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Bayesian Modeling and Network Analysis of Longevity-Relevant Genes

JSHS · 2022

Amy Dong, Dr. Gil Alterovitz · Hinsdale Central High School, Hinsdale, IL

Overview

School Improving human lifespans have always been an important goal for biomedical research. Many “longevity interventions”, such as dietary restrictions and medications, have been shown to expand lifespan, and reduce the onset of aging-related diseases. However, the genetic factors that influence longevity are largely unknown. Though previous studies have linked the effects of longevity interventions to differential gene expression signatures, there currently lacks a computational method to accurately identify true longevity-relevant genes from the large pool of differentially expressed genes. In this project, we developed a Bayesian method to identify potential longevity-relevant genes in a formal probabilistic framework, using a binomial distribution to model the number of times a gene is differentially expressed under multiple longevity interventions, and a beta distribution to model any prior knowledge that a gene is relevant to longevity. In addition, we performed a bipartite and unipartite network analysis to investigate the degree of similarity between different longevity interventions based on shared longevity-relevant genes. By applying our method to a large published mouse study consisting of gene expression data from 23 treatments, we computed the posterior probability distribution of each gene being longevity-relevant. Utilizing these posterior probabilities in our network analysis, we identified three main modules of interventions that affected similar genes, implying similar underlying molecular longevity mechanisms within each module. Besides gene expression data, our method can be applied to other genomic datasets (e.g., identifying genetic mutations relevant to longevity), and used to investigate the genetic factors for other phenotypes of interest (e.g., cancers).

Competition history

  • JSHS 2022 Category not listed

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Source: Junior Science and Humanities Symposium

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