Publications by authors named "Brien P Riley"

66 Publications

Sex-Dependent Shared and Nonshared Genetic Architecture Across Mood and Psychotic Disorders.

Biol Psychiatry 2021 Mar 23. Epub 2021 Mar 23.

Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, Illinois; Department of Psychiatry and Behavioral Sciences, North Shore University Health System, Evanston, Illinois.

Background: Sex differences in incidence and/or presentation of schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BIP) are pervasive. Previous evidence for shared genetic risk and sex differences in brain abnormalities across disorders suggest possible shared sex-dependent genetic risk.

Methods: We conducted the largest to date genome-wide genotype-by-sex (G×S) interaction of risk for these disorders using 85,735 cases (33,403 SCZ, 19,924 BIP, and 32,408 MDD) and 109,946 controls from the PGC (Psychiatric Genomics Consortium) and iPSYCH.

Results: Across disorders, genome-wide significant single nucleotide polymorphism-by-sex interaction was detected for a locus encompassing NKAIN2 (rs117780815, p = 3.2 × 10), which interacts with sodium/potassium-transporting ATPase (adenosine triphosphatase) enzymes, implicating neuronal excitability. Three additional loci showed evidence (p < 1 × 10) for cross-disorder G×S interaction (rs7302529, p = 1.6 × 10; rs73033497, p = 8.8 × 10; rs7914279, p = 6.4 × 10), implicating various functions. Gene-based analyses identified G×S interaction across disorders (p = 8.97 × 10) with transcriptional inhibitor SLTM. Most significant in SCZ was a MOCOS gene locus (rs11665282, p = 1.5 × 10), implicating vascular endothelial cells. Secondary analysis of the PGC-SCZ dataset detected an interaction (rs13265509, p = 1.1 × 10) in a locus containing IDO2, a kynurenine pathway enzyme with immunoregulatory functions implicated in SCZ, BIP, and MDD. Pathway enrichment analysis detected significant G×S interaction of genes regulating vascular endothelial growth factor receptor signaling in MDD (false discovery rate-corrected p < .05).

Conclusions: In the largest genome-wide G×S analysis of mood and psychotic disorders to date, there was substantial genetic overlap between the sexes. However, significant sex-dependent effects were enriched for genes related to neuronal development and immune and vascular functions across and within SCZ, BIP, and MDD at the variant, gene, and pathway levels.
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http://dx.doi.org/10.1016/j.biopsych.2021.02.972DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8458480PMC
March 2021

DECO: a framework for jointly analyzing de novo and rare case/control variants, and biological pathways.

Brief Bioinform 2021 09;22(5)

Virginia Institute for Psychiatric and Behavioral Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA.

Motivation: Rare variant-based analyses are beginning to identify risk genes for neuropsychiatric disorders and other diseases. However, the identified genes only account for a fraction of predicted causal genes. Recent studies have shown that rare damaging variants are significantly enriched in specific gene-sets. Methods which are able to jointly model rare variants and gene-sets to identify enriched gene-sets and use these enriched gene-sets to prioritize additional risk genes could improve understanding of the genetic architecture of diseases.

Results: We propose DECO (Integrated analysis of de novo mutations, rare case/control variants and omics information via gene-sets), an integrated method for rare-variant and gene-set analysis. The method can (i) test the enrichment of gene-sets directly within the statistical model, and (ii) use enriched gene-sets to rank existing genes and prioritize additional risk genes for tested disorders. In simulations, DECO performs better than a homologous method that uses only variant data. To demonstrate the application of the proposed protocol, we have applied this approach to rare-variant datasets of schizophrenia. Compared with a method which only uses variant information, DECO is able to prioritize additional risk genes.

Availability: DECO can be used to analyze rare-variants and biological pathways or cell types for any disease. The package is available on Github https://github.com/hoangtn/DECO.
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http://dx.doi.org/10.1093/bib/bbab067DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8425460PMC
September 2021

Increasing the resolution and precision of psychiatric genome-wide association studies by re-imputing summary statistics using a large, diverse reference panel.

Am J Med Genet B Neuropsychiatr Genet 2021 01 11;186(1):16-27. Epub 2021 Feb 11.

Department of Psychiatry, Virginia Commonwealth University, Richmond, Virginia, USA.

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 for example, ~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: (a) uses a much larger and more diverse reference panel compared to traditional reference panels, and (b) can estimate weights of ethnic-mixture based solely on Z-scores, when allele frequencies 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 GWAS 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.
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http://dx.doi.org/10.1002/ajmg.b.32834DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8247874PMC
January 2021

Recruiting for diversity: a pilot test of recruitment strategies for a national alcohol survey with mail-in genetic data collection.

J Community Genet 2021 Jul 4;12(3):459-468. Epub 2021 Jan 4.

Alcohol Research Group, Public Health Institute, Emeryville, CA, USA.

