MRSL: A phenome-wide causal discovery algorithm based on GWAS summary data

Abstract

Causal discovery is a powerful tool to disclose underlying structures by analyzing purely observational data. Genetic variants can provide useful complementary information for structure learning. Here, we propose a novel algorithm MRSL (Mendelian Randomization (MR)-based Structure Learning algorithm), which combines the graph theory with univariable and multivariable MR to learn the true structure using only GWAS summary statistics. Specifically, MRSL also utilizes topological sorting to improve the precision of structure learning and provides three adjusting categories for multivariable MR. Results of simulation reveal that MRSL has up to two-fold higher F1 score than other eight competitive methods. Additionally, the computing time of MRSL is 100 times faster than other methods. Furthermore, we apply MRSL to 26 biomarkers and 44 ICD10-defined diseases from UK Biobank. The results cover most of expected causal links which have biological interpretations and several new links supported by clinical case reports or previous observational literatures.

Artificial intelligence for dementia genetics and omics

“…131 Another study introduced the MR-based Structure Learning (MRSL) algorithm, which used graph theory combined with multivariable MR to uncover causal and mediating pathways between 44 diseases and 26 biomarkers using publicly available GWAS summary statistics. 132 Together, these results highlight the potential benefits of utilizing ML-based multivariate approaches to model the genetics underlying inter-correlated risk factor traits when performing causal analyses in dementia research.…”

“…Recently, a multivariate GWAS was performed using random forest regression to predict causal SNPs for 56 neuroimaging phenotypes, which identified the APOE SNP rs429358 as the top locus as well as additional lead SNPs that mapped to genes relevant to brain disorders, which were not identified by traditional linear regression methods 131 . Another study introduced the MR‐based Structure Learning (MRSL) algorithm, which used graph theory combined with multivariable MR to uncover causal and mediating pathways between 44 diseases and 26 biomarkers using publicly available GWAS summary statistics 132 . Together, these results highlight the potential benefits of utilizing ML‐based multivariate approaches to model the genetics underlying inter‐correlated risk factor traits when performing causal analyses in dementia research.…”