Browsing by Author "Kember, Rachel L."

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    A multiancestry genome-wide association study of unexplained chronic ALT elevation as a proxy for nonalcoholic fatty liver disease with histological and radiological validation
    (Springer Nature, 2022) Vujkovic, Marijana; Ramdas, Shweta; Lorenz, Kim M.; Guo, Xiuqing; Darlay, Rebecca; Cordell, Heather J.; He, Jing; Gindin, Yevgeniy; Chung, Chuhan; Myers, Robert P.; Schneider, Carolin V.; Park, Joseph; Lee, Kyung Min; Serper, Marina; Carr, Rotonya M.; Kaplan, David E.; Haas, Mary E.; MacLean, Matthew T.; Witschey, Walter R.; Zhu, Xiang; Tcheandjieu, Catherine; Kember, Rachel L.; Kranzler, Henry R.; Verma, Anurag; Giri, Ayush; Klarin, Derek M.; Sun, Yan V.; Huang, Jie; Huffman, Jennifer E.; Townsend Creasy, Kate; Hand, Nicholas J.; Liu, Ching-Ti; Long, Michelle T.; Yao, Jie; Budoff, Matthew; Tan, Jingyi; Li, Xiaohui; Lin, Henry J.; Chen, Yii-Der Ida; Taylor, Kent D.; Chang, Ruey-Kang; Krauss, Ronald M.; Vilarinho, Silvia; Brancale, Joseph; Nielsen, Jonas B.; Locke, Adam E.; Jones, Marcus B.; Verweij, Niek; Baras, Aris; Reddy, K. Rajender; Neuschwander-Tetri, Brent A.; Schwimmer, Jeffrey B.; Sanyal, Arun J.; Chalasani, Naga; Ryan, Kathleen A.; Mitchell, Braxton D.; Gill, Dipender; Wells, Andrew D.; Manduchi, Elisabetta; Saiman, Yedidya; Mahmud, Nadim; Miller, Donald R.; Reaven, Peter D.; Phillips, Lawrence S.; Muralidhar, Sumitra; DuVall, Scott L.; Lee, Jennifer S.; Assimes, Themistocles L.; Pyarajan, Saiju; Cho, Kelly; Edwards, Todd L.; Damrauer, Scott M.; Wilson, Peter W.; Gaziano, J. Michael; O'Donnell, Christopher J.; Khera, Amit V.; Grant, Struan F. A.; Brown, Christopher D.; Tsao, Philip S.; Saleheen, Danish; Lotta, Luca A.; Bastarache, Lisa; Anstee, Quentin M.; Daly, Ann K.; Meigs, James B.; Rotter, Jerome I.; Lynch, Julie A.; Regeneron Genetics Center; Geisinger-Regeneron DiscovEHR Collaboration; EPoS Consortium; VA Million Veteran Program; Rader, Daniel J.; Voight, Benjamin F.; Chang, Kyong-Mi; Medicine, School of Medicine
    Nonalcoholic fatty liver disease (NAFLD) is a growing cause of chronic liver disease. Using a proxy NAFLD definition of chronic elevation of alanine aminotransferase (cALT) levels without other liver diseases, we performed a multiancestry genome-wide association study (GWAS) in the Million Veteran Program (MVP) including 90,408 cALT cases and 128,187 controls. Seventy-seven loci exceeded genome-wide significance, including 25 without prior NAFLD or alanine aminotransferase associations, with one additional locus identified in European American-only and two in African American-only analyses (P < 5 × 10-8). External replication in histology-defined NAFLD cohorts (7,397 cases and 56,785 controls) or radiologic imaging cohorts (n = 44,289) replicated 17 single-nucleotide polymorphisms (SNPs) (P < 6.5 × 10-4), of which 9 were new (TRIB1, PPARG, MTTP, SERPINA1, FTO, IL1RN, COBLL1, APOH and IFI30). Pleiotropy analysis showed that 61 of 77 multiancestry and all 17 replicated SNPs were jointly associated with metabolic and/or inflammatory traits, revealing a complex model of genetic architecture. Our approach integrating cALT, histology and imaging reveals new insights into genetic liability to NAFLD.
