Machine Learning Prediction of Non-Coding Variant Impact in Human Retinal cis-Regulatory Elements.

ABSTRACT:

Purpose

Prior studies have demonstrated the significance of specific cis-regulatory variants in retinal disease; however, determining the functional impact of regulatory variants remains a major challenge. In this study, we utilized a machine learning approach, trained on epigenomic data from the adult human retina, to systematically quantify the predicted impact of cis-regulatory variants.

Methods

We used human retinal DNA accessibility data (ATAC-seq) to determine a set of 18.9k high-confidence, putative cis-regulatory elements. Eighty percent of these elements were used to train a machine learning model utilizing a gapped k-mer support vector machine-based approach. In silico saturation mutagenesis and variant scoring was applied to predict the functional impact of all po

SUBMITTER: VandenBosch LS 

PROVIDER: S-EPMC9034719 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature