Heart rhythm · 2021
Use of machine learning to classify high-risk variants of uncertain significance in lamin A/C cardiac disease.
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Bennett Jeffrey S, Gordon David M, Majumdar Uddalak, Lawrence Patrick J, Matos-Nieves Adrianna, Myers Katherine, Kamp Anna N, Leonard Julie C, McBride Kim L, White Peter, Garg Vidu
Аннотация
BACKGROUND: Variation in lamin A/C results in a spectrum of clinical disease, including arrhythmias and cardiomyopathy. Benign variation is rare, and classification of LMNA missense variants via in silico prediction tools results in a high rate of variants of uncertain significance (VUSs).
OBJECTIVE: The goal of this study was to use a machine learning (ML) approach for in silico prediction of LMNA pathogenic variation.
METHODS: Genetic sequencing was performed on family members with conduction system disease, and patient cell lines were examined for LMNA expression. In silico predictions of conservation and pathogenicity of published LMNA variants were visualized with uniform manifold approximation and projection. K-means clustering was used to identify variant groups with similarly projected scores, allowing the generation of statistically supported risk categories.
RESULTS: We discovered a novel LMNA variant (c.408C>A:p.Asp136Glu) segregating with conduction system disease in a multigeneration pedigree, which was reported as a VUS by a commercial testing company. Additional familial analysis and in vitro testing found it to be pathogenic, which prompted the development of an ML algorithm that used in silico predictions of pathogenicity for known LMNA missense variants. This identified 3 clusters of variation, each with a significantly different incidence of known pathogenic variants (38.8%, 15.0%, and 6.1%). Three hundred thirty-nine of 415 head/rod domain variants (81.7%), including p.Asp136Glu, were in clusters with highest proportions of pathogenic variants.
CONCLUSION: An unsupervised ML method successfully identified clusters enriched for pathogenic LMNA variants including a novel variant associated with conduction system disease. Our ML method may assist in identifying high-risk VUS when familial testing is unavailable.
OBJECTIVE: The goal of this study was to use a machine learning (ML) approach for in silico prediction of LMNA pathogenic variation.
METHODS: Genetic sequencing was performed on family members with conduction system disease, and patient cell lines were examined for LMNA expression. In silico predictions of conservation and pathogenicity of published LMNA variants were visualized with uniform manifold approximation and projection. K-means clustering was used to identify variant groups with similarly projected scores, allowing the generation of statistically supported risk categories.
RESULTS: We discovered a novel LMNA variant (c.408C>A:p.Asp136Glu) segregating with conduction system disease in a multigeneration pedigree, which was reported as a VUS by a commercial testing company. Additional familial analysis and in vitro testing found it to be pathogenic, which prompted the development of an ML algorithm that used in silico predictions of pathogenicity for known LMNA missense variants. This identified 3 clusters of variation, each with a significantly different incidence of known pathogenic variants (38.8%, 15.0%, and 6.1%). Three hundred thirty-nine of 415 head/rod domain variants (81.7%), including p.Asp136Glu, were in clusters with highest proportions of pathogenic variants.
CONCLUSION: An unsupervised ML method successfully identified clusters enriched for pathogenic LMNA variants including a novel variant associated with conduction system disease. Our ML method may assist in identifying high-risk VUS when familial testing is unavailable.