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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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    Diagnostic Interview for Genetic Studies: validity and reliability of the Croatian version
    (Wolters Kluwer, 2017-02) Kralj, Žana; Dedić, Milenka; Kovačević, Anđela; Malički, Mario; Dedić, Jelena; Pelivan, Marina; Vuković, Dubravka; Fisher, Carrie; Kember, Rachel L.; Nurnberger, John; Bućan, Maja; Britvić, Dolores; Psychiatry, School of Medicine
    OBJECTIVE: To test the validity and reliability of the Diagnostic Interview for Genetic Studies (DIGS) in patients with mental illness in Croatia. MATERIALS AND METHODS: Following translation, back-translation, and pilot testing, the Croatian version of DIGS (CRO-DIGS) was administered to a total of 150 inpatients and outpatients diagnosed at the Clinical Hospital in Split with bipolar and major depressive disorder (n=56), schizophrenia and schizoaffective disorder (n=62), and alcohol dependence or use disorders (n=32). Initial testing was performed independently by one interviewer and one observer blinded to the diagnosis, and a retest was performed after 8 weeks by a third examiner. RESULTS: The validity of CRO-DIGS was high (κ=0.916), with an excellent inter-rater (κ=0.824) reliability, especially for bipolar disorder (κ=0.956). Following an 8 week test-retest interval, the reliability for all diagnoses was found to be excellent (κ=0.843). CONCLUSION: Our study has shown excellent validity and reliability of the Croatian version of DIGS, making it a promising instrument to assess mental illness of patients. The development of a valid and reliable diagnostic tool such as the CRO-DIGS will considerably advance the scientific communities' ability to carry out genetic studies of psychiatric illness in the region.
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    Genome-wide association study in individuals of European and African ancestry and multi-trait analysis of opioid use disorder identifies 19 independent genome-wide significant risk loci
    (Springer, 2022-10) Deak, Joseph D.; Zhou, Hang; Galimberti, Marco; Levey, Daniel F.; Wendt, Frank R.; Sanchez-Roige, Sandra; Hatoum, Alexander S.; Johnson, Emma C.; Nunez, Yaira Z.; Demontis, Ditte; Børglum, Anders D.; Rajagopal, Veera M.; Jennings, Mariela V.; Kember, Rachel L.; Justice, Amy C.; Edenberg, Howard J.; Agrawal, Arpana; Polimanti, Renato; Kranzler, Henry R.; Gelernter, Joel; Biochemistry and Molecular Biology, School of Medicine
    Despite the large toll of opioid use disorder (OUD), genome-wide association studies (GWAS) of OUD to date have yielded few susceptibility loci. We performed a large-scale GWAS of OUD in individuals of European (EUR) and African (AFR) ancestry, optimizing genetic informativeness by performing MTAG (Multi-trait analysis of GWAS) with genetically correlated substance use disorders (SUDs). Meta-analysis included seven cohorts: the Million Veteran Program, Psychiatric Genomics Consortium, iPSYCH, FinnGen, Partners Biobank, BioVU, and Yale-Penn 3, resulting in a total N = 639,063 (Ncases = 20,686;Neffective = 77,026) across ancestries. OUD cases were defined as having a lifetime OUD diagnosis, and controls as anyone not known to meet OUD criteria. We estimated