Multi-View Sparse Vector Decomposition to Deal With Missing Values in Alcohol Dependence Study

TL;DR

This paper introduces a novel multi-view sparse vector decomposition method that effectively handles missing data in genetic clustering, improving subtype identification for alcohol dependence and revealing significant genetic associations.

Contribution

It extends multi-view co-clustering techniques to manage missing values, enhancing genetic subtype analysis in alcohol dependence studies.

Findings

Identified rs1229984 as a significant genetic variant for AD subtypes.

Validated the genetic association in an independent replication sample.

Improved clustering accuracy by handling missing data effectively.

Abstract

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.