We assessed the feasibility and acceptability of collecting a saliva sample for DNA through the mail from a national sample of drinkers and examined whether targeted messaging would increase the response rates of Black/African American and Hispanic/Latino participants. We invited respondents from two prior national population surveys to participate in a brief telephone survey regarding recent alcohol use and to mail in a self-administered saliva sample. Blacks/African Americans, Hispanics/Latinos, and Whites had similar rates of consenting to participate. A higher proportion of respondents with a college education and a family history of alcohol problems consented. The differences in participation between respondents receiving targeted and general messaging were not statistically significant. This study provides preliminary evidence for the feasibility of recruiting diverse participants into a genetic study of alcohol use disorder.
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http://dx.doi.org/10.1007/s12687-020-00502-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8241965PMC
July 2021

A large-scale genome-wide association study meta-analysis of cannabis use disorder.

Lancet Psychiatry 2020 12 20;7(12):1032-1045. Epub 2020 Oct 20.

Stanford University Graduate School of Education, Stanford University, Stanford, CA, USA.

Background: Variation in liability to cannabis use disorder has a strong genetic component (estimated twin and family heritability about 50-70%) and is associated with negative outcomes, including increased risk of psychopathology. The aim of the study was to conduct a large genome-wide association study (GWAS) to identify novel genetic variants associated with cannabis use disorder.

Methods: To conduct this GWAS meta-analysis of cannabis use disorder and identify associations with genetic loci, we used samples from the Psychiatric Genomics Consortium Substance Use Disorders working group, iPSYCH, and deCODE (20 916 case samples, 363 116 control samples in total), contrasting cannabis use disorder cases with controls. To examine the genetic overlap between cannabis use disorder and 22 traits of interest (chosen because of previously published phenotypic correlations [eg, psychiatric disorders] or hypothesised associations [eg, chronotype] with cannabis use disorder), we used linkage disequilibrium score regression to calculate genetic correlations.

Findings: We identified two genome-wide significant loci: a novel chromosome 7 locus (FOXP2, lead single-nucleotide polymorphism [SNP] rs7783012; odds ratio [OR] 1·11, 95% CI 1·07-1·15, p=1·84 × 10) and the previously identified chromosome 8 locus (near CHRNA2 and EPHX2, lead SNP rs4732724; OR 0·89, 95% CI 0·86-0·93, p=6·46 × 10). Cannabis use disorder and cannabis use were genetically correlated (r 0·50, p=1·50 × 10), but they showed significantly different genetic correlations with 12 of the 22 traits we tested, suggesting at least partially different genetic underpinnings of cannabis use and cannabis use disorder. Cannabis use disorder was positively genetically correlated with other psychopathology, including ADHD, major depression, and schizophrenia.

Interpretation: These findings support the theory that cannabis use disorder has shared genetic liability with other psychopathology, and there is a distinction between genetic liability to cannabis use and cannabis use disorder.

Funding: National Institute of Mental Health; National Institute on Alcohol Abuse and Alcoholism; National Institute on Drug Abuse; Center for Genomics and Personalized Medicine and the Centre for Integrative Sequencing; The European Commission, Horizon 2020; National Institute of Child Health and Human Development; Health Research Council of New Zealand; National Institute on Aging; Wellcome Trust Case Control Consortium; UK Research and Innovation Medical Research Council (UKRI MRC); The Brain & Behavior Research Foundation; National Institute on Deafness and Other Communication Disorders; Substance Abuse and Mental Health Services Administration (SAMHSA); National Institute of Biomedical Imaging and Bioengineering; National Health and Medical Research Council (NHMRC) Australia; Tobacco-Related Disease Research Program of the University of California; Families for Borderline Personality Disorder Research (Beth and Rob Elliott) 2018 NARSAD Young Investigator Grant; The National Child Health Research Foundation (Cure Kids); The Canterbury Medical Research Foundation; The New Zealand Lottery Grants Board; The University of Otago; The Carney Centre for Pharmacogenomics; The James Hume Bequest Fund; National Institutes of Health: Genes, Environment and Health Initiative; National Institutes of Health; National Cancer Institute; The William T Grant Foundation; Australian Research Council; The Virginia Tobacco Settlement Foundation; The VISN 1 and VISN 4 Mental Illness Research, Education, and Clinical Centers of the US Department of Veterans Affairs; The 5th Framework Programme (FP-5) GenomEUtwin Project; The Lundbeck Foundation; NIH-funded Shared Instrumentation Grant S10RR025141; Clinical Translational Sciences Award grants; National Institute of Neurological Disorders and Stroke; National Heart, Lung, and Blood Institute; National Institute of General Medical Sciences.
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http://dx.doi.org/10.1016/S2215-0366(20)30339-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7674631PMC
December 2020

Assessing the Role of Long Noncoding RNA in Nucleus Accumbens in Subjects With Alcohol Dependence.

Alcohol Clin Exp Res 2020 12 27;44(12):2468-2480. Epub 2020 Nov 27.

Virginia Institute for Psychiatric and Behavioral Genetics, (GOM, ESV, CC, MM, KSK, BPR, MFM, S-AB, VIV), Virginia Commonwealth University, Richmond, Virginia.

