Biology subjects

Bierut, L. J.

Publications and source records attributed to Bierut, L. J..

9 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

Evaluation of methods incorporating biological function and GWAS summary statistics to accelerate discovery

Where sufficiently large genome-wide association study (GWAS) samples are not currently available or feasible, methods that leverage increasing knowledge of the biological function of variants may illuminate discoveries without increasing sample size. We comprehensively evaluated 18 functional weighting methods for identifying novel associations. We assessed the performance of these methods using published results from multiple GWAS waves across each of five complex traits. Although no method achieved both high sensitivity and positive predictive value (PPV) for any trait, a subset of methods utilizing pleiotropy and expression quantitative trait loci nominated variants with high PPV (>75%) for multiple traits. Application of functionally weighting methods to enhance GWAS power for locus discovery is unlikely to circumvent the need for larger sample sizes in truly underpowered GWAS, but these results suggest that applying functional weighting to GWAS can accurately nominate additional novel loci from available samples for follow-up studies.

genetics↗

bioRxiv2021

Expanding the pool of public controls for GWAS via a method for combining genotypes from arrays and sequencing

Genome-wide association studies (GWAS) have made impactful discoveries for complex diseases, often by amassing very large sample sizes. Yet, GWAS of many diseases remain underpowered, especially for non-European ancestries. One cost-effective approach to increase sample size is to combine existing case-only cohorts with public controls, but this approach is limited by the need for a large overlap in variants across genotyping arrays and the scarcity of non-European controls. We developed and validated a protocol, Genotyping Array-WGS Merge (GAWMerge), for combining genotypes from arrays and whole genome sequencing, ensuring complete variant overlap, and allowing for diverse samples like Trans-Omics for Precision Medicine to be used. Our protocol involves phasing, imputation, and filtering. We illustrated its ability to control type I error and recover known disease-associated signals across technologies, independent datasets, and ancestries in smoking-related cohorts. GAWMerge enables genetic studies to leverage existing cohorts to validly increase sample size and enhance discovery.

bioinformatics↗

bioRxiv2020

Expanding the Genetic Architecture of Nicotine Dependence and its Shared Genetics with Multiple Traits: Findings from the Nicotine Dependence GenOmics (iNDiGO) Consortium

Cigarette smoking is the leading cause of preventable morbidity and mortality. Knowledge is evolving on genetics underlying initiation, regular smoking, nicotine dependence (ND), and cessation. We performed a genome-wide association study using the Fagerstrom Test for ND (FTND) in 58,000 smokers of European or African ancestry. Five genome-wide significant loci, including two novel loci MAGI2/GNAI1 (rs2714700) and TENM2 (rs1862416) were identified, and loci reported for other smoking traits were extended to ND. Using the heaviness of smoking index (HSI) in the UK Biobank (N=33,791), rs2714700 was consistently associated, but rs1862416 was not associated, likely reflecting ND features not captured by the HSI. Both variants were cis-eQTLs (rs2714700 for MAGI2-AS3 in hippocampus, rs1862416 for TENM2 in lung), and expression of genes spanning ND-associated variants was enriched in cerebellum. SNP-based heritability of ND was 8.6%, and ND was genetically correlated with 17 other smoking traits (rg=0.40-0.95) and co-morbidities. Our results emphasize the FTND as a composite phenotype that expands genetic knowledge of smoking, including loci specific to ND.

genomics↗

bioRxiv2019

Genome-wide DNA methylation differences in nucleus accumbens of smokers vs. nonsmokers

Numerous DNA methylation (DNAm) biomarkers of cigarette smoking have been identified in peripheral blood studies, but their relevance as neurobiological indicators is unknown due to DNAm tissue-specificity. In contrast, blood-based studies may not detect brain-specific smoking-related DNAm differences that may provide greater insight into the neurobiology of smoking behaviors. We report the first epigenome-wide association study (EWAS) of smoking in human postmortem brain, focusing on nucleus accumbens (NAc) as a key brain region in developing addiction. Following Illumina HumanMethylation EPIC array data generation and quality control, 221 decedents (120 European American [23% current smokers], 101 African American [26% current smokers]) were analyzed. DNAm by smoking (current vs. nonsmoking) was tested using robust linear regression models adjusted for age, sex, cell-type proportion, DNAm-derived negative control principal components (PCs), and genotype-derived PCs. Separate ancestry-specific results were combined via meta-analysis, resulting in 7 CpGs that exceeded false discovery rate (FDR)<0.05. Using published smoking EWAS results in blood, we extended our NAc findings to identify DNAm smoking effects that are unique (tissue-specific) versus shared between tissues (tissue-shared). Of the 7 CpGs identified in NAc, 3 CpGs were located near genes previously indicated with blood-based smoking DNAm biomarkers: ZIC1, ZCCHC24, and PRKDC. The other 4 CpGs are novel for smoking-related DNAm changes: ABLIM3, APCDD1L, MTMR6, and CTCF. Our results provide the first evidence for smoking-related DNAm changes in human NAc, highlighting CpGs that were previously undetected as peripheral biomarkers and may reflect brain-specific processes.

neuroscience↗

bioRxiv2018

Largest genome-wide association study for PTSD identifies genetic risk loci in European and African ancestries and implicates novel biological pathways

