Color Data

Color Data aggregates genotypic and phenotypic data to support research on genetic predisposition and genotype-phenotype correlations in hereditary cancer and cardiovascular conditions.

Key Features:

• Aggregated cohort: Genotypic data from over 68,000 individuals, comprising ~50,000 individuals sequenced for 30 hereditary cancer genes and >18,000 individuals with clinical genetic testing results for 30 hereditary cardiovascular genes.
• Polygenic risk scores: Inclusion of polygenic risk scores for breast cancer, coronary artery disease, and atrial fibrillation.
• Clinical risk models: Implementation of four clinical risk models: the Gail Model, the Claus Model, the simple office-based Framingham Coronary Heart Disease Risk Score, and the CHARGE-AF simple score.
• Phenotypic data: Use of self-reported phenotypic information to support risk model calculations and genotype-phenotype analyses.

Scientific Applications:

• Genotype-phenotype correlation: Correlating genetic variants with self-reported phenotypes and risk model outputs to investigate disease associations.
• Monogenic and polygenic risk assessment: Evaluating contributions of pathogenic variants and polygenic risk scores to breast cancer, coronary artery disease, and atrial fibrillation risk.
• Variant discovery and prioritization: Identifying and prioritizing novel genetic variants for downstream functional analysis.
• Population genomics: Enabling population-scale analyses of hereditary cancer and cardiovascular gene panels.
• Risk model evaluation: Providing datasets for assessment and comparison of clinical risk prediction models (Gail, Claus, Framingham CHD simple, CHARGE-AF simple).

Methodology:

Aggregation of sequencing and clinical genetic testing results for defined 30-gene panels, inclusion of polygenic risk scores for breast cancer, coronary artery disease, and atrial fibrillation, and implementation of the Gail Model, Claus Model, Framingham Coronary Heart Disease Risk Score (simple office-based), and CHARGE-AF simple score using self-reported phenotypic information.