Association of KRAS and TERT Genetic Variants with Opioid Dependence in a Large Clinical Cohort

Association of KRAS and TERT Genetic Variants with Opioid Dependence in a Large Clinical Cohort | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report Association of KRAS and TERT Genetic Variants with Opioid Dependence in a Large Clinical Cohort Raphael E. Cuomo, Ph.D. This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5555328/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction: The present exploratory study seeks to showcase an approach to uncover potential genetic associations predisposing individuals to opioid dependence, in which a large clinical database is utilized to compare results of testing for genetic variants with clinical diagnoses of opioid dependence. Methods Employing a STROBE-compliant study design, this research leveraged the UC Health Data Warehouse, an OMOP CDM-compliant database with EHR data from six University of California academic health centers. Utilizing SQL queries embedded in Python scripts with the Spark framework, the study extracted genetic data, focusing on individuals tested for specific genetic variants. Results Rates of detection for nine genes were evaluated between 222 patients with opioid dependence and 20141 patients without, revealing significantly decreased odds of opioid dependence for individuals with the TERT gene (OR = 0.61, p = 0.010) and significantly increased odds for individuals with the KRAS gene (OR = 1.45, p = 0.014). Significant associations persisted after adjusting for demographics. Conclusions The discovery of KRAS and TERT associations with opioid dependence in our study highlights the intricate genetic landscape of this condition. This study, in triangulation with findings from GWAS and other designs, may pave the way for more personalized approaches to prevention and treatment of opioid dependence. Medical Genetics Psychology Epidemiology Medical Informatics Opioids Genetics Electronic Health Records INTRODUCTION Opioid dependence is a significant health concern on a global scale, affecting millions of individuals and straining healthcare systems [ 1 ]. It is defined as a maladaptive pattern of opioid use leading to clinically significant impairment or distress, characterized by behaviors such as increased tolerance, withdrawal symptoms, uncontrollable consumption, and interference with daily life activities [ 2 ]. The determinants of opioid dependence can be broadly categorized into genetic, environmental, and behavioral components. While considerable research has been dedicated to understanding the socio-behavioral and environmental dimensions of opioid dependence, the genetic facets remain comparatively less explored, leaving a vital aspect of opioid dependence less understood [ 3 ]. Understanding the genetic basis of opioid dependence can provide valuable insights. Identifying specific genes or genetic markers associated with this condition allows us to explore the biological mechanisms and pathways potentially influencing an individual's susceptibility [ 4 ]. Such knowledge can enhance our molecular perspective of opioid dependence and offer a foundation for developing predictive and diagnostic tools. Furthermore, by highlighting potential genetic targets, we may pave the way for the development of personalized therapeutic approaches [ 5 ]. This move towards precision medicine would allow for treatments to be tailored to an individual's unique genetic profile, increasing the likelihood of therapeutic success and reducing adverse reactions. In addition, understanding the genetic links can elucidate potential interactions between genes and environmental or behavioral factors [ 6 ]. This knowledge is paramount as it provides a multi-dimensional view of opioid dependence, integrating both inherited factors and external influences. Such a comprehensive understanding can lead to the development of multi-faceted intervention strategies that can be more effective in preventing or managing opioid dependence. Beyond these applications, delving into the genetic connections also has broader implications for the field of neuropsychiatry. Opioid dependence blurs the lines between psychiatry and neurology [ 7 ]. A detailed genetic mapping can offer insights into the complex neural circuits and neurotransmitter systems involved, potentially providing clarity on the intertwined nature of mental health and substance dependence. Analyzing clinical records, particularly from expansive clinical warehouses of electronic health records (EHRs), presents a comparatively uncommon approach to genetic research that is grounded in the lived experiences and medical journeys of real patients. The depth and breadth of data from such sources encapsulate the multitude of factors at play in clinical settings, offering a more holistic representation of patient health. Traditional methods, such as genome-wide association studies (GWAS), while groundbreaking, have inherent limitations. GWAS relies heavily on curated datasets, often extracted from populations with specific demographics, which may not be representative of the broader public. In contrast, using EHRs from large databases ensures that genetic correlations are founded on definitive clinical diagnoses. This method's value is further accentuated by the fact that EHRs encompass a wide demographic, providing inclusivity and diminishing racial or ethnic biases. In light of these potential benefits, this study endeavors to explore the associations between variants of specific genes and predisposition to opioid dependence in a large data warehouse of EHRs maintained by the University of California Health system. A rigorous and methodical investigation into this aspect not only fills a critical gap in current literature but also offers promising avenues for future research, diagnostics, and therapeutics in the realm of opioid dependence. METHODS We conducted a STROBE-compliant study using the University of California