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

作者：Leah S. VandenBosch, Kelsey Luu, Andrew E. Timms, Shriya Challam, Yue Wu, Aaron Lee, Timothy J. Cherry · 发表于：Translational Vision Science & Technology · 年份：2022 · DOI：10.1167/tvst.11.4.16 · 被引用次数：14 · 研究领域：Retinal Development and Disorders、Genomics and Rare Diseases、Retinal Imaging and Analysis

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 potential single nucleotide variants within cis-regulatory elements. Impact scores were tested in a 20% hold-out dataset and compared to allele population frequency, phylogenetic conservation, transcription factor (TF) binding motifs, and existing massively parallel reporter assay data. Results: We generated a model that distinguishes between human retinal regulatory elements and negative test sequences with 95% accuracy. Among a hold-out test set of 3.7k human retinal CREs, all possible single nucleotide variants were scored. Variants with negative impact scores correlated with higher phylogenetic conservation of the reference allele, disruption of predicted TF binding motifs, and massively parallel repor...