Genetics of 35 blood and urine biomarkers in the UK Biobank

Through UK Biobank-wide GWAS meta-analysis, we report > 10,000 GWAS associations (p < 5e-9) across 35 biomarkers and >5,700 loci. This includes > 450 large-effect (>0.1 s.d.) associations on protein-altering variants, which we highlight in a Fuji plot (gene symbols).

With FINEMAP, we identified >27,000 distinct signals in >5,000 regions across the 35 traits, of which >2,500 signals were fine-mapped to a single variant. We also trained sparse polygenic risk score (PRS) models with Lasso L1- penalized regression using the snpnet package.

To investigate the influence of the identified genetic basis on diseases, we performed single-variant PheWAS, PRS-PheWAS, and causal inference. Motivated by those results, we developed multi-PRS by combining a single-trait PRS model of disease with that of 35 biomarkers.

When we compare the predictive performance, multi-PRS showed improvements over single-trait snpnet PRS across many diseases, which we also replicated in FinnGen, suggesting both large case numbers and multi-trait modeling might complementarily contribute to improving the power.

This work was led by Nasa Sinnott-Armstrong, myself, and Dr. Manuel A. Rivas, with many contributions from colleagues. We thank UK Biobank, FinnGen, their participants, amazing collaborators, and colleagues, as well as funding.