Using genetic information from candidate gene and genome-wide association studies in risk prediction for alcohol dependence

Jia Yan, Fazil Aliev, Bradley T. Webb, Kenneth S. Kendler, Vernell S. Williamson, Howard J. Edenberg, Arpana Agrawal, Mark Z. Kos, Laura Almasy, John I. Nurnberger, Marc A. Schuckit, John R. Kramer, John P. Rice, Samuel Kuperman, Alison M. Goate, Jay A. Tischfield, Bernice Porjesz, Danielle M. Dick

Research output: Contribution to journalArticle

31 Citations (Scopus)

Abstract

Family-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.

Original languageEnglish (US)
Pages (from-to)708-721
Number of pages14
JournalAddiction Biology
Volume19
Issue number4
DOIs
StatePublished - Jul 2014

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Genome-Wide Association Study
Alcoholism
Single Nucleotide Polymorphism
Genes
Aptitude
Area Under Curve
ROC Curve
Alleles

Keywords

  • Clinical validity
  • genetic risk prediction
  • polygenic risk score
  • psychiatric genetic counseling
  • receiver operating characteristic curve analysis

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Psychiatry and Mental health
  • Pharmacology
  • Medicine(all)

Cite this

Using genetic information from candidate gene and genome-wide association studies in risk prediction for alcohol dependence. / Yan, Jia; Aliev, Fazil; Webb, Bradley T.; Kendler, Kenneth S.; Williamson, Vernell S.; Edenberg, Howard J.; Agrawal, Arpana; Kos, Mark Z.; Almasy, Laura; Nurnberger, John I.; Schuckit, Marc A.; Kramer, John R.; Rice, John P.; Kuperman, Samuel; Goate, Alison M.; Tischfield, Jay A.; Porjesz, Bernice; Dick, Danielle M.

In: Addiction Biology, Vol. 19, No. 4, 07.2014, p. 708-721.

Research output: Contribution to journalArticle

Yan, J, Aliev, F, Webb, BT, Kendler, KS, Williamson, VS, Edenberg, HJ, Agrawal, A, Kos, MZ, Almasy, L, Nurnberger, JI, Schuckit, MA, Kramer, JR, Rice, JP, Kuperman, S, Goate, AM, Tischfield, JA, Porjesz, B & Dick, DM 2014, 'Using genetic information from candidate gene and genome-wide association studies in risk prediction for alcohol dependence', Addiction Biology, vol. 19, no. 4, pp. 708-721. https://doi.org/10.1111/adb.12035
Yan, Jia ; Aliev, Fazil ; Webb, Bradley T. ; Kendler, Kenneth S. ; Williamson, Vernell S. ; Edenberg, Howard J. ; Agrawal, Arpana ; Kos, Mark Z. ; Almasy, Laura ; Nurnberger, John I. ; Schuckit, Marc A. ; Kramer, John R. ; Rice, John P. ; Kuperman, Samuel ; Goate, Alison M. ; Tischfield, Jay A. ; Porjesz, Bernice ; Dick, Danielle M. / Using genetic information from candidate gene and genome-wide association studies in risk prediction for alcohol dependence. In: Addiction Biology. 2014 ; Vol. 19, No. 4. pp. 708-721.
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AU - Rice, John P.

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N2 - Family-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.

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