Objective: Determine the incidence of vestibular disorders in patients with SARS-CoV-2 compared to the control population. Study Design: Retrospective. Setting: Clinical data in the National COVID Cohort Collaborative database (N3C). Methods: Deidentified patient data from the National COVID Cohort Collaborative database (N3C) were queried based on variant peak prevalence (untyped, alpha, delta, omicron 21K, and omicron 23A) from covariants.org to retrospectively analyze the incidence of vestibular disorders in patients with SARS-CoV-2 compared to control population, consisting of patients without documented evidence of COVID infection during the same period. Results: Patients testing positive for COVID-19 were significantly more likely to have a vestibular disorder compared to the control population. Compared to control patients, the odds ratio of vestibular disorders was significantly elevated in patients with untyped (odds ratio [OR], 2.39; confidence intervals [CI], 2.29–2.50; P < 0.001), alpha (OR, 3.63; CI, 3.48–3.78; P < 0.001), delta (OR, 3.03; CI, 2.94–3.12; P < 0.001), omicron 21K variant (OR, 2.97; CI, 2.90–3.04; P < 0.001), and omicron 23A variant (OR, 8.80; CI, 8.35–9.27; P < 0.001). Conclusions: The incidence of vestibular disorders differed between COVID-19 variants and was significantly elevated in COVID-19-positive patients compared to the control population. These findings have implications for patient counseling and further research is needed to discern the long-term effects of these findings.

public–private partnership managed by the FNIH aims to address biological, manufacturing and regulatory challenges to the development of gene therapies for rare diseases.

Efficient evidence generation to assess the clinical and economic impact of medical therapies is critical amid rising healthcare costs and aging populations. However, drug development and clinical trials remain far too expensive and inefficient for all stakeholders. On October 25-26, 2023, the Duke Clinical Research Institute brought together leaders from academia, industry, government agencies, patient advocacy, and nonprofit organizations to explore how different entities and influencers in drug development and healthcare can realign incentive structures to efficiently accelerate evidence generation that addresses the highest public health needs. Prominent themes surfaced, including competing research priorities and incentives, inadequate representation of patient population in clinical trials, opportunities to better leverage existing technology and infrastructure in trial design, and a need for heightened transparency and accountability in research practices. The group determined that together these elements contribute to an inefficient and costly clinical research enterprise, amplifying disparities in population health and sustaining gaps in evidence that impede advancements in equitable healthcare delivery and outcomes. The goal of addressing the identified challenges is to ultimately make clinical trials faster, more inclusive, and more efficient across diverse communities and settings.

The All of Us Research Program has prioritized the enrollment of people from backgrounds that are historically under-represented in medical research to bring precision medicine to the full diversity of the US population and to improve health outcomes for all.

