FinnGen: Unique genetic insights from combining isolated population and national health register data

Abstract

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.

Meta-analysis fine-mapping is often miscalibrated at single-variant resolution

“…We further assessed SLALOM performance in the GWAS Catalog meta-analyses by leveraging high-PIP (> 0.9) complex trait and cis -eQTL variants that were rigorously fine-mapped 16,17 in large-scale biobanks (Biobank Japan [BBJ] 58 , FinnGen 20 , and UK Biobank [UKBB] 19 ) and eQTL resources (GTEx 59 v8 and eQTL Catalogue 60 ). Among the 27,713 loci analyzed by SLALOM (maximum PIP > 0.1) that contain a lead variant that was included in biobank fine-mapping, 17% (3,266 / 19,692) of the non-suspicious loci successfully fine-mapped one of the high-PIP GWAS variants in biobank fine-mapping, whereas 7% (589 / 8,021) of suspicious loci did, showing a significant depletion (2.3x) of the high-PIP complex trait variants in suspicious loci (Fisher’s exact P = 4.6 × 10 −100 ; Fig.…”

“…Motivated by successful validation of SLALOM performance, we investigated whether fine-mapping confidence and resolution were improved in the GBMI meta-analyses over individual biobanks. To this end, we used our fine-mapping results 16,17 of nine disease endpoints (asthma 64 , COPD 64 , gout, heart failure 73 , IPF 62 , primary open angle glaucoma 74 , thyroid cancer, stroke 75 , and venous thromboembolism 76 ) in BBJ 58 , FinnGen 20 , and UKBB 19 Europeans that also contributed to the GBMI meta-analyses for the same traits.…”

“…We retrieved our previous fine-mapping results for 1) complex traits in large-scale biobanks (BBJ 58 , FinnGen 20 , and UKBB 19 Europeans) 16,17 and 2) cis -eQTLs in GTEx 59 v8 and eQTL Catalogue 60 . Briefly, we conducted multiple-causal-variant fine-mapping (FINEMAP 21,22 and SuSiE 23 ) of complex trait GWAS (# unique traits = 148) and cis -eQTL gene expression (# unique tissues/cell-types = 69) using summary statistics and in-sample LD.…”

“…To directly compare with fine-mapping results from the GBMI meta-analyses, we used our fine-mapping results of nine disease endpoints (asthma 64 , COPD 64 , gout, heart failure 73 , IPF 62 , primary open angle glaucoma 74 , thyroid cancer, stroke 75 , and venous thromboembolism 76 ) in BBJ 58 , FinnGen 20 , and UKBB 19 Europeans that were also part of the GBMI meta-analyses for the same traits. For comparison, we computed the maximum PIP for each variant and the minimum size of 95% CS across BBJ, FinnGen, and UKBB.…”

“…Despite the great success of past GWAS meta-analyses in locus discovery, individual causal variants in associated loci are largely unresolved. Identifying causal variants from GWAS associations (i.e., finemapping) is challenging due to extensive linkage disequilibrium (LD, the correlation among genetic variants), the presence of multiple causal variants, and limited sample sizes, but is rapidly becoming achievable with high confidence in individual cohorts [14][15][16][17] owing to the recent development of large-scale biobanks [18][19][20] and scalable fine-mapping methods [21][22][23] that enable well-powered, accurate fine-mapping using in-sample LD from large-scale individual-level data. After conducting GWAS meta-analysis, previous studies 2, 7,9,[24][25][26][27][28][29][30] have applied existing summary statistics-based fine-mapping methods (e.g., approximate Bayes factor [ABF] 31,32 , CAVIAR 33 , PAINTOR 34,35 , FINEMAP 21,22 , and SuSiE 23 ) just as they are applied to single-cohort studies, without considering or accounting for the unavoidable heterogeneity among cohorts (e.g.…”