GenoPred

GenoPred implements a Snakemake-based workflow for polygenic scoring that standardizes the process using reference genetic data to generate interpretable polygenic scores and improve transferability to external datasets.

Key Features:

• Reference-standardized framework: Standardizes polygenic scoring using reference genetic data to provide interpretable polygenic scores and improve transferability to external datasets.
• Genotype and GWAS quality control: Performs quality control on genotype data and GWAS summary statistics as part of the comprehensive analysis workflow.
• Ancestry inference: Infers target sample ancestry using reference data, facilitating application to diverse populations within the reference.
• Polygenic score file generation: Generates polygenic score files using a range of leading methods from GWAS summary statistics.
• Target sample scoring: Computes polygenic scores in target samples using the generated score files.
• Reproducible execution: Uses Snakemake with associated Conda software environments to ensure reproducibility.
• Population diversity support: Applicable to GWAS and target data from any population within the reference, facilitating studies of diverse ancestry.

Scientific Applications:

• Disease risk estimation: Estimates an individual's likelihood of a given outcome using genetic effects derived from GWAS summary statistics and individual-level genotype data.
• Cross-population genetic studies: Enables polygenic scoring in ancestrally diverse populations by using a reference-standardized framework applicable to any population within the reference.
• External dataset validation: Improves transferability of polygenic score results to external datasets through reference standardization.
• Large-scale biobank analyses: Demonstrated application to UK Biobank data for efficient, high-performance polygenic scoring at scale.

Methodology:

GenoPred is implemented as a Snakemake pipeline with associated Conda software environments. The workflow requires genotype data and GWAS summary statistics as inputs, performs genotype and GWAS quality control, infers target sample ancestry against reference data, generates polygenic score files using leading methods, and computes target sample scores.