ImportanceThe pharmacokinetics of abatacept and the association between abatacept exposure and outcomes in patients with severe COVID-19 are unknown.ObjectiveTo characterize abatacept pharmacokinetics, relate drug exposure with clinical outcomes, and evaluate the need for dosage adjustments.Design, Setting, and ParticipantsThis study is a secondary analysis of data from the ACTIV-1 (Accelerating COVID-19 Therapeutic Interventions and Vaccines) Immune Modulator (IM) randomized clinical trial conducted between October 16, 2020, and December 31, 2021. The trial included hospitalized adults who received abatacept in addition to standard of care for treatment of COVID-19 pneumonia. Data analysis was performed between September 2022 and February 2024.ExposureSingle intravenous infusion of abatacept (10 mg/kg with a maximum dose of 1000 mg).Main Outcomes and MeasuresMortality at day 28 was the primary outcome of interest, and time to recovery at day 28 was the secondary outcome. Drug exposure was assessed using the projected area under the serum concentration time curve over 28 days (AUC0-28). Logistic regression modeling was used to analyze the association between drug exposure and 28-day mortality, adjusted for age, sex, and disease severity. The association between time to recovery and abatacept exposure was examined using Fine-Gray modeling with death as a competing risk, and was adjusted for age, sex, and disease severity.ResultsOf the 509 patients who received abatacept, 395 patients with 848 serum samples were included in the population pharmacokinetic analysis. Their median age was 55 (range, 19-89) years and most (250 [63.3%]) were men. Abatacept clearance increased with body weight and more severe disease activity at baseline. Drug exposure was higher in patients who survived vs those who died, with a median AUC0-28 of 21 428 (range, 8462-43 378) mg × h/L vs 18 262 (range, 9628-27 507) mg × h/L (P < .001). Controlling for age, sex, and disease severity, an increase of 5000 units in AUC0-28 was associated with lower odds of mortality at day 28 (OR, 0.52 [95% CI, 0.35-0.79]; P = .002). For an AUC0-28 of 19 400 mg × h/L or less, there was a higher probability of recovery at day 28 (hazard ratio, 2.63 [95% CI, 1.70-4.08] for every 5000-unit increase; P < .001). Controlling for age, sex, and disease severity, every 5000-unit increase in AUC0-28 was also associated with lower odds of a composite safety event at 28 days (OR, 0.46 [95% CI, 0.33-0.63]; P < .001). Using the dosing regimen studied in the ACTIV-1 IM trial, 121 of the 395 patients (30.6%) would not achieve an abatacept exposure of at least 19 400 mg × h/L, particularly at the extremes of body weight. Using a modified, higher-dose regimen, only 12 patients (3.0%) would not achieve the hypothesized target abatacept exposure.Conclusions and RelevanceIn this study, patients who were hospitalized with severe COVID-19 and achieved higher projected abatacept exposure had reduced mortality and a higher probability of recovery with fewer safety events. However, abatacept clearance was high in this population, and the current abatacept dosing (10 mg/kg intravenously with a maximum of 1000 mg) may not achieve optimal exposure in all patients.Trial RegistrationClinicalTrials.gov Identifier: NCT04593940

Investment, collaboration, and coordination have been key

Clinical trial processes are unnecessarily inefficient and costly, slowing the translation of medical discoveries into treatments for people living with disease. To reduce redundancies and inefficiencies, a group of clinical trial experts developed a framework for clinical trial site readiness based on existing trial site qualifications from sponsors. The site readiness practices are encompassed within six domains: research team, infrastructure, study management, data collection and management, quality oversight, and ethics and safety. Implementation of this framework for clinical trial sites would reduce inefficiencies in trial conduct and help prepare new sites to enter the clinical trials enterprise, with the potential to improve the reach of clinical trials to underserved communities. Moreover, the framework holds benefits for trial sponsors, contract research organizations, trade associations, trial participants, and the public. For novice sites considering future trials, we provide a framework for site preparation and the engagement of stakeholders. For experienced sites, the framework can be used to assess current practices and inform and engage sponsors, staff, and participants. Details in the supplementary materials provide easy access to key regulatory documents and resources. Invited perspective articles provide greater depth from a systems, DEIA (diversity, equity, inclusion, and accessibility) and decentralized trials perspective.

In this communication, the authors offer considerations for how the scientific community can capitalize on decades of translational science advances and experiential knowledge to develop new education opportunities for a diverse and highly skilled translational science workforce. Continued advancement of the field of translational science will require new education approaches that distill key concepts in translational science from past and ongoing research initiatives and teach this foundational knowledge to current and future translational scientists. These key concepts include generalizable scientific and operational principles to guide translational science, as well as evidence-informed practices. Inspired by this approach, the National Center for Advancing Translational Sciences (NCATS) has developed an initial set of guiding principles for translational science generated via case studies of multiple highly successful translational science initiatives, and is now teaching them via new education activities that aim to reach a broad scientific audience interested in translational science. Our goal with this review is to prompt continued conversation with the translational science community regarding capitalizing on our collective translational science knowledge to advance core content for translational science education and disseminating this content to a broad range of scientific audiences.

The emergence of new cell and gene-based therapies (CGTs) utilizing innovative technologies has recently intensified. Long-standing efforts in publicly funded biomedical research have resulted in breakthrough therapeutic approaches for patients with devastating and life-threatening diseases. Transformative gene-based therapeutic tools include human genome editing technologies, refined transposon systems, and synthetic immunoreceptors, such as chimeric antigen receptor (CAR) T cell and natural killer cell engineered immunotherapies.

