Identifying genes associated with phenotypes using machine and deep learning

TL;DR

This paper presents a machine learning and deep learning pipeline that classifies individuals based on genotype data and identifies phenotype-associated genes, demonstrating high accuracy and potential for aiding disease research.

Contribution

The study introduces a novel ML/DL pipeline for gene identification that outperforms traditional methods in classifying phenotypes and prioritizing associated genes.

Findings

Mean gene identification ratio (GIR) of 0.84 across phenotypes

ML/DL-selected SNPs align with GWAS-identified SNPs

High classification performance metrics (AUC, F1, MCC)

Abstract

Identifying disease-associated genes enables the development of precision medicine and the understanding of biological processes. Genome-wide association studies (GWAS), gene expression data, biological pathway analysis, and protein network analysis are among the techniques used to identify causal genes. We propose a machine-learning (ML) and deep-learning (DL) pipeline to identify genes associated with a phenotype. The proposed pipeline consists of two interrelated processes. The first is classifying people into case/control based on the genotype data. The second is calculating feature importance to identify genes associated with a particular phenotype. We considered 30 phenotypes from the openSNP data for analysis, 21 ML algorithms, and 80 DL algorithms and variants. The best-performing ML and DL models, evaluated by the area under the curve (AUC), F1 score, and Matthews correlation coefficient (MCC), were used to identify important single-nucleotide polymorphisms (SNPs), and the identified SNPs were compared with the phenotype-associated SNPs from the GWAS Catalog. The mean per-phenotype gene identification ratio (GIR) was 0.84. These results suggest that SNPs selected by ML/DL algorithms that maximize classification performance can help prioritise phenotype-associated SNPs and genes, potentially supporting downstream studies aimed at understanding disease mechanisms and identifying candidate therapeutic targets.