GEMINI genome-wide associations study (GWAS) summary statistics

GEMINI: Genetic Evaluation of Multimorbidity towards INdividualisation of Interventions

GWAS summary statistics for 72 long-term conditions. Up to three sources of genetics data are used, depending on the condition: UK Biobank, FinnGen, and consortium-published meta-analyses (where available).

If you use these resources please cite the below and include the resource release version:

Murrin et al. (2024) A systematic analysis of the contribution of genetics to multimorbidity and comparisons with primary care data. eBioMedicine. https://doi.org/10.1016/j.ebiom.2025.105584 

See our GitHub repos for more information: https://github.com/GEMINI-multimorbidity

Changes in v1.1 (29th April 2026)

• A small number of variants are now excluded from UK Biobank results where significant deviation from Hardy-Weinberg Equilibrium was identified,
• Standard errors now account for the Firth correction applied by REGENIE (previously the p-value and SEs did not align),
• Meta-analysis re-run to account for this,
• Unrealistic allele frequencies (<0 or >1) outputted by GWAMA in a small number of cases are now set to NA.

Summary information

See the `conditions.txt` file for a list of conditions included, plus file suffix, studies included, and effective sample size. 

GWAS files are provided in GWAS catalog format, with positions mapped to build 37.

For each GWAS file there is a README, detailing the source of the summary statistics: 1) UK Biobank [UKB], a large population-based prospective study with 450,197 individuals of European genetic ancestry. 2) FinnGen, a large-scale genomics initiative including over 500,000 participants with linked health diagnosis data. 3) Disease-specific GWAS meta-analyses summary statistics when available for each LTC. See the GEMINI GitHub for details on LTC diagnostic codes: https://github.com/GEMINI-multimorbidity 

An extract of the methods from Murrin 2025 (https://doi.org/10.1016/j.ebiom.2025.105584) are included below:

UK Biobank data
To perform the genetic analyses we ascertained diagnosis of LTCs using both primary-care linked data (available for 45% of participants, censoring date: 28/02/2016 – Read v2 and CTV3 codes, truncated to 5 bytes) and hospital inpatient diagnoses (available for all participants, censoring date: 31/10/2022 - ICD-10 codes). Participants were genotyped using two near identical (>95% shared variants, n=805,426 total) microarray platforms: the Affymetrix Axiom UK Biobank array (in 438,427 participants) and the Affymetrix UKBiLEVE array (in 49,950 participants). UK Biobank centrally performed genotype imputation in 487,442 participants using data from the Haplotype Reference Consortium and UK10K reference panels, increasing the number of genetic variants to ~96 million.8 We exclude genetic variants with <0.1% minor allele frequency or with imputed INFO score <0.3, leaving ~16 million for GWAS analysis. GWAS were performed in up to 451,197 participants genetically similar to the 1000 Genomes EUR population (described previously.9 In brief, individuals from the UK Biobank were projected into the 1000 Genomes principal component (PC) space using the SNP loadings derived from the initial PC analysis to minimise confounding of PC values due to varying degrees of relatedness within UK Biobank.10 Using the means derived from the 1000 Genomes reference dataset, we subsequently performed K-means clustering analyses to determine which individuals from UK Biobank could be classified as EUR-like. GWAS were performed in UKB participants genetically similar to the 1000 Genomes EUR reference population for 84 LTCs, using the same clinical code lists as above in CPRD, using the REGENIE software (v3.1.3) to account for population structure and relatedness, adjusted for age at baseline assessment, sex, genotyping chip, and assessment centre. 11 For quality control, we restricted variants to those with a minor allele frequency (MAF) of >0.1%, and an imputation INFO score ≥0.3.

FinnGen data
FinnGen is a large-scale genomics initiative, that contains data from over 500,000 participants and is linked to health diagnosis data. GWAS summary statistics from the FinnGen cohort  (release 9) with 377,277 participants, provided for predetermined disease (“endpoints”), defined using ICD-10-FM (Finnish Modification). 12 

Disease-specific GWAS
Disease-specific GWAS meta-analyses summary statistics when available for each LTC. We used the GWAS Catalog (https://www.ebi.ac.uk/gwas), 13 disease-specific public repositories and contacted authors of the latest GWAS to identify relevant studies with aligned disease definitions and participants of European ancestry to enable comparison with UKB and FinnGen. The below LTCs had available published and available GWAS summary statistics and were used in the genetics analysis (see Supplementary Table 1 for further information).
•    Anxiety disorders.14
•    Asthma.15
•    Atrial fibrillation.16
•    Chronic kidney disease.17
•    Chronic obstructive pulmonary disease.18
•    Coronary heart disease.19
•    Depression.20
•    Erectile dysfunction.21
•    Gastro-oesophageal reflux disease.22
•    Glaucoma.23
•    Gout.24
•    Hearing loss.25
•    Heart failure.26
•    Hyperthyroidism, hypothyroidism.27
•    Irritable bowel syndrome.28
•    Migraine.29
•    Osteoarthritis.30
•    Primary breast malignancy.31
•    Rheumatoid arthritis.32
•    Schizophrenia, schizotypal and delusional disorders.33
•    Type 2 diabetes.34
•    Ulcerative colitis.35

GWAS meta-analysis

For the 72 conditions meeting the heritability criteria above, we meta-analysed genome-wide summary data from up to 3 data sources – UKB, FinnGen and disease-specific GWAS (referred to as Consortium data). See Supplementary Figure 2 for analysis flowchart, and Supplementary Table 1 for effective sample size and other information. A cross-trait LD-score regression framework, that estimates the within-condition, between-dataset genetic correlation, measured the similarity between conditions. 40 The FinnGen and Consortium data were added to the meta-analysis when within-condition genetic correlation (R_g) with UK Biobank was >0.8. Where consortium data included UK Biobank or FinnGen data, the consortium data was used to avoid overlapping datasets (i.e., if UKB was in the consortium GWAS, then we only meta-analysed consortium+FinnGen). Studies were meta-analysed using GWAMA. 41  

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