Integration of multiple epigenomic marks improves prediction of variant impact in saturation mutagenesis reporter assay. Issue 9 (23rd June 2019)

• Record Type: Journal Article
• Authors: Shigaki, Dustin; Adato, Orit; Adhikari, Aashish N.; Dong, Shengcheng; Hawkins‐Hooker, Alex; Inoue, Fumitaka; Juven‐Gershon, Tamar; Kenlay, Henry; Martin, Beth; Patra, Ayoti; Penzar, Dmitry D.; Schubach, Max; Xiong, Chenling; Yan, Zhongxia; Boyle, Alan P.; Kreimer, Anat; Kulakovskiy, Ivan V.; Reid, John; Unger, Ron; Yosef, Nir; Shendure, Jay; Ahituv, Nadav; Kircher, Martin; Beer, Michael A.
• Editors: Moult, John; Brenner, Steven E.
• Other Names: Karchin Rachel guestEditor.; Pal Lipika R. specialEditor.
• Is Part Of: Human mutation. Volume 40:Issue 9(2019)
• Page Start: 1280
• Page End: 1291
• Publication Date: 2019-06-23
• Subjects: enhancers -- gene regulation -- machine learning -- MPRA -- promoters -- regulatory variation; Human chromosome abnormalities -- Periodicals; Mutation (Biology) -- Periodicals; 616.04205
• DOI: 10.1002/humu.23797
• Languages: English
• ISSNs: 1059-7794

Abstract

Abstract: The integrative analysis of high‐throughput reporter assays, machine learning, and profiles of epigenomic chromatin state in a broad array of cells and tissues has the potential to significantly improve our understanding of noncoding regulatory element function and its contribution to human disease. Here, we report results from the CAGI 5 regulation saturation challenge where participants were asked to predict the impact of nucleotide substitution at every base pair within five disease‐associated human enhancers and nine disease‐associated promoters. A library of mutations covering all bases was generated by saturation mutagenesis and altered activity was assessed in a massively parallel reporter assay (MPRA) in relevant cell lines. Reporter expression was measured relative to plasmid DNA to determine the impact of variants. The challenge was to predict the functional effects of variants on reporter expression. Comparative analysis of the full range of submitted prediction results identifies the most successful models of transcription factor binding sites, machine learning algorithms, and ways to choose among or incorporate diverse datatypes and cell‐types for training computational models. These results have the potential to improve the design of future studies on more diverse sets of regulatory elements and aid the interpretation of disease‐associated genetic variation.