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Summary Table

NAME CATEGORY CITATION YEAR
Biobank Japan (BBJ) JENGER Biobanks_Cohorts NA NA
Biobank Japan (BBJ) Phewebjp Biobanks_Cohorts NA NA
Biobank Russia Biobanks_Cohorts Usoltsev D, Kolosov N, Rotar O, Loboda A, ...&, Artomov M. (2024) Complex trait susceptibilities and population diversity in a sample of 4,145 Russians Nat. Commun., 15 (1) 1-10. doi:10.1038/s41467-024-50304-1. PMID 39043636 2024
CARTaGENE PheWeb Biobanks_Cohorts NA NA
China Kadoorie Biobank (CKB) Biobanks_Cohorts NA NA
FinMetSeq Biobanks_Cohorts NA NA
FinnGen Kanta 1st Lab values (October 14 2025 ) Biobanks_Cohorts NA NA
FinnGen R10 (December 18 2023) Biobanks_Cohorts Kurki MI, Karjalainen J, Palta P, Sipilä TP, ...&, Palotie A. (2023) FinnGen provides genetic insights from a well-phenotyped isolated population Nature, 613 (7944) 508-518. doi:10.1038/s41586-022-05473-8. PMID 36653562 2023
FinnGen R10-UKBB meta-analysis Biobanks_Cohorts NA NA
FinnGen R11 (June 24 2024) Biobanks_Cohorts Kurki MI, Karjalainen J, Palta P, Sipilä TP, ...&, Palotie A. (2023) FinnGen provides genetic insights from a well-phenotyped isolated population Nature, 613 (7944) 508-518. doi:10.1038/s41586-022-05473-8. PMID 36653562 2023
FinnGen R12 (November 4 2024) Biobanks_Cohorts Kurki MI, Karjalainen J, Palta P, Sipilä TP, ...&, Palotie A. (2023) FinnGen provides genetic insights from a well-phenotyped isolated population Nature, 613 (7944) 508-518. doi:10.1038/s41586-022-05473-8. PMID 36653562 2023
FinnGen R12-UKBB meta-analysis Biobanks_Cohorts NA NA
FinnGen R4 (November 30 2020) Biobanks_Cohorts Kurki MI, Karjalainen J, Palta P, Sipilä TP, ...&, Palotie A. (2023) FinnGen provides genetic insights from a well-phenotyped isolated population Nature, 613 (7944) 508-518. doi:10.1038/s41586-022-05473-8. PMID 36653562 2023
FinnGen R5 (May 11 2021) Biobanks_Cohorts Kurki MI, Karjalainen J, Palta P, Sipilä TP, ...&, Palotie A. (2023) FinnGen provides genetic insights from a well-phenotyped isolated population Nature, 613 (7944) 508-518. doi:10.1038/s41586-022-05473-8. PMID 36653562 2023
FinnGen R6 (January 24 2022) Biobanks_Cohorts Kurki MI, Karjalainen J, Palta P, Sipilä TP, ...&, Palotie A. (2023) FinnGen provides genetic insights from a well-phenotyped isolated population Nature, 613 (7944) 508-518. doi:10.1038/s41586-022-05473-8. PMID 36653562 2023
FinnGen R7 (June 1 2022) Biobanks_Cohorts Kurki MI, Karjalainen J, Palta P, Sipilä TP, ...&, Palotie A. (2023) FinnGen provides genetic insights from a well-phenotyped isolated population Nature, 613 (7944) 508-518. doi:10.1038/s41586-022-05473-8. PMID 36653562 2023
FinnGen R8 (Dec 1 2022) Biobanks_Cohorts Kurki MI, Karjalainen J, Palta P, Sipilä TP, ...&, Palotie A. (2023) FinnGen provides genetic insights from a well-phenotyped isolated population Nature, 613 (7944) 508-518. doi:10.1038/s41586-022-05473-8. PMID 36653562 2023
FinnGen R9 (May 11 2023) Biobanks_Cohorts Kurki MI, Karjalainen J, Palta P, Sipilä TP, ...&, Palotie A. (2023) FinnGen provides genetic insights from a well-phenotyped isolated population Nature, 613 (7944) 508-518. doi:10.1038/s41586-022-05473-8. PMID 36653562 2023
Generation Scotland Biobanks_Cohorts NA NA
Global Biobank Biobanks_Cohorts Zhou W, Kanai M, Wu KH, Rasheed H, ...&, Neale BM. (2022) Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease Cell Genom., 2 (10) 100192. doi:10.1016/j.xgen.2022.100192. PMID 36777996 2022
KoGES Pheweb Biobanks_Cohorts NA NA
KoreanChip Biobanks_Cohorts NA NA
