Biology subjects

Bacanu, S.-A.

Publications and source records attributed to Bacanu, S.-A..

14 recordsLinked to original sources

bioRxiv2023

Improving the discovery of rare variants associated with alcohol problems by leveraging machine learning phenotype prediction and functional information.

Alcohol use disorder (AUD) is moderately heritable with significant social and economic impact. Genome-wide association studies (GWAS) have identified common variants associated with AUD, however, rare variant investigations have yet to achieve well-powered sample sizes. In this study, we conducted an interval-based exome-wide analysis of the Alcohol Use Disorder Identification Test Problems subscale (AUDIT-P) using both machine learning (ML) predicted risk and empirical functional weights. This research has been conducted using the UK Biobank Resource (application number 30782.) Filtering the 200k exome release to unrelated individuals of European ancestry resulted in a sample of 147,386 individuals with 51,357 observed and 96,029 unmeasured but predicted AUDIT-P for exome analysis. Sequence Kernel Association Test (SKAT/SKAT-O) was used for rare variant (Minor Allele Frequency (MAF) < 0.01) interval analyses using default and empirical weights. Empirical weights were constructed using annotations found significant by stratified LD Score Regression analysis of predicted AUDIT-P GWAS, providing prior functional weights specific to AUDIT-P. Using only samples with observed AUDIT-P yielded no significantly associated intervals. In contrast, ADH1C and THRA gene intervals were significant (False discovery rate (FDR) <0.05) using default and empirical weights in the predicted AUDIT-P sample, with the most significant association found using predicted AUDIT-P and empirical weights in the ADH1C gene (SKAT-O P Default= 1.06 x 10-9 and P Empirical weight = 6.25 x 10-11). These findings provide evidence for rare variant association of the ADH1C gene with the AUDIT-P and highlight the successful leveraging of ML to increase effective sample size and prior empirical functional weights based on common variant GWAS data to refine and increase the statistical significance in underpowered phenotypes.

genetics↗

bioRxiv2022

Phenotype integration improves power and preserves specificity in biobank-based genetic studies of MDD

Biobanks often contain several phenotypes relevant to a given disorder, and researchers face complex tradeoffs between shallow phenotypes (high sample size, low specificity and sensitivity) and deep phenotypes (low sample size, high specificity and sensitivity). Here, we study an extreme case: Major Depressive Disorder (MDD) in UK Biobank. Previous studies found that shallow and deep MDD phenotypes have qualitatively distinct genetic architectures, but it remains unclear which are optimal for scientific study or clinical prediction. We propose a new framework to get the best of both worlds by integrating together information across hundreds of MDD-relevant phenotypes. First, we use phenotype imputation to increase sample size for the deepest available MDD phenotype, which dramatically improves GWAS power (increases #loci ~10 fold) and PRS accuracy (increases R2 ~2 fold). Further, we show the genetic architecture of the imputed phenotype remains specific to MDD using genetic correlation, PRS prediction in external clinical cohorts, and a novel PRS-based pleiotropy metric. We also develop a complementary approach to improve specificity of GWAS on shallow MDD phenotypes by adjusting for phenome-wide PCs. Finally, we study phenotype integration at the level of GWAS summary statistics, which can increase GWAS and PRS power but introduces non-MDD-specific signals. Our work provides a simple and scalable recipe to improve genetic studies in large biobanks by combining the sample size of shallow phenotypes with the sensitivity and specificity of deep phenotypes.

genetics↗

bioRxiv2022

Case-only rare variant analysis of severe alcohol dependence (AD) using a multivariate hierarchical gene clustering approach.

BackgroundVariation in genes involved in ethanol metabolism has been shown to influence risk for alcohol dependence (AD) including protective loss of function alleles in ethanol metabolizing genes. We therefore hypothesized that people with severe AD would exhibit different patterns of rare functional variation in genes with strong prior evidence for influencing ethanol metabolism and response when compared to genes not meeting these criteria. ObjectiveLeverage a novel case only design and Whole Exome Sequencing (WES) of severe AD cases from the island of Ireland to quantify differences in functional variation between genes associated with ethanol metabolism and/or response and their matched control genes. MethodsFirst, three sets of ethanol related genes were identified including those a) involved in alcohol metabolism in humans b) showing altered expression in mouse brain after alcohol exposure, and altering ethanol behavioral responses in invertebrate models. These genes of interest (GOI) sets were matched to control gene sets using multivariate hierarchical clustering of gene-level summary features from gnomAD. Using WES data from 190 individuals with severe AD, GOI were compared to matched control genes using logistic regression to detect aggregate differences in abundance of loss of function, missense, and synonymous variants, respectively. ResultsThree non-independent sets of 10, 117, and 359 genes were queried against control gene sets of 139, 1522, and 3360 matched genes, respectively. Significant differences were not detected in the number of functional variants in the primary set of ethanol-metabolizing genes. In both the mouse expression and invertebrate sets, we observed an increased number of synonymous variants in GOI over matched control genes. Post-hoc simulations showed the estimated effects sizes observed are unlikely to be under-estimated. ConclusionThe proposed method demonstrates a computationally viable and statistically appropriate approach for genetic analysis of case-only data for hypothesized gene sets supported by empirical evidence.

