A Genome-Wide Association Study of Cocaine Use Disorder Accounting for Phenotypic Heterogeneity and Gene–Environment Interaction

Background: Phenotypic heterogeneity and complicated gene-environment interplay in etiology are among the primary factors that hinder the identification of genetic variants associated with cocaine use disorder. Methods: To detect novel genetic variants associated with cocaine use disorder, we derived disease traits with reduced phenotypic heterogeneity using cluster analysis of a study sample (n = 9965). We then used these traits in genome-wide association tests, performed separately for 2070 African Americans and 1570 European Americans, using a new mixed model that accounted for the moderating effects of 5 childhood environmental factors. We used an independent sample (918 African Americans, 1382 European Americans) for replication. Results: The cluster analysis yielded 5 cocaine use disorder subtypes, of which subtypes 4 (n = 3258) and 5 (n = 1916) comprised heavy cocaine users, had high heritability estimates (h2 = 0.66 and 0.64, respectively) and were used in association tests. Seven of the 13 identified genetic loci in the discovery phase were available in the replication sample. In African Americans, rs114492924 (discovery p = 1.23 x E-8), a single nucleotide polymorphism in LINC01411, was replicated in the replication sample (p = 3.63 x E-3). In a meta-analysis that combined the discovery and replication results, 3 loci in African Americans were significant genome-wide: rs10188036 in TRAK2 (p = 2.95 x E-8), del 1:15511771 in TMEM51 = 9.11 x E-10) and rs149843442 near LPHN2 (p = 3.50 x E-8). Limitations: Lack of data prevented us from replicating 6 of the 13 identified loci. Conclusion: Our results demonstrate the importance of considering phenotypic heterogeneity and gene-environment interplay in detecting genetic variations that contribute to cocaine use disorder, because new genetic loci have been identified using our novel analytic method

Characterization of a likelihood based method and effects of markers informativeness in evaluation of admixture and population group assignment

BACKGROUND: Detection and evaluation of population stratification are crucial issues in the conduct of genetic association studies. Statistical approaches useful for understanding these issues have been proposed; these methods rely on information gained from genotyping sets of markers that reflect population ancestry. Before using these methods, a set of markers informative for differentiating population genetic substructure (PGS) is necessary. We have previously evaluated the performance of a Bayesian clustering method implemented in the software STRUCTURE in detecting PGS with a particular informative marker set. In this study, we implemented a likelihood based method (LBM) in evaluating the informativeness of the same selected marker panel, with respect to assessing potential for stratification in samples of European Americans (EAs) and African Americans (AAs), that are known to be admixed. LBM calculates the probability of a set of genotypes based on observations in a reference population with known specific allele frequencies for each marker, assuming Hardy Weinberg equilibrium (HWE) for each marker and linkage equilibrium among markers. RESULTS: In EAs, the assignment accuracy by LBM exceeded 99% using the most efficient marker FY, and reached perfect assignment accuracy using the 10 most efficient markers excluding FY. In AAs, the assignment accuracy reached 96.4% using FY, and >95% when using at least the 9 most efficient markers. The comparison of the observed and reference allele frequencies (which were derived from previous publications and public databases) shows that allele frequencies observed in EAs matched the reference group more accurately than allele frequencies observed in AAs. As a result, the LBM performed better in EAs than AAs, as might be expected given the dependence of LBMs on prior knowledge of allele frequencies. Performance was not dependent on sample size. CONCLUSION: The performance of the LBM depends on the efficiency and number of markers, and depends greatly on how representative the available reference allele frequencies are for those of the population being assigned. This method is of value when the parental population is known and relevant allele frequencies are available

ODACH: A One-shot Distributed Algorithm for Cox model with heterogeneous multi-center data

We developed a One-shot Distributed Algorithm for Cox proportional-hazards model to analyze Heterogeneous multi-center time-to-event data (ODACH) circumventing the need for sharing patient-level information across sites. This algorithm implements a surrogate likelihood function to approximate the Cox log-partial likelihood function that is stratified by site using patient-level data from a lead site and aggregated information from other sites, allowing the baseline hazard functions and the distribution of covariates to vary across sites. Simulation studies and application to a real-world opioid use disorder study showed that ODACH provides estimates close to the pooled estimator, which analyzes patient-level data directly from all sites via a stratified Cox model. Compared to the estimator from meta-analysis, the inverse variance-weighted average of the site-specific estimates, ODACH estimator demonstrates less susceptibility to bias, especially when the event is rare. ODACH is thus a valuable privacy-preserving and communication-efficient method for analyzing multi-center time-to-event data

