Biology subjects

Barr, P. B.

Publications and source records attributed to Barr, P. B..

6 recordsLinked to original sources

bioRxiv2023

Predicting alcohol-related memory problems in older adults: A machine learning study with multi-domain features

Memory problems are common among older adults with a history of alcohol use disorder (AUD). Employing a machine learning framework, the current study investigates the use of multi-domain features to classify individuals with and without alcohol-induced memory problems. A group of 94 individuals (ages 50-81 years) with alcohol-induced memory problems (Memory group) were compared with a matched Control group who did not have memory problems. The Random Forests model identified specific features from each domain that contributed to the classification of Memory vs. Control group (AUC=88.29%). Specifically, individuals from the Memory group manifested a predominant pattern of hyperconnectivity across the default mode network regions except some connections involving anterior cingulate cortex which were predominantly hypoconnected. Other significant contributing features were (i) polygenic risk scores for AUD, (ii) alcohol consumption and related health consequences during the past 5 years, such as health problems, past negative experiences, withdrawal symptoms, and the largest number of drinks in a day during the past 12 months, and (iii) elevated neuroticism and increased harm avoidance, and fewer positive "uplift" life events. At the neural systems level, hyperconnectivity across the default mode network regions, including the connections across the hippocampal hub regions, in individuals with memory problems may indicate dysregulation in neural information processing. Overall, the study outlines the importance of utilizing multidomain features, consisting of resting-state brain connectivity collected [~]18 years ago, together with personality, life experiences, polygenic risk, and alcohol consumption and related consequences, to predict alcohol-related memory problems that arise in later life.

neuroscience↗

bioRxiv2022

A Multivariate Approach to Understanding the Genetic Overlap between Externalizing Phenotypes and Substance Use Disorders

Substance use disorders (SUDs) are phenotypically and genetically correlated with each other and with other psychological traits characterized by behavioral undercontrol, termed externalizing phenotypes. In this study, we used Genomic Structural Equation Modeling to explore the shared genetic architecture among six externalizing phenotypes and four SUDs used in two previous multivariate GWAS of an externalizing and an addiction risk factor, respectively. Using a preregistered set of criteria, we first evaluated the performance of five confirmatory factor analytic models, including a common factor model, alternative parameterizations of two-factor structures, and a bifactor model. We used a combination of model fit, factor reliability, and model characteristics to adjudicate among the models. We next explored the genetic correlations between factors identified in these models and other relevant psychological traits. We found that a common factor model, in which all externalizing phenotypes and SUDs were influenced by a single dimension of genetic risk best characterized the relationships among our phenotypes. Although two two-factor models also performed well, we found that the factors in those models were very highly correlated with each other (rgS > .87) and similarly genetically correlated with external criteria, suggesting they did not represent meaningfully distinct dimensions. Results from this study can be used to inform future efforts to characterize genetic liability for broad externalizing as well as specific externalizing phenotypes.

genetics↗

bioRxiv2020

Multivariate genomic analysis of 1.5 million people identifies genes related to addiction, antisocial behavior, and health

Behaviors and disorders related to self-regulation, such as substance use, antisocial conduct, and ADHD, are collectively referred to as externalizing and have a shared genetic liability. We applied a multivariate approach that leverages genetic correlations among externalizing traits for genome-wide association analyses. By pooling data from ~1.5 million people, our approach is statistically more powerful than single-trait analyses and identifies more than 500 genetic loci. The identified loci were enriched for genes expressed in the brain and related to nervous system development. A polygenic score constructed from our results captures variation in a broad range of behavioral and medical outcomes that were not part of our genome-wide analyses, including traits that until now lacked well-performing polygenic scores, such as opioid use disorder, suicide, HIV infections, criminal convictions, and unemployment. Our findings are consistent with the idea that persistent difficulties in self-regulation can be conceptualized as a neurodevelopmental condition.

genetics↗

bioRxiv2019

Polygenic Prediction of Substance Use Disorders in Clinical and Population Samples

Genome-wide, polygenic risk scores (PRS) have emerged as a useful way to characterize genetic liability using genotypic data. There is growing evidence that PRS may prove useful to identify those at increased risk for developing certain diseases. The current utility of PRS in relation to alcohol use disorders (AUD) remains an open question. Using data from both a population-based sample [the FinnTwin12 (FT12) study] and a high risk sample [the Collaborative Study on the Genetics of Alcoholism (COGA)], we examined the association between PRSs derived from genome-wide association studies (GWASs) of 1) alcohol dependence/alcohol problems, 2) alcohol consumption, and 3) risky behaviors with AUD and other substance use disorder (SUD) symptoms. Individuals in the top 20%, 10%, and 5% of PRSs had increasingly greater odds of having an AUD compared to the lower end of the continuum in both COGA (80th % OR = 1.95; 90th % OR = 2.03; 95th % OR = 2.13) and FT12 (80th % OR = 1.77; 90th % OR = 2.27; 95th % OR = 2.39). Those in the top 5% reported greater levels of licit (alcohol and nicotine) and illicit (cannabis) SUD symptoms. PRSs can predict elevated risk for SUD in independent samples. However, clinical utility of these scores in their current form is modest. As these scores become more predictive of SUD, they may become useful to practitioners. Improvement in predictive ability will likely be dependent on increasing the size of well-phenotyped discovery samples.

