Biology subjects

Kranzler, H. R.

Publications and source records attributed to Kranzler, H. R..

4 recordsLinked to original sources

bioRxiv2019

Meta-analysis of problematic alcohol use in 435,563 individuals identifies 29 risk variants and yields insights into biology, pleiotropy and causality

Problematic alcohol use (PAU) is a leading cause of death and disability worldwide. Although genome-wide association studies (GWASs) 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 genomic conserved and regulatory 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 outcomes.

genetics↗

bioRxiv2019

Factors associated with opioid cessation: a machine learning approach

Background and AimsPeople with opioid use disorder (OUD) can stop using opioids on their own, with help from groups and with treatment, but there is limited research on the factors that influence opioid cessation.\n\nMethodsWe employed multiple machine learning prediction algorithms (LASSO, random forest, deep neural network, and support vector machine) to assess factors associated with ceasing opioid use in a sample comprised of African Americans (AAs) and European Americans (EAs) who met DSM-5 criteria for mild to severe OUD. Values for several thousand demographic, alcohol and other drug use, general health, and behavioral variables, as well as diagnoses for other psychiatric disorders, were obtained for each participant from a detailed semi-structured interview.\n\nResultsSupport vector machine models performed marginally better on average than those derived using other machine learning methods with maximum prediction accuracies of 75.4% in AAs and 79.4% in EAs. Subsequent stepwise regression analyses that considered the 83 most highly ranked variables across all methods and models identified less recent cocaine use (p<5x10-8), a shorter duration of opioid use (p<5x10-6), and older age (p<5x10-9) as the strongest independent predictors of opioid cessation. Factors related to drug use comprised about half of the significant independent predictors, with other predictors related to non-drug use behaviors, psychiatric disorders, overall health, and demographics.\n\nConclusionsThese proof-of-concept findings provide information that can help develop strategies for improving OUD management and the methods we applied provide a framework for personalizing OUD treatment.

neuroscience↗

bioRxiv2019

Genomic Influences on Self-Reported Childhood Maltreatment

Childhood maltreatment is highly prevalent and serves as a risk factor for mental and physical disorders. Self-reported childhood maltreatment appears heritable, but the specific genetic influences on this phenotype are largely unknown. The aims of this study were to 1) identify genetic variation associated with reported childhood maltreatment, 2) calculate the relevant SNP-based heritability estimates, and 3) quantify the genetic overlap of reported childhood maltreatment with mental and physical health-related phenotypes. Genome-wide association analysis for childhood maltreatment was undertaken, using a discovery sample from the UK Biobank (UKBB) (n=124,000) and a replication sample from the Psychiatric Genomics Consortium-posttraumatic stress disorder working group (PGC-PTSD) (n=26,290). Heritability estimations for childhood maltreatment and genetic correlations with mental/physical health traits were calculated using linkage disequilibrium score regression (LDSR). Two genome-wide significant loci associated with childhood maltreatment, located on chromosomes 3p13 (rs142346759, beta=0.015, p=4.35x10-8, FOXP1) and 7q31.1 (rs10262462, beta=-0.016, p=3.24x10-8, FOXP2), were identified in the discovery dataset but were not replicated in the PGC-PTSD sample. SNP-based heritability for childhood maltreatment was estimated to be [~]6%. Childhood maltreatment was most significantly genetically correlated with depressive symptoms (rg=0.70, p=4.65x10-40). This is the first large-scale genetic study to identify specific variants associated with self-reported childhood maltreatment. FOXP genes could influence traits such as depression and thereby be relevant to childhood maltreatment. Alternatively, these variants may be associated with a greater likelihood of reporting maltreatment. A clearer understanding of the genetic relationships of childhood maltreatment, including particular abuse subtypes, with various psychiatric disorders, may ultimately be useful in in developing targeted treatment and prevention strategies.

genomics↗

bioRxiv2019

Molecular correlates of topiramate and GRIK1 rs2832407 genotype in pluripotent stem cell-derived neural cultures

There is growing evidence that the anticonvulsant topiramate is efficacious in reducing alcohol consumption. Further, an intronic single nucleotide polymorphism (rs2832407, C A) in the GRIK1 gene, which encodes the GluK1 subunit of the excitatory kainate receptor, predicted topiramates effectiveness in reducing heavy drinking in a clinical trial. In the current study, we differentiated a total of 22 induced pluripotent stem cell (iPSCs) lines characterized by GRIK1 rs2832407 genotype (10 A/A and 12 C/C) into forebrain-lineage neural cultures to explore molecular correlates of GRIK1 genotype that may relate to topiramates ability to reduce drinking. Our differentiation protocol yielded mixed neural cultures enriched for glutamatergic neurons. Characterization of the GRIK1 locus revealed no effect of rs2832407 genotype on GRIK1 isoform mRNA expression, however a significant difference was observed on GRIK1 antisense-2, with higher expression in C/C neural cultures. Differential effects of acute exposure to 5 M topiramate were observed on the frequency of spontaneous synaptic activity in A/A vs. C/C neurons, with a smaller reduction in excitatory event frequency and a greater reduction in inhibitory event frequency observed in C/C donor neurons. This work highlights the use of iPSC technologies to study pharmacogenetic treatment effects in psychiatric disorders and furthers our understanding of the molecular effects of topiramate exposure in human neural cells.

neuroscience↗