The Open Targets Platform allows prioritisation of drug targets based on the strength of their association with a disease.
We allow for the prioritisation of targets by scoring target-disease associations based on our data sources such as the GWAS catalog, PheWAS catalog. Similar data sources are grouped together into data types (e.g. Genetic associations). Our scoring system varies from 0 to 1, the latter represents the strongest association, and it’s calculated based on the confidence in the evidence.
We assess the key factors that relate to the confidence in the target-disease association. We provide association scores to help you answer these questions:
Which targets have the most evidence for association with a disease?
What is the relative weight of the evidence for different targets associated with a disease?
The association score is a numerical value varying from 0 to 1, which indicates the strength of the association between a target and a disease. A score of 1 refers to the strongest association, whereas a score of 0 corresponds to no evidence supporting an association. In our Platform, we represent the different scores with varying shades of blue: the darker the blue, the stronger the association.
We start by generating a score for each evidence from different data sources (e.g. GWAS catalog, European Variation Archive) within a data type (e.g. Genetic associations). We define the evidence score as:
s = F * S * C
s = score
F = frequency, the relative occurrence of a target-disease evidence
S = severity, the magnitude or strength of the effect described by the evidence
C = confidence, overall confidence for the observation that generates the target-disease evidence
The evidence score summarises the strength of the evidence and depends on factors that affect its relative strength. These factors are specific to the different data sources in the Platform:
Data sources and factors that affect the relative strength of the evidence scores
GWAS and PheWASCatalog (functional consequence score of variants, normalised p-value and normalised sample size); European Variation Archive (functional consequence score of variants e.g. germline variants that cause transcript ablation will have a score of 1, whereas variants that are intronic will have a score of 0.5); UniProt (curator inference score based on how strong the evidence for the gene's involvement in the disease is. If the evidence is strong, the score will be 1. For evidence deemed not to be strong by the curator, the score will be 0.5); Gene2Phenotype (variants are inferred by curators and will have a score of 1, the highest functional consequence score); Genomics England PanelAPP (gene-disease associations are curated and crowdsourced by experts and will have the highest score of 1)
Cancer Gene Census (functional consequence score of variants); European Variation Archive (functional consequence score of variants); IntOgen (binned score based on tumour type categories. If the gene has several signals of positive selections in the tumour, the score will be 0.25. If the gene is already described as a cancer gene and exhibits a signal of positive selection in a tumor type, the score will be 0.5. If in addition to a signal of positive selection, the gene is functionally connected to other genes in the same tumor type, the score will be 0.75)
ChEMBL (Clinical trials phase binned score. Scores will be 0.09 for phase 0, 0.1 for phase I, 0.2 for Phase II, 0.7 for Phase III, and 1 for Phase IV drugs)
Reactome (functional consequence of 1 for a pathway inferred by a curator). SLAPenrich evidence is scored according to Iorio F et al followed by quantifying, in large cohorts of cancer patients, the divergence of the total number of samples with genomic alterations in a Reactome-pathway from its expectation, accounting for mutational burdens and total exonic block lengths of genes in that pathway.
Expression Atlas score (normalised p-value, normalised expression fold change and normalised percentile rank)
Europe PMC (weighting document sections, sentence locations and title for full text articles and abstracts (Kafkas et al 2016))
Once we have the scores for each evidence, we calculate an overall score for a data type (e.g. Genetic associations). In this step, we take into account that although multiple occurrences of evidence can suggest a strong association, the inclusion of further new evidence should not have a great impact on the overall score. For this reason, we calculate the sum of the harmonic progression of each score and adjust the contribution of each of them using a heuristic weighting. Throughout this process, the value of the score is always capped at 1, the most confident association.
The current scoring framework is a modified version of the original one described in "Open Targets: a platform for therapeutic target identification and validation". We now compute the direct relationships between targets and diseases taking into account a sigmoid scaling on the number of expression studies (for RNA Expression) and PubMed IDs (for Text mining) to remove additional spurious relationships.
We will continue to explore and work on alternative statistical models to keep providing robust scoring systems for target-disease associations. For further discussion, please email our Support team.