Identifying the causal genes at cancer risk loci

Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with cancers and other complex diseases. However, most risk variants lie in non-coding regions of the genome, making it challenging to determine which genes they regulate and how they influence disease biology.

Our laboratory combines human genetics with functional genomics to uncover the molecular mechanisms underlying genetic risk. We integrate large-scale sequencing datasets (e.g. single-cell chromatin accessibility, transcriptomics, chromatin interaction data) with statistical and computational approaches to move from variant
association to biological insight. The ultimate goal is to identify the genes, cell types, and pathways that mediate disease risk and may serve as therapeutic targets.

Depending on level and background, student projects may involve:

• Primary analysis of high-throughput functional genomics data (e.g. RNA-seq, ATAC-seq, single-cell multi-omics)

• Development and application of statistical and machine learning models to link genetic variants to regulatory function

• Integration of genetic association data with multi-omic datasets to predict target genes at GWAS loci

• Pathway and network analysis to identify biologically actionable mechanisms

• Identification of candidate genes and pathways for therapeutic targeting or drug repositioning.
  

Students will gain training in:

• Statistical genetics and regulatory genomics

• Reproducible bioinformatics workflows (R/Bioconductor/Python)
• Machine learning approaches for biological data
• Interpretation of large-scale genomic datasets
• Collaborative research across computational and experimental teams.

Projects are embedded within an interdisciplinary environment, collaborating with both computational and wet-lab scientists. Findings from these studies directly inform downstream experimental validation and may contribute to identifying novel
cancer genes and therapeutic strategies.

This project would suit a student with a background in bioinformatics, statistics, or genetics, with an interest in gene regulation and disease biology. Students would work closely with dry and wet lab scientists to identify cancer genes and pathways, which might represent targets for future drug development.