Assessing the functional impact of genetic variants across the human genome is essential for understanding the molecular mechanisms underlying complex traits and disease risk. Genetic variation that causes changes in gene expression can be analyzed through parallel genotyping and functional genomics assays across sets of individuals. In particular, regulatory variants may impact transcription factor regulation.
In this thesis, to map variants that impact the expression of many genes simultaneously through a shared transcription factor (TF), we use an approach in which the protein-level regulatory activity of the TF is inferred from genome-wide expression data and then genetically mapped as a quantitative trait. In Chapter 2, we developed a generalized linear model (GLM) to estimate TF activity levels in an individual-specific manner, and used it to analyze RNA-seq profiles from the Genotype-Tissue Expression (GTEx) project. A key feature is that we fit a beta-binomial GLM at the level of pairs of neighboring genes in order to control for variation in local chromatin structure along the genome and other confounding effects.
As a predictor in our model, we use differential gene expression signatures from TF perturbation experiments. After estimating genotype-specific activities for 55 TFs across 49 tissues, in Chapter 3, we performed genome-wide association analysis on the virtual TF activity trait. This revealed hundreds of TF activity quantitative trait loci, or aQTLs, highlighting the potential of genetic association studies for cellular endophenotypes based on a network-based multi-omic approach.
Lastly, in Chapter 4, we studied the direct impact of genetic variants on TF binding by predicting genetic effects on TF binding affinity. Specifically, we predicted binding affinity on allele-specific binding data using TF binding models derived by the ProBound recently developed by our laboratory, and constructed a likelihood model to assess the performances across different binding models. This indicates that ProBound provides a promising tool for the prediction of genetic effects on in vivo TF binding.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/55t3-5d49 |
| Date | January 2023 |
| Creators | Li, Xiaoting |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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