• DOI:10.1101/173682
• Corpus ID: 8865889

Automating Mendelian randomization through machine learning to construct a putative causal map of the human phenome

@article{Hemani2017AutomatingMR,
  title={Automating Mendelian randomization through machine learning to construct a putative causal map of the human phenome},
  author={Gibran Hemani and Jack Bowden and Philip C. Haycock and Jie Zheng and Oliver S. P. Davis and Peter A. Flach and Tom R. Gaunt and George Davey Smith},
  journal={bioRxiv},
  year={2017}
}

• G. Hemani, J. Bowden, +5 authors G. Smith
• Published 10 August 2017
• Biology
• bioRxiv

A major application for genome-wide association studies (GWAS) has been the emerging field of causal inference using Mendelian randomization (MR), where the causal effect between a pair of traits can be estimated using only summary level data. MR depends on SNPs exhibiting vertical pleiotropy, where the SNP influences an outcome phenotype only through an exposure phenotype. Issues arise when this assumption is violated due to SNPs exhibiting horizontal pleiotropy. We demonstrate that across a range of pleiotropy models, instrument selection will be increasingly liable to selecting invalid instruments as GWAS sample sizes continue to grow. Methods have been developed in an attempt to protect MR from different patterns of horizontal pleiotropy, and here we have designed a mixture-of-experts machine learning framework (MR-MoE 1.0) that predicts the most appropriate model to use for any specific causal analysis, improving on both power and false discovery rates. Using the approach, we systematically estimated the causal effects amongst 2407 phenotypes. Almost 90% of causal estimates indicated some level of horizontal pleiotropy. The causal estimates are organised into a publicly available graph database (http://eve.mrbase.org), and we use it here to highlight the numerous challenges that remain in automated causal inference.