Pleiotropic shared heritability quantifies the shared genetic variance of common diseases

Area of Science:

• Genetics
• Disease Architecture
• Bioinformatics

Background:

• The overall contribution of pleiotropy to complex disease architectures remains largely unquantified.
• Existing methods often estimate genetic correlations individually, limiting a comprehensive understanding of shared genetic influences.

Purpose of the Study:

• To develop and apply a novel method, pleiotropic shared heritability with bias correction (PHBC), for estimating liability-scale genetic variance shared among a set of diseases.
• To quantify the proportion of heritability attributable to pleiotropy across a wide range of common diseases.

Main Methods:

• Developed the PHBC method to estimate shared genetic variance (${h^2}_{\mathrm{pleio}}$) from genetic correlation matrices.
• Employed a Monte Carlo bias correction procedure to mitigate sampling noise.
• Analyzed genetic data from the UK Biobank for 15 diseases and expanded to 62 auxiliary diseases/traits.

Main Results:

• The average ratio of pleiotropic heritability to total SNP heritability (${h^2}_{\mathrm{pleio}}/h^2$) was 27% for 15 diseases, increasing to 48% when using 62 auxiliary traits.
• ${h^2}_{\mathrm{pleio}}/h^2$ was broadly distributed and only modestly decreased when removing informative auxiliary disease categories.
• Genetic pleiotropy explained, on average, 1.51 times more variance than suggested by the phenotypic overlap of auxiliary diseases, indicating stronger genetic than phenotypic sharing.

Conclusions:

• Approximately half of the heritability for common diseases is pleiotropic, influencing a broad spectrum of conditions.
• The PHBC method provides a robust approach to quantify shared genetic variance and understand pleiotropy's role in disease.
• Findings underscore the interconnectedness of genetic factors underlying common diseases.