Endophenotype effect sizes support variant pathogenicity in monogenic disease susceptibility genes.

Area of Science:

• Genetics
• Genomic Medicine
• Biostatistics

Background:

• Accurate classification of variant pathogenicity is crucial for genetic research and clinical applications.
• Existing methods for variant classification face challenges in efficiency and accuracy, particularly for rare variants.

Purpose of the Study:

• To evaluate the utility of population-based associations between rare variants and quantitative endophenotypes in determining variant pathogenicity for monogenic diseases.
• To establish a method for nominating variants with pathogenic potential using effect sizes from endophenotype associations.

Main Methods:

• Utilized data from three large studies encompassing familial hypercholesterolemia, long QT syndrome, and maturity-onset diabetes of the young.
• Analyzed population-based associations between rare variants and quantitative endophenotypes (LDL cholesterol, QTc interval, HbA1c).
• Assessed the correlation between effect sizes and ClinVar pathogenicity assertions, and evaluated the discriminative power of effect sizes for variant classification.

Main Results:

• Population-based variant-endophenotype associations provide significant evidence for variant pathogenicity (P < 0.001).
• Effect sizes effectively discriminate pathogenic from non-pathogenic variants, achieving an area under the curve of 0.82-0.84.
• An effect size threshold (≥ 0.5 SD) can nominate up to 35% of variants of uncertain significance or those not in ClinVar within disease genes.

Conclusions:

• Variant associations with quantitative endophenotypes offer a robust approach to evidence generation for pathogenicity in monogenic diseases.
• This method enhances the classification of rare variants, improving diagnostic yield and genetic research.
• The proposed approach provides a valuable tool for identifying potentially pathogenic variants in clinical and research settings.