Background: Long noncoding RNA (lncRNA) have been implicated in the etiology of alcohol use. Since lncRNA provide another layer of complexity to the transcriptome, assessing their expression in the brain is the first critical step toward understanding lncRNA functions in alcohol use and addiction. Thus, we sought to profile lncRNA expression in the nucleus accumbens (NAc) in a large postmortem alcohol brain sample.

Methods: LncRNA and protein-coding gene (PCG) expressions in the NAc from 41 subjects with alcohol dependence (AD) and 41 controls were assessed via a regression model. Weighted gene coexpression network analysis was used to identify lncRNA and PCG networks (i.e., modules) significantly correlated with AD. Within the significant modules, key network genes (i.e., hubs) were also identified. The lncRNA and PCG hubs were correlated via Pearson correlations to elucidate the potential biological functions of lncRNA. The lncRNA and PCG hubs were further integrated with GWAS data to identify expression quantitative trait loci (eQTL).

Results: At Bonferroni adj. p-value ≤ 0.05, we identified 19 lncRNA and 5 PCG significant modules, which were enriched for neuronal and immune-related processes. In these modules, we further identified 86 and 315 PCG and lncRNA hubs, respectively. At false discovery rate (FDR) of 10%, the correlation analyses between the lncRNA and PCG hubs revealed 3,125 positive and 1,860 negative correlations. Integration of hubs with genotype data identified 243 eQTLs affecting the expression of 39 and 204 PCG and lncRNA hubs, respectively.

Conclusions: Our study identified lncRNA and gene networks significantly associated with AD in the NAc, coordinated lncRNA and mRNA coexpression changes, highlighting potentially regulatory functions for the lncRNA, and our genetic (cis-eQTL) analysis provides novel insights into the etiological mechanisms of AD.
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http://dx.doi.org/10.1111/acer.14479DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7756309PMC
December 2020

TWAS pathway method greatly enhances the number of leads for uncovering the molecular underpinnings of psychiatric disorders.

Am J Med Genet B Neuropsychiatr Genet 2020 12 21;183(8):454-463. Epub 2020 Sep 21.

Department of Psychiatry, Virginia Commonwealth University, Richmond, Virginia, USA.

Genetic signal detection in genome-wide association studies (GWAS) is enhanced by pooling small signals from multiple Single Nucleotide Polymorphism (SNP), for example, 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 to as transcriptomic wide association studies (TWAS), already exist for gene analysis. Due to the possibility of eliminating most of the confounding effects of linkage disequilibrium (LD) from TWAS gene statistics, pathway TWAS methods would be very useful in uncovering the true molecular basis of psychiatric disorders. However, such methods are not yet available for arbitrarily large pathways/gene sets. This is possibly due to the quadratic (as a function of the number of SNPs) computational burden for computing LD across large chromosomal regions. To overcome this obstacle, we propose JEPEGMIX2-P, a novel TWAS pathway method that (a) has a linear computational burden, (b) uses a large and diverse reference panel (33 K subjects), (c) is competitive (adjusts for background enrichment in gene TWAS statistics), and (d) is applicable as-is to ethnically mixed-cohorts. To underline its potential for increasing the power to uncover genetic signals over the commonly used nontranscriptomics methods, for example, 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.
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http://dx.doi.org/10.1002/ajmg.b.32823DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7756231PMC
December 2020

mTADA is a framework for identifying risk genes from de novo mutations in multiple traits.

Nat Commun 2020 06 10;11(1):2929. Epub 2020 Jun 10.

Division of Psychiatric Genomics, Department of Genetics and Genomic Sciences, Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

Joint analysis of multiple traits can result in the identification of associations not found through the analysis of each trait in isolation. Studies of neuropsychiatric disorders and congenital heart disease (CHD) which use de novo mutations (DNMs) from parent-offspring trios have reported multiple putatively causal genes. However, a joint analysis method designed to integrate DNMs from multiple studies has yet to be implemented. We here introduce multiple-trait TADA (mTADA) which jointly analyzes two traits using DNMs from non-overlapping family samples. We first demonstrate that mTADA is able to leverage genetic overlaps to increase the statistical power of risk-gene identification. We then apply mTADA to large datasets of >13,000 trios for five neuropsychiatric disorders and CHD. We report additional risk genes for schizophrenia, epileptic encephalopathies and CHD. We outline some shared and specific biological information of intellectual disability and CHD by conducting systems biology analyses of genes prioritized by mTADA.
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http://dx.doi.org/10.1038/s41467-020-16487-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7287090PMC
June 2020

Leveraging genome-wide data to investigate differences between opioid use vs. opioid dependence in 41,176 individuals from the Psychiatric Genomics Consortium.

Mol Psychiatry 2020 08 26;25(8):1673-1687. Epub 2020 Feb 26.

Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA.