Health Data Warehouse (UCHDW) as a data source. The UCHDW is an OMOP CDM-compliant database of electronic health records (EHR) from the six primary academic health centers within the University of California system, namely UC Davis, UC Irvine, UC Los Angeles, UC Riverside, UC San Diego, and UC San Francisco. Additionally, the data incorporated within the UCHDW includes claim details from the UC self-funded health schemes and external sources like Vizient and the California Department of Health Care Access and Information (HCAI). At the time of this study, the UCHDW housed data for over 8 million patients who received medical care at a UC facility since 2012. This study is exempt from human subject protection under IRB protocol 1604619-1 of the University of California Health System. To extract genetic data for conducting this exploratory cross-sectional analysis of EHRs in the UCHDW, SQL queries were embedded in a Python script in conjunction with the Spark framework. Specifically, data extraction identified all individuals with opioid dependence and all individuals without opioid dependence, and then joined data on identification of all available genetic variants. To enable generalizability, comparisons were limited to gene variants with at least thirty positive identifications within the group of patients having opioid dependence and also with at least thirty positive identifications within the group of patients not having opioid dependence. Gender may confound the association between genetic variants and opioid dependence as it can influence both the genetic tests individuals receive and their risk for opioid dependence. Some genetic conditions or risks might be sex-linked, and opioid dependence prevalence might differ between sexes. Furthermore, populations of different racial and ethnic backgrounds can have distinct genetic markers and profiles, leading to varying risks or propensities for certain medical conditions. Furthermore, there are documented disparities in healthcare access, utilization, and outcomes across different racial and ethnic groups. These disparities can influence the frequency and results of genetic testing in these populations. By including race and ethnicity in the multivariable Poisson model, we aim to convey risk estimates which control for the underlying genetic and socio-environmental differences that might impact opioid dependence risks. In the context of our research, we aimed to discern the relationship between specific genetic variants and the predisposition to opioid dependence. A variant denotes an alteration or mutation in the DNA sequence that deviates from a reference genome. The reference genome represents a standardized DNA sequence for a humans. A variant represents any one of a diverse set of modifications, from single nucleotide polymorphisms (SNPs) to more intricate structural shifts like insertions, deletions, inversions, duplications, and translocations. Depending on their position and nature in the genome, these variants can influence gene function, with potential impacts on protein structure, gene expression, or regulatory operations. In this study, any given genetic variant was denoted as a binary variables, indicating the presence (1) or absence (0) of the specific genetic variant. Opioid dependence was similarly represented as a binary metric based on medical records. Gender, race, and ethnicity were utilized as covariates in regression analyses to adjust for potential confounding effects. Given the binary nature of our outcome, we employed Poisson regression models to ascertain relationships between the presence of specific genetic variants and opioid dependence. The association between genetic variant and opioid dependence was first adjusted for gender to account for potential confounding from these factors, and then further adjusted for race and ethnicity in a fully adjusted model. The outcomes were presented as adjusted risk ratios with a 95% confidence interval for each genetic variant. All analyses were performed using the R programming language. RESULTS Nine genes were identified having at least thirty positive tests in both the group of patients with opioid dependence and the group of patients without. These genes were APC, ARID1A, BRCA2, CDKN2B, KRAS, MLL2, PIK3CA, TERT, and TP53. The cohort of patients having a genetic test for at least one of these nine genes comprised 222 having a diagnosis of opioid dependence and 20,363 without a diagnosis of opioid dependence. There were roughly the same percentages of gender and ethnicity categories across both groups. Proportion of patients who were non-White was 30.6% in the group with opioid dependence but 40.7% in the group without opioid dependence (Table 1 ). Table 1 Demographic characteristics of the patient population in this sample for those with and without opioid dependence. Having Opioid Dependence Not Having Opioid Dependence Sex Female 55.4% 52.7% Male 44.6% 47.3% Race Non-White 30.6% 40.7% White 69.4% 59.3% Ethnicity Hispanic 15.8% 12.3% Non-Hispanic 84.2% 87.7% Results revealed varied levels of association across the genes. In unadjusted models, the KRAS gene showed a statistically significant association with opioid dependence, exhibiting an odds ratio of 1.45 (p = 0.014), indicating a 45% increase in the odds of opioid dependence for individuals with this gene. Similarly, the TERT gene demonstrated a significant inverse relationship, with an odds ratio of 0.61 (p = 0.010), suggesting a 39% decrease in the odds of opioid dependence. Upon adjusting for demographic factors, the significance of these associations were virtually unchanged. Specifically, the odds ratio for KRAS adjusted slightly to 1.43 (p = 0.018), while the odds ratio for TERT remained consistent at 0.61 (p = 0.010). These findings lend support to the assertion that the influence of these genes on opioid dependence may have causal validity, as cellular mechanisms affected by these variants should be consistent across demographic