Genomic regions identified by overlapping clusters of nominally-positive SNPs from genome-wide studies of alcohol and illegal substance dependence

Declaring "replication" from results of genome wide association (GWA) studies is straightforward when major gene effects provide genome-wide significance for association of the same allele of the same SNP in each of multiple independent samples. However, such unambiguous replication is unlikely when phenotypes display polygenic genetic architecture, allelic heterogeneity, locus heterogeneity and when different samples display linkage disequilibria with different fine structures. We seek chromosomal regions that are tagged by clustered SNPs that display nominally-significant association in each of several independent samples. This approach provides one "nontemplate" approach to identifying overall replication of groups of GWA results in the face of difficult genetic architectures. We apply this strategy to 1 M SNP GWA results for dependence on: a) alcohol (including many individuals with dependence on other addictive substances) and b) at least one illegal substance (including many individuals dependent on alcohol). This approach provides high confidence in rejecting the null hypothesis that chance alone accounts for the extent to which clustered, nominally-significant SNPs from samples of the same racial/ethnic background identify the same sets of chromosomal regions. It identifies several genes that are also reported in other independent alcohol-dependence GWA datasets. There is more modest confidence in: a) identification of individual chromosomal regions and genes that are not also identified by data from other independent samples, b) the more modest overlap between results from samples of different racial/ethnic backgrounds and c) the extent to which any gene not identified herein is excluded, since the power of each of these individual samples is modest. Nevertheless, the strong overlap identified among the samples with similar racial/ethnic backgrounds supports contributions to individual differences in vulnerability to addictions that come from newer allelic variants that are common in subsets of current humans.     

Polysubstance Abuse–Vulnerability Genes: Genome Scans for Association, Using 1,004 Subjects and 1,494 Single-Nucleotide Polymorphisms

Strong genetic contributions to drug abuse vulnerability are well documented, but few chromosomal locations for human drug-abuse vulnerability alleles have been confirmed. We now identify chromosomal markers whose alleles distinguish drug abusers from control individuals in each of two samples, on the basis of pooled-sample microarray and association analyses. Reproducibly positive chromosomal regions defined by these markers in conjunction with previous results were especially unlikely to have been identified by chance. Positive markers identify the alcohol dehydrogenase (ADH) locus, flank the brain-derived neurotropic factor (BDNF) locus, and mark seven other regions previously linked to vulnerability to nicotine or alcohol abuse. These data support polygenic contributions of common allelic variants to polysubstance abuse vulnerability.     

Genome Wide Association for Addiction: Replicated Results and Comparisons of Two Analytic Approaches

Background

Vulnerabilities to dependence on addictive substances are substantially heritable complex disorders whose underlying genetic architecture is likely to be polygenic, with modest contributions from variants in many individual genes. “Nontemplate” genome wide association (GWA) approaches can identity groups of chromosomal regions and genes that, taken together, are much more likely to contain allelic variants that alter vulnerability to substance dependence than expected by chance.

Methodology/Principal Findings

We report pooled “nontemplate” genome-wide association studies of two independent samples of substance dependent vs control research volunteers (n = 1620), one European-American and the other African-American using 1 million SNP (single nucleotide polymorphism) Affymetrix genotyping arrays. We assess convergence between results from these two samples using two related methods that seek clustering of nominally-positive results and assess significance levels with Monte Carlo and permutation approaches. Both “converge then cluster” and “cluster then converge” analyses document convergence between the results obtained from these two independent datasets in ways that are virtually never found by chance. The genes identified in this fashion are also identified by individually-genotyped dbGAP data that compare allele frequencies in cocaine dependent vs control individuals.

Conclusions/Significance

These overlapping results identify small chromosomal regions that are also identified by genome wide data from studies of other relevant samples to extents much greater than chance. These chromosomal regions contain more genes related to “cell adhesion” processes than expected by chance. They also contain a number of genes that encode potential targets for anti-addiction pharmacotherapeutics. “Nontemplate” GWA approaches that seek chromosomal regions in which nominally-positive associations are found in multiple independent samples are likely to complement classical, “template” GWA approaches in which “genome wide” levels of significance are sought for SNP data from single case vs control comparisons.     