SNP-heritability (h2SNP) and genetic correlations (rg). Based on genetic correlation, we performed MTAG on OUD, alcohol use disorder (AUD), and cannabis use disorder (CanUD). A leave-one-out polygenic risk score (PRS) analysis was performed to compare OUD and OUD-MTAG PRS as predictors of OUD case status in Yale-Penn 3. The EUR meta-analysis identified three genome-wide significant (GWS; p ≤ 5 × 10−8) lead SNPs—one at FURIN (rs11372849; p = 9.54 × 10−10) and two OPRM1 variants (rs1799971, p = 4.92 × 10−09; rs79704991, p = 1.11 × 10−08; r2 = 0.02). Rs1799971 (p = 4.91 × 10−08) and another OPRM1 variant (rs9478500; p = 1.95 × 10−08; r2 = 0.03) were identified in the cross-ancestry meta-analysis. Estimated h2SNP was 12.75%, with strong rg with CanUD (rg = 0.82; p = 1.14 × 10−47) and AUD (rg = 0.77; p = 6.36 × 10−78). The OUD-MTAG resulted in a GWAS Nequivalent = 128,748 and 18 independent GWS loci, some mapping to genes or gene regions that have previously been associated with psychiatric or addiction phenotypes. The OUD-MTAG PRS accounted for 3.81% of OUD variance (beta = 0.61;s.e. = 0.066; p = 2.00 × 10−16) compared to 2.41% (beta = 0.45; s.e. = 0.058; p = 2.90 × 10−13) explained by the OUD PRS. The current study identified OUD variant associations at OPRM1, single variant associations with FURIN, and 18 GWS associations in the OUD-MTAG. The genetic architecture of OUD is likely influenced by both OUD-specific loci and loci shared across SUDs.
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    Genome-wide meta-analysis of problematic alcohol use in 435,563 individuals yields insights into biology and relationships with other traits
    (Nature, 2020-07) Zhou, Hang; Sealock, Julia M.; Sanchez-Roige, Sandra; Clarke, Toni-Kim; Levey, Daniel F.; Cheng, Zhongshan; Li, Boyang; Polimanti, Renato; Kember, Rachel L.; Smith, Rachel Vickers; Thygesen, Johan H.; Morgan, Marsha Y.; Atkinson, Stephen R.; Thursz, Mark R.; Nyegaard, Mette; Mattheisen, Manuel; Børglum, Anders D.; Johnson, Emma C.; Justice, Amy C.; Palmer, Abraham A.; McQuillin, Andrew; Davis, Lea K.; Edenberg, Howard J.; Agrawal, Arpana; Kranzler, Henry R.; Gelernter, Joel; Medical and Molecular Genetics, School of Medicine
    Problematic alcohol use (PAU) is a leading cause of death and disability worldwide. Although genome-wide association studies have identified PAU risk genes, the genetic architecture of this trait is not fully understood. We conducted a proxy-phenotype meta-analysis of PAU, combining alcohol use disorder and problematic drinking, in 435,563 European-ancestry individuals. We identified 29 independent risk variants, 19 of them novel. PAU was genetically correlated with 138 phenotypes, including substance use and psychiatric traits. Phenome-wide polygenic risk score analysis in an independent biobank sample (BioVU, n = 67,589) confirmed the genetic correlations between PAU and substance use and psychiatric disorders. Genetic heritability of PAU was enriched in brain and in conserved and regulatory genomic regions. Mendelian randomization suggested causal effects on liability to PAU of substance use, psychiatric status, risk-taking behavior and cognitive performance. In summary, this large PAU meta-analysis identified novel risk loci and revealed genetic relationships with numerous other traits.