Abstract Objective Coronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers. Materials and Methods The Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics. Results Organized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access. Conclusions The N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19.

Background Rare diseases (RD) are a diverse collection of more than 7-10,000 different disorders, most of which affect a small number of people per disease. Because of their rarity and fragmentation of patients across thousands of different disorders, the medical needs of RD patients are not well recognized or quantified in healthcare systems (HCS). Methodology We performed a pilot IDeaS study, where we attempted to quantify the number of RD patients and the direct medical costs of 14 representative RD within 4 different HCS databases and performed a preliminary analysis of the diagnostic journey for selected RD patients. Results The overall findings were notable for: (1) RD patients are difficult to quantify in HCS using ICD coding search criteria, which likely results in under-counting and under-estimation of their true impact to HCS; (2) per patient direct medical costs of RD are high, estimated to be around three-fivefold higher than age-matched controls; and (3) preliminary evidence shows that diagnostic journeys are likely prolonged in many patients, and may result in progressive, irreversible, and costly complications of their disease Conclusions The results of this small pilot suggest that RD have high medical burdens to patients and HCS, and collectively represent a major impact to the public health. Machine-learning strategies applied to HCS databases and medical records using sentinel disease and patient characteristics may hold promise for faster and more accurate diagnosis for many RD patients and should be explored to help address the high unmet medical needs of RD patients.

The All of Us Research Program (All of Us) is a national effort to accelerate health research by exploring the relationship between lifestyle, environment, and genetics. It is set to become one of the largest research efforts in U.S. history, aiming to build a national resource of data from at least one million participants. All of Us aims to address the need for more diversity in research and set the stage for that diversity to be leveraged in precision medicine research to come. This paper describes how the program assessed demographic characteristics of participants who have enrolled in other U.S. biomedical research cohorts to better understand which groups are traditionally represented or underrepresented in biomedical research. We 1) reviewed the enrollment characteristics of national cohort studies like All of Us, and 2) surveyed the literature, focusing on key diversity categories essential to the program's enrollment aims. Based on these efforts, All of Us emphasizes enrollment of racial and ethnic minorities, and has formally designated the following additional groups as historically underrepresented: individuals-with inadequate access to medical care; under the age of 18 or over 65; with an annual household income at or below 200% of the federal poverty level; who have a cognitive or physical disability; have less than a high school education or equivalent; are intersex; identify as a sexual or gender minority; or live in rural or non-metropolitan areas. Research accounting for wider demographic variability is critical. Only by ensuring diversity and by addressing the very barriers that limit it, can we position All of Us to better understand and tackle health disparities.

Background: The All of Us Research Program is building a national longitudinal cohort and collecting data from multiple information sources (e.g., biospecimens, electronic health records, and mobile/ wearable technologies) to advance precision medicine. Participant-provided information, collected via surveys, will complement and augment these information sources. We report the process used to develop and refine the initial three surveys for this program. Methods: The All of Us survey development process included: (1) prioritization of domains for scientific needs, (2) examination of existing validated instruments, (3) content creation, (4) evaluation and refinement via cognitive interviews and online testing, (5) content review by key stakeholders, and (6) launch in the All of Us electronic participant portal. All content was translated into Spanish. Results: We conducted cognitive interviews in English and Spanish with 169 participants, and 573 individuals completed online testing. Feedback led to over 40 item content changes. Lessons learned included: (1) validated survey instruments performed well in diverse populations reflective of All of Us; (2) parallel evaluation of multiple languages can ensure optimal survey deployment; (3) recruitment challenges in diverse populations required multiple strategies; and (4) key stakeholders improved integration of surveys into larger Program context. Conclusions: This efficient, iterative process led to successful testing, refinement, and launch of three All of Us surveys. Reuse of All of Us surveys, available at http://researchallofus. org, may facilitate large consortia targeting diverse populations in English and Spanish to capture participant- provided information to supplement other data, such as genetic, physical measurements, or data from electronic health records.

