Deep-learning prediction of gene expression from personal genomes

Abstract

Models that predict RNA levels from DNA sequences show tremendous promise for decoding tissue-specific gene regulatory mechanisms, revealing the genetic architecture of traits, and interpreting noncoding genetic variation. Existing methods take two different approaches: 1) associating expression with linear combinations of common genetic variants (training across individuals on single genes), or 2) learning genome-wide sequence-to-expression rules with neural networks (training across loci using a reference genome). Since limitations of both strategies have been highlighted recently, we sought to combine the sequence context provided by deep learning with the information provided by cross-individual training. We utilized fine-tuning to develop Performer, a model with accuracy approaching the cis-heritability of most genes. Performer prioritizes genetic variants across the allele frequency spectrum that disrupt motifs, fall in annotated regulatory elements, and have functional evidence for modulating gene expression. While obstacles remain in personalized expression prediction, our findings establish deep learning as a viable strategy.