  Due to the heterogeneity of the phenotype defined by Diagnostic and
Statistical Manual of Mental Disorders (DSM) IV, it is not an optimal option to
identify the genetic variation that underlies the risk for alcohol dependence
(AD) and identifying subtypes of AD becomes an important topic. Traditional
unsupervised cluster analysis and latent class analysis are the most commonly
used methods to obtain the subtypes, but without the guidance of the genetic
information, all these methods may lead to subtypes of little utility in
genetic analysis. Recently, some multi-view co-clustering methods are proposed
to ameliorate this drawback. However, these new methods did not take the
missing values inside the data into consideration. To get around this
limitation, we extended one of the multi-view methods to dealing with the
missing values and clustering simultaneously. We applied this method to 2230
European-American sample and found that the well-known generic variant
rs1229984 (in the ADH1B candidate gene) for the subtype is more significant
than that corresponding to case-control association test. Finally, we verify it
on the 1707 replication sample and find it significant, too.
