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2014 ; 38
(6
): 552-9
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TARV: tree-based analysis of rare variants identifying risk modifying variants in
CTNNA2 and CNTNAP2 for alcohol addiction
#MMPMID25041903
Song C
; Zhang H
Genet Epidemiol
2014[Sep]; 38
(6
): 552-9
PMID25041903
show ga
Since the development of next generation sequencing (NGS) technology, researchers
have been extending their efforts on genome-wide association studies (GWAS) from
common variants to rare variants to find the missing inheritance. Although
various statistical methods have been proposed to analyze rare variants data,
they generally face difficulties for complex disease models involving multiple
genes. In this paper, we propose a tree-based analysis of rare variants (TARV)
that adopts a nonparametric disease model and is capable of exploring gene-gene
interactions. We found that TARV outperforms the sequence kernel association test
(SKAT) in most of our simulation scenarios, and by notable margins in some cases.
By applying TARV to the study of addiction: genetics and environment (SAGE) data,
we successfully detected gene CTNNA2 and its 43 specific variants that increase
the risk of alcoholism in women, with an odds ratio (OR) of 1.94. This gene has
not been detected in the SAGE data. Post hoc literature search also supports the
role of CTNNA2 as a likely risk gene for alcohol addiction. In addition, we also
detected a plausible protective gene CNTNAP2, whose 97 rare variants can reduce
the risk of alcoholism in women, with an OR of 0.55. These findings suggest that
TARV can be effective in dissecting genetic variants for complex diseases using
rare variants data.