FinnGen provides genetic insights from a well-phenotyped isolated population

作者：M. Kurki, J. Karjalainen, P. Palta, Timo P. Sipilä, K. Kristiansson, K. Donner, M. Reeve, H. Laivuori, M. Aavikko, M. Kaunisto, A. Loukola, E. Lahtela, Hannele Mattsson, P. Laiho, Pietro della Briotta Parolo, Arto A Lehisto, M. Kanai, N. Mars, Joel T. Rämö, T. Kiiskinen, H. Heyne, K. Veerapen, S. Rüeger, S. Lemmelä, Wei Zhou, S. Ruotsalainen, K. Pärn, T. Hiekkalinna, Sami Koskelainen, T. Paajanen, V. Llorens, Javier Gracia-Tabuenca, H. Siirtola, Kadri Reis, A. Elnahas, Benjamin B. Sun, Christopher N. Foley, K. Aalto-Setälä, K. Alasoo, Mikko Arvas, K. Auro, Shameek Biswas, Argyro Bizaki-Vallaskangas, O. Carpén, Chia-Yen Chen, O. A. Dada, Zhihao Ding, M. Ehm, K. Eklund, M. Färkkilä, H. Finucane, A. Ganna, A. Ghazal, R. Graham, Eric M. Green, A. Hakanen, Marco Hautalahti, Å. Hedman, M. Hiltunen, R. Hinttala, I. Hovatta, Xinli Hu, A. Huertas-Vazquez, L. Huilaja, J. Hunkapiller, H. Jacob, J. Jensen, H. Joensuu, Sally John, V. Julkunen, M. Jung, J. Junttila, K. Kaarniranta, M. Kähönen, Risto Kajanne, Lila Kallio, R. Kälviäinen, J. Kaprio, N. Kerimov, J. Kettunen, Elina Kilpeläinen, T. Kilpi, Katherine W. Klinger, V. Kosma, T. Kuopio, Venla Kurra, T. Laisk, J. Laukkanen, Nathan Lawless, A. Liu, S. Longerich, R. Mägi, J. Mäkelä, Antti A. Mäkitie, A. Malarstig, A. Mannermaa, J. Maranville, A. Matakidou, T. Meretoja, S. Mozaffari, Mari E. K. Niemi, Mari E. K. Niemi, T. Niiranen, C. J. O 'donnell, M. Obeidat, G. Okafo, H. Ollila, A. Palomäki, T. Palotie, J. Partanen, D. Paul, M. Pelkonen, R. Pendergrass, S. Petrovski, A. Pitkäranta, A. Platt, D. Pulford, E. Punkka, P. Pussinen, Neha S. Raghavan, F. Rahimov, D. Rajpal, N. Renaud, B. Riley-Gillis, R. Rodosthenous, E. Saarentaus, A. Salminen, Eveliina Salminen, V. Salomaa, J. Schleutker, R. Serpi, Huei-yi Shen, R. Siegel, K. Silander, S. Siltanen, S. Soini, H. Soininen, J. Sul, I. Tachmazidou, K. Tasanen, P. Tienari, S. Toppila-Salmi, T. Tukiainen, T. Tuomi, J. Turunen, J. Ulirsch, F. Vaura, P. Virolainen, J. Waring, D. Waterworth, Robert Yang, M. Nelis, A. Reigo, A. Metspalu, L. Milani, T. Esko, Caroline Fox, A. Havulinna, M. Perola, S. Ripatti, A. Jalanko, Tarja Laitinen, T. Mäkelä, R. Plenge, M. McCarthy, H. Runz, M. Daly, A. Palotie · 发表于：Nature · 年份：2023 · DOI：10.1038/s41586-022-05473-8 · 被引用次数：3665 · 研究领域：Medicine

Genome-wide association studies of individuals from an isolated population (data from the Finnish biobank study FinnGen) and consequent meta-analyses facilitate the identification of previously unknown coding variant associations for both rare and common diseases. Population isolates such as those in Finland benefit genetic research because deleterious alleles are often concentrated on a small number of low-frequency variants (0.1% ≤ minor allele frequency < 5%). These variants survived the founding bottleneck rather than being distributed over a large number of ultrarare variants. Although this effect is well established in Mendelian genetics, its value in common disease genetics is less explored^ 1 , 2 . FinnGen aims to study the genome and national health register data of 500,000 Finnish individuals. Given the relatively high median age of participants (63 years) and the substantial fraction of hospital-based recruitment, FinnGen is enriched for disease end points. Here we analyse data from 224,737 participants from FinnGen and study 15 diseases that have previously been investigated in large genome-wide association studies (GWASs). We also include meta-analyses of biobank data from Estonia and the United Kingdom. We identified 30 new associations, primarily low-frequency variants, enriched in the Finnish population. A GWAS of 1,932 diseases also identified 2,733 genome-wide significant associations (893 phenome-wide significant (PWS), P  < 2.6 × 10^–11) at 2,496 (771 PWS)...