medRxivGenetic & Genomic MedicinePreprint
Utility of polygenic risk scores in diagnostic genetic testing for dilated cardiomyopathy
Utpala Nanda ChowdhuryiD, Eleni GiannoulatouiD, Diane FatkiniD, John James AthertoniD, Nathan J. PalpantiD, Baptiste Couvy‐DuchesneiD, Sonia H ShahiD
Abstract
Dilated cardiomyopathy (DCM) is a major driver of heart failure and transplantation. Despite its strong genetic basis, clinical genetic testing identifies pathogenic/likely pathogenic (P/LP) variants in only about one-third of cases. Genome-wide association studies (GWAS) indicate that common variants also contribute to genetic risk, but utility of polygenic risk score (PRS) for diagnostic testing is unclear. We derived a DCM PRS from a large GWAS and applied it to 113 Australian Genomics Health Alliance (AGHA) participants of genetically determined European ancestry with clinically diagnosed DCM, with most having a family history of disease. Genetic testing identified a P/LP variant in 34% of patients. Using 450,280 UK Biobank participants of European ancestry as a reference, we selected the best performing PRS (SBayesRC; odds ratio per SD increase in PRS = 1.72, 95% CI 1.58-1.88). We compared the PRS of individuals with a P/LP variant in the TTN gene (TTN_G+) or other genes (nonTTN_G+), and in those without any P/LP or variants of unknown significance identified (G-). Relative to the UK Biobank reference, median PRS values were significantly higher in G- and TTN_G+ groups. Twenty-six percent of G- and 25% of TTN_G+ cases were in the top decile, versus 11% of nonTTN_G+ cases. Notably, TTN_G+ cases were absent from the lowest four deciles. These findings support a substantial polygenic contribution in DCM patients without identifiable monogenic variants as well as in TTN-related DCM. Incorporating PRS alongside routine genetic testing could increase diagnostic yield and inform risk management for patients and families.
- organism: human
- assay: GWAS summary statistics
- research focus: fine-mapping
- research focus: polygenic risk
- approach: statistical model
- Citations
- 0
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