To provide insights into the biology of opioid dependence (OD) and opioid use (i.e., exposure, OE), we completed a genome-wide analysis comparing 4503 OD cases, 4173 opioid-exposed controls, and 32,500 opioid-unexposed controls, including participants of European and African descent (EUR and AFR, respectively). Among the variants identified, rs9291211 was associated with OE (exposed vs. unexposed controls; EUR z = -5.39, p = 7.2 × 10). This variant regulates the transcriptomic profiles of SLC30A9 and BEND4 in multiple brain tissues and was previously associated with depression, alcohol consumption, and neuroticism. A phenome-wide scan of rs9291211 in the UK Biobank (N > 360,000) found association of this variant with propensity to use dietary supplements (p = 1.68 × 10). With respect to the same OE phenotype in the gene-based analysis, we identified SDCCAG8 (EUR + AFR z = 4.69, p = 10), which was previously associated with educational attainment, risk-taking behaviors, and schizophrenia. In addition, rs201123820 showed a genome-wide significant difference between OD cases and unexposed controls (AFR z = 5.55, p = 2.9 × 10) and a significant association with musculoskeletal disorders in the UK Biobank (p = 4.88 × 10). A polygenic risk score (PRS) based on a GWAS of risk-tolerance (n = 466,571) was positively associated with OD (OD vs. unexposed controls, p = 8.1 × 10; OD cases vs. exposed controls, p = 0.054) and OE (exposed vs. unexposed controls, p = 3.6 × 10). A PRS based on a GWAS of neuroticism (n = 390,278) was positively associated with OD (OD vs. unexposed controls, p = 3.2 × 10; OD vs. exposed controls, p = 0.002) but not with OE (p = 0.67). Our analyses highlight the difference between dependence and exposure and the importance of considering the definition of controls in studies of addiction.
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http://dx.doi.org/10.1038/s41380-020-0677-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7392789PMC
August 2020

Shared genetic risk between eating disorder- and substance-use-related phenotypes: Evidence from genome-wide association studies.

Addict Biol 2021 01 16;26(1):e12880. Epub 2020 Feb 16.

Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, RWTH Aachen University, Aachen, Germany.

Eating disorders and substance use disorders frequently co-occur. Twin studies reveal shared genetic variance between liabilities to eating disorders and substance use, with the strongest associations between symptoms of bulimia nervosa and problem alcohol use (genetic correlation [r ], twin-based = 0.23-0.53). We estimated the genetic correlation between eating disorder and substance use and disorder phenotypes using data from genome-wide association studies (GWAS). Four eating disorder phenotypes (anorexia nervosa [AN], AN with binge eating, AN without binge eating, and a bulimia nervosa factor score), and eight substance-use-related phenotypes (drinks per week, alcohol use disorder [AUD], smoking initiation, current smoking, cigarettes per day, nicotine dependence, cannabis initiation, and cannabis use disorder) from eight studies were included. Significant genetic correlations were adjusted for variants associated with major depressive disorder and schizophrenia. Total study sample sizes per phenotype ranged from ~2400 to ~537 000 individuals. We used linkage disequilibrium score regression to calculate single nucleotide polymorphism-based genetic correlations between eating disorder- and substance-use-related phenotypes. Significant positive genetic associations emerged between AUD and AN (r = 0.18; false discovery rate q = 0.0006), cannabis initiation and AN (r = 0.23; q < 0.0001), and cannabis initiation and AN with binge eating (r = 0.27; q = 0.0016). Conversely, significant negative genetic correlations were observed between three nondiagnostic smoking phenotypes (smoking initiation, current smoking, and cigarettes per day) and AN without binge eating (r = -0.19 to -0.23; qs < 0.04). The genetic correlation between AUD and AN was no longer significant after co-varying for major depressive disorder loci. The patterns of association between eating disorder- and substance-use-related phenotypes highlights the potentially complex and substance-specific relationships among these behaviors.
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http://dx.doi.org/10.1111/adb.12880DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7429266PMC
January 2021

Cross-species alcohol dependence-associated gene networks: Co-analysis of mouse brain gene expression and human genome-wide association data.

PLoS One 2019 24;14(4):e0202063. Epub 2019 Apr 24.

VCU Alcohol Research Center, Virginia Commonwealth University, Richmond, Virginia, United States of America.

Genome-wide association studies on alcohol dependence, by themselves, have yet to account for the estimated heritability of the disorder and provide incomplete mechanistic understanding of this complex trait. Integrating brain ethanol-responsive gene expression networks from model organisms with human genetic data on alcohol dependence could aid in identifying dependence-associated genes and functional networks in which they are involved. This study used a modification of the Edge-Weighted Dense Module Searching for genome-wide association studies (EW-dmGWAS) approach to co-analyze whole-genome gene expression data from ethanol-exposed mouse brain tissue, human protein-protein interaction databases and alcohol dependence-related genome-wide association studies. Results revealed novel ethanol-responsive and alcohol dependence-associated gene networks in prefrontal cortex, nucleus accumbens, and ventral tegmental area. Three of these networks were overrepresented with genome-wide association signals from an independent dataset. These networks were significantly overrepresented for gene ontology categories involving several mechanisms, including actin filament-based activity, transcript regulation, Wnt and Syndecan-mediated signaling, and ubiquitination. Together, these studies provide novel insight for brain mechanisms contributing to alcohol dependence.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0202063PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6481773PMC
December 2019

Population-based identity-by-descent mapping combined with exome sequencing to detect rare risk variants for schizophrenia.