Post-traumatic stress disorder (PTSD) is a common and debilitating disorder. The risk of PTSD following trauma is heritable, but robust common variants have yet to be identified by genome-wide association studies (GWAS). We have collected a multi-ethnic cohort including over 30,000 PTSD cases and 170,000 controls. We first demonstrate significant genetic correlations across 60 PTSD cohorts to evaluate the comparability of these phenotypically heterogeneous studies. In this largest GWAS meta-analysis of PTSD to date we identify a total of 6 genome-wide significant loci, 4 in European and 2 in African-ancestry analyses. Follow-up analyses incorporated local ancestry and sex-specific effects, and functional studies. Along with other novel genes, a non-coding RNA (ncRNA) and a Parkinsons Disease gene, PARK2, were associated with PTSD. Consistent with previous reports, SNP-based heritability estimates for PTSD range between 10-20%. Despite a significant shared liability between PTSD and major depressive disorder, we show evidence that some of our loci may be specific to PTSD. These results demonstrate the role of genetic variation contributing to the biology of differential risk for PTSD and the necessity of expanding GWAS beyond European ancestry.

genetics↗

bioRxiv2017

Developmental effects of maternal smoking during pregnancy on the human frontal cortex transcriptome

Cigarette smoking during pregnancy is a major public health concern. While there are well-described consequences in early child development, there is very little known about the effects of maternal smoking on human cortical biology during prenatal life. We therefore performed a genome-wide differential gene expression analysis using RNA sequencing (RNA-seq) on prenatal (N=33; 16 smoking-exposed) as well as adult (N=207; 57 active smokers) human post-mortem prefrontal cortices. Smoking exposure during the prenatal period was directly associated with differential expression of 14 genes; in contrast, during adulthood, despite a much larger sample size, only 2 genes showed significant differential expression (FDR<10%). Moreover, 1,315 genes showed significantly different exposure effects between maternal smoking during pregnancy and direct exposure in adulthood (FDR<10%) - these differences were largely driven by prenatal differences that were enriched for pathways previously implicated in addiction and synaptic function. Furthermore, prenatal and age-dependent differentially expressed genes were enriched for genes implicated in non-syndromic autism spectrum disorder (ASD) and were differentially expressed as a set between patients with ASD and controls in post-mortem cortical regions. These results underscore the enhanced sensitivity to the biological effect of smoking exposure in the developing brain and offer novel insight into the effects of maternal smoking during pregnancy on the prenatal human brain. They also begin to address the relationship between in utero exposure to smoking and the heightened risks for the subsequent development of neuropsychiatric disorders.\n\nOne Sentence SummaryMaternal smoking during pregnancy alters the expression of genes within the developing human cortex and these changes are enriched for genes implicated in neuropsychiatric disorders.

neuroscience↗

bioRxiv2017

Components Of Alcohol Use And All-Cause Mortality

ImportanceCurrent recommendations for low-risk drinking are based on drinking quantity: up to one drink daily for women and two drinks daily for men. Drinking frequency has not been independently examined for its contribution to mortality.\n\nObjectiveTo evaluate the impact of drinking frequency on all-cause mortality after adjusting for drinks per day and binge drinking behavior.\n\nDesignTwo independent observational studies with self-reported alcohol use and subsequent all-cause mortality: the National Health Interview Survey (NHIS), and data from Veterans Health Administration clinics (VA).\n\nSettingEpidemiological sample (NHIS) and VA outpatient database (VA Corporate Data Warehouse).\n\nParticipants208,661 individuals from the NHIS interviewed between 1997 and 2009 at the age of 30 to 70 with mortality follow-up in the last quarter of 2011; 75,515 VA outpatients born between 1948 and 1968 who completed an alcohol survey in 2008 with mortality follow-up in June 2016.\n\nExposuresQuantity of alcohol use when not binging (1-2 drinks on typical day, 3-4 drinks on typical day), frequency of non-binge drinking (never, weekly or less, 2-3 times weekly, 4 or more times weekly), and frequency of binge drinking (never, less than weekly, 1-3 times weekly, 4 or more times weekly). Covariates included age, sex, race, and comorbidity.\n\nMain Outcomes and MeasuresAll-cause mortality.\n\nResultsAfter adjusting for binge drinking behavior, survival analysis showed an increased risk for all-cause mortality among people who typically drink 1-2 drinks four or more times weekly, relative to people who typically drink 1-2 drinks at a time weekly or less (NHIS dataset HR=1.15, 95% CI 1.06-1.26; VA dataset HR=1.31, 95% CI 1.15-1.49).\n\nConclusions and RelevanceDrinking four or more times weekly increased risk of all-cause mortality, even among those who drank only 1 or 2 drinks daily. This was seen in both a large epidemiological database and a large hospital-based database, suggesting that the results can be generalized.

epidemiology↗

bioRxiv2017

Association Between Substance Use Disorder And Polygenic Liability To Schizophrenia

BackgroundThere are high levels of comorbidity between schizophrenia and substance use disorder, but little is known about the genetic etiology of this comorbidity.\n\nMethodsHere, we test the hypothesis that shared genetic liability contributes to the high rates of comorbidity between schizophrenia and substance use disorder. To do this, polygenic risk scores for schizophrenia derived from a large meta-analysis by the Psychiatric Genomics Consortium were computed in three substance use disorder datasets: COGEND (ascertained for nicotine dependence n=918 cases, 988 controls), COGA (ascertained for alcohol dependence n=643 cases, 384 controls), and FSCD (ascertained for cocaine dependence n=210 cases, 317 controls). Phenotypes were harmonized across the three datasets and standardized analyses were performed. Genome-wide genotypes were imputed to 1000 Genomes reference panel.\n\nResultsIn each individual dataset and in the mega-analysis, strong associations were observed between any substance use disorder diagnosis and the polygenic risk score for schizophrenia (mega-analysis pseudo R2 range 0.8%-3.7%, minimum p=4x10-23).\n\nConclusionsThese results suggest that comorbidity between schizophrenia and substance use disorder is partially attributable to shared polygenic liability. This shared liability is most consistent with a general risk for substance use disorder rather than specific risks for individual substance use disorders and adds to increasing evidence of a blurred boundary between schizophrenia and substance use disorder.

genetics↗