Deep mendelian randomization: Investigating the causal knowledge of genomic deep learning models

Abstract

Multi-task deep learning (DL) models can accurately predict diverse genomic marks from sequence, but whether these models learn the causal relationships between genomic marks is unknown. Here, we describe Deep Mendelian Randomization (DeepMR), a method for estimating causal relationships between genomic marks learned by genomic DL models. By combining Mendelian randomization with in silico mutagenesis, DeepMR obtains local (locus specific) and global estimates of (an assumed) linear causal relationship between marks. In a simulation designed to test recovery of pairwise causal relations between transcription factors (TFs), DeepMR gives accurate and unbiased estimates of the ‘true’ global causal effect, but its coverage decays in the presence of sequence-dependent confounding. We then apply DeepMR to examine the global relationships learned by a state-of-the-art DL model, BPNet, between TFs involved in reprogramming. DeepMR’s causal effect estimates validate previously hypothesized relationships between TFs and suggest new relationships for future investigation.

Artificial intelligence for dementia genetics and omics

“…Particularly, DeepCOMBI has been shown to replicate known disease loci, as well as identify novel ones. DeepMR integrates ML with MR by using multi‐task DL models to initially learn the relationship between different sets of genomic marks (e.g., chromatin marks) associated with a pathway or phenotype of interest and then uses MR to examine causal relationships between them, 125 which could help to identify more functionally relevant SNPs for inclusion in the exposure instrumental variable.…”

“…Examples of artificial intelligence methods to potentially address current challenges in the study of dementia genetics and omics. by predictive accuracy and hampered by heritability Novel DL-based model that does not only rely on the addictive effect of risk SNPs, may outperform more traditional PRS models across a variety of disease phenotypes Causal inferences are often underpowered and limited in scope DeepMR125 approaches integrate ML with MR by using multi-task DL models to learn the relationship between different sets of genomic marks associated with a pathway or phenotype of interest and then uses MR to examine causal relationships between them…”

Unravelling the complexity of kidney renal clear cell carcinoma prognosis: integrating chromatin regulators, gene signatures and associated immune landscapes

“…Their primary function revolves around the precise modulation of chromatin architecture and status, exerting profound in uence within the cellular milieu. CRs exert authoritative control over a spectrum of biological processes, including but not limited to cell proliferation, differentiation, DNA repair, and stress response [11][12][13]. Remarkably, these regulatory entities emerge as central gures in the genesis and progression of kidney renal clear cell carcinoma, where their perturbations serve as recurrent hallmarks in various human maladies [14].…”