Knowledge gained from observational cohort studies has dramatically advanced the prevention and treatment of diseases. Many of these cohorts, however, are small, lack diversity, or do not provide comprehensive phenotype data. The All of Us Research Program plans to enroll a diverse group of at least 1 million persons in the United States in order to accelerate biomedical research and improve health. The program aims to make the research results accessible to participants, and it is developing new approaches to generate, access, and make data broadly available to approved researchers. All of Us opened for enrollment in May 2018 and currently enrolls participants 18 years of age or older from a network of more than 340 recruitment sites. Elements of the program protocol include health questionnaires, electronic health records (EHRs), physical measurements, the use of digital health technology, and the collection and analysis of biospecimens. As of July 2019, more than 175,000 participants had contributed biospecimens. More than 80% of these participants are from groups that have been historically underrepresented in biomedical research. EHR data on more than 112,000 participants from 34 sites have been collected. The All of Us data repository should permit researchers to take into account individual differences in lifestyle, socioeconomic factors, environment, and biologic characteristics in order to advance precision diagnosis, prevention, and treatment.

In a Policy Forum, Muin Khoury and colleagues discuss research on the clinical application of genome sequencing data.

The energy substances (mainly carbohydrates and fats) are the basis and guarantee of life activity, especially the oxidative phosphorylation for energy supply. However, excessive absorption and accumulation of these substances can lead to metabolic diseases such as obesity, hyperlipidemia, diabetes, and cancers. A large amount of studies demonstrate that G protein-coupled receptors (GPCRs) play a key role in identification and absorption of energy substances, and the signaling network of nerves, immune, and endocrine regulates their storage and utilization. The gastrointestinal mucus layer not only identifies these substances through identification in diet components but also transfers immune, metabolic, and endocrine signals of hormones, cytokines, and chemokines by promoting interactions between receptors and ligands. These signaling molecules are transferred to corresponding organs, tissues, and cells by the circulatory system, and cell activity is regulated by amplifying of cell signals that constitute the wireless communication network among cells in the body. Absorption, accumulation, and utilization of energy substances in the body obey the law of energy conservation. Energy is stored in the form of fat, and meets the demand of body via two coupled mechanisms: catabolism and oxidative phosphorylation. Under normal physiological conditions, fat consumption involves ketone body metabolism through the circulatory system and glucose consumption requires blood lactic acid cycle. Accumulation of excessive energy leads to the abnormal activation of mammalian target of rapamycin (mTOR), thus promoting the excretion of glucose or glycogen in the form of blood glucose and urine glucose. Alternatively, the body cancels the intercellular contact inhibition and promotes cell proliferation to induce carcinogenesis, which can induce the consumption of large amounts of glucose. Intercellular communication is performed by signaling molecules via sensing, absorption, accumulation, and utilization of energy substances, and anabolism and catabolism are controlled by the central metabolic pathway. Therefore, slower catabolism will result in longer life expectancy, whereas faster catabolism results in shorter life expectancy. Energy substances in diet influence the balance between energy and metabolism in the body through the sensing function of the gastrointestinal system at two levels: cellular communication network and metabolic network. The present review of studies aims to strengthen our knowledge on cellular communication and metabolic networks to offer a dietary guidance on the metabolism and communication role of various foods.

Long-lived postmitotic cells, such as most human neurons, must respond effectively to ongoing changes in neuronal stimulation or microenvironmental cues through transcriptional and epigenomic regulation of gene expression. The role of epigenomic regulation in neuronal function is of fundamental interest to the neuroscience community, as these types of studies have transformed our understanding of gene regulation in postmitotic cells. This perspective article highlights many of the resources available to researchers interested in neuroepigenomic investigations and discusses some of the current obstacles and opportunities in neuroepigenomics.