Variation at APOE and STH loci and Alzheimer's disease

BACKGROUND: The apolipoprotein E (APOE) and tau proteins play important roles in the pathological development of Alzheimer's disease (AD). Many studies have shown an association between the APOE gene and AD. Association between AD and the newly discovered saitohin (STH) gene, nested within the intron of the tau gene, has been reported. The present study aimed to elucidate the association between APOE and AD, and between STH and AD in our sample. METHODS: The functional polymorphisms, rs429358 and rs7412, in the APOE gene (which together define the ε2, ε3, and ε4 alleles), and the Q7R SNP in the STH gene, were genotyped in 369 patients with AD and 289 healthy European-Americans. The associations between these two genes and AD were analyzed in a case-control design. RESULTS: Consistent with previously reported results, the frequencies of the APOE ε4 allele, ε4/ε4 genotype and ε3/ε4 genotype were significantly higher in AD cases than controls; the ε4/ε4 genotype frequency was significantly higher in early-onset AD (EOAD) than late-onset AD (LOAD); the frequencies of the ε2 allele, ε3 allele, ε3/ε3 genotype and ε2/ε3 genotype were significantly lower in AD cases than controls. Positive likelihood ratios (LRs(+)) of APOE alleles and genotypes increased in a linear trend with the number of ε4 alleles and decreased in a linear trend with the number of ε2 or ε3 alleles. There was no significant difference in the STH allele and genotype frequency distributions between AD cases and controls. CONCLUSION: This study confirmed that the ε4 allele is a dose-response risk factor for AD and the ε4/ε4 genotype was associated with a significantly earlier age of onset. Moreover, we found that the ε2 allele was a dose-response protective factor for AD and the ε3 allele exerted a weaker dose-response protective effect for risk of AD compared with ε2. In a clinical setting, APOE genotyping could offer additional biological evidence of whether a subject may develop AD, but it is not robust enough to serve as an independent screening or predictive test in the diagnosis of AD. STH variation was not significantly associated with AD in our sample

Pregnant Smokers Receiving Opioid Agonist Therapy Have an Elevated Nicotine Metabolite Ratio: A Replication Study.

INTRODUCTION: Pregnant women exposed chronically to opioids smoked more cigarettes per day (CPD) and had a higher nicotine metabolite ratio (NMR), 3-hydroxycotinine/cotinine, a biomarker of nicotine metabolism and clearance, than those not receiving opioids. We examined CPD and NMR in a group of pregnant smokers, a quarter of whom were receiving opioid agonist therapy (OAT). AIMS AND METHODS: Pregnant smokers recruited to participate in a placebo-controlled trial of bupropion for smoking cessation provided a blood sample for measurement of NMR. RESULTS: Half (52.4%) of the 124 women with NMR data were African American. OAT-treated women (n = 34, 27.4%; 27 receiving methadone and 7 buprenorphine) were more likely to be white (79% vs. 30%, p \u3c .001) and to have a lower mean PHQ-9 total score (2.91 [SD = 2.83] vs. 4.83 [SD = 3.82], p = .007). OAT-treated women reported smoking more CPD (9.50 [SD = 5.26] vs. 7.20 [SD = 3.65], p = .005) and had higher NMR (0.78 [SD = 0.36] vs. 0.56 [SD = 0.25], p = .001) than the non-OAT-treated group. In a linear regression analysis adjusting for race, depression severity, and CPD, NMR was greater in the OAT group (p = .025), among whom the daily methadone-equivalent dosage correlated with NMR (Spearman\u27s ρ = 0.49, p = .003). CONCLUSIONS: Consistent with the findings of Oncken et al. (2019), we found that OAT smokers smoked more and had higher NMR than non-OAT smokers. As higher NMR is associated with a reduced likelihood of smoking cessation, the effects on NMR of both pregnancy and OAT could contribute to a lower smoking cessation rate in pregnant smokers receiving chronic opioid therapy. IMPLICATIONS: We replicated the finding that the NMR is significantly greater among pregnant smokers receiving OAT than those not receiving this treatment for opioid use disorder. Furthermore, we found that the dosage of the OAT was significantly associated with the NMR level. These findings may contribute to a poorer response to smoking cessation treatment in pregnant women treated with OAT, particularly those receiving high-dose therapy, and raise the question of whether novel approaches are needed to treat smoking in this subgroup of pregnant smokers

Evaluating the role of a galanin enhancer genotype on a range of metabolic, depressive and addictive phenotypes

Funded by •ERC. Grant Number: 284167 •NIH. Grant Number: 1RO1DK0921127-01 •NWO. Grant Numbers: 463-06-001, 451-04-034Peer reviewedPublisher PD

Cognitive Behavioral Therapy for Insomnia in Alcohol‐Dependent Veterans: A Randomized, Controlled Pilot Study

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149521/1/acer14030.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149521/2/acer14030-sup-0001-FigS1-S3.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149521/3/acer14030_am.pd