Am J Med Genet B Neuropsychiatr Genet 2019 04 23;180(3):223-231. Epub 2019 Feb 23.

Cognitive Genetics and Cognitive Therapy Group, Neuroimaging, Cognition & Genomics (NICOG) Centre & NCBES Galway Neuroscience Centre, School of Psychology and Discipline of Biochemistry, National University of Ireland Galway, Galway, Ireland.

Genome-wide association studies (GWASs) are highly effective at identifying common risk variants for schizophrenia. Rare risk variants are also important contributors to schizophrenia etiology but, with the exception of large copy number variants, are difficult to detect with GWAS. Exome and genome sequencing, which have accelerated the study of rare variants, are expensive so alternative methods are needed to aid detection of rare variants. Here we re-analyze an Irish schizophrenia GWAS dataset (n = 3,473) by performing identity-by-descent (IBD) mapping followed by exome sequencing of individuals identified as sharing risk haplotypes to search for rare risk variants in coding regions. We identified 45 rare haplotypes (>1 cM) that were significantly more common in cases than controls. By exome sequencing 105 haplotype carriers, we investigated these haplotypes for functional coding variants that could be tested for association in independent GWAS samples. We identified one rare missense variant in PCNT but did not find statistical support for an association with schizophrenia in a replication analysis. However, IBD mapping can prioritize both individual samples and genomic regions for follow-up analysis but genome rather than exome sequencing may be more effective at detecting risk variants on rare haplotypes.
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http://dx.doi.org/10.1002/ajmg.b.32716DOI Listing
April 2019

Transancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders.

Nat Neurosci 2018 12 26;21(12):1656-1669. Epub 2018 Nov 26.

NIH/NIAAA, Laboratory of Neurogenetics, Bethesda, MD, USA.

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 genome-wide association study 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 = 9.8 × 10) and African ancestries (rs2066702; P = 2.2 × 10). Significant genetic correlations were observed with 17 phenotypes, including schizophrenia, attention deficit-hyperactivity disorder, depression, and use of cigarettes and cannabis. The genetic underpinnings of AD only partially overlap with those for alcohol consumption, underscoring the genetic distinction between pathological and nonpathological drinking behaviors.
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http://dx.doi.org/10.1038/s41593-018-0275-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6430207PMC
December 2018

Cross-species molecular dissection across alcohol behavioral domains.

Alcohol 2018 11 6;72:19-31. Epub 2017 Dec 6.

University of Texas at Austin, Austin, TX, United States. Electronic address:

This review summarizes the proceedings of a symposium presented at the "Alcoholism and Stress: A Framework for Future Treatment Strategies" conference held in Volterra, Italy on May 9-12, 2017. Psychiatric diseases, including alcohol-use disorders (AUDs), are influenced through complex interactions of genes, neurobiological pathways, and environmental influences. A better understanding of the common neurobiological mechanisms underlying an AUD necessitates an integrative approach, involving a systematic assessment of diverse species and phenotype measures. As part of the World Congress on Stress and Alcoholism, this symposium provided a detailed account of current strategies to identify mechanisms underlying the development and progression of AUDs. Dr. Sean Farris discussed the integration and organization of transcriptome and postmortem human brain data to identify brain regional- and cell type-specific differences related to excessive alcohol consumption that are conserved across species. Dr. Brien Riley presented the results of a genome-wide association study of DSM-IV alcohol dependence; although replication of genetic associations with alcohol phenotypes in humans remains challenging, model organism studies show that COL6A3, KLF12, and RYR3 affect behavioral responses to ethanol, and provide substantial evidence for their role in human alcohol-related traits. Dr. Rob Williams expanded upon the systematic characterization of extensive genetic-genomic resources for quantifying and clarifying phenotypes across species that are relevant to precision medicine in human disease. The symposium concluded with Dr. Robert Hitzemann's description of transcriptome studies in a mouse model selectively bred for high alcohol ("binge-like") consumption and a non-human primate model of long-term alcohol consumption. Together, the different components of this session provided an overview of systems-based approaches that are pioneering the experimental prioritization and validation of novel genes and gene networks linked with a range of behavioral phenotypes associated with stress and AUDs.
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http://dx.doi.org/10.1016/j.alcohol.2017.11.036DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6309876PMC
November 2018

Building a schizophrenia genetic network: transcription factor 4 regulates genes involved in neuronal development and schizophrenia risk.

Hum Mol Genet 2018 09;27(18):3246-3256

Department of Pharmacotherapy and Outcomes Science.

