Jin Yutong, Jiang Jiahui, Wang Ruixuan, Qin Zhaohui S
Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, United States.
College of Environmental Sciences and Engineering, Peking University, Beijing, China.
Front Genet. 2021 Sep 9;12:667866. doi: 10.3389/fgene.2021.667866. eCollection 2021.
The majority of the single nucleotide variants (SNVs) identified by genome-wide association studies (GWAS) fall outside of the protein-coding regions. Elucidating the functional implications of these variants has been a major challenge. A possible mechanism for functional non-coding variants is that they disrupted the canonical transcription factor (TF) binding sites that affect the binding of the TF. However, their impact varies since many positions within a TF binding motif are not well conserved. Therefore, simply annotating all variants located in putative TF binding sites may overestimate the functional impact of these SNVs. We conducted a comprehensive survey to study the effect of SNVs on the TF binding affinity. A sequence-based machine learning method was used to estimate the change in binding affinity for each SNV located inside a putative motif site. From the results obtained on 18 TF binding motifs, we found that there is a substantial variation in terms of a SNV's impact on TF binding affinity. We found that only about 20% of SNVs located inside putative TF binding sites would likely to have significant impact on the TF-DNA binding.
全基因组关联研究（GWAS）识别出的大多数单核苷酸变异（SNV）位于蛋白质编码区域之外。阐明这些变异的功能影响一直是一项重大挑战。功能性非编码变异的一种可能机制是它们破坏了影响转录因子（TF）结合的典型转录因子（TF）结合位点。然而，由于TF结合基序内的许多位置保守性不佳，其影响各不相同。因此，简单地注释位于假定TF结合位点的所有变异可能会高估这些SNV的功能影响。我们进行了一项全面调查，以研究SNV对TF结合亲和力的影响。使用基于序列的机器学习方法来估计位于假定基序位点内的每个SNV的结合亲和力变化。从在18个TF结合基序上获得的结果来看，我们发现SNV对TF结合亲和力的影响存在很大差异。我们发现，位于假定TF结合位点内的SNV中，只有约20%可能会对TF-DNA结合产生显著影响。
