Joint Analysis of Multiple Traits in Rare Variant Association Studies.
Ann Hum Genet
; 80(3): 162-71, 2016 May.
Article
en En
| MEDLINE
| ID: mdl-26990300
ABSTRACT
The joint analysis of multiple traits has recently become popular since it can increase statistical power to detect genetic variants and there is increasing evidence showing that pleiotropy is a widespread phenomenon in complex diseases. Currently, the majority of existing methods for the joint analysis of multiple traits test association between one common variant and multiple traits. However, the variant-by-variant methods for common variant association studies may not be optimal for rare variant association studies due to the allelic heterogeneity as well as the extreme rarity of individual variants. Current statistical methods for rare variant association studies are for one single trait only. In this paper, we propose an adaptive weighting reverse regression (AWRR) method to test association between multiple traits and rare variants in a genomic region. AWRR is robust to the directions of effects of causal variants and is also robust to the directions of association of traits. Using extensive simulation studies, we compare the performance of AWRR with canonical correlation analysis (CCA), Single-TOW, and the weighted sum reverse regression (WSRR). Our results show that, in all of the simulation scenarios, AWRR is consistently more powerful than CCA. In most scenarios, AWRR is more powerful than Single-TOW and WSRR.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Variación Genética
/
Estudios de Asociación Genética
Tipo de estudio:
Diagnostic_studies
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
Ann Hum Genet
Año:
2016
Tipo del documento:
Article
País de afiliación:
Estados Unidos