categorizations. Other genes, such as APC, ARID1A, BRCA2, CDKN2B, MLL2, PIK3CA, and TP53, showed no significant associations with opioid dependence in either the unadjusted or adjusted models. The odds ratios for these genes ranged from 0.77 to 1.38 in the unadjusted models and 0.77 to 1.40 in the adjusted models, with corresponding p-values all above the conventional significance threshold of 0.05 (Table 2 ). DISCUSSION This exploratory study illuminates genetic predispositions that may be associated with opioid dependence, possibly leading to a much deeper understanding of the underlying molecular mechanisms. Moreover, this study illustrates the high potential value represented by analysis of risk factors using large systemwide clinical databases. The involvement of KRAS and TERT in neural processes and their potential influence on psychiatric and neurodevelopmental disorders has been substantiated by various studies. For KRAS, research has demonstrated that mutations in this gene are associated with rare neurodevelopmental disorders that can lead to intellectual disabilities. In particular, a prior study had shown that neuron type-specific expression of a mutant KRAS impairs hippocampal-dependent learning and memory. This study indicated that mice expressing a mutant KRAS in neurons displayed deficits in long-term potentiation as well as in spatial memory in adults, implicating KRAS in synaptogenesis during early development, the dysregulation of which subsequently impairs synaptic plasticity and memory in adults [ 8 ]​​. The study also noted the impact of mutant KRAS on the development and function of GABAergic inhibitory neurons, which may contribute to behavioral deficits. For TERT, its role in neuroprotection and influence on neuronal survival and regeneration has been acknowledged in scientific literature. A prior study using in vitro methods discussed findings suggesting a protective role of TERT in neurons with hypoxia-ischemia injury. The study found that TERT expression increases in neurons after oxygen-glucose deprivation, suggesting a neuroprotective role via anti-apoptosis. This neuroprotective mechanism may be associated with regulating the Bcl-2/Bax expression ratio, attenuating reactive oxygen species generation, and increasing mitochondrial membrane potential​​ [ 9 ]. Another study was conducted with mouse models that reported overexpression of telomerase reverse transcriptase induces autism-like excitatory phenotypes in mice, indicating a potential link between TERT expression and neurodevelopmental disorders​​ [ 10 ]. The association of these genes with opioid dependence could also be indicative of broader, multifaceted genetic interactions. Opioid dependence, like many psychiatric and behavioral disorders, is likely influenced by a complex interplay of genetic, environmental, and psychosocial factors. The roles of KRAS and TERT in this context might not be direct but could interact with other genetic variations or environmental exposures to influence susceptibility to opioid dependence. For instance, the impact of chronic opioid use on cell survival and neural plasticity, pathways in which both KRAS and TERT are involved, might be modulated differently in individuals with variations in these genes, thereby influencing the risk or severity of dependence. Further research is needed to elucidate the precise mechanisms through which KRAS and TERT influence opioid dependence. This could include studies focusing on the role of these genes in brain regions implicated in addiction, such as the mesolimbic dopamine system, and examining how their expression or function changes in response to opioid exposure. Animal models and in vitro studies could provide insights into the cellular and molecular pathways involved. Additionally, investigating these genes in larger, more diverse populations would help clarify their role in opioid dependence and whether they interact with other genetic or environmental factors. In attempting to understand potential genetic underpinnings of opioid dependence, previous research methodologies have primarily revolved around GWAS, candidate gene approaches, and twin and family studies [ 11 – 13 ]. While GWAS provides an unbiased assessment of the entire genome, identifying novel genetic markers potentially associated with opioid dependence, candidate gene studies focus on specific genes already believed to be involved in addiction pathways. Twin and family studies, on the other hand, have been instrumental in establishing the heritability of opioid dependence, providing foundational evidence for a genetic component. However, the exploration of vast databases, such as the one utilized in this study, presents a unique advantage. Leveraging large-scale databases allows for exploratory analyses that can refine inclusion criteria and utilize larger sample sizes to facilitate the robustness and generalizability of findings. Furthermore, extensive databases of electronic health records can provide a rich tapestry of clinical and genetic information, facilitating nuanced insights and paving the way for personalized, precision medicine approaches in understanding and treating opioid dependence. A large-scale GWAS was recently conducted on individuals of both European and African ancestry which illustrates the potential complexity and genetic diversity of molecular influences on opioid dependence [ 14 ]. This valuable study provides valuable insights into the genetic landscape of opioid dependence and underscores the challenges inherent in isolating specific genetic triggers through genome scanning. The prior GWAS and this study both found that opioid dependence was associated with genetic variants having downstream cellular functions that operate in disparate and unclearly related ways. Our approach, which amalgamates records from an extensive clinical data warehouse with genetic data, aims to bridge real-world clinical observations with genetic implications. Such a methodology