MANE PheWeb Biobanks_Cohorts NA NA
MGI 1 Biobanks_Cohorts NA NA
MGI 2 Biobanks_Cohorts NA NA
MGI BioUV Biobanks_Cohorts NA NA
MVP-Finngen-UKBB meta-analysis Biobanks_Cohorts NA NA
PLATLAS Biobanks_Cohorts Levin, M. G. et al. Genome-wide assessment of pleiotropy across >1000 traits from global biobanks. medRxiv 2025.04.18.25326074 (2025) doi:10.1101/2025.04.18.25326074. NA
Pan-UKB Biobanks_Cohorts Karczewski, K. J. et al. Pan-UK Biobank genome-wide association analyses enhance discovery and resolution of ancestry-enriched effects. Nat. Genet. 57, 2408–2417 (2025). NA
TPMI PheWeb Biobanks_Cohorts NA NA
Taiwan BioBank Pheweb Biobanks_Cohorts NA NA
Tohoku Medical Megabank (TMM) Jmorp Biobanks_Cohorts NA NA
UKB Neale Biobanks_Cohorts NA NA
UKB TOPMed Biobanks_Cohorts Taliun D, Harris DN, Kessler MD, Carlson J, ...&, Abecasis GR. (2021) Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program Nature, 590 (7845) 290-299. doi:10.1038/s41586-021-03205-y. PMID 33568819 2021
UKB exome Biobanks_Cohorts Wang, Q. et al. Rare variant contribution to human disease in 281,104 UK Biobank exomes. Nature 597, 527–532 (2021). NA
UKB fastgwa (Imputation) Biobanks_Cohorts Jiang L, Zheng Z, Qi T, Kemper KE, ...&, Yang J. (2019) A resource-efficient tool for mixed model association analysis of large-scale data Nat. Genet., 51 (12) 1749-1755. doi:10.1038/s41588-019-0530-8. PMID 31768069 2019
UKB fastgwa (WES) Biobanks_Cohorts Jiang L, Zheng Z, Qi T, Kemper KE, ...&, Yang J. (2019) A resource-efficient tool for mixed model association analysis of large-scale data Nat. Genet., 51 (12) 1749-1755. doi:10.1038/s41588-019-0530-8. PMID 31768069 2019
UKB fastgwa-glmm (Binary) Biobanks_Cohorts Jiang L, Zheng Z, Fang H, Yang J. (2021) A generalized linear mixed model association tool for biobank-scale data Nat. Genet., 53 (11) 1616-1621. doi:10.1038/s41588-021-00954-4. PMID 34737426 2021
UKB gene-based (Genebass) Biobanks_Cohorts Karczewski KJ, Solomonson M, Chao KR, Goodrich JK, ...&, Neale BM. (2022) Systematic single-variant and gene-based association testing of thousands of phenotypes in 394,841 UK Biobank exomes Cell Genom., 2 (9) 100168. doi:10.1016/j.xgen.2022.100168. PMID 36778668 2022
UKB saige Biobanks_Cohorts Zhou W, Nielsen JB, Fritsche LG, Dey R, ...&, Lee S. (2018) Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies Nat. Genet., 50 (9) 1335-1341. doi:10.1038/s41588-018-0184-y. PMID 30104761 2018
Yang Lab xQTL Biobanks_Cohorts NA NA
DIAGRAM Consortiums NA NA
GIANT (Genetic Investigation of ANthropometric Traits) Consortiums NA NA
GLGC (Global Lipids Genetics Consortium) Consortiums NA NA
Megastroke Consortiums NA NA
PGC (Psychiatric Genomics Consortium) Consortiums NA NA
NBDC (hum0197) Database NA NA
CNCR CTGLAB Institution NA NA
CNSGENOMICS Institution NA NA
Cardiovascular Disease Knowledge Portal Platform Costanzo MC, Roselli C, Brandes M, Duby M, ...&, Burtt NP. (2023) Cardiovascular disease knowledge portal: A community resource for cardiovascular disease research Circ. Genom. Precis. Med., 16 (6) e004181. doi:10.1161/CIRCGEN.123.004181. PMID 37814896 2023
GWAS catalog Platform Sollis E, Mosaku A, Abid A, Buniello A, ...&, Harris LW. (2023) The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource Nucleic Acids Res., 51 (D1) D977-D985. doi:10.1093/nar/gkac1010. PMID 36350656 2023
Japan Omics Browser Platform NA NA
OpenGWAS Platform Elsworth, B., Lyon, M., Alexander, T., Liu, Y., Matthews, P., Hallett, J., ... & Hemani, G. (2020). The MRC IEU OpenGWAS data infrastructure. BioRxiv, 2020-08. NA

