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

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Research in Computational Molecular Biology (RECOMB 2017)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 10229))

Abstract

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 in vitro 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 (z-score) and a significance value (p-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.

J. Zhao and D. Li—These authors contributed equally to this work.

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Acknowledgements

This research was supported in part by awards number P01CA142538 from the National Cancer Institute, and R01GM117106 from the National Institute of General Medical Sciences (to RG). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Health.

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Authors and Affiliations

  1. Center for Genomic and Computational Biology, Duke University, Durham, North Carolina, 27708, USA

    Jingkang Zhao, Andrew S. Allen & Raluca Gordân

  2. Program in Computational Biology and Bioinformatics, Duke University, Durham, North Carolina, 27708, USA

    Jingkang Zhao & Jungkyun Seo

  3. Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, 27708, USA

    Dongshunyi Li, Andrew S. Allen & Raluca Gordân

  4. Department of Computer Science, Duke University, Durham, North Carolina, 27708, USA

    Raluca Gordân

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  1. Jingkang Zhao
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  2. Dongshunyi Li
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Correspondence to Raluca Gordân .

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  1. Indiana University Bloomington, Bloomington, Indiana, USA

    S. Cenk Sahinalp

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Zhao, J., Li, D., Seo, J., Allen, A.S., Gordân, R. (2017). Quantifying the Impact of Non-coding Variants on Transcription Factor-DNA Binding. In: Sahinalp, S. (eds) Research in Computational Molecular Biology. RECOMB 2017. Lecture Notes in Computer Science(), vol 10229. Springer, Cham. https://doi.org/10.1007/978-3-319-56970-3_21

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  • DOI: https://doi.org/10.1007/978-3-319-56970-3_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-56969-7

  • Online ISBN: 978-3-319-56970-3

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