should be considered as a complement that triangulates findings with those from traditional GWAS, as well as for potentially amplifying translational applications in patient care. It is worth noting that the etiology of opioid dependence is multifactorial, encompassing a myriad of genetic, environmental, and psychological components [ 15 – 16 ]. As such, while this study identifies specific genes that might be of importance, it remains exploratory in nature, making it essential to approach conclusions with caution. Environmental and behavioral factors, which were not accounted for in this study, can significantly modulate the relationship between these genes and opioid dependence. For a holistic understanding, future research should consider integrating genetics with behavioral, environmental, and physiological data. Conclusions These results contribute to a growing body of evidence suggesting that genetic factors play a crucial role in substance use disorders. Understanding the specific mechanisms through which genes like KRAS and TERT influence opioid dependence could pave the way for more targeted prevention and treatment strategies, tailored to individual genetic profiles. As research in this field continues to evolve, it is increasingly apparent that addressing the opioid crisis will require a multidisciplinary approach, integrating insights from genetics, neuroscience, psychology, and public health. Limitations In any study leveraging EHRs, there are inherent biases that need to be acknowledged. First, diagnostic bias can arise due to variations in the diagnostic practices across different healthcare providers. There's also a possibility of selection bias since the patient population represented in the UCHDW may not be entirely representative of the broader population, potentially skewing the genetic variants identified. Additionally, information bias can be present due to the variability in the recording practices, missing data, or inaccuracies within the EHR. Efforts were made to minimize these biases through rigorous data preprocessing, yet their potential influence on the study outcomes should be taken into consideration when interpreting results. Declarations Ethics Approval : This study is exempt from human subject protection under IRB protocol 1604619-1 of the University of California Health System. Ethical Disclosure : None Financial Disclosure : None Acknowledgements : None Data Sharing Statement : The data used in this study contain sensitive patient information and are subject to strict confidentiality agreements and regulatory restrictions. To ensure the protection of patient privacy and to comply with relevant data protection regulations, the raw data supporting the conclusions of this research cannot be made publicly available. References Degenhardt L, Charlson F, Mathers B, Hall WD, Flaxman AD, Johns N, Vos T (2014) The global epidemiology and burden of opioid dependence: Results from the global burden of disease 2010 study. Addiction 109(8):1320–1333 American Psychiatric Association (2000) Diagnostic and statistical manual of mental disorders (4th ed., text rev.). Washington, DC: Author Molecular psychiatry , 15(4), 417–428 Crist RC, Berrettini WH (2014) Pharmacogenetics of OPRM1. Pharmacol Biochem Behav 123:25–33 Kreek MJ, Bart G, Lilly C, LaForge KS, Nielsen DA (2005) Pharmacogenetics and human molecular genetics of opiate and cocaine addictions and their treatments. Pharmacol Rev 57(1):1–26 Kendler KS, Jacobson KC, Prescott CA, Neale MC (2003) Specificity of genetic and environmental risk factors for use and abuse/dependence of cannabis, cocaine, hallucinogens, sedatives, stimulants, and opiates in male twins. Am J Psychiatry 160(4):687–695 Volkow ND, Morales M (2015) The brain on drugs: From reward to addiction. Cell 162(4):712–725 Ryu HH, Kang M, Hwang KD, Jang HB, Kim SJ, Lee YS (2020) Neuron type-specific expression of a mutant KRAS impairs hippocampal-dependent learning and memory. Sci Rep 10(1):17730 Volkow ND, Koob GF, McLellan AT (2016) Neurobiologic advances from the brain disease model of addiction. N Engl J Med 374(4):363–371 Ducci F, Goldman D (2012) The genetic basis of addictive disorders. Psychiatric Clin 35(2):495–519 Additional Declarations The authors declare no competing interests. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5555328","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":384601169,"identity":"5d3e7c9f-ff5e-4621-89cc-bc16cac6868a","order_by":0,"name":"Raphael E. 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It is defined as a maladaptive pattern of opioid use leading to clinically significant impairment or distress, characterized by behaviors such as increased tolerance, withdrawal symptoms, uncontrollable consumption, and interference with daily life activities [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The determinants of opioid dependence can be broadly categorized into genetic, environmental, and behavioral components. While considerable research has been dedicated to understanding the socio-behavioral and environmental dimensions of opioid dependence, the genetic facets remain comparatively less explored, leaving a vital aspect of opioid dependence less understood [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUnderstanding the genetic basis of opioid dependence can provide valuable insights. Identifying specific genes or genetic markers associated with this condition allows us to explore the biological mechanisms and pathways potentially influencing an individual's susceptibility [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Such knowledge can enhance our molecular perspective of opioid dependence and offer a foundation for developing predictive and diagnostic tools.\u003c/p\u003e \u003cp\u003eFurthermore, by highlighting potential genetic targets, we may pave the way for the development of personalized therapeutic approaches [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This move towards precision medicine would allow for treatments to be tailored to an individual's unique genetic profile, increasing the likelihood of therapeutic success and reducing adverse reactions.