Imputation of coding variants in African Americans: better performance using data from the exome sequencing project | Zendy

Qing Duan | Zendy; Eric Yi Liu | Zendy; Paul L. Auer | Zendy; Guosheng Zhang | Zendy; Ethan M. Lange | Zendy; Goo Jun | Zendy; Chris Bizon | Zendy; Shuo Jiao | Zendy; Steven Buyske | Zendy; Nora Franceschini | Zendy; Chris Carlson | Zendy; Li Hsu | Zendy; Alex P. Reiner | Zendy; Ulrike Peters | Zendy; Jeffrey Haessler | Zendy; Keith R. Curtis | Zendy; Christina L. Wassel | Zendy; Jennifer G. Robinson | Zendy; Lisa W. Martin | Zendy; Christopher A. Haiman | Zendy; Loı̈c Le Marchand | Zendy; Tara C. Matise | Zendy; Lucia A. Hindorff | Zendy; Dana C. Crawford | Zendy; Themistocles L. Assimes | Zendy; Hyun Min Kang | Zendy; Gerardo Heiss | Zendy; Rebecca D. Jackson | Zendy; Charles Kooperberg | Zendy; James G. Wilson | Zendy; Gonçalo R. Abecasis | Zendy; Kari E. North | Zendy; Deborah A. Nickerson | Zendy; Leslie A. Lange | Zendy; Yun Li | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Imputation of coding variants in African Americans: better performance using data from the exome sequencing project

Author(s) -

Qing Duan,

Eric Yi Liu,

Paul L. Auer,

Guosheng Zhang,

Ethan M. Lange,

Goo Jun,

Chris Bizon,

Shuo Jiao,

Steven Buyske,

Nora Franceschini,

Chris Carlson,

Li Hsu,

Alex P. Reiner,

Ulrike Peters,

Jeffrey Haessler,

Keith R. Curtis,

Christina L. Wassel,

Jennifer G. Robinson,

Lisa W. Martin,

Christopher A. Haiman,

Loı̈c Le Marchand,

Tara C. Matise,

Lucia A. Hindorff,

Dana C. Crawford,

Themistocles L. Assimes,

Hyun Min Kang,

Gerardo Heiss,

Rebecca D. Jackson,

Charles Kooperberg,

James G. Wilson,

Gonçalo R. Abecasis,

Kari E. North,

Deborah A. Nickerson,

Leslie A. Lange,

Yun Li

Publication year - 2013

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btt477

Subject(s) - imputation (statistics) , 1000 genomes project , haplotype , exome , minor allele frequency , exome sequencing , quality score , computational biology , allele frequency , biology , genetics , data mining , computer science , missing data , allele , single nucleotide polymorphism , phenotype , gene , genotype , machine learning , engineering , metric (unit) , operations management

Although the 1000 Genomes haplotypes are the most commonly used reference panel for imputation, medical sequencing projects are generating large alternate sets of sequenced samples. Imputation in African Americans using 3384 haplotypes from the Exome Sequencing Project, compared with 2184 haplotypes from 1000 Genomes Project, increased effective sample size by 8.3-11.4% for coding variants with minor allele frequency <1%. No loss of imputation quality was observed using a panel built from phenotypic extremes. We recommend using haplotypes from Exome Sequencing Project alone or concatenation of the two panels over quality score-based post-imputation selection or IMPUTE2's two-panel combination.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research