Systematic analysis of differential transcription factor binding to non-coding variants in the human genome
Activity: Talk/lecture or presentation › Talk/lecture
20 Feb 2019
|Degree of recognition||International|
DescriptionA large number of sequence variants have been linked to complex human traits and diseases, but deciphering their biological function remains a daunting challenge especially for the non-protein-coding variants. To fill this gap, we have systematically assessed the differential binding of transcription factors (TF) to different alleles of non-coding variants in the human genome. Using an ultra-high throughput multiplex protein-DNA binding assay, we examined the binding of 270 human TFs to 95,886 common sequence variants within the 110 type 2 diabetes (T2D) risk loci. We then employed a machine-learning approach to derive computational models to predict differential DNA binding of 124 TFs to other common non-coding variants in the human genome. We showed that the newly derived models outperformed current position-weight matrices (PWM) in describing TF binding to non-coding variants, and facilitated discovery of potential causal variants and dysregulated molecular pathways in human diseases.
Research Unit / Event Journal/Book Series
|Title||The Hong Kong Epigenomics Project Spring 2019 Symposium|
|Date||20/02/19 → …|
|Location||HKUST Tsang Shiu Tim Art Hall|
|Degree of recognition||International event|
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