Heroin addiction in African Americans: a hypothesis-driven association study

Heroin addiction is a chronic complex disease with a substantial genetic contribution. This study was designed to identify gene variants associated with heroin addiction in African Americans. The emphasis was on genes involved in reward modulation, behavioral control, cognitive function, signal transduction and stress response. We have performed a case–control association analysis by screening with 1350 variants of 130 genes. The sample consisted of 202 former severe heroin addicts in methadone treatment and 167 healthy controls with no history of drug abuse. Single nucleotide polymorphism (SNP), haplotype and multi-SNP genotype pattern analyses were performed. Seventeen SNPs showed point-wise significant association with heroin addiction (nominal P < 0.01). These SNPs are from genes encoding several receptors: adrenergic ( ADRA1A ), arginine vasopressin ( AVPR1A ), cholinergic ( CHRM2 ), dopamine (DRD1 ), GABA-A ( GABRB3 ), glutamate ( GRIN2A ) and serotonin ( HTR3A ) as well as alcohol dehydrogenase ( ADH7 ), glutamic acid decarboxylase ( GAD1 and GAD2 ), the nucleoside transporter ( SLC29A1 ) and diazepam-binding inhibitor ( DBI ). The most significant result of the analyses was obtained for the GRIN2A haplotype G-A-T (rs4587976-rs1071502-rs1366076) with protective effect ( P _uncorrected = 9.6E- 05, P _corrected = 0.058). This study corroborates several reported associations with alcohol and drug addiction as well as other related disorders and extends the list of variants that may affect the development of heroin addiction. Further studies will be necessary to replicate these associations and to elucidate the roles of these variants in drug addiction vulnerability.     

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.     

A systematic approach to understand the functional consequences of non-protein coding risk regions

A primary goal of genetic association studies is to elucidate genes and novel biological mechanisms involved in disease. Recently, genome-wide association studies have identified many common genetic variants that are significantly associated with complex diseases such as cancer. In contrast to mutation-causing Mendelian disorders, a sizable fraction of the variants lies outside known protein-coding regions; therefore, understanding their biological consequences presents a major challenge in human genetics. Here we describe an integrated framework to allow non-protein coding loci to be annotated with respect to regulatory functions. This will facilitate identification of target genes as well as prioritize variants for functional testing.     

Powerful multilocus tests of genetic association in the presence of gene-gene and gene-environment interactions

In modern genetic epidemiology studies, the association between the disease and a genomic region, such as a candidate gene, is often investigated using multiple SNPs. We propose a multilocus test of genetic association that can account for genetic effects that might be modified by variants in other genes or by environmental factors. We consider use of the venerable and parsimonious Tukey's 1-degree-of-freedom model of interaction, which is natural when individual SNPs within a gene are associated with disease through a common biological mechanism; in contrast, many standard regression models are designed as if each SNP has unique functional significance. On the basis of Tukey's model, we propose a novel but computationally simple generalized test of association that can simultaneously capture both the main effects of the variants within a genomic region and their interactions with the variants in another region or with an environmental exposure. We compared performance of our method with that of two standard tests of association, one ignoring gene-gene/gene-environment interactions and the other based on a saturated model of interactions. We demonstrate major power advantages of our method both in analysis of data from a case-control study of the association between colorectal adenoma and DNA variants in the NAT2 genomic region, which are well known to be related to a common biological phenotype, and under different models of gene-gene interactions with use of simulated data.     

Genetics of human aggressive behaviour

A consideration of the evolutionary, physiological and anthropological aspects of aggression suggests that individual differences in such behaviour will have important genetic as well as environmental underpinning. Surveys of the likely pathways controlling the physiological and neuronal processes involved highlight, as obvious targets to investigate, genes implicated in sexual differentiation, anxiety, stress response and the serotonin neurotransmitter pathway. To date, however, association studies on single candidates have provided little evidence for any such loci with a major effect size. This may be because genes do not operate independently, but function against a background in which other genetic and environmental factors are crucial. Indeed, a series of recent studies, particularly concentrating on the serotonin and norepinephrine metabolising enzyme, monoamine oxidase A, has emphasised the necessity of examining gene by environmental interactions if the contributions of individual loci are to be understood. These findings will have major significance for the interpretation and analysis of data from detailed whole genome association studies. Functional imaging studies of genetic variants affecting serotonin pathways have also provided valuable insights into potential links between genes, brain and aggressive behaviour.     

