Christopher Wray

My research addresses the "unexplained" genetic landscape of breast cancer, focusing on both inherited predisposition and the somatic evolution of high-risk tumors. Despite advances in sequencing, known alleles explain less than half of familial clustering. I develop and implement bioinformatics pipelines to identify the missing genetic defects that define high-risk individuals, which is a prerequisite for better monitoring and surgical decision-making.

1. Genetic Predisposition to Breast Cancer Using whole-exome sequencing (WES), I investigate rare pathogenic variants within the genetically isolated Northern Finnish population. This unique cohort increases the likelihood of identifying moderate-to-high risk alleles. My work involves processing NGS data to call point mutations and copy number variants, followed by rigorous in silico filtering and statistical validation against large-scale replication case and control cohorts.

2. Resolving the Structural Variant (SV) Landscape A major part of my work focuses on large-scale genomic structural variants in tumors, which are often invisible to classical cytogenetics and standard NGS. I use Optical Genome Mapping (OGM) to achieve high-coverage (300-500x) detection of complex rearrangements and clonality. By integrating OGM data with nucleotide-level information and mutational signature analysis, I aim to provide a complete view of tumor tumorigenesis and identify new diagnostic markers.

In practice, I manage these multi-omic workflows using R, Python, and Unix-based HPC environments. My goal is to translate these complex genomic findings into peer-reviewed research that improves risk assessment and targeted treatment strategies.