Research Question: What inherited genetic factors explain intergenerational differences in the age of onset of familial women’s cancers, as observed in large-scale population cohorts?
Sub-questions: Which categories of germline variants are associated with earlier onset across generations?
Do families exhibiting earlier onset in daughters versus mothers carry higher cumulative genetic risk burdens or distinct variant
combinations within known cancer susceptibility loci?
How can integration of genetic data with electronic medical records (EMRs) improve modeling of intergenerational onset differences?
Objectives: Analyze whole-exome and genotyping data with documented familial women’s cancers to detect variants correlated with earlier onset in first-degree relatives.
Link germline variant profiles with structured EMR data to quantify how genetic factors modulate age-of-onset distribution.
Construct statistical and machine-learning models incorporating variant load, polygenic risk scores, and EMR variables to explain and
predict onset differences within families.
Replicate key associations in external cohorts derived from UK Biobank’s cross-ancestry subsets and related international biobanks where family history and EMR data are available.
Scientific Rationale:
Familial women’s cancers demonstrate intergenerational variability in age of onset, even among carriers of the same high-risk mutations. This project will systematically test whether additive or
interactive effects of germline variation can explain this variability when analyzed alongside longitudinal EMR data. Leveraging the scale and depth of the UK Biobank enables high-resolution mapping of these
patterns, linking genetic architecture to clinical trajectory.
Understanding these genetic and EMR-linked determinants will refine risk-prediction models, personalize surveillance timing for at-risk families, and enhance counseling for carriers of known and novel susceptibility variants.