Biobanks_Cohorts

Biobank Japan (BBJ) JENGER

Biobank Japan (BBJ) Phewebjp

Biobank Russia

  • NAME : Biobank Russia
  • URL : https://biobank.almazovcentre.ru/#
  • MAIN_ANCESTRY : EUR
  • TITLE : Complex trait susceptibilities and population diversity in a sample of 4,145 Russians
  • ABSTRACT : AbstractThe population of Russia consists of more than 150 local ethnicities. The ethnic diversity and geographic origins, which extend from eastern Europe to Asia, make the population uniquely positioned to investigate the shared properties of inherited disease risks between European and Asian ancestries. We present the analysis of genetic and phenotypic data from a cohort of 4,145 individuals collected in three metro areas in western Russia. We show the presence of multiple admixed genetic ancestry clusters spanning from primarily European to Asian and high identity-by-descent sharing with the Finnish population. As a result, there was notable enrichment of Finnish-specific variants in Russia. We illustrate the utility of Russian-descent cohorts for discovery of novel population-specific genetic associations, as well as replication of previously identified associations that were thought to be population-specific in other cohorts. Finally, we provide access to a database of allele frequencies and GWAS results for 464 phenotypes.
  • COPYRIGHT : https://creativecommons.org/licenses/by/4.0
  • CITATION : Usoltsev D, Kolosov N, Rotar O, Loboda A, ...&, Artomov M. (2024) Complex trait susceptibilities and population diversity in a sample of 4,145 Russians Nat. Commun., 15 (1) 1-10. doi:10.1038/s41467-024-50304-1. PMID 39043636
  • JOURNAL_INFO : Nature communications ; Nat. Commun. ; 2024 ; 15 ; 1 ; 1-10
  • PUBMED_LINK : 39043636

CARTaGENE PheWeb

China Kadoorie Biobank (CKB)

FinMetSeq

FinnGen Kanta 1st Lab values (October 14 2025 )

FinnGen R10 (December 18 2023)

  • NAME : FinnGen R10 (December 18 2023)
  • URL : https://r10.finngen.fi/
  • MAIN_ANCESTRY : EUR
  • RELATED_BIOBANK : Finngen
  • TITLE : 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 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 <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.
  • COPYRIGHT : https://creativecommons.org/licenses/by/4.0
  • CITATION : Kurki MI, Karjalainen J, Palta P, Sipilä TP, ...&, Palotie A. (2023) FinnGen provides genetic insights from a well-phenotyped isolated population Nature, 613 (7944) 508-518. doi:10.1038/s41586-022-05473-8. PMID 36653562
  • JOURNAL_INFO : Nature ; Nature ; 2023 ; 613 ; 7944 ; 508-518
  • PUBMED_LINK : 36653562

FinnGen R10-UKBB meta-analysis

FinnGen R11 (June 24 2024)

  • NAME : FinnGen R11 (June 24 2024)
  • URL : https://r11.finngen.fi/
  • MAIN_ANCESTRY : EUR
  • RELATED_BIOBANK : Finngen
  • TITLE : 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 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 <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.
  • COPYRIGHT : https://creativecommons.org/licenses/by/4.0
  • CITATION : Kurki MI, Karjalainen J, Palta P, Sipilä TP, ...&, Palotie A. (2023) FinnGen provides genetic insights from a well-phenotyped isolated population Nature, 613 (7944) 508-518. doi:10.1038/s41586-022-05473-8. PMID 36653562
  • JOURNAL_INFO : Nature ; Nature ; 2023 ; 613 ; 7944 ; 508-518
  • PUBMED_LINK : 36653562

FinnGen R12 (November 4 2024)

  • NAME : FinnGen R12 (November 4 2024)
  • URL : https://r12.finngen.fi/
  • MAIN_ANCESTRY : EUR
  • RELATED_BIOBANK : Finngen
  • TITLE : 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 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 <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.
  • COPYRIGHT : https://creativecommons.org/licenses/by/4.0
  • CITATION : Kurki MI, Karjalainen J, Palta P, Sipilä TP, ...&, Palotie A. (2023) FinnGen provides genetic insights from a well-phenotyped isolated population Nature, 613 (7944) 508-518. doi:10.1038/s41586-022-05473-8. PMID 36653562
  • JOURNAL_INFO : Nature ; Nature ; 2023 ; 613 ; 7944 ; 508-518
  • PUBMED_LINK : 36653562