genetics↗

bioRxiv2020

Network Preservation Reveals Shared and Unique Biological Processes Associated with Chronic Alcohol Abuse in NAc and PFC.

BackgroundExcessive alcohol consumption has become a growing public health concern worldwide due to the potential development of alcohol dependence (AD). Prolonged alcohol abuse leads to dysregulation of the mesocorticolimbic pathway (MCL), effectively disrupting executive functioning and the allostatic conditioning of reward response. MethodsWe utilized weighted gene co-expressed network analysis (WGCNA) and network preservation using a case/control study design (n=35) to identify unique and shared biological processes dysregulated in AD in the prefrontal cortex (PFC) and nucleus accumbens (NAc). We used correlation and regression analyses to identify mRNA/miRNA interactions and local expression quantitative trait loci (cis-eQTL) to identify genetic regulatory mechanisms for networks significantly associated with AD. ResultsNetwork analyses revealed 6 and 3 significant mRNA modules from the NAc and PFC, respectively. Network preservation revealed immune response upregulation in both regions, whereas cellular morphogenesis/localization and cilia-based cell projection processes were upregulated only in the NAc. We observed 4 significantly correlated module eigengenes (ME) between the significant mRNA and miRNA modules in PFC, and 6 significant miRNA/mRNA ME correlations in NAc, with the mir-449a/b cluster emerging as a potential regulator for cellular morphogenesis/localization dysregulation in this brain region. Finally, we identified cis-eQTLs (37 mRNA and 9 miRNA in NAc, and 17 mRNA and 16 miRNA in PFC) which potentially mediate alcohols effect in a brain region-specific manner. ConclusionIn agreement with previous reports, we observed a generalized upregulation of immune response processes in subjects with AD, that highlights alcohols neurotoxic properties, while simultaneously demonstrating distinct molecular changes in subcortical brain regions as a result of chronic alcohol abuse. Such changes further support previous neuroimaging and physiological studies that emphasize the distinct roles PFC and NAc play in the development of addictive behaviors.

genomics↗

bioRxiv2019

The Impact on Estimations of Genetic Correlations of the Use, in Genome Wide Case-Control Studies, of Super-Normal, Unscreened and Family-History Screened Controls

Traditionally, in normal case-control studies of disorder A, individuals with disorder A are screened-out of controls. However, in genome wide association (GWA) studies, controls are sometimes unscreened or screened for disorder A and disorder B, producing super-normal controls. Using simulations, we examine how the observed genetic correlations between two disorders (A and B) are influenced by the use of unscreened, normal, and super-normal controls. Normal controls produce unbiased estimates of the genetic correlation. However, unscreened and super-normal controls both bias upward the genetic correlations. The strength of the bias increases with increasing population prevalences for the two disorders. With super-normal controls, the magnitude of bias is stronger when the true genetic correlation is low. The opposite is seen with the use of unscreened controls. Adding screening of first-degree relatives of controls substantially increases the bias in genetic correlations with super-normal controls but has minimal impact when normal controls are used.

genetics↗

bioRxiv2019

Assessing the role of long-noncoding RNA in nucleus accumbens in subjects with alcohol dependence

