Systematic Cell-Based Phenotyping of Missense Alleles Empowers Rare Variant Association Studies: A Case for LDLR and Myocardial Infarction
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thormaehlen-2015-systematic cell.pdf
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Author(s)
Thormaehlen, Aenne S.
Schuberth, Christian
Won, Hong-Hee
Blattmann, Peter
Joggerst-Thomalla, Brigitte
Theiss, Susanne
Asselta, Rosanna
Duga, Stefano
Merlini, Piera Angelica
Ardissino, Diego
Date Issued
February 2015
Journal
PLOS Genetics
Publisher
Public Library of Science
Citation
Thormaehlen, Aenne S., Christian Schuberth, Hong-Hee Won, Peter Blattmann, Brigitte Joggerst-Thomalla, Susanne Theiss, Rosanna Asselta, et al. “Systematic Cell-Based Phenotyping of Missense Alleles Empowers Rare Variant Association Studies: A Case for LDLR and Myocardial Infarction.” Edited by Chris Cotsapas. PLoS Genet 11, no. 2 (February 3, 2015): e1004855.
Version
Final published version
Abstract
A fundamental challenge to contemporary genetics is to distinguish rare missense alleles that disrupt protein functions from the majority of alleles neutral on protein activities. High-throughput experimental tools to securely discriminate between disruptive and non-disruptive missense alleles are currently missing. Here we establish a scalable cell-based strategy to profile the biological effects and likely disease relevance of rare missense variants in vitro. We apply this strategy to systematically characterize missense alleles in the low-density lipoprotein receptor (LDLR) gene identified through exome sequencing of 3,235 individuals and exome-chip profiling of 39,186 individuals. Our strategy reliably identifies disruptive missense alleles, and disruptive-allele carriers have higher plasma LDL-cholesterol (LDL-C). Importantly, considering experimental data refined the risk of rare LDLR allele carriers from 4.5- to 25.3-fold for high LDL-C, and from 2.1- to 20-fold for early-onset myocardial infarction. Our study generates proof-of-concept that systematic functional variant profiling may empower rare variant-association studies by orders of magnitude.
MIT Department
Massachusetts Institute of Technology. Department of Biology
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DOI of Published Version
https://doi.org/10.1371/journal.pgen.1004855
