Improved methods to identify stable, highly heritable subtypes of opioid use and related behaviors.
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.
Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA.
Jiangwen
J
Sun
Jinbo
J
Bi
Grace
G
Chan
David
D
Oslin
Lindsay
L
Farrer
Joel
J
Gelernter
Henry R
HR
Kranzler
D000328:Adult; D016739:Behavior, Addictive / Q000235:genetics*; D016022:Case-Control Studies; D057225:Data Mining / Q000379:methods; D005260:Female; D008040:Genetic Linkage / Q000235:genetics*; D006801:Humans; D008297:Male; D008875:Middle Aged; D009293:Opioid-Related Disorders / Q000235:genetics*; D010641:Phenotype