Clinical significance of apolipoprotein A5

PURPOSE OF REVIEW: We have examined the evidence from recent human studies examining the role of apolipoprotein A-V in triglyceride-rich lipoprotein metabolism and cardiovascular disease risk. Special emphasis was placed on the evidence emerging from the association between genetic variability at the apolipoprotein A5 locus, lipid phenotypes and disease outcomes. Moreover, we address recent reports evaluating apolipoprotein A5 gene-environment interactions in relation to cardiovascular disease and its common risk factors. RECENT FINDINGS: Several genetic association studies have continued to strengthen the position of APOA5 as a major gene that is involved in triglyceride metabolism and modulated by dietary factors and pharmacological therapies. Moreover, genetic variants at this locus have been significantly associated with both coronary disease and stroke risks. SUMMARY: Apolipoprotein A-V has an important role in lipid metabolism, specifically for triglyceride-rich lipoproteins. However, its mechanism of action is still poorly understood. Clinical significance at present comes largely from genetic studies showing a consistent association with plasma triglyceride concentrations. Moreover, the effects of common genetic variants on triglyceride concentrations and disease risk are further modulated by other factors such as diet, pharmacological interventions and BMI. Therefore, these genetic variants could be potentially used to predict cardiovascular disease risk and individualize therapeutic options to decrease cardiovascular disease risk.     

Exome sequencing followed by large-scale genotyping suggests a limited role for moderately rare risk factors of strong effect in schizophrenia

Schizophrenia is a severe psychiatric disorder with strong heritability and marked heterogeneity in symptoms, course, and treatment response. There is strong interest in identifying genetic risk factors that can help to elucidate the pathophysiology and that might result in the development of improved treatments. Linkage and genome-wide association studies (GWASs) suggest that the genetic basis of schizophrenia is heterogeneous. However, it remains unclear whether the underlying genetic variants are mostly moderately rare and can be identified by the genotyping of variants observed in sequenced cases in large follow-up cohorts or whether they will typically be much rarer and therefore more effectively identified by gene-based methods that seek to combine candidate variants. Here, we consider 166 persons who have schizophrenia or schizoaffective disorder and who have had either their genomes or their exomes sequenced to high coverage. From these data, we selected 5,155 variants that were further evaluated in an independent cohort of 2,617 cases and 1,800 controls. No single variant showed a study-wide significant association in the initial or follow-up cohorts. However, we identified a number of case-specific variants, some of which might be real risk factors for schizophrenia, and these can be readily interrogated in other data sets. Our results indicate that schizophrenia risk is unlikely to be predominantly influenced by variants just outside the range detectable by GWASs. Rather, multiple rarer genetic variants must contribute substantially to the predisposition to schizophrenia, suggesting that both very large sample sizes and gene-based association tests will be required for securely identifying genetic risk factors.     

Estimating Genetic Effects and Quantifying Missing Heritability Explained by Identified Rare-Variant Associations

Next-generation sequencing has led to many complex-trait rare-variant (RV) association studies. Although single-variant association analysis can be performed, it is grossly underpowered. Therefore, researchers have developed many RV association tests that aggregate multiple variant sites across a genetic region (e.g., gene), and test for the association between the trait and the aggregated genotype. After these aggregate tests detect an association, it is only possible to estimate the average genetic effect for a group of RVs. As a result of the "winner’s curse," such an estimate can be biased. Although for common variants one can obtain unbiased estimates of genetic parameters by analyzing a replication sample, for RVs it is desirable to obtain unbiased genetic estimates for the study where the association is identified. This is because there can be substantial heterogeneity of RV sites and frequencies even among closely related populations. In order to obtain an unbiased estimate for aggregated RV analysis, we developed bootstrap-sample-split algorithms to reduce the bias of the winner’s curse. The unbiased estimates are greatly important for understanding the population-specific contribution of RVs to the heritability of complex traits. We also demonstrate both theoretically and via simulations that for aggregate RV analysis the genetic variance for a gene or region will always be underestimated, sometimes substantially, because of the presence of noncausal variants or because of the presence of causal variants with effects of different magnitudes or directions. Therefore, even if RVs play a major role in the complex-trait etiologies, a portion of the heritability will remain missing, and the contribution of RVs to the complex-trait etiologies will be underestimated.     

