IPGWAS

IPGWAS performs quality control, association analysis, format conversion, and visualization to support genome-wide association studies (GWAS) by identifying QC thresholds and detecting significant genetic associations.

Key Features:

• Quality Control: Removes individuals or markers with low genotyping quality and provides a systematic approach to determine QC thresholds for GWAS datasets.
• Association Analysis: Implements statistical association analysis and generates Manhattan plots and quantile-quantile (QQ) plots to identify significant associations and assess p-value distributions.
• Data Conversion: Performs format conversion to enable compatibility with downstream analyses such as meta-analysis, genotype phasing, and imputation.
• Multiplatform Compatibility: Implemented in Perl and developed for multiplatform deployment.

Scientific Applications:

• Genetic Epidemiology: Supports GWAS workflows to identify genetic variants associated with diseases or traits, improving reliability of association findings.
• Personalized Medicine: Aids identification of variants that can inform potential therapeutic targets and stratification in translational research.

Methodology:

Integrates data quality assessment including removal of low-quality individuals/markers and determination of QC thresholds, statistical association analysis, generation of Manhattan and QQ plots for visualization, and format conversion for meta-analysis, genotype phasing, and imputation.