Mapping candidate hotspots of meiotic recombination in segments of human DNA cloned in the yeast<Emphasis Type="Italic"> Saccharomyces cerevisiae</Emphasis>

The hotspots of meiotic recombination in the human genome can be localized by genetic techniques. The resolution of these techniques is in the range of kilobases and depends on the density of the physical markers identifying allelic variants of the chromosomal loci. We thought it would be interesting to localize these sites with higher resolution. Assuming that some human chromosomal sites conserve their propensity for recombination when cloned in yeast, we localized the hotspots of recombination in several yeast artificial chromosomes (YACs) carrying human DNA. A number of potential recombination hotspots could be identified in the clones studied. Among them there are two classes of sites that are particularly recombination prone also in human meiotic cells: sites associated with CpG islands and sites located in the vicinity of long minisatellite sequences.Communicated by G. P. Georgiev     

Genetic vulnerability and susceptibility to substance dependence

The development of substance dependence requires the initiation of substance use and the conversion from experimental use to established use before development of dependence. Numerous large twin studies have indicated a significant genetic contribution to this process. Genetic studies to date have been most successful at identifying genetic factors that influence the transition from regular use to dependence. The availability of large cohort samples for nicotine and alcohol dependence has resulted in significant progress being made in understanding at least some of the genetic contributions to these addictions. Fewer studies have replicated specific genetic contributions to illicit drug use, though it is clear that there is a strong genetic component involved here as well. Substance dependence can be thought of as a pharmacogenetic illness, and most likely hundreds and more probably thousands of genetic variants will be required to fully explain the genetic input to this disease.     

The search for human osteoporosis genes

Osteoporosis is the most prevalent metabolic bone disease and a major clinical and public health problem. Heredity plays an important and well-established role in determining the lifetime risk of this disease. Major efforts are currently underway to identify the specific genes and their allelic variations that contribute to the heritable component to osteoporosis. A number of laboratories are using quantitative trait locus (QTL) methods of genome scanning in families and animal models to identify candidate genomic regions and, ultimately, the genes and genetic variations that lead to osteoporosis. Several chromosomal regions of the human genome have now been linked to osteoporosis-related phenotypes. Although the specific genes contributing to the majority of these linkage signals have not been identified, two positional candidate genes have now been identified: low density lipoprotein receptor-related protein 5 (LRP5) and bone morphogenetic protein 2 (BMP2). A number of QTL has also been identified by cross-breeding strains of mice with variable bone density and several of these QTL have been fine mapped, providing a rich new base for understanding osteoporosis. Genetic association analyses have also provided evidence for a modest relationship between allelic variants in several biological candidate genes and bone mass and the risk of fracture. These ongoing animal and human studies will provide a continuing source of new insight into the genetic regulation of bone and mineral metabolism and the molecular etiology of osteoporosis. The new insight that will emerge from this ongoing research should lead to new ways of diagnosing, preventing and treating the growing clinical and public health problem of osteoporosis.     

Strategies for performing genotype–phenotype association studies in nonhuman primates

Anthropoid primate models offer opportunities to study genetic influence on alcohol consumption and alcohol-related intermediate phenotypes in socially and behaviorally complex animal models that are closely related to humans, and in which functionally equivalent or orthologous genetic variants exist. This review will discuss the methods commonly used for performing candidate gene-based studies in rhesus macaques in order to model how functional genetic variation moderates risk for human psychiatric disorders. Various in silico and in vitro approaches to identifying functional genetic variants for performance of these studies will be discussed. Next, I will provide examples of how this approach can be used for performing candidate gene-based studies and for examining gene by environment interactions. Finally, these approaches will then be placed in the context of how function-guided studies can inform us of genetic variants that may be under selection across species, demonstrating how functional genetic variants that may have conferred selective advantage at some point in the evolutionary history of humans could increase risk for addictive disorders in modern society.     