\u003c/p\u003e \u003cp\u003eIn addition, understanding the genetic links can elucidate potential interactions between genes and environmental or behavioral factors [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This knowledge is paramount as it provides a multi-dimensional view of opioid dependence, integrating both inherited factors and external influences. Such a comprehensive understanding can lead to the development of multi-faceted intervention strategies that can be more effective in preventing or managing opioid dependence.\u003c/p\u003e \u003cp\u003eBeyond these applications, delving into the genetic connections also has broader implications for the field of neuropsychiatry. Opioid dependence blurs the lines between psychiatry and neurology [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. A detailed genetic mapping can offer insights into the complex neural circuits and neurotransmitter systems involved, potentially providing clarity on the intertwined nature of mental health and substance dependence.\u003c/p\u003e \u003cp\u003eAnalyzing clinical records, particularly from expansive clinical warehouses of electronic health records (EHRs), presents a comparatively uncommon approach to genetic research that is grounded in the lived experiences and medical journeys of real patients. The depth and breadth of data from such sources encapsulate the multitude of factors at play in clinical settings, offering a more holistic representation of patient health. Traditional methods, such as genome-wide association studies (GWAS), while groundbreaking, have inherent limitations. GWAS relies heavily on curated datasets, often extracted from populations with specific demographics, which may not be representative of the broader public. In contrast, using EHRs from large databases ensures that genetic correlations are founded on definitive clinical diagnoses. This method's value is further accentuated by the fact that EHRs encompass a wide demographic, providing inclusivity and diminishing racial or ethnic biases.\u003c/p\u003e \u003cp\u003eIn light of these potential benefits, this study endeavors to explore the associations between variants of specific genes and predisposition to opioid dependence in a large data warehouse of EHRs maintained by the University of California Health system. A rigorous and methodical investigation into this aspect not only fills a critical gap in current literature but also offers promising avenues for future research, diagnostics, and therapeutics in the realm of opioid dependence.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eWe conducted a STROBE-compliant study using the University of California Health Data Warehouse (UCHDW) as a data source. The UCHDW is an OMOP CDM-compliant database of electronic health records (EHR) from the six primary academic health centers within the University of California system, namely UC Davis, UC Irvine, UC Los Angeles, UC Riverside, UC San Diego, and UC San Francisco. Additionally, the data incorporated within the UCHDW includes claim details from the UC self-funded health schemes and external sources like Vizient and the California Department of Health Care Access and Information (HCAI). At the time of this study, the UCHDW housed data for over 8\u0026nbsp;million patients who received medical care at a UC facility since 2012. This study is exempt from human subject protection under IRB protocol 1604619-1 of the University of California Health System.\u003c/p\u003e \u003cp\u003eTo extract genetic data for conducting this exploratory cross-sectional analysis of EHRs in the UCHDW, SQL queries were embedded in a Python script in conjunction with the Spark framework. Specifically, data extraction identified all individuals with opioid dependence and all individuals without opioid dependence, and then joined data on identification of all available genetic variants. To enable generalizability, comparisons were limited to gene variants with at least thirty positive identifications within the group of patients having opioid dependence and also with at least thirty positive identifications within the group of patients not having opioid dependence.\u003c/p\u003e \u003cp\u003eGender may confound the association between genetic variants and opioid dependence as it can influence both the genetic tests individuals receive and their risk for opioid dependence. Some genetic conditions or risks might be sex-linked, and opioid dependence prevalence might differ between sexes. Furthermore, populations of different racial and ethnic backgrounds can have distinct genetic markers and profiles, leading to varying risks or propensities for certain medical conditions. Furthermore, there are documented disparities in healthcare access, utilization, and outcomes across different racial and ethnic groups. These disparities can influence the frequency and results of genetic testing in these populations. By including race and ethnicity in the multivariable Poisson model, we aim to convey risk estimates which control for the underlying genetic and socio-environmental differences that might impact opioid dependence risks.\u003c/p\u003e \u003cp\u003eIn the context of our research, we aimed to discern the relationship between specific genetic variants and the predisposition to opioid dependence. A variant denotes an alteration or mutation in the DNA sequence that deviates from a reference genome. The reference genome represents a standardized DNA sequence for a humans. A variant represents any one of a diverse set of modifications, from single nucleotide polymorphisms (SNPs) to more intricate structural shifts like insertions, deletions, inversions, duplications, and translocations. Depending on their position and nature in the genome, these variants can influence gene function, with potential impacts on protein structure, gene expression, or regulatory operations.