FinnGen R12-UKBB meta-analysis

FinnGen R4 (November 30 2020)

  • NAME : FinnGen R4 (November 30 2020)
  • URL : https://r4.finngen.fi/about
  • MAIN_ANCESTRY : EUR
  • RELATED_BIOBANK : Finngen
  • TITLE : 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 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 <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.
  • COPYRIGHT : https://creativecommons.org/licenses/by/4.0
  • CITATION : Kurki MI, Karjalainen J, Palta P, Sipilä TP, ...&, Palotie A. (2023) FinnGen provides genetic insights from a well-phenotyped isolated population Nature, 613 (7944) 508-518. doi:10.1038/s41586-022-05473-8. PMID 36653562
  • JOURNAL_INFO : Nature ; Nature ; 2023 ; 613 ; 7944 ; 508-518
  • PUBMED_LINK : 36653562

FinnGen R5 (May 11 2021)

  • NAME : FinnGen R5 (May 11 2021)
  • URL : https://r5.finngen.fi/about
  • MAIN_ANCESTRY : EUR
  • RELATED_BIOBANK : Finngen
  • TITLE : 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 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 <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.
  • COPYRIGHT : https://creativecommons.org/licenses/by/4.0
  • CITATION : Kurki MI, Karjalainen J, Palta P, Sipilä TP, ...&, Palotie A. (2023) FinnGen provides genetic insights from a well-phenotyped isolated population Nature, 613 (7944) 508-518. doi:10.1038/s41586-022-05473-8. PMID 36653562
  • JOURNAL_INFO : Nature ; Nature ; 2023 ; 613 ; 7944 ; 508-518
  • PUBMED_LINK : 36653562

FinnGen R6 (January 24 2022)

  • NAME : FinnGen R6 (January 24 2022)
  • URL : https://r6.finngen.fi/about
  • MAIN_ANCESTRY : EUR
  • RELATED_BIOBANK : Finngen
  • TITLE : 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 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 <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.
  • COPYRIGHT : https://creativecommons.org/licenses/by/4.0
  • CITATION : Kurki MI, Karjalainen J, Palta P, Sipilä TP, ...&, Palotie A. (2023) FinnGen provides genetic insights from a well-phenotyped isolated population Nature, 613 (7944) 508-518. doi:10.1038/s41586-022-05473-8. PMID 36653562
  • JOURNAL_INFO : Nature ; Nature ; 2023 ; 613 ; 7944 ; 508-518
  • PUBMED_LINK : 36653562

FinnGen R7 (June 1 2022)

  • NAME : FinnGen R7 (June 1 2022)
  • URL : https://r7.finngen.fi/about
  • MAIN_ANCESTRY : EUR
  • RELATED_BIOBANK : Finngen
  • TITLE : 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 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 <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.
  • COPYRIGHT : https://creativecommons.org/licenses/by/4.0
  • CITATION : Kurki MI, Karjalainen J, Palta P, Sipilä TP, ...&, Palotie A. (2023) FinnGen provides genetic insights from a well-phenotyped isolated population Nature, 613 (7944) 508-518. doi:10.1038/s41586-022-05473-8. PMID 36653562
  • JOURNAL_INFO : Nature ; Nature ; 2023 ; 613 ; 7944 ; 508-518
  • PUBMED_LINK : 36653562

FinnGen R8 (Dec 1 2022)

  • NAME : FinnGen R8 (Dec 1 2022)
  • URL : https://r8.finngen.fi/about
  • MAIN_ANCESTRY : EUR
  • RELATED_BIOBANK : Finngen
  • TITLE : 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 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 <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.
  • COPYRIGHT : https://creativecommons.org/licenses/by/4.0
  • CITATION : Kurki MI, Karjalainen J, Palta P, Sipilä TP, ...&, Palotie A. (2023) FinnGen provides genetic insights from a well-phenotyped isolated population Nature, 613 (7944) 508-518. doi:10.1038/s41586-022-05473-8. PMID 36653562
  • JOURNAL_INFO : Nature ; Nature ; 2023 ; 613 ; 7944 ; 508-518
  • PUBMED_LINK : 36653562

FinnGen R9 (May 11 2023)

