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FinnGen provides genetic insights from a well-phenotyped isolated population

Authors :
Kurki, Mitja I
Karjalainen, Juha
Palta, Priit
Sipilä, Timo P
Kristiansson, Kati
Donner, Kati M
Reeve, Mary P
Laivuori, Hannele
Aavikko, Mervi
Kaunisto, Mari A
Loukola, Anu
Lahtela, Elisa
Mattsson, Hannele
Laiho, Päivi
Della Briotta Parolo, Pietro
Lehisto, Arto A
Kanai, Masahiro
Mars, Nina
Rämö, Joel
Kiiskinen, Tuomo
Heyne, Henrike O
Veerapen, Kumar
Rüeger, Sina
Lemmelä, Susanna
Zhou, Wei
Ruotsalainen, Sanni
Pärn, Kalle
Hiekkalinna, Tero
Koskelainen, Sami
Paajanen, Teemu
Llorens, Vincent
Gracia-Tabuenca, Javier
Siirtola, Harri
Reis, Kadri
Elnahas, Abdelrahman G
Sun, Benjamin
Foley, Christopher N
Aalto-Setälä, Katriina
Alasoo, Kaur
Arvas, Mikko
Auro, Kirsi
Biswas, Shameek
Bizaki-Vallaskangas, Argyro
Carpen, Olli
Chen, Chia-Yen
Dada, Oluwaseun A
Ding, Zhihao
Ehm, Margaret G
Eklund, Kari
Färkkilä, Martti
Finucane, Hilary
Ganna, Andrea
Ghazal, Awaisa
Graham, Robert R
Green, Eric M
Hakanen, Antti
Hautalahti, Marco
Hedman, Åsa K
Hiltunen, Mikko
Hinttala, Reetta
Hovatta, Iiris
Hu, Xinli
Huertas-Vazquez, Adriana
Huilaja, Laura
Hunkapiller, Julie
Jacob, Howard
Jensen, Jan-Nygaard
Joensuu, Heikki
John, Sally
Julkunen, Valtteri
Jung, Marc
Junttila, Juhani
Kaarniranta, Kai
Kähönen, Mika
Kajanne, Risto
Kallio, Lila
Kälviäinen, Reetta
Kaprio, Jaakko
FinnGen
Kerimov, Nurlan
Kettunen, Johannes
Kilpeläinen, Elina
Kilpi, Terhi
Klinger, Katherine
Kosma, Veli-Matti
Kuopio, Teijo
Kurra, Venla
Laisk, Triin
Laukkanen, Jari
Lawless, Nathan
Liu, Aoxing
Longerich, Simonne
Mägi, Reedik
Mäkelä, Johanna
Mäkitie, Antti
Malarstig, Anders
Mannermaa, Arto
Maranville, Joseph
Matakidou, Athena
Meretoja, Tuomo
Mozaffari, Sahar V
Niemi, Mari EK
Niemi, Marianna
Niiranen, Teemu
O Donnell, Christopher J
Obeidat, Ma En
Okafo, George
Ollila, Hanna M
Palomäki, Antti
Palotie, Tuula
Partanen, Jukka
Paul, Dirk S
Pelkonen, Margit
Pendergrass, Rion K
Petrovski, Slavé
Pitkäranta, Anne
Platt, Adam
Pulford, David
Punkka, Eero
Pussinen, Pirkko
Raghavan, Neha
Rahimov, Fedik
Rajpal, Deepak
Renaud, Nicole A
Riley-Gillis, Bridget
Rodosthenous, Rodosthenis
Saarentaus, Elmo
Salminen, Aino
Salminen, Eveliina
Salomaa, Veikko
Schleutker, Johanna
Serpi, Raisa
Shen, Huei-Yi
Siegel, Richard
Silander, Kaisa
Siltanen, Sanna
Soini, Sirpa
Soininen, Hilkka
Sul, Jae Hoon
Tachmazidou, Ioanna
Tasanen, Kaisa
Tienari, Pentti
Toppila-Salmi, Sanna
Tukiainen, Taru
Tuomi, Tiinamaija
Turunen, Joni A
Ulirsch, Jacob C
Vaura, Felix
Virolainen, Petri
Waring, Jeffrey
Waterworth, Dawn
Yang, Robert
Nelis, Mari
Reigo, Anu
Metspalu, Andres
Milani, Lili
Esko, Tõnu
Fox, Caroline
Havulinna, Aki S
Perola, Markus
Ripatti, Samuli
Jalanko, Anu
Laitinen, Tarja
Mäkelä, Tomi P
Plenge, Robert
McCarthy, Mark
Runz, Heiko
Daly, Mark J
Palotie, Aarno
Palotie, Aarno [0000-0002-2527-5874]
Apollo - University of Cambridge Repository
Publication Year :
2023
Publisher :
Springer Science and Business Media LLC, 2023.

Abstract

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 explored1,2. 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) independent loci with 807 (247 PWS) end points. Among these, fine-mapping implicated 148 (73 PWS) coding variants associated with 83 (42 PWS) end points. Moreover, 91 (47 PWS) had an allele frequency of

Details

Database :
OpenAIRE
Accession number :
edsair.doi.dedup.....0d32f429d6345a0300cb72f40f8b18ba
Full Text :
https://doi.org/10.17863/cam.94317