PopInf

PopInf infers and visualizes genetic ancestry of genomic samples by projecting genotypes onto a reference population panel using principal components analysis to enable population assignment and comparison with self-reported race.

Key Features:

• Ancestry Assignment: Assigns population affiliation to genomic samples by projecting genotypes onto a reference population panel, enabling inference when ancestry metadata are missing and comparison to self-reported race.
• Visualization of Principal Components Analysis (PCA): Visualizes PCA outputs to facilitate interpretation of genetic ancestry, sample clustering, and population structure.
• Reproducibility via Snakemake: Implements the workflow in Snakemake to ensure consistent, reproducible computational execution.
• Integration with Cancer Genetics Studies: Includes a pre-processed reference population panel and has been applied to The Cancer Genome Atlas (TCGA) liver cancer data to highlight discrepancies between reported race and inferred genetic ancestry.

Scientific Applications:

• Oncology: Enables ancestry-aware analyses in cancer genetics and identification of mismatches between reported race and genetic ancestry in tumor cohorts.
• Epidemiology: Supports epidemiological studies that require accurate population stratification to control confounding in disease risk analyses.
• Population Genetics: Facilitates investigation of population structure and comparative analyses across reference populations using PCA projections.

Methodology:

Principal components analysis is used to project sample genotypes onto a reference population panel; the pipeline is implemented as a Snakemake workflow and includes a pre-processed reference population panel.