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

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. ### Competing Interest Statement T.R.G receives funding from Biogen for unrelated research. ### Funding Statement M.V. is supported by the University of Bristol Alumni Fund (Professor Sir Eric Thomas Scholarship). T.R. is supported by NIHR Development and Skills Enhancement Award (NIHR 302363) and has received grants to attend educational workshops from Daiichi-Sankyo and Amgen. M.V., T.R., Y.L., T.R.G, work in the Medical Research Council Integrative Epidemiology Unit at the University of Bristol supported by the Medical Research Council (MC\_UU\_00011/4). This work was also supported by a Cancer Research UK programme grant (the Integrative Cancer Epidemiology Programme) (C18281/A29019). This study was also supported by the NIHR Biomedical Research Centre at University Hospitals Bristol NHS Foundation Trust and the University of Bristol. The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study used only openly available human data. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All data produced in the present work are contained in the manuscript or publicly available. The MR-EvE estimates and literature-mined relationships used in this work are available from EpiGraphDB (). The GWAS data used to represent literature-identified intermediates was taken from OpenGWAS (). BCAC 2020 molecular subtype data is available at