Clinical laboratory tests are a critical component of the continuum of care. We evaluate the genetic basis of 35 blood and urine laboratory measurements in the UK Biobank (n=363,228 individuals). We identify 1,857 loci associated with at least one trait, containing 3,374 fine-mapped associations, and additional sets of large-effect (> 0.1 sd) protein-altering, HLA, and copy-number variant associations. Through Mendelian Randomization analysis, we discover 51 causal relationships, including previously known agonistic effects of urate on gout and cystatin C on stroke. Finally, we develop polygenic risk scores for each biomarker and built ‘multi-PRS’ models for diseases using 35 PRSs simultaneously, which improved chronic kidney disease, type 2 diabetes, gout, and alcoholic cirrhosis genetic risk stratification in an independent dataset (FinnGen; n=135,500) relative to single-disease PRSs. Together, our results delineate the genetic basis of biomarkers, their causal influences on diseases, and improve genetic risk stratification for common diseases.

Polygenic risk scores (PRS) have shown promise in predicting susceptibility to common diseases. 1-3 We estimated their added value in clinical risk prediction of five common diseases, using largescale biobank data (FinnGen; N=135,300), and the FINRISK study with clinical risk factors to test genome-wide PRSs for coronary heart disease (CHD), type 2 diabetes (T2D), atrial fibrillation (AF), and breast and prostate cancer. We evaluated the lifetime risk at different PRS levels, and the impact on disease onset and on prediction together with clinical risk scores. Compared to average PRS, having a high PRS contributed to 21% to 38% higher lifetime risk, and 4 to 9 years earlier disease onset. PRS improved model discrimination over age and sex in T2D, AF, breast cancer, and prostate cancer, and over clinical risk in T2D, breast cancer, and prostate cancer. In all diseases, PRS improved reclassification over clinical thresholds, with largest net reclassification improvements for early-onset CHD, AF, and prostate cancer. This study provides evidence for the additional value of PRS in clinical disease prediction. The practical applications of polygenic risk information for stratified screening or for guiding lifestyle and medical interventions in the clinical setting remain to be defined in further studies.

Population isolates such as Finland provide benefits in genetic studies because the allelic spectrum of damaging alleles in any gene is often concentrated on a small number of low-frequency variants (0.1% ≤ minor allele frequency < 5%), which survived the founding bottleneck, as opposed to being distributed over a much larger number of ultra--rare variants. While this advantage is well-- established in Mendelian genetics, its value in common disease genetics has been less explored. FinnGen aims to study the genome and national health register data of 500,000 Finns, already reaching 224,737 genotyped and phenotyped participants. Given the relatively high median age of participants (63 years) and dominance of hospital-based recruitment, FinnGen is enriched for many disease endpoints often underrepresented in population-based studies (e.g., rarer immune-mediated diseases and late onset degenerative and ophthalmologic endpoints). We report here a genome-wide association study (GWAS) of 1,932 clinical endpoints defined from nationwide health registries. We identify genome--wide significant associations at 2,491 independent loci. Among these, finemapping implicates 148 putatively causal coding variants associated with 202 endpoints, 104 with low allele frequency (AF<10%) of which 62 were over two-fold enriched in Finland.We studied a benchmark set of 15 diseases that had previously been investigated in large genome-wide association studies. FinnGen discovery analyses were meta-analysed in Estonian and UK biobanks. We identify 30 novel associations, primarily low-frequency variants strongly enriched, in or specific to, the Finnish population and Uralic language family neighbors in Estonia and Russia.These findings demonstrate the power of bottlenecked populations to find unique entry points into the biology of common diseases through low-frequency, high impact variants. Such high impact variants have a potential to contribute to medical translation including drug discovery.