\u003c/p\u003e \u003cp\u003eIn this study, any given genetic variant was denoted as a binary variables, indicating the presence (1) or absence (0) of the specific genetic variant. Opioid dependence was similarly represented as a binary metric based on medical records. Gender, race, and ethnicity were utilized as covariates in regression analyses to adjust for potential confounding effects.\u003c/p\u003e \u003cp\u003eGiven the binary nature of our outcome, we employed Poisson regression models to ascertain relationships between the presence of specific genetic variants and opioid dependence. The association between genetic variant and opioid dependence was first adjusted for gender to account for potential confounding from these factors, and then further adjusted for race and ethnicity in a fully adjusted model. The outcomes were presented as adjusted risk ratios with a 95% confidence interval for each genetic variant. All analyses were performed using the R programming language.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eNine genes were identified having at least thirty positive tests in both the group of patients with opioid dependence and the group of patients without. These genes were APC, ARID1A, BRCA2, CDKN2B, KRAS, MLL2, PIK3CA, TERT, and TP53. The cohort of patients having a genetic test for at least one of these nine genes comprised 222 having a diagnosis of opioid dependence and 20,363 without a diagnosis of opioid dependence. There were roughly the same percentages of gender and ethnicity categories across both groups. Proportion of patients who were non-White was 30.6% in the group with opioid dependence but 40.7% in the group without opioid dependence (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic characteristics of the patient population in this sample for those with and without opioid dependence.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHaving Opioid Dependence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot Having Opioid Dependence\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEthnicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e84.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eResults revealed varied levels of association across the genes. In unadjusted models, the KRAS gene showed a statistically significant association with opioid dependence, exhibiting an odds ratio of 1.45 (p\u0026thinsp;=\u0026thinsp;0.014), indicating a 45% increase in the odds of opioid dependence for individuals with this gene. Similarly, the TERT gene demonstrated a significant inverse relationship, with an odds ratio of 0.61 (p\u0026thinsp;=\u0026thinsp;0.010), suggesting a 39% decrease in the odds of opioid dependence.\u003c/p\u003e \u003cp\u003eUpon adjusting for demographic factors, the significance of these associations were virtually unchanged. Specifically, the odds ratio for KRAS adjusted slightly to 1.43 (p\u0026thinsp;=\u0026thinsp;0.018), while the odds ratio for TERT remained consistent at 0.61 (p\u0026thinsp;=\u0026thinsp;0.010). These findings lend support to the assertion that the influence of these genes on opioid dependence may have causal validity, as cellular mechanisms affected by these variants should be consistent across demographic categorizations.\u003c/p\u003e \u003cp\u003eOther genes, such as APC, ARID1A, BRCA2, CDKN2B, MLL2, PIK3CA, and TP53, showed no significant associations with opioid dependence in either the unadjusted or adjusted models. The odds ratios for these genes ranged from 0.77 to 1.38 in the unadjusted models and 0.77 to 1.40 in the adjusted models, with corresponding p-values all above the conventional significance threshold of 0.05 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"961\" height=\"452\"\u003e\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis exploratory study illuminates genetic predispositions that may be associated with opioid dependence, possibly leading to a much deeper understanding of the underlying molecular mechanisms. Moreover, this study illustrates the high potential value represented by analysis of risk factors using large systemwide clinical databases.\u003c/p\u003e \u003cp\u003eThe involvement of KRAS and TERT in neural processes and their potential influence on psychiatric and neurodevelopmental disorders has been substantiated by various studies. For KRAS, research has demonstrated that mutations in this gene are associated with rare neurodevelopmental disorders that can lead to intellectual disabilities. In particular, a prior study had shown that neuron type-specific expression of a mutant KRAS impairs hippocampal-dependent learning and memory. This study indicated that mice expressing a mutant KRAS in neurons displayed deficits in long-term potentiation as well as in spatial memory in adults, implicating KRAS in synaptogenesis during early development, the dysregulation of which subsequently impairs synaptic plasticity and memory in adults [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]​​. The study also noted the impact of mutant KRAS on the development and function of GABAergic inhibitory neurons, which may contribute to behavioral deficits.