Training deep learning models on personalized genomic sequences improves variant effect prediction
Year: 2024
Type: preprint
Abstract: Sequence-to-function models have broad applications in interpreting the molecular impact of genetic variation, yet have been criticized for poor performance in this task. Here we show that training models on functional genomic data with matched personal genomes improves their performance at variant effect prediction. Variant effect representations are retained even when transfer learning models to unseen cellular contexts and experimental readouts. Our results have implications for interpreting trait-associated genetic variation.
Source: Research Square
Authors: Charles Danko, Adam He, Nathan Palamuttam
Institution: Cornell University
Language: English
Topic: Genomics and Rare Diseases
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W4403745771