A Consensus-Based Machine Learning Framework to Determine Genetically Inferred Ancestry (GIA) from Comprehensive Genomic Profiling (CGP) Sequencing Results

This study developed and validated a workflow for accurately determining the genetically inferred ancestry (GIA) of patients from comprehensive genomic profiling (CGP) sequencing results, which can enable ancestry-aware biomarker research and contribute to reducing cancer disparities and improving representation in clinical trials.