Journal article
Using genetic information from candidate gene and genome-wide association studies in risk prediction for alcohol dependence
Addiction biology, Vol.19(4), pp.708-721
01/30/2013
DOI: 10.1111/adb.12035
PMCID: PMC3664249
PMID: 23362995
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 to 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 to 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.
Details
- Title: Subtitle
- Using genetic information from candidate gene and genome-wide association studies in risk prediction for alcohol dependence
- Creators
- Jia Yan - Virginia Commonwealth UniversityFazil Aliev - Virginia Commonwealth UniversityBradley T Webb - Virginia Commonwealth UniversityKenneth S Kendler - Virginia Commonwealth UniversityVernell S Williamson - Virginia Commonwealth UniversityHoward J Edenberg - Indiana UniversityArpana Agrawal - Washington University in St. LouisMark Z Kos - Texas Biomedical Research InstituteLaura Almasy - Texas Biomedical Research InstituteJohn I Nurnberger - Indiana UniversityMarc A Schuckit - University of California San DiegoJohn R Kramer - University of IowaJohn P Rice - Washington University in St. LouisSamuel Kuperman - University of IowaAlison M Goate - Washington University in St. LouisJay A Tischfield - Rutgers, The State University of New JerseyBernice Porjesz - University at Albany, State University of New YorkDanielle M Dick - Virginia Commonwealth University
- Resource Type
- Journal article
- Publication Details
- Addiction biology, Vol.19(4), pp.708-721
- DOI
- 10.1111/adb.12035
- PMID
- 23362995
- PMCID
- PMC3664249
- NLM abbreviation
- Addict Biol
- ISSN
- 1355-6215
- eISSN
- 1369-1600
- Language
- English
- Date published
- 01/30/2013
- Academic Unit
- Psychiatry
- Record Identifier
- 9984293653402771
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