Peter Kraft

Speaking of ripple effects: any guidance for NIH researchers who have registered for #ASHG25 and have a poster or presentation but may not be able to attend because of the shutdown?

Julie-Alexia Dias, MSc

@ajhgnews.bsky.social sat with Julie-Alexia Dias, MSc, in the latest "Inside AJHG" to discuss her recently published paper, “Evaluating multi-ancestry genome-wide association methods: statistical power, population structure, and practical implications.”➡️ ashg.org/ajhg/inside-... #ASHG #humangenetics

I see this playing out in slow motion, accompanied by Barber’s Adagio.

Curious to hear others’ thoughts and experience here! /fin

(iv) And the simulations and applications assume individual-level data or in-sample LD is available—typically not the case in large meta-analyses for complex traits. See Wenmin Zhang et al for a discussion of this issue and a possible fix. www.biorxiv.org/content/10.1... 12/n

On a prosaic level, defining discrete genetic ancestry clusters often means excluding participants who don’t fall into any of the clusters, lowering sample size and limiting generalizability. 11/n

(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

(ii) The simulations are focused on two ancestry groups, with imbalance maxing out at 1:2. In the complex trait setting, it’s not unusual for there to be 4 or 5 ancestry groups, with imbalances on the order of 1:5 or more. 9/n

Some caveats and open Qs: (i) The simulations and data applications are focused on the context of molecular QTL (large effects, small sample sizes) not complex traits (small effects, large sample size). Not clear (but plausible) that qualitative results transfer to that setting. 8/n

Points (ii) and (iii) may be the flip side of this: in low power situations, the extra degrees of freedom allowing for group-specific effects may cost power. Betting on near similar effects borrows information across groups and improves power here. 7/n

…then SuShiE and other methods that allow effects to differ across ancestry groups should be more powerful. On the other hand, if genetic effects are nearly identical (e.g. the causal variant is typed and marginalized GxE and GxG effects are negligible) then pooling should be more powerful. 6/n

Point (i) makes sense: if the genetic effects differ across ancestry groups—perhaps due to linkage disequilibrium differences if the causal variant is not typed or due to subtle differences in marginal genetic effects due to GxE and GxG interactions… 5/n

Quick take-homes: SuShiE outperforms pooled SuSiE—except when (i) the correlation in genetic effects across ancestries is very high (0.99), (ii) sample sizes across ancestries are imbalanced, or (iii) the overall sample size is low relative to the strength of the genetic effects. 4/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

This was neat work by @nmancuso.bsky.social et al developing and benchmarking a new multi-ancestry fine-mapping method (“SuShiE”). I learned something about the performance of pooled v stratified analyses but still have some Qs. 1/n

In epi & biostats it’s not unusual for chapters to be published papers with full author list (often including advisor). If chapter is draft of a paper yet to be shared with coauthors, no author list included (contributions in acknowledgements). Student meets first author criteria in both scenarios.

Genetic analyses of diverse populations improves discovery for complex traits - Nature Genetic analyses of ancestrally diverse populations show evidence of heterogeneity across ancestries and provide insights into clinical implications, highlighting the importance of including ancestral...

The observation that joint analysis can safely improve power strikes me as being a similar observation to that of @genandgenes.bsky.social et al in their Nature 2019 manuscript: www.nature.com/articles/s41...

Thanks to all coauthors (including @madduri on here) for important substantive and technical contributions. 7/7

Genome-wide Association Studies in Ancestrally Diverse Populations: Opportunities, Methods, Pitfalls, and Recommendations Genome-wide association studies (GWASs) have focused primarily on populations of European descent, but it is essential that diverse populations become…

A 2019 review by Peterson et al. flagged “to pool or not to pool” as one of the key unresolved issues in the analysis of GWAS in ancestrally diverse samples. We hope our paper provides some guidance. 6/7 www.sciencedirect.com/science/arti...

These are encouraging results, given the ongoing expansion of GWAS to include more diverse samples (e.g. the Confluence Project, which will double the sample size of the largest breast-cancer GWAS to date and more than quadruple the number of Latina participants). 5/7 confluence.cancer.gov

Also: trust the maths, but verify. A pooled analysis is a good place to start, but do your due diligence and check lambda-GCs and LDSC intercepts for evidence of concerning inflation. Assessing the correlations between the trait of interest and global and local PCs can also help. 4/7

Your mileage may vary: power gains and confounding are trait- and context-specific. Our simulations and real-data examples are focused on anthropometry, biomarkers, and complex diseases. Confounding could be more of an issue for socially defined traits (e.g. educational attainment). 3/7

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

Evaluating multi-ancestry genome-wide association methods: Statistical power, population structure, and practical implications Multi-ancestry GWASs enhance discovery in diverse populations, but optimal methods remain debated. Using theory, simulations, and analyses from the UK Biobank and All of Us, we show that pooled analys...

Multi-ancestry GWAS can increase power and precision, but how should we analyze them? Pooled or stratified? We answer that question in a paper out today in AJHG, led by Julie Dias and Haoyu Zhang. 1/7 www.cell.com/ajhg/fulltex...

An evergreen thread: Race/Ethnicity is *not the same* as genetics, and you can't use Race/Ethnicity as a sort of stand-in for genetics. These two concepts are connected via aspects like skin colour, but the connection is alot less profound and categorical than most people think.

Discovery and characterisation of gene by environment and epistatic genetic effects in a vertebrate model Phenotypic variation arises from the interplay between genetic and environmental factors. However, disentangling these interactions for complex traits remains challenging in observational cohorts such...

Nice blog and good to see this also from the twins/shared environment side. We (with my colleagues in @wittbrodtlab.bsky.social) have tried to tackle the non-additive in experimental settings (in medaka fish) which we can map to human (as the medaka fish are "wild") www.biorxiv.org/content/10.1...

Beneath the surface of the sum When genetic interactions matter and when they don't

I wrote about gene-gene interactions (epistasis) and the implications for heritability, trait definitions, natural selection, and therapeutic interventions. Biology is clearly full of causal interactions, so why don't we see them in the data? A 🧵:

“[The NIH] should encourage​ incentive structure reform that rewards productivity beyond publishing in traditional journals [e.g.]: providing research experience to trainees, sharing research data and code, participating in preprint peer-review, and communicating their research to policymakers.” 3/3

The NIH should “clearly communicate that [contrary to whispers and rumors] authors are always able to self-deposit their peer-reviewed author-accepted manuscripts into PubMed Central without cost.” 2/3