Browsing by Author "Crist, Richard C."

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    Candidate Genes from an FDA-Approved Algorithm Fail to Predict Opioid Use Disorder Risk in Over 450,000 Veterans
    (medRxiv, 2024-05-16) Davis, Christal N.; Jinwala, Zeal; Hatoum, Alexander S.; Toikumo, Sylvanus; Agrawal, Arpana; Rentsch, Christopher T.; Edenberg, Howard J.; Baurley, James W.; Hartwell, Emily E.; Crist, Richard C.; Gray, Joshua C.; Justice, Amy C.; Gelernter, Joel; Kember, Rachel L.; Kranzler, Henry R.; Biochemistry and Molecular Biology, School of Medicine
    Importance: Recently, the Food and Drug Administration gave pre-marketing approval to algorithm based on its purported ability to identify genetic risk for opioid use disorder. However, the clinical utility of the candidate genes comprising the algorithm has not been independently demonstrated. Objective: To assess the utility of 15 variants in candidate genes from an algorithm intended to predict opioid use disorder risk. Design: This case-control study examined the association of 15 candidate genetic variants with risk of opioid use disorder using available electronic health record data from December 20, 1992 to September 30, 2022. Setting: Electronic health record data, including pharmacy records, from Million Veteran Program participants across the United States. Participants: Participants were opioid-exposed individuals enrolled in the Million Veteran Program (n = 452,664). Opioid use disorder cases were identified using International Classification of Disease diagnostic codes, and controls were individuals with no opioid use disorder diagnosis. Exposures: Number of risk alleles present across 15 candidate genetic variants. Main outcome and measures: Predictive performance of 15 genetic variants for opioid use disorder risk assessed via logistic regression and machine learning models. Results: Opioid exposed individuals (n=33,669 cases) were on average 61.15 (SD = 13.37) years old, 90.46% male, and had varied genetic similarity to global reference panels. Collectively, the 15 candidate genetic variants accounted for 0.4% of variation in opioid use disorder risk. The accuracy of the ensemble machine learning model using the 15 genes as predictors was 52.8% (95% CI = 52.1 - 53.6%) in an independent testing sample. Conclusions and relevance: Candidate genes that comprise the approved algorithm do not meet reasonable standards of efficacy in predicting opioid use disorder risk. Given the algorithm's limited predictive accuracy, its use in clinical care would lead to high rates of false positive and negative findings. More clinically useful models are needed to identify individuals at risk of developing opioid use disorder.
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    Clinical Pharmacogenetics Implementation Consortium Guideline for CYP2B6 Genotype and Methadone Therapy
    (Wiley, 2024) Robinson, Katherine M.; Eum, Seenae; Desta, Zeruesenay; Tyndale, Rachel F.; Gaedigk, Andrea; Crist, Richard C.; Haidar, Cyrine E.; Myers, Alan L.; Samer, Caroline F.; Somogyi, Andrew A.; Zubiaur, Pablo; Iwuchukwu, Otito F.; Whirl-Carrillo, Michelle; Klein, Teri E.; Caudle, Kelly E.; Donnelly, Roseann S.; Kharasch, Evan D.; Medicine, School of Medicine
    Methadone is a mu (μ) opioid receptor agonist used clinically in adults and children to manage opioid use disorder, neonatal abstinence syndrome, and acute and chronic pain. It is typically marketed as a racemic mixture of R- and S-enantiomers. R-methadone has 30-to 50-fold higher analgesic potency than S-methadone, and S-methadone has a greater adverse effect (prolongation) on the cardiac QTc interval. Methadone undergoes stereoselective metabolism. CYP2B6 is the primary enzyme responsible for catalyzing the metabolism of both enantiomers to the inactive metabolites, S- and R-2-ethylidene-1,5-dimethyl-3,3-diphenylpyrrolidine (S- and R-EDDP). Genetic variation in the CYP2B6 gene has been investigated in the context of implications for methadone pharmacokinetics, dose, and clinical outcomes. Most CYP2B6 variants result in diminished or loss of CYP2B6 enzyme activity, which can lead to higher plasma methadone concentrations (affecting S- more than R-methadone). However, the data do not consistently indicate that CYP2B6-based metabolic variability has a clinically significant effect on methadone dose, efficacy, or QTc prolongation. Expert analysis of the published literature does not support a change from standard methadone prescribing based on CYP2B6 genotype (updates at www.cpicpgx.org).
