Improved methods to identify stable, highly heritable subtypes of opioid use and related behaviors

• Published in: Addictive behaviors Vol. 37; no. 10; pp. 1138 - 1144
• Main Authors: Sun, Jiangwen, Bi, Jinbo, Chan, Grace, Oslin, David, Farrer, Lindsay, Gelernter, Joel, Kranzler, Henry R.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.10.2012; Elsevier Science Ltd
• Subjects: Addictive behaviors; Adult; Behavior, Addictive - genetics; Case-Control Studies; Cluster analysis; Data mining; Data Mining - methods; Drug abuse; Drug addiction; Female; Genetic Linkage - genetics; Genetics; Heritability; Hierarchical clustering; Humans; k-medoids clustering; Male; Middle Aged; Opioid dependence; Opioid-Related Disorders - genetics; Phenotype; Psychiatry; Subtypes; Opioid dependence; Subtypes; Phenotype; Heritability; Hierarchical clustering; k-medoids clustering
• ISSN: 0306-4603; 1873-6327; 1873-6327
• DOI: 10.1016/j.addbeh.2012.05.010

Although there is evidence that opioid dependence (OD) is heritable, efforts to identify genes contributing to risk for the disorder have been hampered by its complex etiology and variable clinical manifestations. Decomposition of a complex set of opioid users into homogeneous subgroups could enhance genetic analysis. We applied a series of data mining techniques, including multiple correspondence analysis, variable selection and cluster analysis, to 69 opioid-related measures from 5390 subjects aggregated from family-based and case–control genetic studies to identify homogeneous subtypes and estimate their heritability. Novel aspects of this work include our use of 1) heritability estimates of specific clinical features of OD to enhance the heritability of the subtypes and 2) a k-medoids clustering method in combination with hierarchical clustering to yield replicable clusters that are less sensitive to noise than previous methods. We identified five homogeneous groups, including two large groups comprised of 762 and 1353 heavy opioid users, with estimated heritability of 0.69 and 0.76, respectively. These methods represent a promising approach to the identification of highly heritable subtypes in complex, heterogeneous disorders. ► We applied data mining techniques to opioid-related measures. ► The approach was less sensitive to noise in the data than a prior approach. ► We identified five homogeneous groups, including two heavy opioid use groups. ► The heritability of the two largest clusters was 0.76 and 0.69, respectively.