Recently, long noncoding RNA (lncRNA) were implicated in the etiology of alcohol dependence (AD). As lncRNA provide another layer of complexity to the transcriptome, assessing their expression in the brain is the first critical step towards understanding lncRNA functions in AD. To that end, we profiled the expression of lncRNA and protein coding genes (PCG) in nucleus accumbens (NAc) from 41 subjects with AD and 41 controls. At false discovery rate (FDR) of 5%, we identified 69 and 309 differentially expressed lncRNA and PCG genes, respectively. Using co-expression network analyses, we identified three lncRNA and five PCG modules significantly correlated with AD at Bonferroni adj. p[≤]0.05. To better understand lncRNA functions, we integrated the lncRNA and PCG hubs from the significant AD modules; at FDR of 5%, we identified 3 151 positive and 2 255 negative correlations supporting the functional role of lncRNA in the development of AD. Gene enrichment analysis revealed that PCG significantly correlated with lncRNA are, among others, enriched for neuronal and immune related processes. To highlight the mechanisms, by which genetic variants contribute to AD, we integrated lncRNA and PCG hubs with genome-wide SNP data. At FDR[≤]0.3, we identified 276 expression quantitative trait loci (eQTL), affecting the expression of 20 and 256 lncRNA and PCG hubs, respectively. Our study is the first to profile lncRNA expression in nucleus accumbens in a large postmortem alcohol brain sample and our results may provide novel insights into the regulation of the brain transcriptome across disease.

neuroscience↗

bioRxiv2019

Analysis of Genetically Regulated Gene Expression identifies a trauma type specific PTSD gene, SNRNP35

PTSD has significant genetic heritability; however, it is unclear how genetic risk influences tissue-specific gene expression. We used brain and non-brain transcriptomic imputation models to impute genetically regulated gene expression (GReX) in 9,087 PTSD-cases and 23,811 controls and identified thirteen significant GReX-PTSD associations. The results suggest substantial genetic heterogeneity between civilian and military PTSD cohorts. The top study-wide significant PTSD-association was with predicted downregulation of the Small Nuclear Ribonucleoprotein U11/U12 Subunit 35 (SNRNP35) in the BA9 region of the prefrontal cortex (PFC) in military cohorts. In peripheral leukocytes from 175 U.S. Marines, the observed PTSD differential gene expression correlated with the predicted blood GReX differences for these individuals, and deployment stress downregulated SNRNP35 expression, primarily in Marines with post-deployment PTSD. SNRNP35 is a subunit of the minor spliceosome complex and SNRNP35 knockdown in cells validated its functional importance in U12-intron splicing. Finally, mimicking acute activation of the endogenous stress axis in mice downregulated PFC Snrnp35 expression.

genetics↗

bioRxiv2018

DISTMIX2: robust imputation of summary statistics for cosmopolitan cohorts using a large and diverse reference panel

Genotype imputation across populations of mixed ancestry is critical for optimal discovery in large-scale genome-wide association studies (GWAS). Methods for direct imputation of GWAS summary statistics were previously shown to be practically as accurate as summary statistics produced after raw genotype imputation, while incurring orders of magnitude lower computational burden. Given that direct imputation needs a precise estimation of linkage-disequilibrium (LD) and that most of the methods using a small reference panel e.g., ~2,500 subject coming from the 1000 Genome Project, there is a great need for much larger and more diverse reference panels. To accurately estimate the LD needed for an exhaustive analysis of any cosmopolitan cohort, we developed DISTMIX2. DISTMIX2: i) uses a much larger and more diverse reference panel and ii) estimates weights of ethnic mixture based solely on Z-scores (when AFs are not available). We applied DISTMIX2 to GWAS summary statistics from the Psychiatric Genetic Consortium (PGC). DISTMIX2 uncovered signals in numerous new regions, with most of these findings coming from the rarer variants. Rarer variants provide much sharper location for the signals compared with common variants, as the LD for rare variants extends over a lower distance than for common ones. For example, while the original PGC post-traumatic stress disorder (PTSD) study found only 3 marginal signals for common variants, we now uncover a very strong signal for a rare variant in PKN2, a gene associated with neuronal and hippocampal development. Thus, DISTMIX2 provides a robust and fast (re)imputation approach for most Psychiatric GWAS studies.

bioinformatics↗

bioRxiv2018

JEPEGMIX2-P: Novel pathway transcriptomic method greatly increases detection of molecular pathways in cosmopolitan cohorts

