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Updated: Sep 12, 2025

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
Published on: January 16, 2019
Complexities in Variant Analysis, Classification, and Interpretation in Kidney Disease-Related Genes
Maggie Westemeyer1, Michelle S Bloom1, Alicia B Byrne2
1Natera, Inc, Austin, TX.
Abstract:
Advancements in chronic kidney disease (CKD) genetic research and next-generation sequencing have improved CKD diagnosis and personalized treatment. Broad gene panel testing or whole exome/genome sequencing has greatly improved understanding of the genetic etiology of kidney disease but has also increased the complexity of interpretation. Standardized variant classification guidelines help, but challenges remain due to subjective evidence and limited functional and phenotypic data. Careful consideration of genetic and clinical evidence, along with collaboration between clinicians, genetics experts, and laboratories, is essential for accurate interpretation and patient care. This article examines nephrology genetic testing, focusing on the complexities of variant analysis, classification, and interpretation. Variant classification in monogenic kidney diseases is crucial for accurate diagnosis and patient management. We outline the classification methods highlighting several variant examples using the ACMG/AMP framework and quantitative approaches for pathogenicity assessment. We highlight challenges in integrating genetic findings into nephrology and emphasize the clinical impact of accurate genetic diagnoses for precision medicine in CKD.
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