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2016 ; 17
(2
): 221-34
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Group association test using a hidden Markov model
#MMPMID26420797
Cheng Y
; Dai JY
; Kooperberg C
Biostatistics
2016[Apr]; 17
(2
): 221-34
PMID26420797
show ga
In the genomic era, group association tests are of great interest. Due to the
overwhelming number of individual genomic features, the power of testing for
association of a single genomic feature at a time is often very small, as are the
effect sizes for most features. Many methods have been proposed to test
association of a trait with a group of features within a functional unit as a
whole, e.g. all SNPs in a gene, yet few of these methods account for the fact
that generally a substantial proportion of the features are not associated with
the trait. In this paper, we propose to model the association for each feature in
the group as a mixture of features with no association and features with non-zero
associations to explicitly account for the possibility that a fraction of
features may not be associated with the trait while other features in the group
are. The feature-level associations are first estimated by generalized linear
models; the sequence of these estimated associations is then modeled by a hidden
Markov chain. To test for global association, we develop a modified likelihood
ratio test based on a log-likelihood function that ignores higher order
dependency plus a penalty term. We derive the asymptotic distribution of the
likelihood ratio test under the null hypothesis. Furthermore, we obtain the
posterior probability of association for each feature, which provides evidence of
feature-level association and is useful for potential follow-up studies. In
simulations and data application, we show that our proposed method performs well
when compared with existing group association tests especially when there are
only few features associated with the outcome.
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