Genetic signal detection in genome-wide association studies (GWAS) is enhanced by pooling small signals from multiple Single Nucleotide Polymorphism (SNP), e.g. across genes and pathways. Because genes are believed to influence traits via gene expression, it is of interest to combine information from expression Quantitative Trait Loci (eQTLs) in a gene or genes in the same pathway. Such methods, widely referred as transcriptomic wide association analysis (TWAS), already exist for gene analysis. Due to the possibility of eliminating most of the confounding effect of linkage disequilibrium (LD) from TWAS gene statistics, pathway TWAS methods would be very useful in uncovering the true molecular bases of psychiatric disorders. However, such methods are not yet available for arbitrarily large pathways/gene sets. This is possibly due to it quadratic (in the number of SNPs) computational burden for computing LD across large regions. To overcome this obstacle, we propose JEPEGMIX2-P, a novel TWAS pathway method that i) has a linear computational burden, ii) uses a large and diverse reference panel (33K subjects), iii) is competitive (adjusts for background enrichment in gene TWAS statistics) and iv) is applicable as-is to ethnically mixed cohorts. To underline its potential for increasing the power to uncover genetic signals over the state-of-the-art and commonly used non-transcriptomics methods, e.g. MAGMA, we applied JEPEGMIX2-P to summary statistics of most large meta-analyses from Psychiatric Genetics Consortium (PGC). While our work is just the very first step toward clinical translation of psychiatric disorders, PGC anorexia results suggest a possible avenue for treatment.

bioinformatics↗

bioRxiv2018

Trans-ancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders

Liability to alcohol dependence (AD) is heritable, but little is known about its complex polygenic architecture or its genetic relationship with other disorders. To discover loci associated with AD and characterize the relationship between AD and other psychiatric and behavioral outcomes, we carried out the largest GWAS to date of DSM - IV diagnosed AD. Genome - wide data on 14,904 individuals with AD and 37,944 controls from 28 case / control and family - based studies were meta - analyzed, stratified by genetic ancestry (European, N = 46,568; African; N = 6,280). Independent, genome - wide significant effects of different ADH1B variants were identified in European (rs1229984; p = 9.8E - 13) and African ancestries (rs2066702; p = 2.2E - 9). Significant genetic correlations were observed with schizophrenia, ADHD, depression, and use of cigarettes and cannabis. There was only modest genetic correlation with alcohol consumption and inconsistent associations with problem drinking. The genetic underpinnings of AD only partially overlap with those for alcohol consumption, underscoring the genetic distinction between pathological and non - pathological drinking behaviors.

genetics↗

bioRxiv2017

Method to estimate the approximate samples size that yield a certain number of significant GWAS signals in polygenic traits

To argue for increased sample collection for disorders without significant findings, researchers retorted to plotting, for multiple traits, the number of significant findings as a function of the sample size. However, for polygenic traits, the prevalence of the disorder confounds the relationship between the number of significant findings and the sample size. To adjust the number of significant findings for prevalence, we develop a method that uses the expected noncentrality of the contrast between liabilities of cases and controls. We empirically find that, when compared to the sample size, this measure is a better predictor of number of significant findings. Even more, we show that the sample size effect on the number of signals is explained by the noncetrality measure. Finally, we provide an R script to estimate the required sample size (non-centrality) needed to yield a pre-specified number of significant findings.

genetics↗

bioRxiv2017

Pathway-based polygenic risk implicates GO: 17144 drug metabolism in recurrent depressive disorder

The Psychiatric Genomics Consortium (PGC) has made major advances in the molecular etiology of MDD, confirming that MDD is highly polygenic, with any top risk loci conferring a very small proportion of variance in case-control status (1). Pathway enrichment results from PGC meta-analyses can also be used to help inform molecular drug targets. Prior to any knowledge of molecular biomarkers for MDD, drugs targeting molecular pathways have proved successful in treating MDD. However, it is possible that with information from PGC analyses, examining specific molecular pathway(s) implicated in MDD can further inform our study of molecular drug targets. Using a large case-control GWAS based on low-coverage whole genome sequencing (N = 10,640), we derived polygenic risk scores for MDD and for MDD specific to each of over 300 molecular pathways. We then used these data to identify sets of scores significantly predictive of case status, accounting for critical covariates. Over and above global polygenic risk for MDD, polygenic risk within the GO: 17144 drug metabolism pathway significantly predicted recurrent depression. In transcriptomic analyses, two pathway genes yielded suggestive signals at FDR q-values = .054: CYP2C19 (family of Cytochrome P450) and CBR1 (Carbonyl Reductase 1). Because the neuroleptic carbamazepine is a known inducer of CYP2C19, future research might examine whether drug metabolism PRS has any influence on clinical presentation and treatment response. Overall, results indicate that pathway-based risk might inform treatment of severe depression. We discuss limitations to the generalizability of these preliminary findings, and urge replication in future research.

genetics↗

bioRxiv2017

Genome-wide Association Study of Clinical Features in the Schizophrenia Psychiatric Genomics Consortium: Confirmation of Polygenic Effect on Negative Symptoms