Pathways and Networks-Based Analysis of Candidate Genes Associated with Nicotine Addiction

Nicotine is the addictive substance in tobacco and it has a broad impact on both the central and peripheral nervous systems. Over the past decades, an increasing number of genes potentially involved in nicotine addiction have been identified by different technical approaches. However, the molecular mechanisms underlying nicotine addiction remain largely unclear. Under such situation, a comprehensive analysis focusing on the overall functional characteristics of these genes, as well as how they interact with each other will provide us valuable information to understand nicotine addiction. In this study, we presented a systematic analysis on nicotine addiction-related genes to identify the major underlying biological themes. Functional analysis revealed that biological processes and biochemical pathways related to neurodevelopment, immune system and metabolism were significantly enriched in the nicotine addiction-related genes. By extracting the nicotine addiction-specific subnetwork, a number of novel genes associated with addiction were identified. Moreover, we constructed a schematic molecular network for nicotine addiction via integrating the pathways and network, providing an intuitional view to understand the development of nicotine addiction. Pathway and network analysis indicated that the biological processes related to nicotine addiction were complex. Results from our work may have important implications for understanding the molecular mechanism underlying nicotine addiction.     

Genetic insights into age-related macular degeneration: controversies addressing risk, causality, and therapeutics

Age-related macular degeneration (AMD) is a common condition among the elderly population that leads to the progressive central vision loss and serious compromise of quality of life for its sufferers. It is also one of the few disorders for whom the investigation of its genetics has yielded rich insights into its diversity and causality and holds the promise of enabling clinicians to provide better risk assessments for individuals as well as to develop and selectively deploy new therapeutics to either prevent or slow the development of disease and lessen the threat of vision loss. The genetics of AMD began initially with the appreciation of familial aggregation and increase risk and expanded with the initial association of APOE variants with the disease. The first major breakthroughs came with family-based linkage studies of affected (and discordant) sibs, which identified a number of genetic loci and led to the targeted search of the 1q31 and 10q26 loci for associated variants. Three of the initial four reports for the CFH variant, Y402H, were based on regional candidate searches, as were the two initial reports of the ARMS2/HTRA1 locus variants. Case-control association studies initially also played a role in discovering the major genetic variants for AMD, and the success of those early studies have been used to fuel enthusiasm for the methodology for a number of diseases. Until 2010, all of the subsequent genetic variants associated with AMD came from candidate gene testing based on the complement factor pathway. In 2010, several large-scale genome-wide association studies (GWAS) identified genes that had not been previously identified. Much of this historical information is available in a number of recent reviews (Chen et al., 2010b; Deangelis et al., 2011; Fafowora and Gorin, 2012b; Francis and Klein, 2011; Kokotas et al., 2011). Large meta analysis of AMD GWAS has added new loci and variants to this collection (Chen et al., 2010a; Kopplin et al., 2010; Yu et al., 2011). This paper will focus on the ongoing controversies that are confronting AMD genetics at this time, rather than attempting to summarize this field, which has exploded in the past 5 years.     

Genetic association analyses of PDYN polymorphisms with heroin and cocaine addiction

Genetic factors are believed to account for 30-50% of the risk for cocaine and heroin addiction. Dynorphin peptides, derived from the prodynorphin (PDYN) precursor, bind to opioid receptors, preferentially the kappa-opioid receptor, and may mediate the aversive effects of drugs of abuse. Dynorphin peptides produce place aversion in animals and produce dysphoria in humans. Cocaine and heroin have both been shown to increase expression of PDYN in brain regions relevant for drug reward and use. Polymorphisms in PDYN are therefore hypothesized to increase risk for addiction to drugs of abuse. In this study, 3 polymorphisms in PDYN (rs1022563, rs910080 and rs1997794) were genotyped in opioid-addicted [248 African Americans (AAs) and 1040 European Americans (EAs)], cocaine-addicted (1248 AAs and 336 EAs) and control individuals (674 AAs and 656 EAs). Sex-specific analyses were also performed as a previous study identified PDYN polymorphisms to be more significantly associated with female opioid addicts. We found rs1022563 to be significantly associated with opioid addiction in EAs [P = 0.03, odds ratio (OR) = 1.31; false discovery rate (FDR) corrected q-value]; however, when we performed female-specific association analyses, the OR increased from 1.31 to 1.51. Increased ORs were observed for rs910080 and rs199774 in female opioid addicts also in EAs. No statistically significant associations were observed with cocaine or opioid addiction in AAs. These data show that polymorphisms in PDYN are associated with opioid addiction in EAs and provide further evidence that these risk variants may be more relevant in females.     