The transcription factor 4 (TCF4) locus is a robust association finding with schizophrenia (SCZ), but little is known about the genes regulated by the encoded transcription factor. Therefore, we conducted chromatin immunoprecipitation sequencing (ChIP-seq) of TCF4 in neural-derived (SH-SY5Y) cells to identify genome-wide TCF4 binding sites, followed by data integration with SCZ association findings. We identified 11 322 TCF4 binding sites overlapping in two ChIP-seq experiments. These sites are significantly enriched for the TCF4 Ebox binding motif (>85% having ≥1 Ebox) and implicate a gene set enriched for genes downregulated in TCF4 small-interfering RNA (siRNA) knockdown experiments, indicating the validity of our findings. The TCF4 gene set was also enriched among (1) gene ontology categories such as axon/neuronal development, (2) genes preferentially expressed in brain, in particular pyramidal neurons of the somatosensory cortex and (3) genes downregulated in postmortem brain tissue from SCZ patients (odds ratio, OR = 2.8, permutation P < 4x10-5). Considering genomic alignments, TCF4 binding sites significantly overlapped those for neural DNA-binding proteins such as FOXP2 and the SCZ-associated EP300. TCF4 binding sites were modestly enriched among SCZ risk loci from the Psychiatric Genomic Consortium (OR = 1.56, P = 0.03). In total, 130 TCF4 binding sites occurred in 39 of the 108 regions published in 2014. Thirteen genes within the 108 loci had both a TCF4 binding site ±10kb and were differentially expressed in siRNA knockdown experiments of TCF4, suggesting direct TCF4 regulation. These findings confirm TCF4 as an important regulator of neural genes and point toward functional interactions with potential relevance for SCZ.
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http://dx.doi.org/10.1093/hmg/ddy222DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6354221PMC
September 2018

Building a schizophrenia genetic network: transcription factor 4 regulates genes involved in neuronal development and schizophrenia risk.

Hum Mol Genet 2018 09;27(18):3246-3256

Department of Pharmacotherapy and Outcomes Science.

The transcription factor 4 (TCF4) locus is a robust association finding with schizophrenia (SCZ), but little is known about the genes regulated by the encoded transcription factor. Therefore, we conducted chromatin immunoprecipitation sequencing (ChIP-seq) of TCF4 in neural-derived (SH-SY5Y) cells to identify genome-wide TCF4 binding sites, followed by data integration with SCZ association findings. We identified 11 322 TCF4 binding sites overlapping in two ChIP-seq experiments. These sites are significantly enriched for the TCF4 Ebox binding motif (>85% having ≥1 Ebox) and implicate a gene set enriched for genes downregulated in TCF4 small-interfering RNA (siRNA) knockdown experiments, indicating the validity of our findings. The TCF4 gene set was also enriched among (1) gene ontology categories such as axon/neuronal development, (2) genes preferentially expressed in brain, in particular pyramidal neurons of the somatosensory cortex and (3) genes downregulated in postmortem brain tissue from SCZ patients (odds ratio, OR = 2.8, permutation P < 4x10-5). Considering genomic alignments, TCF4 binding sites significantly overlapped those for neural DNA-binding proteins such as FOXP2 and the SCZ-associated EP300. TCF4 binding sites were modestly enriched among SCZ risk loci from the Psychiatric Genomic Consortium (OR = 1.56, P = 0.03). In total, 130 TCF4 binding sites occurred in 39 of the 108 regions published in 2014. Thirteen genes within the 108 loci had both a TCF4 binding site ±10kb and were differentially expressed in siRNA knockdown experiments of TCF4, suggesting direct TCF4 regulation. These findings confirm TCF4 as an important regulator of neural genes and point toward functional interactions with potential relevance for SCZ.
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http://dx.doi.org/10.1093/hmg/ddy222DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6354221PMC
September 2018

Polygenic Risk Score Prediction of Alcohol Dependence Symptoms Across Population-Based and Clinically Ascertained Samples.

Alcohol Clin Exp Res 2018 Mar 5;42(3):520-530. Epub 2018 Feb 5.

Department of Psychology, Virginia Commonwealth University, Richmond, Virginia.

Background: Despite consistent evidence of the heritability of alcohol use disorders (AUDs), few specific genes with an etiological role have been identified. It is likely that AUDs are highly polygenic; however, the etiological pathways and genetic variants involved may differ between populations. The aim of this study was thus to evaluate whether aggregate genetic risk for AUDs differed between clinically ascertained and population-based epidemiological samples.

Methods: Four independent samples were obtained: 2 from unselected birth cohorts (Avon Longitudinal Study of Parents and Children [ALSPAC], N = 4,304; FinnTwin12 [FT12], N = 1,135) and 2 from families densely affected with AUDs, identified from treatment-seeking patients (Collaborative Study on the Genetics of Alcoholism, N = 2,097; Irish Affected Sib Pair Study of Alcohol Dependence, N = 706). AUD symptoms were assessed with clinical interviews, and participants of European ancestry were genotyped. Genomewide association was conducted separately in each sample, and the resulting association weights were used to create polygenic risk scores in each of the other samples (12 total discovery-validation pairs), and from meta-analyses within sample type. We then tested how well these aggregate genetic scores predicted AUD outcomes within and across sample types.

