Clinical lab tests are often used in clinical practice to guide diagnoses and treatment plans. Thanks to UK Biobank, we have a fortunate to analyze the genetic basis of those biomarkers and investigate their influence on diseases.
Through UK Biobank-wide GWAS meta-analysis, we report > 10,000 GWAS asscotiaions (p < 5e-9) across 35 biomarkers and >1,800 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 complementary 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.
- Supplementary Data (500GB+) is available at NIH’s archive instance of figshare
- Analysis and visualization scripts used in this manuscript is available at GitHub repo
- Full text
Citation: N. Sinnott-Armstrong*, Y. Tanigawa*, D. Amar, N. J. Mars, C. Benner, M. Aguirre, G. R. Venkataraman, M. Wainberg, H. M. Ollila, T. Kiiskinen, A. S. Havulinna, J. P. Pirruccello, J. Qian, A. Shcherbina, FinnGen, F. Rodriguez, T. L. Assimes, V. Agarwala, R. Tibshirani, T. Hastie, S. Ripatti, J. K. Pritchard, M. J. Daly, M. A. Rivas, Genetics of 35 blood and urine biomarkers in the UK Biobank. Nature Genetics. 53(2), 185-194 (2021). https://doi.org/10.1038/s41588-020-00757-z