Evolutionary meta-analysis of association studies reveals ancient constraints affecting disease marker discovery

Genome-wide disease association studies contrast genetic variation between disease cohorts and healthy populations to discover single nucleotide polymorphisms (SNPs) and other genetic markers revealing underlying genetic architectures of human diseases. Despite scores of efforts over the past decade, many reproducible genetic variants that explain substantial proportions of the heritable risk of common human diseases remain undiscovered. We have conducted a multispecies genomic analysis of 5,831 putative human risk variants for more than 230 disease phenotypes reported in 2,021 studies. We find that the current approaches show a propensity for discovering disease-associated SNPs (dSNPs) at conserved genomic positions because the effect size (odds ratio) and allelic P value of genetic association of an SNP relates strongly to the evolutionary conservation of their genomic position. We propose a new measure for ranking SNPs that integrates evolutionary conservation scores and the P value (E-rank). Using published data from a large case-control study, we demonstrate that E-rank method prioritizes SNPs with a greater likelihood of bona fide and reproducible genetic disease associations, many of which may explain greater proportions of genetic variance. Therefore, long-term evolutionary histories of genomic positions offer key practical utility in reassessing data from existing disease association studies, and in the design and analysis of future studies aimed at revealing the genetic basis of common human diseases.     

The fatty acid amide hydrolase 385 A/A (P129T) variant: haplotype analysis of an ancient missense mutation and validation of risk for drug addiction

The human fatty acid amide hydrolase (FAAH) missense mutation c.385 C→A, which results in conversion of a conserved proline residue to threonine (P129T), has been associated with street drug use and problem drug abuse. Although a link between the FAAH P129T variant and human drug abuse has been reported, the extent of risk and specific types of substance addiction vulnerability remain to be determined. Here, we investigated the relationship of the FAAH P129T variant to a number of linked single nucleotide polymorphisms to establish a haplotyping system, calculate the estimated age and origin of the FAAH 385 C→A mutation and evaluate its association with clinically significant drug addiction in a case control study. The results showed a significant over-representation of the FAAH P129T homozygotes in 249 subjects with documented multiple different drug addictions compared to drug free individuals of the same ethnic backgrounds (P = 0.05) using logistic regression analysis controlling for ethnicity. To increase the logistic regression analysis power by increasing the sample size, the data from our previous study (Sipe et al. in Proc Natl Acad Sci USA 99:8394–8399, 2002) were pooled with the present cohort which increased the significance to P = 0.00003. Investigation of the FAAH chromosomal backgrounds of the P129T variant in both multiple different drug addicted and control subjects revealed a common ancestral haplotype, marked population differences in haplotype genetic diversity and an estimated P129T mutation age of 114,425–177,525 years. Collectively, these results show that the P129T mutation is the only common mutation in the FAAH gene and is significantly associated with addictive traits. Moreover, this mutation appears to have arisen early in human evolution and this study validates the previous link between the FAAH P129T variant and vulnerability to addiction of multiple different drugs.     

Introduction to Deep Sequencing and Its Application to Drug Addiction Research with a Focus on Rare Variants

Through linkage analysis, candidate gene approach, and genome-wide association studies (GWAS), many genetic susceptibility factors for substance dependence have been discovered such as the alcohol dehydrogenase gene (ALDH2) for alcohol dependence (AD) and nicotinic acetylcholine receptor (nAChR) subunit variants on chromosomes 8 and 15 for nicotine dependence (ND). However, these confirmed genetic factors contribute only a small portion of the heritability responsible for each addiction. Among many potential factors, rare variants in those identified and unidentified susceptibility genes are supposed to contribute greatly to the missing heritability. Several studies focusing on rare variants have been conducted by taking advantage of next-generation sequencing technologies, which revealed that some rare variants of nAChR subunits are associated with ND in both genetic and functional studies. However, these studies investigated variants for only a small number of genes and need to be expanded to broad regions/genes in a larger population. This review presents an update on recently developed methods for rare-variant identification and association analysis and on studies focused on rare-variant discovery and function related to addictions.     

The VMAT2 gene in mice and humans: amphetamine responses, locomotion, cardiac arrhythmias, aging, and vulnerability to dopaminergic toxins.

Monoamine compartmentalization in monoaminergic neurons uses serial action of the plasma membrane and vesicular monoamine (VAMT2) transporters. We can now define the sequences of the genes encoding these transporters in mice and humans, examine influences of deletions of this gene and alteration in its expression levels in transgenic mice, and identify sequence polymorphisms in the human VMAT2 gene. Examination of VMAT2 variants can provide potential insights into roles for allelic variants at these loci in variant drug responses and in diseases linked to monoaminergic systems, including substance abuse and Parkinson's disease.     