  • NAME : FinnGen R9 (May 11 2023)
  • URL : https://r9.finngen.fi/about
  • MAIN_ANCESTRY : EUR
  • RELATED_BIOBANK : Finngen
  • TITLE : 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 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 <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.
  • COPYRIGHT : https://creativecommons.org/licenses/by/4.0
  • CITATION : Kurki MI, Karjalainen J, Palta P, Sipilä TP, ...&, Palotie A. (2023) FinnGen provides genetic insights from a well-phenotyped isolated population Nature, 613 (7944) 508-518. doi:10.1038/s41586-022-05473-8. PMID 36653562
  • JOURNAL_INFO : Nature ; Nature ; 2023 ; 613 ; 7944 ; 508-518
  • PUBMED_LINK : 36653562

Generation Scotland

Global Biobank

  • NAME : Global Biobank
  • URL : http://results.globalbiobankmeta.org/
  • MAIN_ANCESTRY : ALL
  • TITLE : Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease
  • ABSTRACT : Biobanks facilitate genome-wide association studies (GWASs), which have mapped genomic loci across a range of human diseases and traits. However, most biobanks are primarily composed of individuals of European ancestry. We introduce the Global Biobank Meta-analysis Initiative (GBMI)-a collaborative network of 23 biobanks from 4 continents representing more than 2.2 million consented individuals with genetic data linked to electronic health records. GBMI meta-analyzes summary statistics from GWASs generated using harmonized genotypes and phenotypes from member biobanks for 14 exemplar diseases and endpoints. This strategy validates that GWASs conducted in diverse biobanks can be integrated despite heterogeneity in case definitions, recruitment strategies, and baseline characteristics. This collaborative effort improves GWAS power for diseases, benefits understudied diseases, and improves risk prediction while also enabling the nomination of disease genes and drug candidates by incorporating gene and protein expression data and providing insight into the underlying biology of human diseases and traits.
  • COPYRIGHT : http://creativecommons.org/licenses/by-nc-nd/4.0/
  • CITATION : Zhou W, Kanai M, Wu KH, Rasheed H, ...&, Neale BM. (2022) Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease Cell Genom., 2 (10) 100192. doi:10.1016/j.xgen.2022.100192. PMID 36777996
  • JOURNAL_INFO : Cell genomics ; Cell Genom. ; 2022 ; 2 ; 10 ; 100192
  • PUBMED_LINK : 36777996

KoGES Pheweb

KoreanChip

MANE PheWeb

MGI 1

MGI 2

MGI BioUV

MVP-Finngen-UKBB meta-analysis

PLATLAS

  • NAME : PLATLAS
  • URL : https://platlas.cels.anl.gov/
  • FULL NAME : PLeiotropic ATLAS
  • MAIN_ANCESTRY : ALL
  • CITATION : Levin, M. G. et al. Genome-wide assessment of pleiotropy across >1000 traits from global biobanks. medRxiv 2025.04.18.25326074 (2025) doi:10.1101/2025.04.18.25326074.
  • PUBMED_LINK : 40313291

Pan-UKB

  • NAME : Pan-UKB
  • URL : https://pan.ukbb.broadinstitute.org/
  • MAIN_ANCESTRY : EUR
  • RELATED_BIOBANK : UK Biobank
  • CITATION : Karczewski, K. J. et al. Pan-UK Biobank genome-wide association analyses enhance discovery and resolution of ancestry-enriched effects. Nat. Genet. 57, 2408–2417 (2025).
  • PUBMED_LINK : 40968291

TPMI PheWeb

Taiwan BioBank Pheweb

Tohoku Medical Megabank (TMM) Jmorp

UKB Neale

  • NAME : UKB Neale
  • URL : https://pheweb.org/UKB-Neale/
  • DESCRIPTION : https://www.nealelab.is/blog/2017/9/11/details-and-considerations-of-the-uk-biobank-gwas
  • MAIN_ANCESTRY : EUR
  • RELATED_BIOBANK : UK Biobank

UKB TOPMed

  • NAME : UKB TOPMed
  • URL : https://pheweb.org/UKB-TOPMed/
  • DESCRIPTION : Imputed: GWAS summary statistics for 2,173 traits from fastGWA analysis of the UKB imputed data. About 8 million SNPs with MAF > 0.01 are available.
  • MAIN_ANCESTRY : EUR
  • RELATED_BIOBANK : UK Biobank
  • TITLE : Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program
  • ABSTRACT : The Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases. The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data. The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes)1. In the first 53,831 TOPMed samples, we detected more than 400 million single-nucleotide and insertion or deletion variants after alignment with the reference genome. Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci. Among the more than 400 million detected variants, 97% have frequencies of less than 1% and 46% are singletons that are present in only one individual (53% among unrelated individuals). These rare variants provide insights into mutational processes and recent human evolutionary history. The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 0.01%.
  • CITATION : Taliun D, Harris DN, Kessler MD, Carlson J, ...&, Abecasis GR. (2021) Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program Nature, 590 (7845) 290-299. doi:10.1038/s41586-021-03205-y. PMID 33568819
  • JOURNAL_INFO : Nature ; Nature ; 2021 ; 590 ; 7845 ; 290-299
  • PUBMED_LINK : 33568819