\u003c/p\u003e \u003cp\u003eFor TERT, its role in neuroprotection and influence on neuronal survival and regeneration has been acknowledged in scientific literature. A prior study using \u003cem\u003ein vitro\u003c/em\u003e methods discussed findings suggesting a protective role of TERT in neurons with hypoxia-ischemia injury. The study found that TERT expression increases in neurons after oxygen-glucose deprivation, suggesting a neuroprotective role via anti-apoptosis. This neuroprotective mechanism may be associated with regulating the Bcl-2/Bax expression ratio, attenuating reactive oxygen species generation, and increasing mitochondrial membrane potential​​ [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Another study was conducted with mouse models that reported overexpression of telomerase reverse transcriptase induces autism-like excitatory phenotypes in mice, indicating a potential link between TERT expression and neurodevelopmental disorders​​ [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe association of these genes with opioid dependence could also be indicative of broader, multifaceted genetic interactions. Opioid dependence, like many psychiatric and behavioral disorders, is likely influenced by a complex interplay of genetic, environmental, and psychosocial factors. The roles of KRAS and TERT in this context might not be direct but could interact with other genetic variations or environmental exposures to influence susceptibility to opioid dependence. For instance, the impact of chronic opioid use on cell survival and neural plasticity, pathways in which both KRAS and TERT are involved, might be modulated differently in individuals with variations in these genes, thereby influencing the risk or severity of dependence.\u003c/p\u003e \u003cp\u003eFurther research is needed to elucidate the precise mechanisms through which KRAS and TERT influence opioid dependence. This could include studies focusing on the role of these genes in brain regions implicated in addiction, such as the mesolimbic dopamine system, and examining how their expression or function changes in response to opioid exposure. Animal models and in vitro studies could provide insights into the cellular and molecular pathways involved. Additionally, investigating these genes in larger, more diverse populations would help clarify their role in opioid dependence and whether they interact with other genetic or environmental factors.\u003c/p\u003e \u003cp\u003eIn attempting to understand potential genetic underpinnings of opioid dependence, previous research methodologies have primarily revolved around GWAS, candidate gene approaches, and twin and family studies [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. While GWAS provides an unbiased assessment of the entire genome, identifying novel genetic markers potentially associated with opioid dependence, candidate gene studies focus on specific genes already believed to be involved in addiction pathways. Twin and family studies, on the other hand, have been instrumental in establishing the heritability of opioid dependence, providing foundational evidence for a genetic component. However, the exploration of vast databases, such as the one utilized in this study, presents a unique advantage. Leveraging large-scale databases allows for exploratory analyses that can refine inclusion criteria and utilize larger sample sizes to facilitate the robustness and generalizability of findings. Furthermore, extensive databases of electronic health records can provide a rich tapestry of clinical and genetic information, facilitating nuanced insights and paving the way for personalized, precision medicine approaches in understanding and treating opioid dependence.\u003c/p\u003e \u003cp\u003eA large-scale GWAS was recently conducted on individuals of both European and African ancestry which illustrates the potential complexity and genetic diversity of molecular influences on opioid dependence [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This valuable study provides valuable insights into the genetic landscape of opioid dependence and underscores the challenges inherent in isolating specific genetic triggers through genome scanning. The prior GWAS and this study both found that opioid dependence was associated with genetic variants having downstream cellular functions that operate in disparate and unclearly related ways. Our approach, which amalgamates records from an extensive clinical data warehouse with genetic data, aims to bridge real-world clinical observations with genetic implications. Such a methodology should be considered as a complement that triangulates findings with those from traditional GWAS, as well as for potentially amplifying translational applications in patient care.\u003c/p\u003e \u003cp\u003eIt is worth noting that the etiology of opioid dependence is multifactorial, encompassing a myriad of genetic, environmental, and psychological components [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. As such, while this study identifies specific genes that might be of importance, it remains exploratory in nature, making it essential to approach conclusions with caution. Environmental and behavioral factors, which were not accounted for in this study, can significantly modulate the relationship between these genes and opioid dependence. For a holistic understanding, future research should consider integrating genetics with behavioral, environmental, and physiological data.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThese results contribute to a growing body of evidence suggesting that genetic factors play a crucial role in substance use disorders. Understanding the specific mechanisms through which genes like KRAS and TERT influence opioid dependence could pave the way for more targeted prevention and treatment strategies, tailored to individual genetic profiles. As research in this field continues to evolve, it is increasingly apparent that addressing the opioid crisis will require a multidisciplinary approach, integrating insights from genetics, neuroscience, psychology, and public health.\u003c/p\u003e"},{"header":"Limitations","content":"\u003cp\u003eIn any study leveraging EHRs, there are inherent biases that need to be acknowledged. First, diagnostic bias can arise due to variations in the diagnostic practices across different healthcare providers. There's also a possibility of selection bias since the patient population represented in the UCHDW may not be entirely representative of the broader population, potentially skewing the genetic variants identified. Additionally, information bias can be present due to the variability in the recording practices, missing data, or inaccuracies within the EHR. Efforts were made to minimize these biases through rigorous data preprocessing, yet their potential influence on the study outcomes should be taken into consideration when interpreting results.