Quality Control of Common and Rare Variants

Area of Science:

• Genetics
• Bioinformatics
• Genomic Data Analysis

Background:

• High-throughput genotyping is essential for modern genetic studies.
• Data quality control (QC) is a critical prerequisite for successful genotyping.
• Existing research has defined numerous criteria for sample and variant QC.

Purpose of the Study:

• To outline established criteria and thresholds for data quality control in high-throughput genotyping.
• To detail the specific parameters evaluated during sample-level QC.
• To describe the metrics used for variant-level QC and refinement.

Main Methods:

• Sample QC involves assessing call rates, sex concordance, heterozygosity, relatedness, ethnicity, batch effects, and contamination.
• Variant QC includes evaluating call rates, phenotypic associations, Hardy-Weinberg Equilibrium (HWE) deviations, genotype quality, batch effects, allele frequency comparisons, minor allele counts (MAC), imputation quality, variant quality scores, and read depth.

Main Results:

• Specific QC metrics have been developed for both individual samples and genetic variants.
• These QC measures facilitate the identification and appropriate removal or refinement of low-quality data.
• Adherence to these QC standards is vital for the integrity of high-throughput genotyping data.

Conclusions:

• Comprehensive data quality control at both sample and variant levels is indispensable for high-throughput genotyping.
• The application of established QC criteria ensures the reliability and accuracy of genetic association studies.
• Systematic QC procedures enhance the overall quality and interpretability of genomic datasets.