Contextualizing genetic risk score for disease screening and rare variant discovery

Area of Science:

• Genetics
• Genomic Medicine
• Complex Trait Genetics

Background:

• Complex traits are influenced by both common, small-effect variants (polygenic burden) and rare, large-effect variants (LEV).
• Understanding the interplay between these variant types is crucial for genetic discovery and clinical applications.

Purpose of the Study:

• To identify conditions where polygenic burden (PB) derived from GWAS can facilitate rare pathogenic variant (LEV) discovery.
• To evaluate PB as a tool for prioritizing samples in whole-genome or exome sequencing studies.
• To explore the clinical utility of PB in relation to LEV-derived risk.

Main Methods:

• Extensive simulations of genetic architectures and disease liability models using empirical data.
• Quantification of the power to detect differences in PB between LEV carriers and non-carriers among cases.
• Development and application of a summary-statistics-based methodology (PB-LEV-SCAN) for predicting LEV screening utility.

Main Results:

• Identified conditions where GWAS-derived PB can effectively support rare pathogenic variant discovery.
• Demonstrated that PB can serve as a valuable sample prioritization tool for sequencing.
• Uncovered clinically relevant scenarios where PB-derived risk is comparable to LEV-derived risk.
• PB-LEV-SCAN successfully predicted LEV screening utility for 36 complex traits, validated in UK Biobank data.

Conclusions:

• Polygenic burden is a powerful tool for enhancing rare variant discovery and sample selection in complex trait genetics.
• The PB-LEV-SCAN methodology offers a practical approach for clinical decision-making regarding genetic screening.
• Integrating polygenic burden into genetic studies has significant implications for understanding and managing complex diseases.