SNPs Associated with Cerebrospinal Fluid Phospho-Tau Levels Influence Rate of Decline in Alzheimer's Disease

Alzheimer''s Disease (AD) is a complex and multifactorial disease. While large genome-wide association studies have had some success in identifying novel genetic risk factors for AD, case-control studies are less likely to uncover genetic factors that influence progression of disease. An alternative approach to identifying genetic risk for AD is the use of quantitative traits or endophenotypes. The use of endophenotypes has proven to be an effective strategy, implicating genetic risk factors in several diseases, including anemia, osteoporosis and heart disease. In this study we identify a genetic factor associated with the rate of decline in AD patients and present a methodology for identification of other such factors. We have used an established biomarker for AD, cerebrospinal fluid (CSF) tau phosphorylated at threonine 181 (ptau₁₈₁) levels as an endophenotype for AD, identifying a SNP, rs1868402, in the gene encoding the regulatory sub-unit of protein phosphatase B, associated with CSF ptau₁₈₁ levels in two independent CSF series . We show no association of rs1868402 with risk for AD or age at onset, but detected a very significant association with rate of progression of disease that is consistent in two independent series . Our analyses suggest that genetic variants associated with CSF ptau₁₈₁ levels may have a greater impact on rate of progression, while genetic variants such as APOE4, that are associated with CSF Aβ₄₂ levels influence risk and onset but not the rate of progression. Our results also suggest that drugs that inhibit or decrease tau phosphorylation may slow cognitive decline in individuals with very mild dementia or delay the appearance of memory problems in elderly individuals with low CSF Aβ₄₂ levels. Finally, we believe genome-wide association studies of CSF tau/ptau₁₈₁ levels should identify novel genetic variants which will likely influence rate of progression of AD.     

G = E: What GWAS Can Tell Us about the Environment

As our understanding of genetics has improved, genome-wide association studies (GWAS) have identified numerous variants associated with lifestyle behaviours and health outcomes. However, what is sometimes overlooked is the possibility that genetic variants identified in GWAS of disease might reflect the effect of modifiable risk factors as well as direct genetic effects. We discuss this possibility with illustrative examples from tobacco and alcohol research, in which genetic variants that predict behavioural phenotypes have been seen in GWAS of diseases known to be causally related to these behaviours. This consideration has implications for the interpretation of GWAS findings.     

Rare Variants Association Analysis in Large-Scale Sequencing Studies at the Single Locus Level

Genetic association analyses of rare variants in next-generation sequencing (NGS) studies are fundamentally challenging due to the presence of a very large number of candidate variants at extremely low minor allele frequencies. Recent developments often focus on pooling multiple variants to provide association analysis at the gene instead of the locus level. Nonetheless, pinpointing individual variants is a critical goal for genomic researches as such information can facilitate the precise delineation of molecular mechanisms and functions of genetic factors on diseases. Due to the extreme rarity of mutations and high-dimensionality, significances of causal variants cannot easily stand out from those of noncausal ones. Consequently, standard false-positive control procedures, such as the Bonferroni and false discovery rate (FDR), are often impractical to apply, as a majority of the causal variants can only be identified along with a few but unknown number of noncausal variants. To provide informative analysis of individual variants in large-scale sequencing studies, we propose the Adaptive False-Negative Control (AFNC) procedure that can include a large proportion of causal variants with high confidence by introducing a novel statistical inquiry to determine those variants that can be confidently dispatched as noncausal. The AFNC provides a general framework that can accommodate for a variety of models and significance tests. The procedure is computationally efficient and can adapt to the underlying proportion of causal variants and quality of significance rankings. Extensive simulation studies across a plethora of scenarios demonstrate that the AFNC is advantageous for identifying individual rare variants, whereas the Bonferroni and FDR are exceedingly over-conservative for rare variants association studies. In the analyses of the CoLaus dataset, AFNC has identified individual variants most responsible for gene-level significances. Moreover, single-variant results using the AFNC have been successfully applied to infer related genes with annotation information.     

A Robust Model-free Approach for Rare Variants Association Studies Incorporating Gene-Gene and Gene-Environmental Interactions

Recently more and more evidence suggest that rare variants with much lower minor allele frequencies play significant roles in disease etiology. Advances in next-generation sequencing technologies will lead to many more rare variants association studies. Several statistical methods have been proposed to assess the effect of rare variants by aggregating information from multiple loci across a genetic region and testing the association between the phenotype and aggregated genotype. One limitation of existing methods is that they only look into the marginal effects of rare variants but do not systematically take into account effects due to interactions among rare variants and between rare variants and environmental factors. In this article, we propose the summation of partition approach (SPA), a robust model-free method that is designed specifically for detecting both marginal effects and effects due to gene-gene (G×G) and gene-environmental (G×E) interactions for rare variants association studies. SPA has three advantages. First, it accounts for the interaction information and gains considerable power in the presence of unknown and complicated G×G or G×E interactions. Secondly, it does not sacrifice the marginal detection power; in the situation when rare variants only have marginal effects it is comparable with the most competitive method in current literature. Thirdly, it is easy to extend and can incorporate more complex interactions; other practitioners and scientists can tailor the procedure to fit their own study friendly. Our simulation studies show that SPA is considerably more powerful than many existing methods in the presence of G×G and G×E interactions.     

