Investigating Causality Between Genotype And Clinical Phenotype In Neurological Disorders Using Structural Causal Model and Normalizing Flow

Published: 27 Oct 2023, Last Modified: 11 Nov 2023DGM4H NeurIPS 2023 PosterEveryoneRevisionsBibTeX
Keywords: Normalizing Flow, Structural Causal Model, Causal Inference, Alzheimer's Disease, Cancer
TL;DR: The paper leverages structural causal model with normalizing flows to investigate the causality between genotype and phenotype in Alzheimer's disease and glioblastoma.
Abstract: Understanding the causal relationship between genotype and clinical phenotype is crucial for disease treatment and prognosis. Despite the existing literature on exploring associations of genetics with clinical phenotypes such as imaging patterns and survival in various diseases, there are few to none work address the causation of these correlated genotypes. This paper leverages recent advances in causal deep learning to formulate the phenotypical outcome given the change in genotype as a causal inference problem. We build upon structural causal model (SCM) with normalizing flows parameterized by deep networks to perform the counterfactual query to investigate the causal relationship between genotype and clinical phenotype in two types of neurological disorders. Specifically, we focus on the causal effect of (1) APOE4 allele on brain volumetric measures in Alzheimer's disease; (2) key driver gene mutations on overall survival (OS) in glioblastoma. Experimental results show that APOE4 noncarriers causally lead to greater gray matter atrophy in the frontal lobe, and survival-correlated genes do not exhibit causal effect on OS in glioblastoma.
Submission Number: 44