The sequence kernel association test for multicategorical outcomes.
Date
2023-04-19ICR Author
Author
Jiang, Z
Zhang, H
Ahearn, TU
Garcia-Closas, M
Chatterjee, N
Zhu, H
Zhan, X
Zhao, N
Type
Journal Article
Metadata
Show full item recordAbstract
Disease heterogeneity is ubiquitous in biomedical and clinical studies. In genetic studies, researchers are increasingly interested in understanding the distinct genetic underpinning of subtypes of diseases. However, existing set-based analysis methods for genome-wide association studies are either inadequate or inefficient to handle such multicategorical outcomes. In this paper, we proposed a novel set-based association analysis method, sequence kernel association test (SKAT)-MC, the sequence kernel association test for multicategorical outcomes (nominal or ordinal), which jointly evaluates the relationship between a set of variants (common and rare) and disease subtypes. Through comprehensive simulation studies, we showed that SKAT-MC effectively preserves the nominal type I error rate while substantially increases the statistical power compared to existing methods under various scenarios. We applied SKAT-MC to the Polish breast cancer study (PBCS), and identified gene FGFR2 was significantly associated with estrogen receptor (ER)+ and ER- breast cancer subtypes. We also investigated educational attainment using UK Biobank data ( N = 127 , 127 $N=127,127$ ) with SKAT-MC, and identified 21 significant genes in the genome. Consequently, SKAT-MC is a powerful and efficient analysis tool for genetic association studies with multicategorical outcomes. A freely distributed R package SKAT-MC can be accessed at https://github.com/Zhiwen-Owen-Jiang/SKATMC.
Collections
Subject
SKAT
multicategorical data
the generalized logit model
the proportional odds model
Research team
Integrative Cancer Epidem
Language
eng
Date accepted
2023-03-30
License start date
2023-04-19
Citation
Genetic Epidemiology, 2023,
Publisher
WILEY