Systematic analysis of binding of transcription factors to noncoding variants
- 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.
- Consortium data used in this publication
- Sequencing data generated in this study can be accessed via Gene Expression Omnibus (GEO) under accession number GSE118725. The raw sequencing data for transcription factor ChIP–seq of GM12878 is extracted from the ENCODE portal (https://www.encodeproject.org). The specific transcription factor data can be accessed by searching the accession numbers listed in Supplementary Table 4. The web portal (http://renlab.sdsc.edu/GVATdb/) provides a searchable interface for SNPs and transcription factors tested in the current study. Enriched motifs for SNP-SELEX experiments using Homer are presented in Supplementary File 1. Scores for all tested SNP–transcription factor pairs from SNP-SELEX experiments are shown in Supplementary File 2. The data for high-confidence allelic binding of 94 transcription factors to all common SNPs in the human genome predicted by deltaSVM models are presented in Supplementary File 3.
- DSR976GBT, DSR942HZR, DSR594DXF, DSR090FJR, DSR661CGV, DSR894OMX, DSR595NST, DSR121DOQ, DSR458GVO, DSR107WVY, DSR353BOX, DSR297LBX, DSR737SFI, DSR066MCS, DSR537YLU, DSR916CWH, DSR715HEW, DSR434KET, DSR801DSZ, DSR744JJN, DSR886NPX, DSR795VYC, DSR763ZRB, DSR855KJU, DSR135GLZ, DSR404VKE, DSR557VDM, DSR009OCX, DSR697ONB, DSR651JFK, DSR239JDC, DSR220WQU, DSR573CEH, DSR734ORP, DSR460KEW, DSR246XUG, DSR433YZO