SAQC

SAQC evaluates genome-wide single-nucleotide polymorphism (SNP) array data quality by computing allele-frequency (AF) deviation indices and applying empirical distribution–based confidence-interval methods to detect poor-quality arrays and samples.

Key Features:

• Quality indices development: Introduces quality indices that measure deviation of estimated individual-level allele frequencies (AFs) from expected values using standardized distances.
• Statistical properties evaluation: Investigates statistical properties of the quality indices and reports empirical lognormal distributions across large genomic studies.
• Reference data establishment: Provides AF reference data and quality-index reference data for multiple SNP array platforms derived from diverse reference populations.
• Confidence interval methodology: Implements a confidence-interval method that leverages empirical distributions of quality indices to identify poor-quality SNP arrays or DNA samples.
• Data visualization and evaluation: Provides visualization tools to interpret data quality metrics for SNP array assessments.

Scientific Applications:

• Genome-wide association studies (GWAS): Ensures accuracy and precision of downstream analyses in GWAS by identifying and mitigating poor-quality SNP array data.
• SNP array–based genetic research: Supports quality assessment across genetic studies that use genome-wide SNP arrays and reference-population comparisons.

Methodology:

Quantifies deviations in allele frequencies via standardized distances, establishes empirical distributions for these quality indices (noting lognormal behavior), and applies an empirical confidence-interval method to detect poor-quality arrays or samples.