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval\u003c/strong\u003e: This study is exempt from human subject protection under IRB protocol 1604619-1 of the University of California Health System.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Disclosure\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial Disclosure\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e None\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Sharing Statement\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eThe data used in this study contain sensitive patient information and are subject to strict confidentiality agreements and regulatory restrictions. To ensure the protection of patient privacy and to comply with relevant data protection regulations, the raw data supporting the conclusions of this research cannot be made publicly available.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDegenhardt L, Charlson F, Mathers B, Hall WD, Flaxman AD, Johns N, Vos T (2014) The global epidemiology and burden of opioid dependence: Results from the global burden of disease 2010 study. Addiction 109(8):1320\u0026ndash;1333\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmerican Psychiatric Association (2000) Diagnostic and statistical manual of mental disorders (4th ed., text rev.). Washington, DC: Author\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cem\u003eMolecular psychiatry\u003c/em\u003e, 15(4), 417\u0026ndash;428\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrist RC, Berrettini WH (2014) Pharmacogenetics of OPRM1. Pharmacol Biochem Behav 123:25\u0026ndash;33\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKreek MJ, Bart G, Lilly C, LaForge KS, Nielsen DA (2005) Pharmacogenetics and human molecular genetics of opiate and cocaine addictions and their treatments. Pharmacol Rev 57(1):1\u0026ndash;26\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKendler KS, Jacobson KC, Prescott CA, Neale MC (2003) Specificity of genetic and environmental risk factors for use and abuse/dependence of cannabis, cocaine, hallucinogens, sedatives, stimulants, and opiates in male twins. Am J Psychiatry 160(4):687\u0026ndash;695\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVolkow ND, Morales M (2015) The brain on drugs: From reward to addiction. Cell 162(4):712\u0026ndash;725\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRyu HH, Kang M, Hwang KD, Jang HB, Kim SJ, Lee YS (2020) Neuron type-specific expression of a mutant KRAS impairs hippocampal-dependent learning and memory. Sci Rep 10(1):17730\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVolkow ND, Koob GF, McLellan AT (2016) Neurobiologic advances from the brain disease model of addiction. N Engl J Med 374(4):363\u0026ndash;371\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDucci F, Goldman D (2012) The genetic basis of addictive disorders. Psychiatric Clin 35(2):495\u0026ndash;519\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of California, San Diego","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Opioids, Genetics, Electronic Health Records","lastPublishedDoi":"10.21203/rs.3.rs-5555328/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5555328/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e \u003cp\u003eThe present exploratory study seeks to showcase an approach to uncover potential genetic associations predisposing individuals to opioid dependence, in which a large clinical database is utilized to compare results of testing for genetic variants with clinical diagnoses of opioid dependence.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eEmploying a STROBE-compliant study design, this research leveraged the UC Health Data Warehouse, an OMOP CDM-compliant database with EHR data from six University of California academic health centers. Utilizing SQL queries embedded in Python scripts with the Spark framework, the study extracted genetic data, focusing on individuals tested for specific genetic variants.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eRates of detection for nine genes were evaluated between 222 patients with opioid dependence and 20141 patients without, revealing significantly decreased odds of opioid dependence for individuals with the TERT gene (OR\u0026thinsp;=\u0026thinsp;0.61, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010) and significantly increased odds for individuals with the KRAS gene (OR\u0026thinsp;=\u0026thinsp;1.45, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014). Significant associations persisted after adjusting for demographics.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe discovery of KRAS and TERT associations with opioid dependence in our study highlights the intricate genetic landscape of this condition. This study, in triangulation with findings from GWAS and other designs, may pave the way for more personalized approaches to prevention and treatment of opioid dependence.\u003c/p\u003e","manuscriptTitle":"Association of KRAS and TERT Genetic Variants with Opioid Dependence in a Large Clinical Cohort","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-09 07:46:38","doi":"10.21203/rs.3.rs-5555328/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1287044e-1f77-42fd-89c3-71c1cc867cce","owner":[],"postedDate":"December 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":40974834,"name":"Medical Genetics"},{"id":40974835,"name":"Psychology"},{"id":40974836,"name":"Epidemiology"},{"id":40974837,"name":"Medical Informatics"}],"tags":[],"updatedAt":"2024-12-09T07:46:38+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-09 07:46:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5555328","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5555328","identity":"rs-5555328","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}