Dataset Information

FinnGen provides genetic insights from a well-phenotyped isolated population.

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 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) 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 <5% in non-Finnish European individuals, of which 62 (32 PWS) were enriched by more than twofold in Finland. These findings demonstrate the power of bottlenecked populations to find entry points into the biology of common diseases through low-frequency, high impact variants.

SUBMITTER: Kurki MI

PROVIDER: S-EPMC9849126 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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FinnGen provides genetic insights from a well-phenotyped isolated population.

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

Nature 20230118 7944

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<sup>1,2</sup>. FinnGen aims to study the genome and nation ...[more]

PMID: 36653562

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Project description:BackgroundDue to a significant decline in the costs associated with next-generation sequencing, it has become possible to decipher the genetic architecture of a population by sequencing a large number of individuals to a deep coverage. The Korean Personal Genomes Project (KPGP) recently sequenced 35 Korean genomes at high coverage using the Illumina Hiseq platform and made the deep sequencing data publicly available, providing the scientific community opportunities to decipher the genetic architecture of the Korean population.MethodsIn this study, we used two single nucleotide variant (SNV) calling pipelines: mapping the raw reads obtained from whole genome sequencing of 35 Korean individuals in KPGP using BWA and SOAP2 followed by SNV calling using SAMtools and SOAPsnp, respectively. The consensus SNVs obtained from the two SNV pipelines were used to represent the SNVs of the Korean population. We compared these SNVs to those from 17 other populations provided by the HapMap consortium and the 1000 Genomes Project (1KGP) and identified SNVs that were only present in the Korean population. We studied the mutation spectrum and analyzed the genes of non-synonymous SNVs only detected in the Korean population.ResultsWe detected a total of 8,555,726 SNVs in the 35 Korean individuals and identified 1,213,613 SNVs detected in at least one Korean individual (SNV-1) and 12,640 in all of 35 Korean individuals (SNV-35) but not in 17 other populations. In contrast with the SNVs common to other populations in HapMap and 1KGP, the Korean only SNVs had high percentages of non-silent variants, emphasizing the unique roles of these Korean only SNVs in the Korean population. Specifically, we identified 8,361 non-synonymous Korean only SNVs, of which 58 SNVs existed in all 35 Korean individuals. The 5,754 genes of non-synonymous Korean only SNVs were highly enriched in some metabolic pathways. We found adhesion is the top disease term associated with SNV-1 and Nelson syndrome is the only disease term associated with SNV-35. We found that a significant number of Korean only SNVs are in genes that are associated with the drug term of adenosine.ConclusionWe identified the SNVs that were found in the Korean population but not seen in other populations, and explored the corresponding genes and pathways as well as the associated disease terms and drug terms. The results expand our knowledge of the genetic architecture of the Korean population, which will benefit the implementation of personalized medicine for the Korean population.

Dataset Information

FinnGen provides genetic insights from a well-phenotyped isolated population.

Publications

FinnGen provides genetic insights from a well-phenotyped isolated population.

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