Large scale association analysis for drug addiction: results from SNP to gene.
Many genetic association studies used single nucleotide polymorphisms (SNPs) data to identify genetic variants for complex diseases. Although SNP-based associations are most common in genome-wide association studies (GWAS), gene-based association analysis has received increasing attention in understanding genetic etiologies for complex diseases. While both methods have been used to analyze the same data, few genome-wide association studies compare the results or observe the connection between them. We performed a comprehensive analysis of the data from the Study of Addiction: Genetics and Environment (SAGE) and compared the results from the SNP-based and gene-based analyses. Our results suggest that the gene-based method complements the individual SNP-based analysis, and conceptually they are closely related. In terms of gene findings, our results validate many genes that were either reported from the analysis of the same dataset or based on animal studies for substance dependence.
Department of Biostatistics, Yale University School of Public Health, New Haven, CT 06520, USA.
Xiaobo
X
Guo
Zhifa
Z
Liu
Xueqin
X
Wang
Heping
H
Zhang
D000465:Algorithms; D002874:Chromosome Mapping; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D015894:Genome, Human / Q000235:genetics; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide; D019966:Substance-Related Disorders / Q000235:genetics*