Genetic heterogeneity analysis using genetic algorithm and network science

TL;DR

This paper presents FCSNet, a novel feature selection method combining genetic algorithms and network science to identify genetic heterogeneity and feature interactions in GWAS data, improving disease association detection.

Contribution

It introduces FCSNet, a new GWAS feature selection approach that leverages genetic algorithms and network analysis to detect heterogeneous genetic variables and interactions.

Findings

Effective identification of feature interactions demonstrated on synthetic data.

Successful application to colorectal cancer GWAS dataset.

Enhanced understanding of genetic heterogeneity in disease association.

Abstract

Through genome-wide association studies (GWAS), disease susceptible genetic variables can be identified by comparing the genetic data of individuals with and without a specific disease. However, the discovery of these associations poses a significant challenge due to genetic heterogeneity and feature interactions. Genetic variables intertwined with these effects often exhibit lower effect-size, and thus can be difficult to be detected using machine learning feature selection methods. To address these challenges, this paper introduces a novel feature selection mechanism for GWAS, named Feature Co-selection Network (FCSNet). FCS-Net is designed to extract heterogeneous subsets of genetic variables from a network constructed from multiple independent feature selection runs based on a genetic algorithm (GA), an evolutionary learning algorithm. We employ a non-linear machine learning algorithm to detect feature interaction. We introduce the Community Risk Score (CRS), a synthetic feature designed to quantify the collective disease association of each variable subset. Our experiment showcases the effectiveness of the utilized GA-based feature selection method in identifying feature interactions through synthetic data analysis. Furthermore, we apply our novel approach to a case-control colorectal cancer GWAS dataset. The resulting synthetic features are then used to explain the genetic heterogeneity in an additional case-only GWAS dataset.