Back

The genetic determinants of plasma protein variance across ancestries and effects on cardiometabolic disease risk

Ben-Eghan, C. R.; Persyn, E.; Foguet, C.; Wu, Z.; Jiang, X.; Xu, Y.; Ritchie, S. C.; Lambert, S. A.; Butterworth, A. S.; Burgess, S.; Inouye, M.

2025-11-21 genetic and genomic medicine
10.1101/2025.11.20.25340688 medRxiv
Show abstract

Variance quantitative trait loci (vQTLs), which capture genetic contributions to phenotypic variability, remain underexplored in proteomic studies, particularly across diverse ancestries. We systematically mapped cis-vQTLs for 2,923 plasma proteins in 52,706 UK Biobank participants of European (EUR, N = 45,486), African (AFR, N = 1,336), and Central/South Asian (CSA, N = 934) ancestries, identifying 2,162 vQTLs (PVE < 5 x 10-8) for 781 proteins. We identified ancestry-specific and shared cis-vQTLs, including those for 30 proteins which were shared across all ancestries, with a few proteins, exhibiting stronger associations in non-EUR ancestry groups despite smaller sample sizes. Across ancestries, 7% (EUR), 25% (AFR), and 14% (CSA) of associations had variance effects only (vQTLonly), lacking corresponding mean effects (PME > 0.05), with chromosome X enriched for vQTLonly associations. Finally, multivariable Mendelian randomization revealed that, independent of genetically predicted mean protein levels, genetically predicted variance of three proteins influenced disease risk of coronary artery disease (Lp(a) and VAMP5) or type 2 diabetes (ANGPTL4). The MR effects for protein levels and variance were independent yet directionally consistent and significant (FDR < 0.05). Taken together, this study identifies novel protein vQTLs, highlights their transferability and demonstrates the potential therapeutic relevance of protein variance.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.