New quality measure for SNP array based CNV detection

Area of Science:

• Genetics
• Bioinformatics
• Computational Biology

Background:

• Large-scale genome-wide association studies (GWAS) are crucial for understanding common diseases.
• Copy Number Variants (CNVs) are significant genetic factors in common diseases, but their accurate identification from array data remains challenging.
• Existing methods for filtering false positive CNV calls are empirical and lack robust validation.

Purpose of the Study:

• To develop and validate a novel quality score (QS) for copy number variants (CNVs) identified by PennCNV.
• To improve the reliability and statistical power of genome-wide CNV association studies.
• To enhance the accuracy of CNV calling for better understanding of CNV-trait associations.

Main Methods:

• Developed a new quality score (QS) to estimate the concordance of CNV calls across different software.
• Evaluated the QS using out-of-sample comparisons and Receiver Operating Characteristic (ROC) curves.
• Assessed the impact of QS on statistical power through simulations and by re-analyzing known CNV-trait associations.

Main Results:

• The proposed QS demonstrated a twofold higher correlation with consensus CNV status compared to existing filters.
• ROC curve analysis yielded an Area Under the Curve (AUC) greater than 0.8, indicating high discriminative ability.
• Simulations showed up to a 20% increase in statistical power when employing the QS, with improved performance in identifying CNV-trait associations.

Conclusions:

• The developed quality score significantly enhances the accuracy and reliability of CNV detection in GWAS.
• This approach offers a robust method for filtering false positive CNV calls, leading to increased statistical power.
• The QS facilitates more accurate identification of disease-associated CNVs, advancing the study of genetic contributions to common diseases.