TY - JOUR TI - The MR-Base platform supports systematic causal inference across the human phenome AU - Hemani, Gibran AU - Zheng, Jie AU - Elsworth, Benjamin AU - Wade, Kaitlin H AU - Haberland, Valeriia AU - Baird, Denis AU - Laurin, Charles AU - Burgess, Stephen AU - Bowden, Jack AU - Langdon, Ryan AU - Tan, Vanessa Y AU - Yarmolinsky, James AU - Shihab, Hashem A AU - Timpson, Nicholas J AU - Evans, David M AU - Relton, Caroline AU - Martin, Richard M AU - Davey Smith, George AU - Gaunt, Tom R AU - Haycock, Philip C A2 - Loos, Ruth VL - 7 PY - 2018 DA - 2018/05/30 SP - e34408 C1 - eLife 2018;7:e34408 DO - 10.7554/eLife.34408 UR - https://doi.org/10.7554/eLife.34408 AB - Results from genome-wide association studies (GWAS) can be used to infer causal relationships between phenotypes, using a strategy known as 2-sample Mendelian randomization (2SMR) and bypassing the need for individual-level data. However, 2SMR methods are evolving rapidly and GWAS results are often insufficiently curated, undermining efficient implementation of the approach. We therefore developed MR-Base (http://www.mrbase.org): a platform that integrates a curated database of complete GWAS results (no restrictions according to statistical significance) with an application programming interface, web app and R packages that automate 2SMR. The software includes several sensitivity analyses for assessing the impact of horizontal pleiotropy and other violations of assumptions. The database currently comprises 11 billion single nucleotide polymorphism-trait associations from 1673 GWAS and is updated on a regular basis. Integrating data with software ensures more rigorous application of hypothesis-driven analyses and allows millions of potential causal relationships to be efficiently evaluated in phenome-wide association studies. KW - causal inference KW - Mendelian randomization KW - GWAS JF - eLife SN - 2050-084X PB - eLife Sciences Publications, Ltd ER -