Robel Alemu

Our results are broadly consistent with Wang (2020), especially for AFR, where MAF+LD explain much of the portability loss. However, we find trait- and ancestry-specific differences. By adding behavioral/social traits and a more comprehensive analytical framework, we uncover additional patterns.

Recomputing LoA(LD+MAF) with fGWAS PGIs gives interesting picture: downward shift for most phenotypes in AFR, mixed in SAS, & upward for many in EAS. fGWAS-based PGIs can alter the relative contribution of LD and MAF to prediction loss, with direction and magnitude varying by phenotype and ancestry.

Observed portability is broadly similar for standard vs family-based GWAS (fGWAS) PGIs across traits/ancestries, with notable gains for BMI in AFR—consistent with population-specific confounds affecting select traits rather than a uniform shift.

Because h2 was imprecisely estimated in SAS/EAS, we evaluate LoA(LD+MAF+ h2) in AFR. Adding h2 closes most remaining gap for height and increases explained loss for BMI.

Inside biomarkers, patterns differ, e.g. LDL and non-HDL exhibit some of the lowest LoA(LD+MAF) in EAS and SAS, but these are some of the highest observed in AFR. Traits like COPD and prostate cancer show lower LoA(LD+MAF) across all ancestries—highlighting joint trait- and ancestry-specificity.

We estimate the share of observed loss in PGI relative accuracy (LoA) explained by cross ancestry MAF and LD differences. LoA(LD+MAF) is highest in AFR, lower in EAS/SAS, and varies by category: blood biomarkers highest; fertility/sexual development & substance use lower.

Biologically proximal traits (blood lipids, diabetes, height) are more portable than behavioral/social traits (e.g., educational attainment, neuroticism, risk tolerance), likely reflecting stronger environmental influences interacting with genetics for the latter.

We dissect why EUR-trained PGIs lose accuracy in non-EURs across 54 traits. We build on model by Wang et al (2020), but use genome-wide, LD-adjusted PGIs, model ancestry-specific h2, and compare standard vs family-based GWAS. Average portability is lowest in AFR (~24%), then EAS (~37%), SAS (~51%).

PGI predictive accuracy is substantially smaller when weights obtained from one ancestry are applied to another. The first step in addressing this portability problem is to understand how different factors influence the loss of PGI predictive accuracy across ancestries.

Excited to share our preprint on PGI portability across ancestries—now on bioRxiv. With co-authors @aysuo.bsky.social, @paturley.bsky.social, @alextisyoung.bsky.social, and @Dan_J_Benjamin. Preprint: doi.org/10.1101/2025... Thread below for details.

Direct effect of genetic ancestry on complex traits in a Mexican population Human populations differ in disease prevalences and in average values of phenotypes, but the extent to which differences are caused by genetic or environmental factors is unknown for most complex trai...

Brilliant paper by Visscher et al.

Populations differ in traits/disease burden. Are these differences due to genetics?

Comparing single variants or polygenic scores between populations is biased due to environmental confounders correlated with the variants.

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www.medrxiv.org/content/10.1...

ICYMI: New online! Harnessing functional annotation to improve the accuracy and transferability of polygenic scores

Eight academic health policy researchers present their global health projects—from Ethiopia to China to Argentina and beyond—during our annual #RosenkranzGlobal health symposium. See the slideshow.
healthpolicy.fsi.stanford.edu/content/2025...

Robel Alemu, a postdoc research scientist at UCLA and
the @broadinstitute.org, shows abrupt and prolonged loss of iodized salt in Ethiopian children harms later-life academic achievement, earnings & survival. @robel-alemu.bsky.social Learn More: bit.ly/3H0V5go

PGI Repository v2.0 preprint out! A 🧵 on the main results and updates @robel-alemu.bsky.social @paturley.bsky.social @alextisyoung.bsky.social

Advanced analytical methods needed for better insights into chronic diseases

www.adelaide.edu.au/newsroom/new...

Multi-omics approaches for understanding gene-environment interactions in noncommunicable diseases: techniques, translation, and equity issues - Human Genomics Non-communicable diseases (NCDs) such as cardiovascular diseases, chronic respiratory diseases, cancers, diabetes, and mental health disorders pose a significant global health challenge, accounting fo...

In our lastest review article, we explore the evolving landscape of multi-omics research in NCDs.

A huge thank you to collaborators Nigussie T. Sharew, Yodit Y. Arsano, Muktar Ahmed, Fasil Tekola-Ayele, Tesfaye B. Mersha, and Azmeraw T. Amare

📖 Read the full article here: doi.org/10.1186/s402...