Meta-analysis of quantitative pleiotropic traits for next-generation sequencing with multivariate functional linear models.
Eur J Hum Genet
; 25(3): 350-359, 2017 02.
Article
en En
| MEDLINE
| ID: mdl-28000696
ABSTRACT
To analyze next-generation sequencing data, multivariate functional linear models are developed for a meta-analysis of multiple studies to connect genetic variant data to multiple quantitative traits adjusting for covariates. The goal is to take the advantage of both meta-analysis and pleiotropic analysis in order to improve power and to carry out a unified association analysis of multiple studies and multiple traits of complex disorders. Three types of approximate F -distributions based on Pillai-Bartlett trace, Hotelling-Lawley trace, and Wilks's Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants. Simulation analysis is performed to evaluate false-positive rates and power of the proposed tests. The proposed methods are applied to analyze lipid traits in eight European cohorts. It is shown that it is more advantageous to perform multivariate analysis than univariate analysis in general, and it is more advantageous to perform meta-analysis of multiple studies instead of analyzing the individual studies separately. The proposed models require individual observations. The value of the current paper can be seen at least for two reasons (a) the proposed methods can be applied to studies that have individual genotype data; (b) the proposed methods can be used as a criterion for future work that uses summary statistics to build test statistics to meta-analyze the data.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Análisis de Secuencia de ADN
/
Secuenciación de Nucleótidos de Alto Rendimiento
/
Pleiotropía Genética
/
Modelos Genéticos
Tipo de estudio:
Prognostic_studies
/
Systematic_reviews
Límite:
Humans
Idioma:
En
Revista:
Eur J Hum Genet
Asunto de la revista:
GENETICA MEDICA
Año:
2017
Tipo del documento:
Article