Prediction of disease‐associated functional variants in noncoding regions through a comprehensive analysis by integrating datasets and features. Issue 6 (23rd April 2021)

• Record Type: Journal Article
• Authors: Lu, Yu; Wu, Yiming; Liu, Yuan; Li, Yizhou; Jing, Runyu; Li, Menglong
• Is Part Of: Human mutation. Volume 42:Issue 6(2021)
• Page Start: 667
• Page End: 684
• Publication Date: 2021-04-23
• Subjects: complex diseases -- feature importance -- functional variants -- noncoding regions -- random forest; Human chromosome abnormalities -- Periodicals; Mutation (Biology) -- Periodicals; 616.04205
• DOI: 10.1002/humu.24203
• Languages: English
• ISSNs: 1059-7794

Abstract

Abstract: One of the greatest challenges in human genetics is deciphering the link between functional variants in noncoding sequences and the pathophysiology of complex diseases. To address this issue, many methods have been developed to sort functional single‐nucleotide variants (SNVs) for neutral SNVs in noncoding regions. In this study, we integrated well‐established features and commonly used datasets and merged them into large‐scale datasets based on a random forest model, which yielded promising performance and outperformed some cutting‐edge approaches. Our analyses of feature importance and data coverage also provide certain clues for future research in enhancing the prediction of functional noncoding SNVs. Abstract : To explore the relationship between disease‐related functional variants in non‐coding regions and the pathophysiology of complex diseases, researchers in this field were working on developing a tool for precisely evaluating and prioritizing the impact of variants in non‐coding regions. In this study, we presented a novel supervised learning method that performed a comprehensive analysis of all five groups of features and three datasets. The prediction results showed that this study achieved a comparable prediction performance in comparison with seven cutting‐edge approaches in detecting functional non‐coding variants.