genetics↗

bioRxiv2019

Polygenic Risk for Alcohol Misuse is Moderated by Romantic Partnerships: Primarily in Men

ImportanceProblematic alcohol use remains a leading influence on preventable mortality and morbidity across the globe. Those in committed relationships consistently report lower levels of alcohol misuse and problems. ObjectiveTo determine 1) whether genetic risk for alcohol misuse is moderated by romantic relationships (gene-environment interaction; GxE), and 2) whether GxE results are consistent across sex. DesignData came from the young adult wave of the Finnish Twin Study (FinnTwin12), a nationally representative sample of twins. Predictors included genome-wide polygenic scores (GPS), derived from a recent genome-wide association study (GWAS) of alcohol consumption in ~1 million participants; and participant reports of relationship status. SettingFinland ParticipantsAn intensively studied subset of FinnTwin12 received a diagnostic interview during the young adult phase (1,312 of 1,347 individuals provided genotypic data). The analytic sample includes those with complete interview and genetic data (N=1,201, 54% female). ExposureSelf-reported involvement in a romantic partnership. Main Outcomes and MeasuresDrinking frequency, intoxication frequency, and DSM-IV alcohol dependence (AD) symptoms from a diagnostic interview. ResultsGPS predicted drinking frequency (b = 0.109; 95% CI = 0.051, 0.167), intoxication frequency (b = 0.111; 95% CI = 0.054, 0.168), and AD symptoms (b = 0.123; 95% CI = 0.064, 0.182). Relationship moderated the association between GPS and drinking frequency (b = -0.105; 95% CI = -0.211, -0.001), intoxication frequency (b = -0.118; 95% CI = -0.220, -0.015), and AD symptoms (b = -0.119; 95% CI = -0.229, -0.010). The interaction for drinking frequency was not significant after correcting for covariates. There was a 3-way interaction between sex, relationship status, and GPS for intoxication frequency (b = 0.223; 95% CI = 0.014, 0.432), with the two-way interaction of relationship status and PRS on intoxication frequency being significant only in men. Conclusions and RelevanceBeing in a relationship reduced the association between genetic predisposition and high risk drinking. Part of the protective effect of committed partnerships on alcohol misuse observed in epidemiological research may be in limiting genetic liability. However, this protective effect was largely limited to males, mapping onto earlier findings suggesting that males benefit more from romantic partnerships. Key PointsO_ST_ABSQuestionC_ST_ABSDo romantic relationships moderate polygenic risk on alcohol misuse in young adulthood? FindingsInvolvement in romantic relationships moderated the polygenic risk on frequency of intoxication and DSM-IV alcohol dependence symptoms, such that polygenic associations with alcohol misuse were stronger among those not in a romantic relationship. Males experienced a stronger protective effect of romantic relationship in limiting the manifestation of genetic predispositions toward intoxication frequency. MeaningThe interplay between genes and environment is important in understanding etiology of problematic alcohol use, and romantic relationships appear to buffer genetic risk for alcohol misuse in young adulthood. Findings underscore how social relationships may alter the risk posed by genetic predispositions.

epidemiology↗

bioRxiv2017

GWAS meta-analysis (N=279,930) identifies new genes and functional links to intelligence

Intelligence is highly heritable1 and a major determinant of human health and well-being2. Recent genome-wide meta-analyses have identified 24 genomic loci linked to intelligence3-7, but much about its genetic underpinnings remains to be discovered. Here, we present the largest genetic association study of intelligence to date (N=279,930), identifying 206 genomic loci (191 novel) and implicating 1,041 genes (963 novel) via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping, and gene-based association analysis. We find enrichment of genetic effects in conserved and coding regions and identify 89 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain and specifically in striatal medium spiny neurons and cortical and hippocampal pyramidal neurons. Gene-set analyses implicate pathways related to neurogenesis, neuron differentiation and synaptic structure. We confirm previous strong genetic correlations with several neuropsychiatric disorders, and Mendelian Randomization results suggest protective effects of intelligence for Alzheimers dementia and ADHD, and bidirectional causation with strong pleiotropy for schizophrenia. These results are a major step forward in understanding the neurobiology of intelligence as well as genetically associated neuropsychiatric traits.

genetics↗