Genetics of GABAergic signaling in nicotine and alcohol dependence

Both nicotine and alcohol addictions are common chronic brain disorders that are of great concern to individuals and society. Although genetics contributes significantly to these disorders, the susceptibility genes and variants underlying them remain largely unknown. Many years of genome-wide linkage and association studies have implicated a number of genes and pathways in the etiology of nicotine and alcohol addictions. In this communication, we focus on current evidence, primarily from human genetic studies, supporting the involvement of genes and variants in the GABAergic signaling system in the etiology of nicotine dependence and alcoholism based on linkage, association, and gene-by-gene interaction studies. Current efforts aim not only to replicate these findings in independent samples, but also to identify which variant contributes to the detected associations and through what molecular mechanisms.     

Progress and promise in understanding the genetic basis of common diseases

Susceptibility to common human diseases is influenced by both genetic and environmental factors. The explosive growth of genetic data, and the knowledge that it is generating, are transforming our biological understanding of these diseases. In this review, we describe the technological and analytical advances that have enabled genome-wide association studies to be successful in identifying a large number of genetic variants robustly associated with common disease. We examine the biological insights that these genetic associations are beginning to produce, from functional mechanisms involving individual genes to biological pathways linking associated genes, and the identification of functional annotations, some of which are cell-type-specific, enriched in disease associations. Although most efforts have focused on identifying and interpreting genetic variants that are irrefutably associated with disease, it is increasingly clear that—even at large sample sizes—these represent only the tip of the iceberg of genetic signal, motivating polygenic analyses that consider the effects of genetic variants throughout the genome, including modest effects that are not individually statistically significant. As data from an increasingly large number of diseases and traits are analysed, pleiotropic effects (defined as genetic loci affecting multiple phenotypes) can help integrate our biological understanding. Looking forward, the next generation of population-scale data resources, linking genomic information with health outcomes, will lead to another step-change in our ability to understand, and treat, common diseases.     

Multi‐allelic exact tests for Hardy–Weinberg equilibrium that account for gender

Statistical tests for Hardy–Weinberg equilibrium are important elementary tools in genetic data analysis. X‐chromosomal variants have long been tested by applying autosomal test procedures to females only, and gender is usually not considered when testing autosomal variants for equilibrium. Recently, we proposed specific X‐chromosomal exact test procedures for bi‐allelic variants that include the hemizygous males, as well as autosomal tests that consider gender. In this study, we present the extension of the previous work for variants with multiple alleles. A full enumeration algorithm is used for the exact calculations of tri‐allelic variants. For variants with many alternate alleles, we use a permutation test. Some empirical examples with data from the 1,000 genomes project are discussed.     

Use of denaturing HPLC to map human and murine genes and to validate single-nucleotide polymorphisms

Linkage mapping has been extensively applied in the murine and human genomes. It remains a powerful approach to mapping genes and identifying genetic variants. As genome efforts identify large numbers of single-nucleotide polymorphisms, it will be critical to validate these polymorphisms and confirm their gene assignment and chromosomal location. The presence of pseudogenes can confuse such efforts. We have used denaturing HPLC to identify polymorphisms in human genes and to genotype individuals in selected CEPH pedigrees. The same approach has been applied to the mapping of murine genes in interspecies backcross animals. This strategy is rapid, accurate and superior in several respects to other technologies.     

The genetic architecture of autism spectrum disorders(ASDs) and the potential importance of common regulatory genetic variants

