SNP2Vec: Scalable Self-Supervised Pre-Training for Genome-Wide Association Study

TL;DR

SNP2Vec is a scalable self-supervised pre-training method designed to understand genetic variations, specifically SNPs, improving genome-wide association studies by outperforming existing methods in predicting Alzheimer's disease risk.

Contribution

The paper introduces SNP2Vec, a novel self-supervised pre-training approach tailored for SNP understanding and long-sequence genomics modeling.

Findings

Outperforms existing polygenic risk score methods

Effective in predicting Alzheimer's disease risk

Trained on long genomic sequences

Abstract

Self-supervised pre-training methods have brought remarkable breakthroughs in the understanding of text, image, and speech. Recent developments in genomics has also adopted these pre-training methods for genome understanding. However, they focus only on understanding haploid sequences, which hinders their applicability towards understanding genetic variations, also known as single nucleotide polymorphisms (SNPs), which is crucial for genome-wide association study. In this paper, we introduce SNP2Vec, a scalable self-supervised pre-training approach for understanding SNP. We apply SNP2Vec to perform long-sequence genomics modeling, and we evaluate the effectiveness of our approach on predicting Alzheimer's disease risk in a Chinese cohort. Our approach significantly outperforms existing polygenic risk score methods and all other baselines, including the model that is trained entirely with haploid sequences. We release our code and dataset on https://github.com/HLTCHKUST/snp2vec.