UKB exome

  • NAME : UKB exome
  • URL : https://azphewas.com/
  • MAIN_ANCESTRY : EUR
  • RELATED_BIOBANK : UK Biobank
  • CITATION : Wang, Q. et al. Rare variant contribution to human disease in 281,104 UK Biobank exomes. Nature 597, 527–532 (2021).
  • PUBMED_LINK : 34375979

UKB fastgwa (Imputation)

  • NAME : UKB fastgwa (Imputation)
  • URL : https://yanglab.westlake.edu.cn/data/ukb_fastgwa/imp/
  • DESCRIPTION : GWAS summary statistics for 2,048 traits from fastGWA analysis of the UKB WES data (50k individuals from the first release). About 150k SNPs with MAF > 0.01 are available.
  • MAIN_ANCESTRY : EUR
  • RELATED_BIOBANK : UK Biobank
  • TITLE : A resource-efficient tool for mixed model association analysis of large-scale data
  • ABSTRACT : The genome-wide association study (GWAS) has been widely used as an experimental design to detect associations between genetic variants and a phenotype. Two major confounding factors, population stratification and relatedness, could potentially lead to inflated GWAS test statistics and hence to spurious associations. Mixed linear model (MLM)-based approaches can be used to account for sample structure. However, genome-wide association (GWA) analyses in biobank samples such as the UK Biobank (UKB) often exceed the capability of most existing MLM-based tools especially if the number of traits is large. Here, we develop an MLM-based tool (fastGWA) that controls for population stratification by principal components and for relatedness by a sparse genetic relationship matrix for GWA analyses of biobank-scale data. We demonstrate by extensive simulations that fastGWA is reliable, robust and highly resource-efficient. We then apply fastGWA to 2,173 traits on array-genotyped and imputed samples from 456,422 individuals and to 2,048 traits on whole-exome-sequenced samples from 46,191 individuals in the UKB.
  • CITATION : Jiang L, Zheng Z, Qi T, Kemper KE, ...&, Yang J. (2019) A resource-efficient tool for mixed model association analysis of large-scale data Nat. Genet., 51 (12) 1749-1755. doi:10.1038/s41588-019-0530-8. PMID 31768069
  • JOURNAL_INFO : Nature genetics ; Nat. Genet. ; 2019 ; 51 ; 12 ; 1749-1755
  • PUBMED_LINK : 31768069

UKB fastgwa (WES)

  • NAME : UKB fastgwa (WES)
  • URL : https://yanglab.westlake.edu.cn/data/ukb_fastgwa/wes/
  • DESCRIPTION : Binary: GWAS summary statistics for 2,989 binary traits from the fastGWA-GLMM analysis of the UKB imputed data. About 11 million SNPs with MAF > 1e-4 are available.
  • MAIN_ANCESTRY : EUR
  • RELATED_BIOBANK : UK Biobank
  • TITLE : A resource-efficient tool for mixed model association analysis of large-scale data
  • ABSTRACT : The genome-wide association study (GWAS) has been widely used as an experimental design to detect associations between genetic variants and a phenotype. Two major confounding factors, population stratification and relatedness, could potentially lead to inflated GWAS test statistics and hence to spurious associations. Mixed linear model (MLM)-based approaches can be used to account for sample structure. However, genome-wide association (GWA) analyses in biobank samples such as the UK Biobank (UKB) often exceed the capability of most existing MLM-based tools especially if the number of traits is large. Here, we develop an MLM-based tool (fastGWA) that controls for population stratification by principal components and for relatedness by a sparse genetic relationship matrix for GWA analyses of biobank-scale data. We demonstrate by extensive simulations that fastGWA is reliable, robust and highly resource-efficient. We then apply fastGWA to 2,173 traits on array-genotyped and imputed samples from 456,422 individuals and to 2,048 traits on whole-exome-sequenced samples from 46,191 individuals in the UKB.
  • CITATION : Jiang L, Zheng Z, Qi T, Kemper KE, ...&, Yang J. (2019) A resource-efficient tool for mixed model association analysis of large-scale data Nat. Genet., 51 (12) 1749-1755. doi:10.1038/s41588-019-0530-8. PMID 31768069
  • JOURNAL_INFO : Nature genetics ; Nat. Genet. ; 2019 ; 51 ; 12 ; 1749-1755
  • PUBMED_LINK : 31768069

