Show Notes
️ Episode 73: Assessing Family History in Dementia Genetics: Enhancing Diagnostic Yield
In this episode of PaperCast Base by Base, we explore a study that evaluates how stratifying dementia patients by family history and age at onset can optimize the identification of diagnostically significant genetic variants in a clinical memory clinic setting, illustrating gene–disease association and variant interpretation through exome sequencing discoveries in neurodegenerative disease.
Study Highlights:
The authors analyzed 701 patients diagnosed with Alzheimer’s disease, frontotemporal dementia and other dementia subtypes and classified them into risk categories using a modified Goldman family history score combined with an early-onset cutoff of 65 years. Exome sequencing and targeted genotyping in the 51 high-risk individuals uncovered pathogenic variants in APP, PSEN1, MAPT, GRN, C9ORF72 and APOE4 homozygosity in 39% of cases, which more than doubled the diagnostic yield compared to routine assessments. Applying a strict 60-year threshold for early onset was shown to miss several APOE4 homozygotes and a C9ORF72 repeat expansion, underscoring the value of a slightly higher age cutoff. Limitations include the retrospective design and incomplete sequencing data for some high-risk patients, highlighting the need for prospective validation and broader genetic infrastructure in memory clinics.
Conclusion:
Implementing a standardized classification scheme that combines family history and age at onset significantly improves the efficiency of genetic testing in dementia patients and supports more personalized diagnostic and therapeutic strategies.
Reference:
König T, Silvaieh S, Parvizi T, Wurm R, Goeschl S, Uhlik E, Farr C, Berger-Sieczkowski E, Untersteiner H, Zimprich A, Stögmann E. Assessing family history and approaches for identifying dementia patients with diagnostically significant genetic findings. Genetics in Medicine. Pre-proof published online June 27, 2025. doi:10.1016/j.gim.2025.101517
License:
This episode is based on an open-access article published under the Creative Commons Attribution 4.0 International License (CC BY 4.0) – https://creativecommons.org/licenses/by/4.0/
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