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1.  Validation of the Bayesian Alcoholism Test Compared to Single Biomarkers in Detecting Harmful Drinking 
Aims: Conventional tests for alcohol dependence often fail to detect hazardous and harmful alcohol use (HHAU) accurately. We previously validated the Bayesian Alcoholism Test (BAT) for the detection of HHAU among males. This uses 15 biochemical and clinical variables, including questionnaire data to calculate the probability of harmful (>80 g alcohol/day), hazardous (40–80 g/day) and ‘moderate’ (<40 g/day) drinking. Here we investigate the BAT's diagnostic performance when more limited clinical data are available. Methods: The WHO/ISBRA Collaborative Project recruited subjects from the general community and alcohol dependence treatment services. We analysed data from male drinkers: 318 alcohol dependent, 220 heavy and 712 moderate drinkers. Drinking was assessed using the Alcohol-Use Disorders and Associated Disabilities Interview Schedule. Eight of 15 markers used in the original BAT could be extracted from the WHO/ISBRA dataset. Results: Comparing harmful to moderate drinkers, the area under the ROC curve for BAT (0.90) was significantly higher than that for CDT (0.82), GGT (0.77) and AST (0.76). Comparing hazardous to moderate drinkers, the area under the ROC curve for BAT (0.78) was significantly higher than that for AST (0.65) but not significantly higher than that for CDT (0.71) and GGT (0.70). For all 1250 subjects, the amount consumed correlated significantly better with BAT (0.65) than with CDT (0.52), GGT (0.44) or AST (0.40) alone. Conclusions: The BAT is more accurate than commonly used single biological markers in detecting harmful alcohol use, even when only half the input requirements are available. Computerized record keeping increases the practicality of use of algorithms in the detection of harmful drinking.
doi:10.1093/alcalc/agp011
PMCID: PMC3122887  PMID: 19293144
2.  Addictions Biology: Haplotype-Based Analysis for 130 Candidate Genes on a Single Array 
Aims: To develop a panel of markers able to extract full haplotype information for candidate genes in alcoholism, other addictions and disorders of mood and anxiety. Methods: A total of 130 genes were haplotype tagged and genotyped in 7 case/control populations and 51 reference populations using Illumina GoldenGate SNP genotyping technology, determining haplotype coverage. We also constructed and determined the efficacy of a panel of 186 ancestry informative markers. Results: An average of 1465 loci were genotyped at an average completion rate of 91.3%, with an average call rate of 98.3% and replication rate of 99.7%. Completion and call rates were lowered by the performance of two datasets, highlighting the importance of the DNA quality in high throughput assays. A comparison of haplotypes captured by the Addictions Array tagging SNPs and commercially available whole-genome arrays from Illumina and Affymetrix shows comparable performance of the tag SNPs to the best whole-genome array in all populations for which data are available. Conclusions: Arrays of haplotype-tagged candidate genes, such as this addictions-focused array, represent a cost-effective approach to generate high-quality SNP genotyping data useful for the haplotype-based analysis of panels of genes such as these 130 genes of interest to alcohol and addictions researchers. The inclusion of the 186 ancestry informative markers allows for the detection and correction for admixture and further enhances the utility of the array.
doi:10.1093/alcalc/agn032
PMCID: PMC2724863  PMID: 18477577

Results 1-2 (2)