UKB fastgwa-glmm (Binary)

  • NAME : UKB fastgwa-glmm (Binary)
  • URL : https://yanglab.westlake.edu.cn/data/ukb_fastgwa/imp_binary/
  • MAIN_ANCESTRY : EUR
  • RELATED_BIOBANK : UK Biobank
  • TITLE : A generalized linear mixed model association tool for biobank-scale data
  • ABSTRACT : Compared with linear mixed model-based genome-wide association (GWA) methods, generalized linear mixed model (GLMM)-based methods have better statistical properties when applied to binary traits but are computationally much slower. In the present study, leveraging efficient sparse matrix-based algorithms, we developed a GLMM-based GWA tool, fastGWA-GLMM, that is severalfold to orders of magnitude faster than the state-of-the-art tools when applied to the UK Biobank (UKB) data and scalable to cohorts with millions of individuals. We show by simulation that the fastGWA-GLMM test statistics of both common and rare variants are well calibrated under the null, even for traits with extreme case-control ratios. We applied fastGWA-GLMM to the UKB data of 456,348 individuals, 11,842,647 variants and 2,989 binary traits (full summary statistics available at http://fastgwa.info/ukbimpbin ), and identified 259 rare variants associated with 75 traits, demonstrating the use of imputed genotype data in a large cohort to discover rare variants for binary complex traits.
  • COPYRIGHT : https://www.springernature.com/gp/researchers/text-and-data-mining
  • CITATION : Jiang L, Zheng Z, Fang H, Yang J. (2021) A generalized linear mixed model association tool for biobank-scale data Nat. Genet., 53 (11) 1616-1621. doi:10.1038/s41588-021-00954-4. PMID 34737426
  • JOURNAL_INFO : Nature genetics ; Nat. Genet. ; 2021 ; 53 ; 11 ; 1616-1621
  • PUBMED_LINK : 34737426

UKB gene-based (Genebass)

  • NAME : UKB gene-based (Genebass)
  • URL : https://genebass.org/
  • MAIN_ANCESTRY : EUR
  • RELATED_BIOBANK : UK Biobank
  • TITLE : Systematic single-variant and gene-based association testing of thousands of phenotypes in 394,841 UK Biobank exomes
  • ABSTRACT : Genome-wide association studies have successfully discovered thousands of common variants associated with human diseases and traits, but the landscape of rare variations in human disease has not been explored at scale. Exome-sequencing studies of population biobanks provide an opportunity to systematically evaluate the impact of rare coding variations across a wide range of phenotypes to discover genes and allelic series relevant to human health and disease. Here, we present results from systematic association analyses of 4,529 phenotypes using single-variant and gene tests of 394,841 individuals in the UK Biobank with exome-sequence data. We find that the discovery of genetic associations is tightly linked to frequency and is correlated with metrics of deleteriousness and natural selection. We highlight biological findings elucidated by these data and release the dataset as a public resource alongside the Genebass browser for rapidly exploring rare-variant association results.
  • COPYRIGHT : http://creativecommons.org/licenses/by/4.0/
  • CITATION : Karczewski KJ, Solomonson M, Chao KR, Goodrich JK, ...&, Neale BM. (2022) Systematic single-variant and gene-based association testing of thousands of phenotypes in 394,841 UK Biobank exomes Cell Genom., 2 (9) 100168. doi:10.1016/j.xgen.2022.100168. PMID 36778668
  • JOURNAL_INFO : Cell genomics ; Cell Genom. ; 2022 ; 2 ; 9 ; 100168
  • PUBMED_LINK : 36778668

