Genetic heterogeneity analysis using genetic algorithm and network
  science
Zhendong Sha Yuanzhu Chen Ting Hu
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.