Open AccessThe copy number variation and stroke (CaNVAS) risk and outcome study
Author(s) -
John W. Cole,
Taiwo Adigun,
Rufus Akinyemi,
Onoja Akpa,
Steven Bell,
Bowang Chen,
Jordi Jiménez Conde,
Uxue Lazcano,
Israel Fernandez,
Myriam Fornage,
Cristina Gallego-Fábrega,
Christina Jern,
Michael Krawczak,
Arne Lindgren,
Hugh S. Markus,
Olle Melander,
Mayowa Owolabi,
Kristina Schlicht,
Martin Söderholm,
Vinodh Srinivasasainagendra,
Carolina Soriano-Tárraga,
Martin Stenman,
Hemant K. Tiwari,
Margaret Corasaniti,
Natalie Fecteau,
Beth Guizzardi,
Haley Lopez,
Kevin A. Nguyen,
Brady Gaynor,
Timothy D. O’Connor,
O. Colin Stine,
Steven J. Kittner,
Patrick F. McArdle,
Braxton D. Mitchell,
Huichun Xu,
Caspar GrondGinsbach
Publication year - 2021
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0248791
Subject(s) - copy number variation , genome wide association study , stroke (engine) , exome , genotyping , biomarker , medicine , bioinformatics , oncology , biology , genetics , single nucleotide polymorphism , exome sequencing , phenotype , genotype , genome , gene , mechanical engineering , engineering
Background and purpose The role of copy number variation (CNV) variation in stroke susceptibility and outcome has yet to be explored. The Copy Number Variation and Stroke (CaNVAS) Risk and Outcome study addresses this knowledge gap. Methods Over 24,500 well-phenotyped IS cases, including IS subtypes, and over 43,500 controls have been identified, all with readily available genotyping on GWAS and exome arrays, with case measures of stroke outcome. To evaluate CNV-associated stroke risk and stroke outcome it is planned to: 1) perform Risk Discovery using several analytic approaches to identify CNVs that are associated with the risk of IS and its subtypes, across the age-, sex- and ethnicity-spectrums; 2) perform Risk Replication and Extension to determine whether the identified stroke-associated CNVs replicate in other ethnically diverse datasets and use biomarker data (e.g. methylation, proteomic, RNA, miRNA, etc.) to evaluate how the identified CNVs exert their effects on stroke risk, and lastly; 3) perform outcome-based Replication and Extension analyses of recent findings demonstrating an inverse relationship between CNV burden and stroke outcome at 3 months (mRS), and then determine the key CNV drivers responsible for these associations using existing biomarker data. Results The results of an initial CNV evaluation of 50 samples from each participating dataset are presented demonstrating that the existing GWAS and exome chip data are excellent for the planned CNV analyses. Further, some samples will require additional considerations for analysis, however such samples can readily be identified, as demonstrated by a sample demonstrating clonal mosaicism. Conclusion The CaNVAS study will cost-effectively leverage the numerous advantages of using existing case-control data sets, exploring the relationships between CNV and IS and its subtypes, and outcome at 3 months, in both men and women, in those of African and European-Caucasian descent, this, across the entire adult-age spectrum.