Despite the wide spread use of highly active antiretroviral therapy (HAART), mild forms of HIV-associated neuro cognitive disorders (HAND) remain commonplace. HAART treated patients now show low levels of viremia and more subtle yet biologically important signs of brain macrophage and microglial activation. Adjunctive therapeutic strategies are required to eliminate HIV-1 infection and suppress immune activation and its associated neuroinflammation. In this regard, cannabinoid receptor-2(CB2) activation is a promising means to attenuate HAND by inhibiting HIV replication, down regulating inflammation, and suppressing chemokine-like activity of viral neurotoxic proteins (for example, Tat and HIV-1gp120), and thereby prevent neuronal and synaptic loss. Inhibiting even low level HIV replication can attenuate neuronal injury by decreasing the production of neurotoxins. Down regulation of inflammation by CB2 activation is mediated through blunted activation of peri vascular macrophages and microglia; decreased production of tumor necrosis factor-α, chemokines and virotoxins. Down regulated neuroinflammation can decrease blood brain barrier permeability and leukocyte infiltration resulting in reduced neuronal injury. It is suggested that CB2 agonists may further attenuate HAND in HIVinfected patients on HAART. In addition, CB2 activation may also blunt brain injury by attenuating drug addiction.

Genetic and epigenetic research have expanded our understanding of the neurobiological basis of substance use (SUD) and related disorders through an interdisciplinary process of human and molecular genetic research. Concomitantly, significant advances in neuroimaging have led to the recognition of the crucial role that the orchestration of gene regulation plays at specific developmental stages in human brain development. Taken together, this work has led to an increased recognition that genetic variation and gene regulation modulate vulnerability (or resilience) for SUD and that these processes are mediated though their influence on brain development, connectivity, and function, which in turn influences brain responses. Drug addiction is a term applied across substance use disorders (SUDs) and defined as a chronic, relapsing complex brain disease characterized by compulsive drug seeking, craving, loss of self-control, and impaired decision making (National Institute on Drug Abuse 2010). Drug addiction persists in spite of harmful consequences and cuts across all demographics. Addiction results from the interactive effects among multiple genes, acting in multiple environments, and across multiple stages of development. It is this intersection that roots SUDs, in concert with other biological processes, in genetic and epigenetic mechanisms that provide a scaffold for normal brain development, for learning and memory, and for pathophysiology. The heritability of addictions ranges from moderate to high. Twin studies have shown that genetic factors account for approximately half of the variance for addictive disorders (Kendler et al. 2008). It is thought that the remaining heritability comes from environmental and developmental aspects, including epigenetic factors that contribute to gene expression miscues of otherwise highly synchronized gene regulation. Over the last decade, genetic research on SUDs has transitioned from candidate gene and linkage methods to genome-wide association studies (GWAS) and sequencing-based approaches. Many studies have been fruitful mainly because studying the genetics of SUDs benefits from a vast knowledge of a given drug's mechanism of action (Rutter 2006). Among these are genes encoding for the enzymes involved with drug metabolism such as alcohol and aldehyde dehydrogenases, which offer protection against alcoholism (Chen et al. 2009) and CYP2A6, which offers protections against nicotine addiction (Malaiyandi et al. 2005). Studies have also identified gene variants that modulate the response to the rewarding or aversive effects of drugs such as the nicotinic cholinergic receptor subunit gene (CHRNA5, α5) for nicotine (Fowler et al. 2011) and the μ-opioid receptor gene (ORPM1) for alcohol and opioids (Arias et al. 2006). Finally genes have been identified that may help predict outcomes for naltrexone treatment for alcohol dependence, such as the A118G OPRM1 single-nucleotide polymorphism (SNP) (Enoch 2013) and for response to nicotine replacement therapy for smoking cessation, such as the D398N SNP in the α5 gene and CYP2A6 genetic polymorphisms (Chen et al. 2014a,b). As with many complex disorders, clear GWAS successes have been hard to come by. However, a striking example in the field of nicotine dependence is the α5-α3-β4 nicotinic cholinergic receptor subunit gene cluster on chromosome 15q, which sparked the discovery of several loci related to smoking and its more dire health consequences, such as lung diseases and peripheral artery disease (Volkow et al. 2008a; Welter et al. 2014). While found initially via a hybrid candidate gene-GWAS design (Saccone et al. 2007), the α5-α3-β4 gene cluster was replicated by GWAS and meta-analysis approaches to become one of the most replicated variants in complex disease genetics (Liu et al.2010; Thorgeirsson et al. 2010; Tobacco and Genetics Consortium 2010). This seminal finding pointed to lesser known targets for nicotine addiction (the α5-α3-β4 nicotine subunit receptors) in addition to the more common α4/β2 nicotinic receptors, which are thought to mediate nicotine reward and dependence (Picciotto et al. 1998). This GWAS finding was also crucial for identifying the importance of the medial habenula (MHb) and the interpeduncular nucleus (IPN), brain regions that express high levels of the α5 subunit containing receptors (Changeux 2010), and which form a brain circuit that plays a role in the aversive effects of nicotine. Since the MHb-IPN inhibits the activity of brain dopamine neurons involved with reward, the α5 gene pointed to the importance of the aversive effects of nicotine in the process of addiction. Indeed α5 knockout mice are much less sensitive to the aversive effects of high nicotine doses than are wild-type mice which avoid high nicotine doses (Fowler et al. 2011). Combined with the dense expression of α5 in the MHb-IPN, a picture emerged which implicates nicotinic receptors containing α5 as a “gate” for nicotine intake. When the α5 gene contains the common SNP the gate is intact and the MHb-IPN aversive circuit functions properly. However, when the α5 gene contains the variant SNP, the gate is weakened and the aversive protection is reduced (Fowler et al. 2011; Fowler and Kenny 2012). This could explain why the heaviest smokers are more likely to have the associated risk allele. In parallel to these large-scale genomic studies, significant advances in neuroimaging have led to the recognition of the crucial role that the orchestration of gene regulation plays at specific developmental stages in the development of the human brain. This has also led to an increased recognition that the regulation of genes that modulate vulnerability (or resilience) for SUD is mediated though their influence on brain development, connectivity, and function, which in turn influences brain responses. This more integrated perspective on the role of genes and gene regulation in SUD provides a better understanding of why there is significant comorbidity between SUD and mental illness, including not only the frequent co-occurrence of both of these disorders but also overlap in brain circuits, in risk alleles and in environmental insults. Particularly prominent are circuits involved with “self regulation and control,” which implicate frontal networks and those involved with saliency circuits, which implicate striatal and limbic regions (Volkow et al. 2008b). For example, early social stressful exposure during childhood, the sensitivity of which is modulated by the gene that encodes for the serotonin transporter (Drabant et al. 2012) increases the risk of depression and SUD and is associated with disrupted frontal control networks. Another example is the 7-repeat variable number tandem repeat (VNTR) in the DRD4 gene with individuals showing a greater risk for attention deficit hyperactivity disorder (ADHD) and for SUD. These individuals also show an increased sensitivity to both adverse as well as positive environmental factors resulting in either worse or better outcomes than those without the 7-repeat DRD4 VNTR (Grady et al. 2013; Olsson et al. 2013). This is interpreted to reflect the DRD4 role in modulating dopamine signaling in frontal striatal terminals (Nikolova et al. 2011). Outside of the large-scale studies, the molecular genetic contributions to SUD indicate a staged effect on the transitions that uncover points of entry into addiction. These include genes that modulate early experimentation with drugs such as those involved with personality (Belcher et al. 2014); genes that determine sensitivity to rewarding versus aversive effects of drugs (Shabani et al. 2011); genes that modulate conditioning to the drug, which implicate genes involved with neuronal plasticity and learning (Drgon et al. 2010); genes that involve the transition into compulsive drug intake which implicate genes involved with regulation of self-control networks (Gustavson et al. 2014); genes that mediate the sensitivity to symptoms of drug withdrawal and likely involved in the regulation of the amygdala and the habenula and other networks involved in stress reactivity and dysphoria (Wankerl et al. 2010); genes involved in the regulation of interoceptive awareness such as the insula and the default mode network (Vergara et al. 2014); and finally genes that regulate the response to treatment (Gonzalez et al. 2012). A prospective longitudinal study points out that much of the genetic variants that regulate the growth and development of the human brain have been important for the evolution of the cerebral cortex (Schmitt et al. 2014). Indeed, some of these genes are involved in the very basic neural-synaptic