EasyQC

EasyQC performs quality control and data harmonization for genome-wide association meta-analyses (GWAMAs), assessing and correcting file-level and meta-level issues across genome-wide association (GWA) datasets to enable accurate aggregation of study statistics.

Key Features:

• File-Level QC: Performs detailed quality control on individual GWA datasets, identifying and correcting dataset-specific issues prior to inclusion in meta-analyses.
• Meta-Level QC: Evaluates consistency and compatibility across multiple GWA datasets to detect cross-study discrepancies and harmonize inputs for meta-analysis.
• Data-Handling Simplification: Provides computational functionality to manage and process large-scale GWA datasets for downstream meta-analysis workflows.

Scientific Applications:

• Genome-wide association meta-analyses (GWAMAs): Facilitates GWAMAs by providing QC at the study file level, across studies at the meta-level, and on meta-analysis outputs.
• Consortium-scale meta-analysis (e.g., GIANT Consortium): Supports consortium-scale meta-analyses such as the GIANT Consortium, which combined data from over 125 studies and more than 330,000 individuals.

Methodology:

Implements a general GWAMA QC protocol that incorporates real-world examples and consortium-derived solutions.