# Deep-Neural-Network-Aided Genetic Association Testing in Samples with Related Individuals

## TL;DR

This paper introduces a deep learning method to improve genetic association studies, especially when individuals in the sample are related.

## Contribution

A novel deep neural network-based approach for genetic association testing in related individuals is proposed.

## Key findings

- Simulation studies show increased power for detecting genetic associations using the proposed method.
- The method complements conventional statistical approaches and improves predictive performance.
- Application to Framingham Heart Study data identifies SNPs associated with systolic blood pressure.

## Abstract

Genome-wide association studies (GWAS) have successfully identified thousands of genetic loci associated with complex traits and diseases, providing critical insights into genetic architecture, biological pathways, and disease mechanisms. With the advance of machine learning, the analytical scope of GWAS can be substantially expanded by enabling joint modeling, nonlinear effects, and integrative analysis. However, deep learning approaches remain underutilized in augmenting traditional GWAS frameworks, particularly in the presence of cryptic relatedness among sampled individuals. In this paper, we propose a deep neural network (DNN)-based machine learning method to assist genetic association testing in samples with related individuals. By approximating the phenotype–genotype relationships in classical association tests and combining approximations across multiple tests, the proposed method aims to improve predictive performance in the identification of associated variants. Simulation studies demonstrate that our approach effectively complements conventional statistical methods and generally achieves increased power for detecting genetic associations. We further apply the method to data from the Framingham Heart Study, illustrating how DNN-based machine learning can facilitate the identification of genome-wide SNPs associated with average systolic blood pressure.