Currently, there is great interest in identifying genetic variants that contribute to the risk of developing autism spectrum disorders(ASDs), due in part to recent increases in the frequency of diagnosis of these disorders worldwide. While there is nearly universal agreement that ASDs are complex diseases, with multiple genetic and environmental contributing factors, there is less agreement concerning the relative importance of common vs rare genetic variants in ASD liability. Recent observations that rare mutations and copy number variants(CNVs) are frequently associated with ASDs, combined with reduced fecundity of individuals with these disorders, has led to the hypothesis that ASDs are caused primarily by de novo or rare genetic mutations. Based on this model, large-scale whole-genome DNA sequencing has been proposed as the most appropriate method for discovering ASD liability genes. While this approach will undoubtedly identify many novel candidate genes and produce important new insights concerning the genetic causes of these disorders, a full accounting of the genetics of ASDs will be incomplete absent an understanding of the contributions of common regulatory variants, which are likely to influence ASD liability by modifying the effects of rare variants or, by assuming unfavorable combinations, directly produce these disorders. Because it is not yet possible to identify regulatory genetic variants by examination of DNA sequences alone, their identification will require experimentation. In this essay, I discuss these issues and describe the advantages of measurements of allelic expression imbalance(AEI) of m RNA expression for identifying cis-acting regulatory variants that contribute to ASDs.     

Addictive drugs and their relationship with infectious diseases

The use of drugs of abuse, both recreationally and medicinally, may be related to serious public health concerns. There is a relationship between addictive drugs of abuse such as alcohol and nicotine in cigarette smoke, as well as illegal drugs such as opiates, cocaine and marijuana, and increased susceptibility to infections. The nature and mechanisms of immunomodulation induced by such drugs of abuse are described in this review. The effects of opiates and marijuana, using animal models as well as in vitro studies with immune cells from experimental animals and humans, have shown that immunomodulation induced by these drugs is mainly receptor-mediated, either directly by interaction with specific receptors on immune cells or indirectly by reaction with similar receptors on cells of the nervous system. Similar studies also show that cocaine and nicotine have marked immunomodulatory effects, which are mainly receptor-mediated. Both cocaine, an illegal drug, and nicotine, a widely used legal addictive component of cigarettes, are markedly immunomodulatory and increase susceptibility to infection. The nature and mechanism of immunomodulation induced by alcohol, the most widely used addictive substance of abuse, are similar but immunomodulatory effects, although not receptor-mediated. The many research studies on the effects of these drugs on immunity and increased susceptibility to infectious diseases, including AIDS, are providing a better understanding of the complex interactions between immunity, infections and substance abuse.     

Pooled analysis of loss of heterozygosity in breast cancer: a genome scan provides comparative evidence for multiple tumor suppressors and identifies novel candidate regions

Somatic loss of heterozygosity (LOH) has been widely reported in breast cancer as a means of identifying putative tumor-suppressor genes. However, individual studies have rarely spanned more than a single chromosome, and the varying criteria used to declare LOH complicate efforts to formally differentiate regions of consistent versus sporadic (random) loss. We report here the compilation of an extensive database from 151 published LOH studies of breast cancer, with summary data from >15,000 tumors and primary allelotypes from >4,300 tumors. Allelic loss was evaluated at 1,168 marker loci, with large variation in the density of informative observations across the genome. Using studies in which primary allelotype information was available, we employed a likelihood-based approach with a formal chromosomal instability and selection model. The approach seeks direct evidence for preferential loss at each locus compared with nearby loci, accounts for heterogeneity across studies, and enables the direct comparison of candidate regions across the genome. Striking preferential loss was observed (in descending order of significance) in specific regions of chromosomes 7q, 16q, 13q, 17p, 8p, 21q, 3p, 18q, 2q, and 19p, as well as other regions, in many cases coinciding with previously identified candidate genes or known fragile sites. Many of these observations were not possible from any single LOH study, and our results suggest that many previously reported LOH results are not systematic or reproducible. Our approach provides a comparative framework for further investigation of regions exhibiting LOH and identifies broad genomic regions for which there exist few data.     

New developments in cytogenetics

Novel methods allowing to analyze the human genome make it possible to assess old questions such as the molecular basis of structural chromosome anomalies and the diathesis to aneuploidy. The architecture of the human genome as unravelled by the human genome sequencing project allows to explain the recurrence of microdeletions and microduplications caused by a non allelic homologous recombination involving segmental duplications created during the evolution of primates. This structural feature of the human genome is associated with a novel class of genetic diseases called genomic disorders as opposed to genetic diseases due to gene mutations. The study of the parental and cellular origin of aneuploidy shed new light on the different mechanisms controlling meiosis in man and woman. In addition it contributes to define the role of maternal age and genetic recombination on the behavior of chromosomes during meiosis. These new data greatly contribute to our understanding of human chromosomal diseases.