Ultra-potent antibodies against respiratory syncytial virus: effects of binding kinetics and binding valence on viral neutralization

We describe here the selection of ultra-potent anti-respiratory syncytial virus (RSV) antibodies for preventing RSV infection. A large number of antibody variants derived from Synagis (palivizumab), an anti-RSV monoclonal antibody that targets RSV F protein, were generated by a directed evolution approach that allowed convenient manipulation of the binding kinetics. Palivizumab variants with about 100-fold slower dissociation rates or with fivefold faster association rates were identified and tested for their ability to neutralize virus in a microneutralization assay. Our data reveal a major differential effect of the association and dissociation rates on the RSV neutralization, particularly for intact antibodies wherein the association rate plays the predominant role. Furthermore, we found that antibody binding valence also plays a critical role in mediating the viral neutralization through a mechanism that is likely unrelated to antibody size or binding avidity. We applied an iterative mutagenesis approach, and thereafter were able to identify palivizumab Fab variants with up to 1500-fold improvement and palivizumab IgG variants with up to 44-fold improvement in the ability to neutralize RSV. These anti-RSV antibodies likely will offer great clinical potential for RSV immunoprophylaxis. In addition, our findings provide insights into engineering potent antibody therapeutics for other disease targets.     

Copy number variants and genetic traits: closer to the resolution of phenotypic to genotypic variability

A considerable and unanticipated plasticity of the human genome, manifested as inter-individual copy number variation, has been discovered. These structural changes constitute a major source of inter-individual genetic variation that could explain variable penetrance of inherited (Mendelian and polygenic) diseases and variation in the phenotypic expression of aneuploidies and sporadic traits, and might represent a major factor in the aetiology of complex, multifactorial traits. For these reasons, an effort should be made to discover all common and rare copy number variants (CNVs) in the human population. This will also enable systematic exploration of both SNPs and CNVs in association studies to identify the genomic contributors to the common disorders and complex traits.     

Genetic association studies of complex traits: design and analysis issues

Most common diseases and many important quantitative traits are complex genetic traits, with multiple genetic and environmental variables contributing to the observed phenotype. Because of the multi-factorial nature of complex traits, each individual genetic variant generally has only a modest effect, and the interaction of genetic variants with each other or with environmental factors can potentially be quite important in determining the observed phenotype. It remains largely unknown what sort of genetic variants explain inherited variation in complex traits, but recent evidence suggests that common genetic variants will explain at least some of the inherited variation in susceptibility to common disease. Genetic association studies, in which the allele or genotype frequencies at markers are determined in affected individuals and compared with those of controls (either population- or family-based), may be an effective approach to detecting the effects of common variants with modest effects. With the explosion in single nucleotide polymorphism (SNP) discovery and genotyping technologies, large-scale association studies have become feasible, and small-scale association studies have become plentiful. We review the different types of association studies and discuss issues that are important to consider when performing and interpreting association studies of complex genetic traits. Heritable and accurately measured phenotypes, carefully matched large samples, well-chosen genetic markers, and adequate standards in genotyping, analysis, and interpretation are all integral parts of a high-quality association study.     

Segregation of acrocentric chromosome association in familial dicentric Robertsonian translocation t(14p;22p), aneuploidy (trisomy-21) and heteromorphism

The frequency and types of acrocentric chromosome association were quantitatively analysed in a Down syndrome child with unusual karyotype, 46, XX, -14, -22, t dic (14p;22p), +21, 21S+. Father and 4 sibs were heterozygous carriers for t dic (14p;22p). The variant 21S+ was inherited from the mother. The occurrence of translocation and trisomy in the same individual is extremely rare. Acrocentric chromosome association was analysed in this interesting family to understand the interrelationship of acrocentric chromosome association, Robertsonian translocation and heteromorphism, as possible predisposing factors for nondisjunction. Our findings suggest that acrocentric chromosome association is a heritable and nonrandom phenomenon. Heterozygous carriers for translocations and variants are likely to be at increased risk of nondisjunction. Long term family studies will enable to ascertain the causal-relationship of these factors more precisely.