2 posts tagged with "knowledge graph"

Genome-wide association studies have mapped thousands of genetic associations, but interpreting what those associations mean biologically remains a central challenge. In a new medRxiv preprint, Andrew Elmore, Aimee Hanson, Genevieve Leyden and colleagues introduce the Human Genotype-Phenotype Map (GPMap), an open resource for tracing shared genetic signals across complex traits and molecular measurements.

Figure: GPMap processing pipeline, from GWAS summary statistics through imputation, fine-mapping, colocalisation and views by trait, gene, variant and tissue. Source: Elmore et al., medRxiv, 2026, Fig. 1 (CC BY-ND 4.0).

Breast cancer research spans epidemiology, molecular biology, clinical trials, and a vast and rapidly growing literature. One challenge is triangulating across these evidence types: when different sources point in the same direction, we can be more confident we are seeing something causal rather than correlational.

In a paper led by Marina Vabistsevits published in the Journal of Biomedical Informatics, we show how to bring two complementary sources together:

1. Mendelian randomization (MR) evidence generated at scale using MR-EvE (“Everything-vs-Everything”), and
2. Literature-mined relationships stored in EpiGraphDB, our biomedical knowledge graph.