activities that differentiate the patterned expression in mouse and humans (Miller et al. 2014). By the same token, given that drug addiction is comorbid with many psychiatric conditions such as depression, anxiety, ADHD, and schizophrenia (Buckley et al. 2009), it is difficult to interpret whether the drug addiction is a cause or a consequence of that comorbidity. One possibility is that these distinct clinical disorders have common biological underpinnings. A case in point is a study that found that SNPs associated with euphoric responses to d-amphetamine also showed decreased susceptibility to schizophrenia and ADHD and suggest a point of convergence of SNPs within the dopamine system genes affecting multiple disorders (Hart et al. 2014). Furthermore, marijuana, which is the most frequently used illicit substance and is more commonly used during early adolescence, has been associated with twofold increased risk of schizophrenia (Andreasson et al. 1987; Arseneault et al. 2002). Recognizing that most schizophrenia patients have no prior marijuana use, and most adolescents who abuse marijuana do not develop schizophrenia, the link spurred investigations into the impact of genetic variation in the cannabinoid receptor 1 (CNR1). CNR1 is densely localized in the hippocampus, the amygdala, and the prefrontal cortex (Herkenham et al. 1991) and is the primary receptor for Δ9-tetrahydrocannabinol (THC), the psychoactive component in marijuana. Reduced hippocampal volumes are more likely seen in heavy marijuana users relative to healthy controls (Ashtari et al. 2011), and this volume reduction is mediated by a common variant in CNR1 (Schact et al. 2012). Furthermore, several reports have found that polymorphisms within CNR1 are associated with white matter brain volumes, which might represent a gene × environment relationship for the brain volume deficits in schizophrenia (Ho et al. 2011). The high prevalence of cigarette smoking among schizophrenics also reinforces the hypothesis that addictive behavior and schizophrenia may rely on shared neurocircuitry. For instance, ~90% of people with schizophrenia smoke and use nicotine as a mechanism of self-medication to increase cognition (Chambers et al. 2001). Schizophrenics may have underlying genetic differences in regions of the brain that are similar to those seen in long-term substance users but without the prior drug exposure. For example, studies of resting state functional connectivity (rsFC) in schizophrenia patients found that they had reduced circuit strength in the dorsal anterior cingulate cortex (dACC) and ventral striatum regardless of smoking status, whereas control heavy smokers had reduced connectivity in this pathway compared to control nonsmokers (Moran et al. 2012). These imaging data suggest that schizophrenia patients may be vulnerable to smoking due to these dACC circuit weaknesses. In turn the D398N α5 gene variant is associated with strength of connectivity in the dACC-ventral striatum/extended amygdala circuit, such that the risk allele associates with decreased rsFC (Hong et al. 2010). In addition to the direct role of genetics in SUD and related comorbidities, genetic variation also plays a role in the more subtle traits that are commonly linked with addiction. Personality traits, for example, are influenced by a myriad of factors. Genes and circuits involved with novelty seeking, impulsivity, low reward sensitivity, and conduct disorder or antisocial personality disorder are generally thought to be unifying traits in SUDs – that is, they are largely associated with early-life behaviors that tend to manifest as later-life problems with SUDs. However, there seems to be a high degree of disease heterogeneity that is dependent on how and when those early traits develop in the context of environmental stressors, such as maltreatment and abuse, as well as the genetic vulnerabilities that might be at play (Dick et al. 2013). Examples of such genes include gamma-aminobutyric acid receptor alpha 2, serotonin transporter 5-HTT, and monoamine oxidase A (Caspi et al. 2002; Dick et al. 2013; Ernst et al. 2014). It is not surprising that genetic variants within these neurotransmitter genes can converge to impact mood, aggression, and impulsivity, and to impact later-life addiction vulnerability. Similarly, genes that modulate homeostatic control may help counteract the risk of compulsive drug use, or overeating in food-rich environments. The CNR1 gene is a good example of a gene involved in both reward and homeostatic pathways. It has been associated with body mass index (BMI) and obesity risk (Schleinitz et al. 2010), as well as with addiction (Benyamina et al. 2010). Similarly endogenous opioids are involved in hedonic responses to food and to drugs, and the functional A118G polymorphism in the OPRM1 has been associated with vulnerability for binge eating disorders (Davis et al. 2009) and for alcoholism (Ray et al. 2011). A more recent study used GWAS and meta-analysis approaches to examine the observation that