Low-dimensional genotype embeddings for predictive models

Abstract

We develop methods for constructing low-dimensional vector representations (embeddings) of large-scale genotyping data, capable of reducing genotypes of hundreds of thousands of SNPs to 100-dimensional embeddings that retain substantial predictive power for inferring medical phenotypes. We demonstrate that embedding-based models yield an average F-score of 0.605 on a test of ten phenoypes (including BMI prediction, genetic relatedness, and depression) versus 0.339 for baseline models. Genotype embeddings also hold promise for creating sharing data while preserving subject anonymity: we show that they retain substantial predictive power even after anonymization by adding Gaussian noise to each dimension.