Journal
ANNALS OF HUMAN GENETICS
Volume 79, Issue 4, Pages 294-309Publisher
WILEY
DOI: 10.1111/ahg.12117
Keywords
Ordered logistic model; set-valued system identification; multiple thresholds; genetic association study; rare variants
Categories
Funding
- American Lebanese and Syrian Associated Charities (ALSAC), grants from the National Natural Science Foundation of China [11171333, 61134013]
- National Science Foundation [DMS-1209112]
- National Institutes of Health [R01 HG006292]
- NIH [R01 GM031575]
Ask authors/readers for more resources
In genetic association studies of an ordered categorical phenotype, it is usual to either regroup multiple categories of the phenotype into two categories and then apply the logistic regression (LG), or apply ordered logistic (oLG), or ordered probit (oPRB) regression, which accounts for the ordinal nature of the phenotype. However, they may lose statistical power or may not control type I error due to their model assumption and/or instable parameter estimation algorithm when the genetic variant is rare or sample size is limited. To solve this problem, we propose a set-valued (SV) system model to identify genetic variants associated with an ordinal categorical phenotype. We couple this model with a SV system identification algorithm to identify all the key system parameters. Simulations and two real data analyses show that SV and LG accurately controlled the Type I error rate even at a significance level of 10(-6) but not oLG and oPRB in some cases. LG had significantly less power than the other three methods due to disregarding of the ordinal nature of the phenotype, and SV had similar or greater power than oLG and oPRB. We argue that SV should be employed in genetic association studies for ordered categorical phenotype.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available