RECENT RESULTS FROM GENOME-WIDE SCANS FOR ADDICTION AND OTHER BRAIN DISEASES
Pooled Association Genome-Scanning for Addictions: Convergent Observations
George R. Uhl, M.D., Ph.D.
[Slides not available]
Classical genetic studies document strong, complex genetic contributions to the abuse of multiple addictive substances and to the ability to quit smoking. Mnemonic processes that are likely to include those involved with substance dependence, and to the volumes of brain gray matter in regions that are likely to contribute to mnemonic/cognitive and addictive processes, also show substantially heritable individual differences. Whole genome association studies have been increasingly recognized as methods of choice for elucidating the genetic underpinnings of complex genetically and environmentally influenced disorders, such as addictions.
We can now identify the results of whole genome association studies—using multiple samples from individuals addicted to drugs and matched controls—that now identify many of the loci and genes that contain allelic variants that are likely to provide the heritable components of human addiction vulnerability. These data identify a surprising number of haplotypes in genes that are involved with cell adhesion processes, whereby neurons recognize each other during development and in the synaptic modifications that occur in adult brains. Data from these addiction-vulnerability genes, as well as data from the genes that distinguish successful versus unsuccessful quitters, are likely to inform our understanding of addictions and also to provide sets of markers that are clinically useful for better assignment of individuals to appropriate treatment and prevention modalities. Medium-sized clinical trials of nicotine cessation, for example, could gain substantial power and reduce costs by using genotypic stratification.
Understanding the Genetic Architecture of Common Disease:
A Comparison of Genome Scans
Dennis G. Ballinger, Ph.D.
Perlegen Sciences has conducted several genome-wide SNP association studies. The different designs of these studies will be compared and contrasted. In addition, common and distinct features of the genetic architecture of the various phenotypes will be discussed. The studies involved will include predisposition to Parkinson’s disease with both sibling and unrelated controls; late onset Alzheimer’s disease cases and matched controls; and nicotine-addicted and matched exposed nonaddicted controls.
A Hybrid Approach to Genetic Studies: Genome-Wide Association Study and Comprehensive Candidate Gene Analysis of Nicotine Dependence
Laura J. Bierut, M.D.
Smoking is the leading source of preventable death in the United States, and twin studies consistently demonstrate strong genetic contributions to smoking. The NICSNP Project is a hybrid study of a genome-wide association (GWA) study in tandem with the systematic coverage of biologically relevant candidate genes. The sample consisted of 1,050 nicotine-dependent cases and 879 nondependent smokers as controls. All participants were selected from two community-based studies, the Collaborative Genetics Study of Nicotine Dependence (United States) and the Nicotine Addiction Genetics Project (Australia). The GWA study performed pooled genotyping of 2.4 million single nucleotide polymorphisms (SNPs) followed by individual genotyping of the top 40,000 signals. The second arm of the study was a comprehensive candidate gene study, where individual genotyping was conducted in more than 300 genes chosen for their biological significance by experts in the field of addiction. There were convergent findings in these complementary approaches. Common variants in candidate genes exhibited the highest level of statistical significance and are among the top 25 signals from the GWA study. In addition, the GWA study results identified novel loci not previously associated with the risk for nicotine dependence. These findings are a major advance in the genetics of complex human disease, and data will be available upon publication for further analyses by the scientific community.
Statistical Methodologies for Analyzing Whole Genome Association Data
John P. Rice, Ph.D.
Although it is clear that whole genome association (WGA) studies will be done in the near future on many genetic disorders, it is less clear how to design, analyze, and interpret these studies. We will address several of these issues and will underscore that there are limitations in most current methods. It is essential to understand these limitations and to evaluate the underlying assumptions.
We will cover the distinction between linkage disequilibrium (LD) blocks and bins and the different properties of D-Prime and R-Square in single nucleotide polymorphism (SNP) selection. We will describe analytic issues—problems of multiple testing, use of prior linkage information, use of genomic controls, and the problem of whether to base the analysis on allelic or genotypic differences. We will also discuss the analysis of haplotypes and SNPs in different genes.
Finally, we will consider the implications of WGA studies for our understanding of the genetics of substance use disorders. The public availability of genotypic data and DNA to qualified investigators is a key area of debate.