|
KMID : 0381120210430010069
|
|
Genes and Genomics
2021 Volume.43 No. 1 p.69 ~ p.77
|
|
|
A fast and powerful aggregated Cauchy association test for joint analysis of multiple phenotypes
|
|
Chen Lili
Zhou Yajing
|
|
|
Abstract
|
|
|
Background: Pleiotropy is a widespread phenomenon in complex human diseases. Jointly analyzing multiple phenotypes can improve power performance of detecting genetic variants and uncover the underlying genetic mechanism.
Objective: This study aims to detect the association between genetic variants in a genomic region and multiple phenotypes.
Methods: We develop the aggregated Cauchy association test to detect the association between rare variants in a genomic region and multiple phenotypes (abbreviated as “Multi-ACAT”). Multi-ACAT first detects the association between each rare variant and multiple phenotypes based on reverse regression and obtains variant-level p-values, then takes linear combination of transformed p-values as the test statistic which approximately follows Cauchy distribution under the null hypothesis.
Results: Extensive simulation studies show that when the proportion of causal variants in a genomic region is extremely small, Multi-ACAT is more powerful than the other several methods and is robust to bi-directional effects of causal variants. Finally, we illustrate our proposed method by analyzing two phenotypes [systolic blood pressure (SBP) and diastolic blood pressure (DBP)] from Genetic Analysis Workshop 19 (GAW19).
Conclusion: The Multi-ACAT computes extremely fast, does not consider complex distributions of multiple correlated phenotypes, and can be applied to the case with noise phenotypes.
|
|
|
KEYWORD
|
|
|
Association analysis, Rare variant, Pleiotropy, Multiple phenotypes
|
|
|
FullTexts / Linksout information
|
|
|
|
|
|
Listed journal information
|
|
 
|