Interpreting artificial neural networks to detect genome-wide association signals for complex traits

TL;DR

This study employs artificial neural networks to identify genomic loci linked to complex traits, overcoming limitations of traditional GWAS by capturing nonlinear and interactive effects, and validating findings with real and simulated data.

Contribution

The paper introduces a novel neural network-based approach for detecting associated loci in GWAS, including methods for interpretability and p-value estimation, improving detection accuracy.

Findings

Neural networks can identify associated loci with high precision.

Application to schizophrenia data revealed loci consistent with known biology.

Enrichment analyses linked identified loci to brain-related functions.

Abstract

Investigating the genetic architecture of complex diseases is challenging due to the multifactorial and interactive landscape of genomic and environmental influences. Although genome-wide association studies (GWAS) have identified thousands of variants for multiple complex traits, conventional statistical approaches can be limited by simplified assumptions such as linearity and lack of epistasis in models. In this work, we trained artificial neural networks to predict complex traits using both simulated and real genotype-phenotype datasets. We extracted feature importance scores via different post hoc interpretability methods to identify potentially associated loci (PAL) for the target phenotype and devised an approach for obtaining p-values for the detected PAL. Simulations with various parameters demonstrated that associated loci can be detected with good precision using strict selection criteria. By applying our approach to the schizophrenia cohort in the Estonian Biobank, we detected multiple loci associated with this highly polygenic and heritable disorder. There was significant concordance between PAL and loci previously associated with schizophrenia and bipolar disorder, with enrichment analyses of genes within the identified PAL predominantly highlighting terms related to brain morphology and function. With advancements in model optimization and uncertainty quantification, artificial neural networks have the potential to enhance the identification of genomic loci associated with complex diseases, offering a more comprehensive approach for GWAS and serving as initial screening tools for subsequent functional studies.