CNest: A novel copy number association discovery method uncovers 862 new associations from 200,629 whole-exome sequence datasets in the UK Biobank.
CNest: A novel copy number association discovery method uncovers 862 new associations from 200,629 whole-exome sequence datasets in the UK Biobank.
复制标题
DOI:
10.1016/j.xgen.2022.100167
复制
发表时间:
2022-08-10
期刊:
影响因子:
--
通讯作者:
Birney, Ewan
中科院分区:
文献类型:
--
作者:
Fitzgerald, Tomas;Birney, Ewan
Copy number variation (CNV) is known to influence human traits, having a rich history of research into common and rare genetic disease, and although CNV is accepted as an important class of genomic variation, progress on copy-number-based genome-wide association studies (GWASs) from next-generation sequencing (NGS) data has been limited. Here we present a novel method for large-scale copy number analysis from NGS data generating robust copy number estimates and allowing copy number GWASs (CN-GWASs) to be performed genome-wide in discovery mode. We provide a detailed analysis in the UK Biobank resource and a specifically designed software package. We use these methods to perform CN-GWAS analysis across 78 human traits, discovering over 800 genetic associations that are likely to contribute strongly to trait distributions. Finally, we compare CNV and SNP association signals across the same traits and samples, defining specific CNV association classes. Novel copy number variation (CNV) associations in a large human cohort Large-scale CNV association testing from next-generation sequencing data Specifically designed GWAS framework for CNV operating under GA4GH standards Investigation of SNP and CNV associations and CNV tagging SNPs CNest copy number variation (CNV) genome-wide association study (GWAS) testing brings CNVs into a similar framework as those widely used in SNP GWAS. We made over 800 new discoveries in the UK Biobank human cohort at exon-level resolution, paving the way for further CNV GWAS studies using next-generation sequencing data.
登录
查看更多内容
影响因子:
64.8
作者:
Bycroft C;Freeman C;Petkova D;Band G;Elliott LT;Sharp K;Motyer A;Vukcevic D;Delaneau O;O'Connell J;Cortes A;Welsh S;Young A;Effingham M;McVean G;Leslie S;Allen N;Donnelly P;Marchini J
通讯作者:
Marchini J
影响因子:
9.8
作者:
通讯作者:
--
影响因子:
14.9
作者:
Buniello, Annalisa;MacArthur, Jacqueline A. L.;Parkinson, Helen
通讯作者:
Parkinson, Helen
影响因子:
56.9
作者:
Duerr, Richard H.;Taylor, Kent D.;Cho, Judy H.
通讯作者:
Cho, Judy H.
影响因子:
64.5
作者:
Astle, William J.;Elding, Heather;Soranzo, Nicole
通讯作者:
Soranzo, Nicole