov/40902600/), so I was particularly interested in the comparisons of SuShiE and other methods that rely on genetic-ancestry-group-stratified analyses to SuSiE applied to the pooled data. 3/n

If fine-mapping, mol-QTL, or [fill-in-the-blank]WAS analyses are your jam, do check this paper out, if only for the nice review and assessment of contemporary multi-ancestry fine-mapping methods. If fine-mapping is not your jam, this is gonna get technical & jargony. 2/n

Through a combination of maths, simulations, and real-data analyses, we show that pooled analysis is generally more powerful than meta-analysis while controlling Type I error rates. 2/7

This paper is appropriately getting lots of press for this top-line result, but it’s also a nice example of using multimodal data (genetics, questionnaires, biomarkers) to understand the biologic mechanisms behind the observed associations. pubmed.ncbi.nlm.nih.gov/40855194/ (4/4)

Protective effect of Mediterranean diet is much larger for folks with two copies of the APOE4 allele. This “genetics is not destiny” result is typical of many chronic diseases: whether you’re at high risk due to one large effect gene or many small effects genes, your risk can be modified. (2/4)