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    Genome-Wide Association Study Meta-Analysis of the Alcohol Use Disorders Identification Test (AUDIT) in Two Population-Based Cohorts
    (American Psychiatric Publishing, 2019-02-01) Sanchez-Roige, Sandra; Palmer, Abraham A.; Fontanillas, Pierre; Elson, Sarah L.; The 23andMe Research Team; Substance Use Disorder Working Group of the Psychiatric Genomics Consortium; Adams, Mark J.; Howard, David M.; Edenberg, Howard J.; Davies, Gail; Crist, Richard C.; Deary, Ian J.; McIntosh, Andrew M.; Clarke, Toni-Kim; Biochemistry and Molecular Biology, School of Medicine
    OBJECTIVE: Alcohol use disorders are common conditions that have enormous social and economic consequences. Genome-wide association analyses were performed to identify genetic variants associated with a proxy measure of alcohol consumption and alcohol misuse and to explore the shared genetic basis between these measures and other substance use, psychiatric, and behavioral traits. METHOD: This study used quantitative measures from the Alcohol Use Disorders Identification Test (AUDIT) from two population-based cohorts of European ancestry (UK Biobank [N=121,604] and 23andMe [N=20,328]) and performed a genome-wide association study (GWAS) meta-analysis. Two additional GWAS analyses were performed, a GWAS for AUDIT scores on items 1-3, which focus on consumption (AUDIT-C), and for scores on items 4-10, which focus on the problematic consequences of drinking (AUDIT-P). RESULTS: The GWAS meta-analysis of AUDIT total score identified 10 associated risk loci. Novel associations localized to genes including JCAD and SLC39A13; this study also replicated previously identified signals in the genes ADH1B, ADH1C, KLB, and GCKR. The dimensions of AUDIT showed positive genetic correlations with alcohol consumption (rg=0.76-0.92) and DSM-IV alcohol dependence (rg=0.33-0.63). AUDIT-P and AUDIT-C scores showed significantly different patterns of association across a number of traits, including psychiatric disorders. AUDIT-P score was significantly positively genetically correlated with schizophrenia (rg=0.22), major depressive disorder (rg=0.26), and attention deficit hyperactivity disorder (rg=0.23), whereas AUDIT-C score was significantly negatively genetically correlated with major depressive disorder (rg=-0.24) and ADHD (rg=-0.10). This study also used the AUDIT data in the UK Biobank to identify thresholds for dichotomizing AUDIT total score that optimize genetic correlations with DSM-IV alcohol dependence. Coding individuals with AUDIT total scores ≤4 as control subjects and those with scores ≥12 as case subjects produced a significant high genetic correlation with DSM-IV alcohol dependence (rg=0.82) while retaining most subjects. CONCLUSIONS: AUDIT scores ascertained in population-based cohorts can be used to explore the genetic basis of both alcohol consumption and alcohol use disorders.