Results: Polygenic scores derived from 1 population-based sample (ALSPAC) significantly predicted AUD symptoms in another population-based sample (FT12), but not in either clinically ascertained sample. Trend-level associations (uncorrected p < 0.05) were found for polygenic score predictions within sample types but no or negative predictions across sample types. Polygenic scores accounted for 0 to 1% of the variance in AUD symptoms.

Conclusions: Though preliminary, these results provide suggestive evidence of differences in the genetic etiology of AUDs based on sample characteristics such as treatment-seeking status, which may index other important clinical or demographic factors that moderate genetic influences. Although the variance accounted for by genomewide polygenic scores remains low, future studies could improve gene identification efforts by amassing very large samples, or reducing genetic heterogeneity by informing analyses with other phenotypic information such as sample characteristics. Multiple complementary approaches may be needed to make progress in gene identification for this complex disorder.
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http://dx.doi.org/10.1111/acer.13589DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5832589PMC
March 2018

Polygenic prediction of the phenome, across ancestry, in emerging adulthood.

Psychol Med 2018 08 27;48(11):1814-1823. Epub 2017 Nov 27.

Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University School of Medicine, Richmond, VA, USA.

Background: Identifying genetic relationships between complex traits in emerging adulthood can provide useful etiological insights into risk for psychopathology. College-age individuals are under-represented in genomic analyses thus far, and the majority of work has focused on the clinical disorder or cognitive abilities rather than normal-range behavioral outcomes.

Methods: This study examined a sample of emerging adults 18-22 years of age (N = 5947) to construct an atlas of polygenic risk for 33 traits predicting relevant phenotypic outcomes. Twenty-eight hypotheses were tested based on the previous literature on samples of European ancestry, and the availability of rich assessment data allowed for polygenic predictions across 55 psychological and medical phenotypes.

Results: Polygenic risk for schizophrenia (SZ) in emerging adults predicted anxiety, depression, nicotine use, trauma, and family history of psychological disorders. Polygenic risk for neuroticism predicted anxiety, depression, phobia, panic, neuroticism, and was correlated with polygenic risk for cardiovascular disease.

Conclusions: These results demonstrate the extensive impact of genetic risk for SZ, neuroticism, and major depression on a range of health outcomes in early adulthood. Minimal cross-ancestry replication of these phenomic patterns of polygenic influence underscores the need for more genome-wide association studies of non-European populations.
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http://dx.doi.org/10.1017/S0033291717003312DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5971142PMC
August 2018

Molecular Genetic Influences on Normative and Problematic Alcohol Use in a Population-Based Sample of College Students.

Front Genet 2017 15;8:30. Epub 2017 Mar 15.

Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth UniversityRichmond, VA, USA; Department of Psychiatry, Virginia Commonwealth UniversityRichmond, VA, USA.

Genetic factors impact alcohol use behaviors and these factors may become increasingly evident during emerging adulthood. Examination of the effects of individual variants as well as aggregate genetic variation can clarify mechanisms underlying risk. We conducted genome-wide association studies (GWAS) in an ethnically diverse sample of college students for three quantitative outcomes including typical monthly alcohol consumption, alcohol problems, and maximum number of drinks in 24 h. Heritability based on common genetic variants () was assessed. We also evaluated whether risk variants in aggregate were associated with alcohol use outcomes in an independent sample of young adults. Two genome-wide significant markers were observed: rs11201929 in for maximum drinks in 24 h, with supportive evidence across all ancestry groups; and rs73317305 in (alcohol problems), tested only in the African ancestry group. The estimate was 0.19 (SE = 0.11) for consumption, and was non-significant for other outcomes. Genome-wide polygenic scores were significantly associated with alcohol outcomes in an independent sample. These results robustly identify genetic risk for alcohol use outcomes at the variant level and in aggregate. We confirm prior evidence that genetic variation in impacts alcohol use, and identify novel loci of interest for multiple alcohol outcomes in emerging adults. These findings indicate that genetic variation influencing normative and problematic alcohol use is, to some extent, convergent across ancestry groups. Studying college populations represents a promising avenue by which to obtain large, diverse samples for gene identification.
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http://dx.doi.org/10.3389/fgene.2017.00030DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5350109PMC
March 2017

Genomewide Association Study of Alcohol Dependence Identifies Risk Loci Altering Ethanol-Response Behaviors in Model Organisms.

Alcohol Clin Exp Res 2017 May 30;41(5):911-928. Epub 2017 Mar 30.

Privatklinik Meiringen, Meiringen, Switzerland.

Background: Alcohol dependence (AD) shows evidence for genetic liability, but genes influencing risk remain largely unidentified.

Methods: We conducted a genomewide association study in 706 related AD cases and 1,748 unscreened population controls from Ireland. We sought replication in 15,496 samples of European descent. We used model organisms (MOs) to assess the role of orthologous genes in ethanol (EtOH)-response behaviors. We tested 1 primate-specific gene for expression differences in case/control postmortem brain tissue.