Schizophrenia is a clinically heterogeneous disorder. Proposed revisions in DSM - 5 included dimensional measurement of different symptom domains. We sought to identify common genetic variants influencing these dimensions, and confirm a previous association between polygenic risk of schizophrenia and the severity of negative symptoms. The Psychiatric Genomics Consortium study of schizophrenia comprised 8,432 cases of European ancestry with available clinical phenotype data. Symptoms averaged over the course of illness were assessed using the OPCRIT, PANSS, LDPS, SCAN, SCID, and CASH. Factor analyses of each constituent PGC study identified positive, negative, manic, and depressive symptom dimensions. We examined the relationship between the resultant symptom dimensions and aggregate polygenic risk scores indexing risk of schizophrenia. We performed genome - wide association study (GWAS) of each quantitative traits using linear regression and adjusting for significant effects of sex and ancestry. The negative symptom factor was significantly associated with polygene risk scores for schizophrenia, confirming a previous, suggestive finding by our group in a smaller sample, though explaining only a small fraction of the variance. In subsequent GWAS, we observed the strongest evidence of association for the positive and negative symptom factors, with SNPs in RFX8 on 2q11.2 (P = 6.27x10-8) and upstream of WDR72 / UNC13C on 15q21.3 (P = 7.59x10-8), respectively. We report evidence of association of novel modifier loci for schizophrenia, though no single locus attained established genome - wide significance criteria. As this may have been due to insufficient statistical power, follow - up in additional samples is warranted. Importantly, we replicated our previous finding that polygenic risk explains at least some of the variance in negative symptoms, a core illness dimension.

genetics↗

bioRxiv2017

Proof Of Concept: Molecular Prediction Of Schizophrenia Risk

Key PointsO_ST_ABSQuestionC_ST_ABSTo what extent do global polygenic risk scores (PRS), molecular pathway-specific PRS, complement component (C4) gene expression, MHC loci, sex, and ancestry jointly contribute to risk for schizophrenia-spectrum disorders (SZ)?\n\nFindingsGlobal polygenic risk for schizophrenia, sex, and their interaction most robustly predict risk in a classification and regression tree model, with highest risk groups having 50/50 chance of SZ.\n\nMeaningPsychometric risk indicators, such as prodromal symptom assessments, may be enhanced by the examination of genetic risk metrics. Preliminary results suggest that of genetic risk metrics, global polygenic information has the most potential to significantly aide in the prediction of SZ.\n\nAbstractO_ST_ABSImportanceC_ST_ABSSchizophrenia (SZ) has a complex, heterogeneous symptom presentation with limited established associations between biological markers and illness onset. Many (gene) molecular pathways (MPs) are enriched for SZ signal, but it is still unclear how these MPs, global PRS, major histocompatibility complex (MHC) complement component (C4) gene expression, and MHC loci might jointly contribute to SZ and its clinical presentation. It is also unclear whether sex or ancestry interacts with these metrics to increase risk in certain individuals.\n\nObjectiveTo examine multiple genetic metrics, sex, and their interactions as possible predictors of SZ risk. Genetic information could aid in the clinical prediction of risk, but it is still unclear which genetic metrics are most promising, and how sex interacts with genetic risk metrics.\n\nDesign, Setting, and ParticipantsTo examine molecular risk in a proof-of-concept study, we used the Wellcome Trust case-control cohort and classified cases as a function of 1) polygenic risk score (PRS) for both whole genome and for 345 implicated molecular pathways, 2) predicted C4 expression, 3) SZ-relevant MHC loci, 4) sex, and 5) ancestry.\n\nMain Outcomes and MeasuresPRSs, C4 expression, SZ-relevant MHC loci, sex, and ancestry as joint risk factors for SZ.\n\nResultsRecursive partitioning yielded 15 molecular risk classes and retained as significant psychosis classifiers only sex, genome-wide SZ polygenic risk, and one MP PRS. Sex was the most robust classifier in a stepwise regression, and there was a significant interaction of sex with SZ PRS on case status, suggesting males have a lower polygenic risk threshold. By down-sampling case proportion to 1% and 1.4% population base rates in males and females, respectively, high-risk subtypes defined by this model had roughly a 52% odds of developing SZ (individuals with SZ PRS elevated by 2.6 SDs; incidence = 51.8%).\n\nConclusions and RelevanceThis proof-of-concept suggests that global SZ PRS, sex, and their interaction are robust predictors of risk and that males have a lower PRS threshold for onset. Implications for the integration of these metrics with psychometrically-identified risk are discussed.

genomics↗