RICOPILI

RICOPILI performs end-to-end processing of large-scale multi-cohort genome-wide association studies (GWAS), implementing quality control, genome-wide phasing and imputation, association and meta-analysis, polygenic risk scoring, and replication analysis to support genetic discovery and validation.

Key Features:

• Implementation: Perl-based pipeline for automated GWAS processing and management.
• Quality Control (QC): Performs technical and genomic QC on case-control and trio cohorts to ensure input data integrity.
• Genome-wide Phasing and Imputation: Facilitates genome-wide phasing and imputation to predict unobserved genotypes and increase variant resolution.
• Association Analysis: Conducts association analyses to identify genetic variants linked to complex traits.
• Meta-Analysis: Supports meta-analysis to combine results across multiple GWAS cohorts for increased statistical power.
• Polygenic Risk Scoring: Computes polygenic risk scores to quantify genetic predisposition to traits or diseases.
• Replication Analysis: Enables replication analyses for validating GWAS findings across independent cohorts.
• Automated Parallelization and Cluster Job Management: Uses automated parallelization and cluster job management for scalable processing.
• Job Scheduler Integration: Integrates with job schedulers QSUB, BSUB, and SLURM for high-performance computing environments.
• Simulated GWAS and Visualization: Supports analysis of simulated GWAS data and generates visualization plots for result interpretation.

Scientific Applications:

• Psychiatric genetics: Applied to discovery and validation of genetic variants associated with psychiatric disorders.
• Large-scale GWAS meta-analysis: Combines multi-cohort GWAS data to increase power for detecting associations.
• Polygenic risk estimation: Produces polygenic risk scores for quantifying individual genetic risk for traits and diseases.
• Replication and cross-cohort validation: Validates GWAS findings across independent cohorts and study designs.
• Simulation-based method evaluation: Uses simulated GWAS datasets to evaluate and illustrate analytical workflows and results.

Methodology:

Perl-based pipeline implementing technical and genomic quality control, genome-wide phasing and imputation, association analysis, meta-analysis, polygenic risk scoring, replication analysis, automated parallelization and cluster job management with integration for QSUB, BSUB, and SLURM.