Quantifying the Impact of Non-coding Variants on Transcription Factor-DNA Binding.

TitleQuantifying the Impact of Non-coding Variants on Transcription Factor-DNA Binding.
Publication TypeJournal Article
Year of Publication2017
AuthorsZhao, Jingkang, Dongshunyi Li, Jungkyun Seo, Andrew S. Allen, and Raluca Gordân
JournalRes Comput Mol Biol
Date Published2017 May

Many recent studies have emphasized the importance of genetic variants and mutations in cancer and other complex human diseases. The overwhelming majority of these variants occur in non-coding portions of the genome, where they can have a functional impact by disrupting regulatory interactions between transcription factors (TFs) and DNA. Here, we present a method for assessing the impact of non-coding mutations on TF-DNA interactions, based on regression models of DNA-binding specificity trained on high-throughput data. We use ordinary least squares (OLS) to estimate the parameters of the binding model for each TF, and we show that our predictions of TF-binding changes due to DNA mutations correlate well with measured changes in gene expression. In addition, by leveraging distributional results associated with OLS estimation, for each predicted change in TF binding we also compute a normalized score (-score) and a significance value (-value) reflecting our confidence that the mutation affects TF binding. We use this approach to analyze a large set of pathogenic non-coding variants, and we show that these variants lead to significant differences in TF binding between alleles, compared to a control set of common variants. Thus, our results indicate that there is a strong regulatory component to the pathogenic non-coding variants identified thus far.

Alternate JournalRes Comput Mol Biol
Original PublicationQuantifying the impact of non-coding variants on transcription factor-DNA binding.
PubMed ID28691125
PubMed Central IDPMC5501730
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
R01 GM117106 / GM / NIGMS NIH HHS / United States