Integrating information from data sources representing different study designs has the potential to strengthen evidence in population health research. However, this concept of evidence "triangulation" presents a number of challenges for systematically identifying and integrating relevant information. We present ASQ (Annotated Semantic Queries), a natural language query interface to the integrated biomedical entities and epidemiological evidence in EpiGraphDB, which enables users to extract "claims" from a piece of unstructured text, and then investigate the evidence that could either support, contradict the claims, or offer additional information to the query. This approach has the potential to support the rapid review of pre-prints, grant applications, conference abstracts and articles submitted for peer review. ASQ implements strategies to harmonize biomedical entities in different taxonomies and evidence from different sources, to facilitate evidence triangulation and interpretation. ASQ is openly available at https://asq.epigraphdb.org.

Motivation: Human traits are typically represented in both the biomedical literature and large population studies as descriptive text strings. Whilst a number of ontologies exist, none of these perfectly represent the entire human phenome and exposome. Mapping trait names across large datasets is therefore time-consuming and challenging. Recent developments in language modelling have created new methods for semantic representation of words and phrases, and these methods offer new opportunities to map human trait names in the form of words and short phrases, both to ontologies and to each other. Here we present a comparison between a range of established and more recent language modelling approaches for the task of mapping trait names from UK Biobank to the Experimental Factor Ontology (EFO), and also explore how they compare to each other in direct trait-to-trait mapping. Results: In our analyses of 1191 traits from UK Biobank with manual EFO mappings, the BioSentVec model performed best at predicting these, matching 40.3% of the manual mappings correctly. The BlueBERT-EFO model (finetuned on EFO) performed nearly as well (38.8% of traits matching the manual mapping). In contrast, Levenshtein edit distance only mapped 22% of traits correctly. Pairwise mapping of traits to each other demonstrated that many of the models can accurately group similar traits based on their semantic similarity. Availability and Implementation: Our code is available at https://github.com/MRCIEU/vectology.

An increasing challenge in population health research is efficiently utilising the wealth of data available from multiple sources to investigate the mechanisms of disease and identify potential intervention targets. The use of biomedical data integration platforms can facilitate evidence triangulation from these different sources, improving confidence in causal relationships of interest. In this work, we aimed to integrate Mendelian randomization (MR) and literature-mined evidence from the EpiGraphDB knowledge graph to build a comprehensive overview of risk factors for developing breast cancer. We utilised MR-EvE ("Everything-vs-Everything") data to generate a list of causal risk factors for breast cancer, integrated this data with literature-mined relationships and identified potential mediators. We used multivariable MR to evaluate mediation and estimate the direct effects of these traits. We identified 213 novel and established lifestyle and molecular traits with evidence of an effect on breast cancer. We present the results of this evidence integration for four case studies (insulin-like growth factor I, cardiotrophin-1, childhood body size and age at menopause). We demonstrate that using MR-EvE to identify disease risk factors is an efficient hypothesis-generating approach. Moreover, we show that integrating MR evidence with literature-mined data may identify causal intermediates and uncover the mechanisms behind disease.