Systematic analysis of binding of transcription factors to noncoding variants

Jian Yan (), Yunjiang Qiu, André M. Ribeiro dos Santos, Yimeng Yin, Yang E. Li, Nick Vinckier, Naoki Nariai, Paola Benaglio, Anugraha Raman, Xiaoyu Li, Shicai Fan, Joshua Chiou, Fulin Chen, Kelly A. Frazer, Kyle J. Gaulton, Maike Sander, Jussi Taipale () and Bing Ren ()
Additional contact information
Jian Yan: Northwest University
Yunjiang Qiu: Ludwig Institute for Cancer Research
André M. Ribeiro dos Santos: Ludwig Institute for Cancer Research
Yimeng Yin: Karolinska Institutet
Yang E. Li: Ludwig Institute for Cancer Research
Nick Vinckier: University of California San Diego
Naoki Nariai: University of California San Diego
Paola Benaglio: University of California San Diego
Anugraha Raman: Ludwig Institute for Cancer Research
Xiaoyu Li: Northwest University
Shicai Fan: University of California San Diego
Joshua Chiou: University of California San Diego
Fulin Chen: Northwest University
Kelly A. Frazer: University of California San Diego
Kyle J. Gaulton: University of California San Diego
Maike Sander: University of California San Diego
Jussi Taipale: Karolinska Institutet
Bing Ren: Ludwig Institute for Cancer Research

Nature, 2021, vol. 591, issue 7848, 147-151

Abstract: Abstract Many sequence variants have been linked to complex human traits and diseases1, but deciphering their biological functions remains challenging, as most of them reside in noncoding DNA. Here we have systematically assessed the binding of 270 human transcription factors to 95,886 noncoding variants in the human genome using an ultra-high-throughput multiplex protein–DNA binding assay, termed single-nucleotide polymorphism evaluation by systematic evolution of ligands by exponential enrichment (SNP-SELEX). The resulting 828 million measurements of transcription factor–DNA interactions enable estimation of the relative affinity of these transcription factors to each variant in vitro and evaluation of the current methods to predict the effects of noncoding variants on transcription factor binding. We show that the position weight matrices of most transcription factors lack sufficient predictive power, whereas the support vector machine combined with the gapped k-mer representation show much improved performance, when assessed on results from independent SNP-SELEX experiments involving a new set of 61,020 sequence variants. We report highly predictive models for 94 human transcription factors and demonstrate their utility in genome-wide association studies and understanding of the molecular pathways involved in diverse human traits and diseases.

Date: 2021
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DOI: 10.1038/s41586-021-03211-0

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