Integrating Mendelian randomization and literature-mined evidence for breast cancer risk factors

Abstract

AbstractAn 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.