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    Multi-trait genome-wide association study of opioid addiction: OPRM1 and beyond
    (Springer Nature, 2022-10-07) Gaddis, Nathan; Mathur, Ravi; Marks, Jesse; Zhou, Linran; Quach, Bryan; Waldrop, Alex; Levran, Orna; Agrawal, Arpana; Randesi, Matthew; Adelson, Miriam; Jeffries, Paul W.; Martin, Nicholas G.; Degenhardt, Louisa; Montgomery, Grant W.; Wetherill, Leah; Lai, Dongbing; Bucholz, Kathleen; Foroud, Tatiana; Porjesz, Bernice; Runarsdottir, Valgerdur; Tyrfingsson, Thorarinn; Einarsson, Gudmundur; Gudbjartsson, Daniel F.; Webb, Bradley Todd; Crist, Richard C.; Kranzler, Henry R.; Sherva, Richard; Zhou, Hang; Hulse, Gary; Wildenauer, Dieter; Kelty, Erin; Attia, John; Holliday, Elizabeth G.; McEvoy, Mark; Scott, Rodney J.; Schwab, Sibylle G.; Maher, Brion S.; Gruza, Richard; Kreek, Mary Jeanne; Nelson, Elliot C.; Thorgeirsson, Thorgeir; Stefansson, Kari; Berrettini, Wade H.; Gelernter, Joel; Edenberg, Howard J.; Bierut, Laura; Hancock, Dana B.; Johnson, Eric Otto; Medical and Molecular Genetics, School of Medicine
    Opioid addiction (OA) is moderately heritable, yet only rs1799971, the A118G variant in OPRM1, has been identified as a genome-wide significant association with OA and independently replicated. We applied genomic structural equation modeling to conduct a GWAS of the new Genetics of Opioid Addiction Consortium (GENOA) data together with published studies (Psychiatric Genomics Consortium, Million Veteran Program, and Partners Health), comprising 23,367 cases and effective sample size of 88,114 individuals of European ancestry. Genetic correlations among the various OA phenotypes were uniformly high (rg > 0.9). We observed the strongest evidence to date for OPRM1: lead SNP rs9478500 (p = 2.56 × 10-9). Gene-based analyses identified novel genome-wide significant associations with PPP6C and FURIN. Variants within these loci appear to be pleiotropic for addiction and related traits.
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    Utility of Candidate Genes From an Algorithm Designed to Predict Genetic Risk for Opioid Use Disorder
    (American Medical Association, 2025-01-02) Davis, Christal N.; Jinwala, Zeal; Hatoum, Alexander S.; Toikumo, Sylvanus; Agrawal, Arpana; Rentsch, Christopher T.; Edenberg, Howard J.; Baurley, James W.; Hartwell, Emily E.; Crist, Richard C.; Gray, Joshua C.; Justice, Amy C.; Gelernter, Joel; Kember, Rachel L.; Kranzler, Henry R.; VA Million Veteran Program; Biochemistry and Molecular Biology, School of Medicine
    Importance: Recently, the US Food and Drug Administration gave premarketing approval to an algorithm based on its purported ability to identify individuals at genetic risk for opioid use disorder (OUD). However, the clinical utility of the candidate genetic variants included in the algorithm has not been independently demonstrated. Objective: To assess the utility of 15 genetic variants from an algorithm intended to predict OUD risk. Design, setting, and participants: This case-control study examined the association of 15 candidate genetic variants with risk of OUD using electronic health record data from December 20, 1992, to September 30, 2022. Electronic health record data, including pharmacy records, were accrued from participants in the Million Veteran Program across the US with opioid exposure (n = 452 664). Cases with OUD were identified using International Classification of Diseases, Ninth Revision, or International Classification of Diseases, Tenth Revision, diagnostic codes, and controls were individuals with no OUD diagnosis. Exposures: Number of risk alleles present across 15 candidate genetic variants. Main outcome and measures: Performance of 15 genetic variants for identifying OUD risk assessed via logistic regression and machine learning models. Results: A total of 452 664 individuals with opioid exposure (including 33 669 with OUD) had a mean (SD) age of 61.15 (13.37) years, and 90.46% were male; the sample was ancestrally diverse (with individuals of genetically inferred European, African, and admixed American ancestries). Using Nagelkerke R2, collectively, the 15 candidate genes accounted for 0.40% of variation in OUD risk. In comparison, age and sex alone accounted for 3.27% of the variation. The ensemble machine learning. The ensemble machine learning model using the 15 variants as predictive factors correctly classified 52.83% (95% CI, 52.07%-53.59%) of individuals in an independent testing sample. Conclusions and relevance: Results of this study suggest that the candidate genetic variants included in the approved algorithm do not meet reasonable standards of efficacy in identifying OUD risk. Given the algorithm's limited predictive accuracy, its use in clinical care would lead to high rates of both false-positive and false-negative findings. More clinically useful models are needed to identify individuals at risk of developing OUD.