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

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