A rare variant analysis framework using public genotype summary counts to prioritize disease-predisposition genes

Area of Science:

• Genetics
• Bioinformatics
• Computational Biology

Background:

• Prioritizing germline disease-predisposition genes is challenging without matched healthy controls.
• Using public genotype data as controls can introduce systematic inflation and false positives if confounding factors are not managed.
• Rare pathogenic variants play a crucial role in genetic disease predisposition.

Purpose of the Study:

• To develop a robust framework for identifying disease-predisposition genes using public genotype data.
• To address challenges of confounding factors, inflation, and false positives in rare variant association studies.
• To provide a cost-effective method for prioritizing genes enriched with rare pathogenic variants.

Main Methods:

• The proposed framework, consistent summary counts based rare variant burden test (CoCoRV), implements consistent variant quality control and filtering.
• CoCoRV performs ethnicity-stratified rare variant association tests and accurately estimates inflation factors.
• The framework includes powerful false discovery rate (FDR) control and detects rare variant pairs in high linkage disequilibrium.

Main Results:

• Application of CoCoRV to pediatric cancer cohorts identified known cancer-predisposition genes.
• CoCoRV was successfully applied to identify disease-predisposition genes in adult brain tumors and amyotrophic lateral sclerosis cohorts.
• The framework effectively controlled for potential confounding factors, demonstrating its reliability.

Conclusions:

• CoCoRV offers a cost-effective solution for prioritizing disease-risk genes enriched with rare pathogenic variants.
• The framework enhances the ability to identify genetic factors contributing to various diseases, including cancers and neurological disorders.
• Consistent control of confounding factors is essential for accurate rare variant association studies.