Recall by genotype and cascade screening for familial hypercholesterolemia in a population-based biobank from Estonia
Name
s41436-018-0311-2.pdf
Description
Published version
Size
844.22 KB
Format
Adobe PDF
Checksum (MD5)
d5a643e09794460cc1b5135485d08814
Author(s)
Zekavat, Seyedeh M.
Gabriel, Stacey
Lander, Eric Steven
Philippakis, Anthony A.
Date Issued
October 2018
Journal
Genetics in medicine
Publisher
Springer Science and Business Media LLC
Citation
Alver, Maris et al. “Recall by genotype and cascade screening for familial hypercholesterolemia in a population-based biobank from Estonia.” Genetics in medicine 21 (2019): 1173-1180 © 2019 The Author(s)
Version
Final published version
Abstract
Purpose: Large-scale, population-based biobanks integrating health records and genomic profiles may provide a platform to identify individuals with disease-predisposing genetic variants. Here, we recall probands carrying familial hypercholesterolemia (FH)-associated variants, perform cascade screening of family members, and describe health outcomes affected by such a strategy. Methods: The Estonian Biobank of Estonian Genome Center, University of Tartu, comprises 52,274 individuals. Among 4776 participants with exome or genome sequences, we identified 27 individuals who carried FH-associated variants in the LDLR, APOB, or PCSK9 genes. Cascade screening of 64 family members identified an additional 20 carriers of FH-associated variants. Results: Via genetic counseling and clinical management of carriers, we were able to reclassify 51% of the study participants from having previously established nonspecific hypercholesterolemia to having FH and identify 32% who were completely unaware of harboring a high-risk disease-associated genetic variant. Imaging-based risk stratification targeted 86% of the variant carriers for statin treatment recommendations. Conclusion: Genotype-guided recall of probands and subsequent cascade screening for familial hypercholesterolemia is feasible within a population-based biobank and may facilitate more appropriate clinical management.
MIT Department
Massachusetts Institute of Technology. Department of Biology
Terms of Use
Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1038/S41436-018-0311-2
