Revealing the impact of sequence variants on transcription factor binding and gene expression

Transcription factors (TFs) can bind to specific regulatory regions to control the expression of target genes. Disruption of TF binding is regarded as one of the key mechanisms by which regulatory variants could act to cause disease. However predicting the functional impact of variants on TF binding remains a major challenge for the field, standing as a key obstacle to achieving the potential of clinical genome analysis. This thesis confronts this challenge from a bioinformatics perspective and addresses two unresolved problems. The first problem is the determination of which genetic variants alter TF binding. Only a small number of allele-specific binding (ASB) events, in which TFs preferentially bind to one of two alleles at heterozygous sites in the genome, have been determined. To study the impact of variants on TF binding, access to a large, gold standard collection of ASB events could facilitate the development of new predictive methods. In Chapter 2, we implemented a pipeline to identify ASB events from ChIP-seq data and applied it to produce one of the largest ASB datasets. We found that ASB events were associated with allelic alterations of TF motifs, chromatin accessibility and histone modifications. Using the available features, classifiers were trained to predict the impact of variants on TF binding. To improve ASB calling, Chapter 3 evaluated five statistical methods, ultimately supporting a method that pooled ChIP-seq replicates and utilized a binomial distribution to model allelic read counts. The second problem is to determine how altered TF binding events impact the expression of target genes. In Chapter 4, we implemented regression-based models to predict gene expression changes based on altered TF binding events across 358 individuals. The models showed predictive capacity for 19.2% of genes, and the key TF binding events in the model provided mechanistic insights as to how these regulatory variants alter gene expression. In summary, this thesis both generated the largest, high-quality collection of ASB events, and developed algorithms to predict variant impact on TF binding and gene expression. The presented work advances the capacity of the field to interpret regulatory variants and will facilitate future clinical genome analysis.Science, Faculty ofGraduat