Using genetic information from candidate gene and genome-wide association studies in risk prediction for alcohol dependence.
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Webb, Bradley T
Kendler, Kenneth S
Williamson, Vernell S
Edenberg, Howard J
Kos, Mark Z
Nurnberger, John I
Schuckit, Marc A
Kramer, John R
Rice, John P
Goate, Alison M
Tischfield, Jay A
Dick, Danielle M
genetic risk prediction
polygenic risk score
psychiatric genetic counseling
receiver operating characteristic curve analysis
Journal titleAddiction biology
Publication Begin page708
Publication End page21
MetadataShow full item record
AbstractFamily-based and genome-wide association studies (GWAS) of alcohol dependence (AD) have reported numerous associated variants. The clinical validity of these variants for predicting AD compared with family history information has not been reported. Using the Collaborative Study on the Genetics of Alcoholism (COGA) and the Study of Addiction: Genes and Environment (SAGE) GWAS samples, we examined the aggregate impact of multiple single nucleotide polymorphisms (SNPs) on risk prediction. We created genetic sum scores by adding risk alleles associated in discovery samples, and then tested the scores for their ability to discriminate between cases and controls in validation samples. Genetic sum scores were assessed separately for SNPs associated with AD in candidate gene studies and SNPs from GWAS analyses that met varying P-value thresholds. Candidate gene sum scores did not exhibit significant predictive accuracy. Family history was a better classifier of case-control status, with a significant area under the receiver operating characteristic curve (AUC) of 0.686 in COGA and 0.614 in SAGE. SNPs that met less stringent P-value thresholds of 0.01-0.50 in GWAS analyses yielded significant AUC estimates, ranging from mean estimates of 0.549 for SNPs with P < 0.01 to 0.565 for SNPs with P < 0.50. This study suggests that SNPs currently have limited clinical utility, but there is potential for enhanced predictive ability with better understanding of the large number of variants that might contribute to risk.
CitationYan J, Aliev F, Webb BT, Kendler KS, Williamson VS, Edenberg HJ, Agrawal A, Kos MZ, Almasy L, Nurnberger JI Jr, Schuckit MA, Kramer JR, Rice JP, Kuperman S, Goate AM, Tischfield JA, Porjesz B, Dick DM. Using genetic information from candidate gene and genome-wide association studies in risk prediction for alcohol dependence. Addict Biol. 2014 Jul;19(4):708-21. doi: 10.1111/adb.12035. Epub 2013 Jan 30. PMID: 23362995; PMCID: PMC3664249.
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- Creative Commons
Except where otherwise noted, this item's license is described as © 2013 The Authors, Addiction Biology © 2013 Society for the Study of Addiction.
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