GLOGS: a fast and powerful method for GWAS of binary traits with risk covariates in related populations.
Bioinformatics
; 28(11): 1553-4, 2012 Jun 01.
Article
em En
| MEDLINE
| ID: mdl-22522135
ABSTRACT
SUMMARY:
Mixed model-based approaches to genome-wide association studies (GWAS) of binary traits in related individuals can account for non-genetic risk factors in an integrated manner. However, they are technically challenging. GLOGS (Genome-wide LOGistic mixed model/Score test) addresses such challenges with efficient statistical procedures and a parallel implementation. GLOGS has high power relative to alternative approaches as risk covariate effects increase, and can complete a GWAS in minutes.AVAILABILITY:
Source code and documentation are provided at http//www.bioinformatics.org/~stanhope/GLOGS.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Modelos Logísticos
/
Estudo de Associação Genômica Ampla
/
Modelos Genéticos
Tipo de estudo:
Etiology_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2012
Tipo de documento:
Article