smoking influences weight gain, a common reason for relapse especially for women. The α5/α3/β4 cluster most known for its role in nicotine dependence is also associated with BMI where the risk allele for nicotine dependence correlates with reduced BMI in current and former smokers, but not never-smokers (Freathy et al. 2011). Another study approached the question by examining common variants identified in a GWAS of BMI and compared smokers to never-smokers and found that transmembrane protein 18, mitochondrial translational initiation factor 3, and brain-derived neurotrophic factor (BDNF) are also associated with smoking initiation and/or cigarettes per day (Thorgeirsson et al. 2013). Taken together, these data support the notion that phenotypic outcomes such as obesity, nicotine dependence, alcoholism, inter alia are a complex manifestation of multiple genetic variants acting within a common biological network. By the same token, genetic differences in personality and emotion influence an individual's stress reactivity, which has implications on drug use vulnerability. The opioid and cholinergic systems provide classic examples that link the stress systems to SUDs. Responses to stress invoke very rapid and robust brain responses through the opioid, norepinephrine (NE), orexin, and corticotropin-releasing factor (CRF) systems and they can remain engaged even when the stressor is no longer present (Koob 1999). The CRF system is highly responsive to the environment and genetic studies support the role of CRF serving as a key interface between environmental stressors and an individual's vulnerability to stress-related psychiatric disorders (Binder and Nemeroff 2010). Sensitivity to stress has a propensity to associate with sensitivity to pain, where the opioid system is involved. A study using rats that were selectively bred for high responding to emotional reactivity and exploratory behavior showed that in the high responding rats, the metabotropic glutamate receptors were found to modulate neuronal excitability and plasticity most notably in the hippocampus during neurodevelopment and synaptogenesis (Clinton et al. 2011), illustrating a caricature of the stress/anxiety systems that intersect with the vulnerability to addiction. It has become increasingly evident that epigenetic factors are instrumental in their ability to affect several genes simultaneously with or without the underlying genetic variation. Epigenomic regulation mediates cellular responses to external stimuli such as environmental toxins, infection, stress, and drugs of abuse. Epigenomic regulation of cocaine responses in brain reward circuitry has been particularly well studied (Nester 2014). Exposure to drugs of abuse also may have epigenetic transgenerational influences that affect offspring (Vassoler and Sadri-Vakili 2014). For example, male rat offspring sired by cocaine exposed males were slower to acquire cocaine self-administration than were offspring sired by unexposed males and the effects were linked to increased acetylation of the promoter region of the BDNF gene (Vassoler et al. 2013). A recent study of THC exposure during adolescence found that the unexposed offspring showed an increased propensity to self-administer heroin (Szutorisz et al. 2014). Taken together, these examples indicate the pervasive influence of environmental factors, which are mediated through epigenetic mechanisms; and that these influences may not only affect cellular events in the exposed, but may also manifest across generations. In summary, genetics and epigenetics have expanded our understanding of the neurobiological basis of SUDs and related disorders through an interdisciplinary process of human and molecular genetic research (Fig. 1). The remarkable success using genomics, especially in the field of nicotine dependence and smoking cessation, is now starting to enter the realm of genomic medicine (Bergen et al. 2013; Chen et al. 2014a,b). Treatment approaches that capitalize on genomic information about an individual's genetic or metabolic profile signal a pharmacogenetic game changer in choosing medical and treatment decisions. Likewise, this work weaves a broader perspective on the role of genes, their expression, and on the effects of environmental exposures and experiences on behavior. New approaches such as the CRISPR/Cas9 and TALEN gene editing systems, used for silencing, enhancing, or changing specific genes, will be important tools to allow more specific exploration of how genes associated with SUD influence behavior in animal models of addiction. Research toward understanding the spatial and temporal aspects of the precise molecular mechanisms and pathways influenced by genes at the cellular and circuit levels is beginning to reveal that addiction is a disease of the developing brain influenced by genetic and epigenetic mechanisms. A more comprehensive genomic and epigenomic understanding of SUDs will guide future efforts toward a new way of thinking about treatments and prevention. None declared.