Genetic associations

Genetic associations are available for both common and rare (Mendelian) diseases and come from six data sources summarised below.

Common diseases

Open Targets Genetics Portal

Genome wide association studies (GWAS) provide a link between common diseases and genomic loci, where each association is represented by a lead variant where the evidence for the association is the strongest, based on the reported p-value. The lead variant is not necessarily the causal (or the only causal) variant for the association. Moreover, the causal gene(s) is not necessarily the closest to the lead variant. Due to these reasons, identifying target-disease associations based on GWAS data is extremely challenging. Open Targets Genetics has been developed to address these challenges. Firstly, the Open Targets Genetics pipeline identifies tag variants from the lead (reported) variant through LD expansion or fine mapping, the latter for when summary statistics are available (e.g. UK Biobank and some GWAS curated by NHGRI-EBI GWAS Catalog). Once the set of tag variants are identified, Open Targets Genetics then uses a machine learning method to link the associated locus to the most likely causal gene(s) by integrating and summarising the effect of tag variants based on genetic and functional genomic data namely:

  • Expression QTLs

  • Protein abundance QTLs

  • Chromatin interaction

  • In silico prediction of variant functional consequence

  • Distance of the variant to the transcript start site of the gene

The weight of an evidence is given by the locus to gene score describing the likelihood of the gene being causal for the associated locus.

For more details, check Open Targets Genetics overview.

The Genetics portal import summary statistics and manually curated lead variants from the NHGRI-EBI GWAS Catalog and UK Biobank summary statistics from the Neale lab. In order to generate evidence connecting targets with diseases, significant GWAS associations (p-value < 5*10^-8) are assigned to the most likely causal gene(s) using the L2G machine learning method. The score provided by the method is used for evidence scoring. To enrich the number of true positive evidence, associations where the score is below 0.05 are removed.

From February 2020 (release 20.02), genome-wide association based evidence is sourced from the Open Targets Genetics portal.

PheWAS catalog

The PheWAS (phenome-wide association studies) resources provide associations between a genetic variant and multiple phenotypes. It contains clinical phenotypes derived from the electronic medical record (EMR)-linked DNA biobank BioVU by the Center for Precision Medicine at the Vanderbilt University Medical Centre. The EMR-based PheWAS uses ICD9 (International Classification of Disease, 9th edition), which were mapped to EFO using OLS and Zooma. The catalog contains all associations with p < 0.05 (uncorrected).

Rare (Mendelian) diseases

Genomics England PanelApp

The Genomics England PanelApp is a knowledgebase that combines crowdsourced expertise with curation to provide gene-disease relationships. Virtual gene panels related to human disorders are reviewed by experts within the clinical and scientific community to support interpretation of genomes within the 100,000 Genomes Project. Within a panel, genes are rated based on the level of evidence supporting the association with the phenotypes identified by the panel. Genes are then classified according to a traffic light system with red/stop, amber/pause, and green/go classifications. To receive a green rating (diagnostic-grade) on a version 1+ panel, the gene requires "evidence from 3 or more unrelated families or from 2 - 3 unrelated families where there is strong additional functional data" and "genes that do not meet these criteria are rated as Amber (borderline) or Red (low level of evidence)." The Open Targets Platform includes "green" and "amber" genes from version 1+ panels along with their phenotypes, providing the latter can be mapped to a disease or phenotype ontology. As we standardise our evidence to the EFO ontology, some of the phenotypes cannot be mapped and included in our Platform - please visit the Genomics England PanelApp website for the full set.

UniProt literature

The Universal Protein Resource (UniProt) provides protein sequence and functional information. They manually curate associations between genes and rare (Mendelian) diseases reported in OMIM ( and we integrate the ones that are purely based on literature curation given that the ones with genetic variants are imported from ClinVar via EVA. We classify these evidence as UniProt Literature.

European Variation Archive (EVA)

The EMBL-EBI European Variation Archive (EVA) is a database of publicly available genetic variants, both short scale (e.g. SNPs) and large structural variants (e.g. larger deletions). For rare (Mendelian) diseases, EVA provides clinically relevant data for the variants that are pathogenic or likely pathogenic. This clinical information comes from ClinVar, and includes OMIM data.


The data in Gene2Phenotype (G2P) is produced and curated from the literature by consultant clinical geneticists in the UK. This data is comprised of both short scale (i.e. sequence variants such as SNPs) and large structural variants (e.g. copy number variants), and can allow targeted filtering of genome-wide data for diagnostic purposes. The variants were provided by DECIPHER, a database of genomic variants and phenotypes in patients with developmental disorders.


The ClinGen Gene-Disease Clinical Validity curation process involves evaluating the strength of evidence supporting or refuting a claim that variation in a particular gene causes a particular disease. Gene-disease relationships are evaluated using a framework to provide a semiquantitative measurement for the strength of evidence. This results in a qualitative classification for each curated gene-disease relationship: "Definitive," "Strong," "Moderate," "Limited," "No Reported Evidence," or "Conflicting Evidence." For more details on ClinGen's curation approach, see Strande et al., 2017.