UKB saige

  • NAME : UKB saige
  • URL : https://pheweb.org/UKB-SAIGE/
  • MAIN_ANCESTRY : EUR
  • RELATED_BIOBANK : UK Biobank
  • TITLE : Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies
  • ABSTRACT : In genome-wide association studies (GWAS) for thousands of phenotypes in large biobanks, most binary traits have substantially fewer cases than controls. Both of the widely used approaches, the linear mixed model and the recently proposed logistic mixed model, perform poorly; they produce large type I error rates when used to analyze unbalanced case-control phenotypes. Here we propose a scalable and accurate generalized mixed model association test that uses the saddlepoint approximation to calibrate the distribution of score test statistics. This method, SAIGE (Scalable and Accurate Implementation of GEneralized mixed model), provides accurate P values even when case-control ratios are extremely unbalanced. SAIGE uses state-of-art optimization strategies to reduce computational costs; hence, it is applicable to GWAS for thousands of phenotypes by large biobanks. Through the analysis of UK Biobank data of 408,961 samples from white British participants with European ancestry for > 1,400 binary phenotypes, we show that SAIGE can efficiently analyze large sample data, controlling for unbalanced case-control ratios and sample relatedness.
  • CITATION : Zhou W, Nielsen JB, Fritsche LG, Dey R, ...&, Lee S. (2018) Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies Nat. Genet., 50 (9) 1335-1341. doi:10.1038/s41588-018-0184-y. PMID 30104761
  • JOURNAL_INFO : Nature genetics ; Nat. Genet. ; 2018 ; 50 ; 9 ; 1335-1341
  • PUBMED_LINK : 30104761

Yang Lab xQTL

Consortiums

DIAGRAM

GIANT (Genetic Investigation of ANthropometric Traits)

GLGC (Global Lipids Genetics Consortium)

Megastroke

PGC (Psychiatric Genomics Consortium)

Database

NBDC (hum0197)

Institution

CNCR CTGLAB

CNSGENOMICS

Platform

Cardiovascular Disease Knowledge Portal

  • NAME : Cardiovascular Disease Knowledge Portal
  • URL : https://cvd.hugeamp.org/
  • TITLE : Cardiovascular disease knowledge portal: A community resource for cardiovascular disease research
  • CITATION : Costanzo MC, Roselli C, Brandes M, Duby M, ...&, Burtt NP. (2023) Cardiovascular disease knowledge portal: A community resource for cardiovascular disease research Circ. Genom. Precis. Med., 16 (6) e004181. doi:10.1161/CIRCGEN.123.004181. PMID 37814896
  • JOURNAL_INFO : Circulation. Genomic and precision medicine ; Circ. Genom. Precis. Med. ; 2023 ; 16 ; 6 ; e004181
  • PUBMED_LINK : 37814896

GWAS catalog

  • NAME : GWAS catalog
  • URL : https://www.ebi.ac.uk/gwas/
  • TITLE : The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource
  • ABSTRACT : The NHGRI-EBI GWAS Catalog (www.ebi.ac.uk/gwas) is a FAIR knowledgebase providing detailed, structured, standardised and interoperable genome-wide association study (GWAS) data to >200 000 users per year from academic research, healthcare and industry. The Catalog contains variant-trait associations and supporting metadata for >45 000 published GWAS across >5000 human traits, and >40 000 full P-value summary statistics datasets. Content is curated from publications or acquired via author submission of prepublication summary statistics through a new submission portal and validation tool. GWAS data volume has vastly increased in recent years. We have updated our software to meet this scaling challenge and to enable rapid release of submitted summary statistics. The scope of the repository has expanded to include additional data types of high interest to the community, including sequencing-based GWAS, gene-based analyses and copy number variation analyses. Community outreach has increased the number of shared datasets from under-represented traits, e.g. cancer, and we continue to contribute to awareness of the lack of population diversity in GWAS. Interoperability of the Catalog has been enhanced through links to other resources including the Polygenic Score Catalog and the International Mouse Phenotyping Consortium, refinements to GWAS trait annotation, and the development of a standard format for GWAS data.
  • COPYRIGHT : https://creativecommons.org/licenses/by/4.0/
  • CITATION : Sollis E, Mosaku A, Abid A, Buniello A, ...&, Harris LW. (2023) The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource Nucleic Acids Res., 51 (D1) D977-D985. doi:10.1093/nar/gkac1010. PMID 36350656
  • JOURNAL_INFO : Nucleic acids research ; Nucleic Acids Res. ; 2023 ; 51 ; D1 ; D977-D985
  • PUBMED_LINK : 36350656

Japan Omics Browser

OpenGWAS

  • NAME : OpenGWAS
  • URL : https://gwas.mrcieu.ac.uk/
  • PREPRINT_DOI : 10.1101/2020.08.10.244293
  • SERVER : biorxiv
  • CITATION : Elsworth, B., Lyon, M., Alexander, T., Liu, Y., Matthews, P., Hallett, J., ... & Hemani, G. (2020). The MRC IEU OpenGWAS data infrastructure. BioRxiv, 2020-08.