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    Investigation of convergent and divergent genetic influences underlying schizophrenia and alcohol use disorder
    (Cambridge University Press, 2023) Johnson, Emma C.; Kapoor, Manav; Hatoum, Alexander S.; Zhou, Hang; Polimanti, Renato; Wendt, Frank R.; Walters, Raymond K.; Lai, Dongbing; Kember, Rachel L.; Hartz, Sarah; Meyers, Jacquelyn L.; Peterson, Roseann E.; Ripke, Stephan; Bigdeli, Tim B.; Fanous, Ayman H.; Pato, Carlos N.; Pato, Michele T.; Goate, Alison M.; Kranzler, Henry R.; O’Donovan, Michael C.; Walters, James T. R.; Gelernter, Joel; Edenberg, Howard J.; Agrawal, Arpana; Medical and Molecular Genetics, School of Medicine
    Background: Alcohol use disorder (AUD) and schizophrenia (SCZ) frequently co-occur, and large-scale genome-wide association studies (GWAS) have identified significant genetic correlations between these disorders. Methods: We used the largest published GWAS for AUD (total cases = 77 822) and SCZ (total cases = 46 827) to identify genetic variants that influence both disorders (with either the same or opposite direction of effect) and those that are disorder specific. Results: We identified 55 independent genome-wide significant single nucleotide polymorphisms with the same direction of effect on AUD and SCZ, 8 with robust effects in opposite directions, and 98 with disorder-specific effects. We also found evidence for 12 genes whose pleiotropic associations with AUD and SCZ are consistent with mediation via gene expression in the prefrontal cortex. The genetic covariance between AUD and SCZ was concentrated in genomic regions functional in brain tissues (p = 0.001). Conclusions: Our findings provide further evidence that SCZ shares meaningful genetic overlap with AUD.
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    Multi-ancestry study of the genetics of problematic alcohol use in over 1 million individuals
    (Springer Nature, 2023) Zhou, Hang; Kember, Rachel L.; Deak, Joseph D.; Xu, Heng; Toikumo, Sylvanus; Yuan, Kai; Lind, Penelope A.; Farajzadeh, Leila; Wang, Lu; Hatoum, Alexander S.; Johnson, Jessica; Lee, Hyunjoon; Mallard, Travis T.; Xu, Jiayi; Johnston, Keira J. A.; Johnson, Emma C.; Galimberti, Marco; Dao, Cecilia; Levey, Daniel F.; Overstreet, Cassie; Byrne, Enda M.; Gillespie, Nathan A.; Gordon, Scott; Hickie, Ian B.; Whitfield, John B.; Xu, Ke; Zhao, Hongyu; Huckins, Laura M.; Davis, Lea K.; Sanchez-Roige, Sandra; Madden, Pamela A. F.; Heath, Andrew C.; Medland, Sarah E.; Martin, Nicholas G.; Ge, Tian; Smoller, Jordan W.; Hougaard, David M.; Børglum, Anders D.; Demontis, Ditte; Krystal, John H.; Gaziano, J. Michael; Edenberg, Howard J.; Agrawal, Arpana; Million Veteran Program; Justice, Amy C.; Stein, Murray B.; Kranzler, Henry R.; Gelernter, Joel; Biochemistry and Molecular Biology, School of Medicine
    Problematic alcohol use (PAU), a trait that combines alcohol use disorder and alcohol-related problems assessed with a questionnaire, is a leading cause of death and morbidity worldwide. Here we conducted a large cross-ancestry meta-analysis of PAU in 1,079,947 individuals (European, N = 903,147; African, N = 122,571; Latin American, N = 38,962; East Asian, N = 13,551; and South Asian, N = 1,716 ancestries). We observed a high degree of cross-ancestral similarity in the genetic architecture of PAU and identified 110 independent risk variants in within- and cross-ancestry analyses. Cross-ancestry fine mapping improved the identification of likely causal variants. Prioritizing genes through gene expression and chromatin interaction in brain tissues identified multiple genes associated with PAU. We identified existing medications for potential pharmacological studies by a computational drug repurposing analysis. Cross-ancestry polygenic risk scores showed better performance of association in independent samples than single-ancestry polygenic risk scores. Genetic correlations between PAU and other traits were observed in multiple ancestries, with other substance use traits having the highest correlations. This study advances our knowledge of the genetic etiology of PAU, and these findings may bring possible clinical applicability of genetics insights-together with neuroscience, biology and data science-closer.