Results: We detected significant association in COL6A3 and suggestive association in 2 previously implicated loci, KLF12 and RYR3. None of these signals are significant in replication. A suggestive signal in the long noncoding RNA LOC339975 is significant in case:control meta-analysis, but not in a population sample. Knockdown of a COL6A3 ortholog in Caenorhabditis elegans reduced EtOH sensitivity. Col6a3 expression correlated with handling-induced convulsions in mice. Loss of function of the KLF12 ortholog in C. elegans impaired development of acute functional tolerance (AFT). Klf12 expression correlated with locomotor activation following EtOH injection in mice. Loss of function of the RYR3 ortholog reduced EtOH sensitivity in C. elegans and rapid tolerance in Drosophila. The ryanodine receptor antagonist dantrolene reduced motivation to self-administer EtOH in rats. Expression of LOC339975 does not differ between cases and controls but is reduced in carriers of the associated rs11726136 allele in nucleus accumbens (NAc).

Conclusions: We detect association between AD and COL6A3, KLF12, RYR3, and LOC339975. Despite nonreplication of COL6A3, KLF12, and RYR3 signals, orthologs of these genes influence behavioral response to EtOH in MOs, suggesting potential involvement in human EtOH response and AD liability. The associated LOC339975 allele may influence gene expression in human NAc. Although the functions of long noncoding RNAs are poorly understood, there is mounting evidence implicating these genes in multiple brain functions and disorders.
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http://dx.doi.org/10.1111/acer.13362DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5404949PMC
May 2017

The Genetic Architecture of Major Depressive Disorder in Han Chinese Women.

JAMA Psychiatry 2017 02;74(2):162-168

Virginia Institute for Psychiatric and Behavioral Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond.

Importance: Despite the moderate, well-demonstrated heritability of major depressive disorder (MDD), there has been limited success in identifying replicable genetic risk loci, suggesting a complex genetic architecture. Research is needed to quantify the relative contribution of classes of genetic variation across the genome to inform future genetic studies of MDD.

Objectives: To apply aggregate genetic risk methods to clarify the genetic architecture of MDD by estimating and partitioning heritability by chromosome, minor allele frequency, and functional annotations and to test for enrichment of rare deleterious variants.

Design, Setting, And Participants: The CONVERGE (China, Oxford, and Virginia Commonwealth University Experimental Research on Genetic Epidemiology) study collected data on 5278 patients with recurrent MDD from 58 provincial mental health centers and psychiatric departments of general medical hospitals in 45 cities and 23 provinces of China. Screened controls (n = 5196) were recruited from a range of locations, including general hospitals and local community centers. Data were collected from August 1, 2008, to October 31, 2012.

Main Outcomes And Measures: Genetic risk for liability to recurrent MDD was partitioned using sparse whole-genome sequencing.

Results: In aggregate, common single-nucleotide polymorphisms (SNPs) explained between 20% and 29% of the variance in MDD risk, and the heritability in MDD explained by each chromosome was proportional to its length (r = 0.680; P = .0003), supporting a common polygenic etiology. Partitioning heritability by minor allele frequency indicated that the variance explained was distributed across the allelic frequency spectrum, although relatively common SNPs accounted for a disproportionate fraction of risk. Partitioning by genic annotation indicated a greater contribution of SNPs in protein-coding regions and within 3'-UTR regions of genes. Enrichment of SNPs associated with DNase I-hypersensitive sites was also found in many tissue types, including brain tissue. Examining burden scores from singleton exonic SNPs predicted to be deleterious indicated that cases had significantly more mutations than controls (odds ratio, 1.009; 95% CI, 1.003-1.014; P = .003), including those occurring in genes expressed in the brain (odds ratio, 1.011; 95% CI, 1.003-1.018; P = .004) and within nuclear-encoded genes with mitochondrial gene products (odds ratio, 1.075; 95% CI, 1.018-1.135; P = .009).

Conclusions And Relevance: Results support a complex etiology for MDD and highlight the value of analyzing components of heritability to clarify genetic architecture.
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http://dx.doi.org/10.1001/jamapsychiatry.2016.3578DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5319866PMC
February 2017

Contribution of copy number variants to schizophrenia from a genome-wide study of 41,321 subjects.

Nat Genet 2017 01 21;49(1):27-35. Epub 2016 Nov 21.

Department of Psychiatry, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.

Copy number variants (CNVs) have been strongly implicated in the genetic etiology of schizophrenia (SCZ). However, genome-wide investigation of the contribution of CNV to risk has been hampered by limited sample sizes. We sought to address this obstacle by applying a centralized analysis pipeline to a SCZ cohort of 21,094 cases and 20,227 controls. A global enrichment of CNV burden was observed in cases (odds ratio (OR) = 1.11, P = 5.7 × 10), which persisted after excluding loci implicated in previous studies (OR = 1.07, P = 1.7 × 10). CNV burden was enriched for genes associated with synaptic function (OR = 1.68, P = 2.8 × 10) and neurobehavioral phenotypes in mouse (OR = 1.18, P = 7.3 × 10). Genome-wide significant evidence was obtained for eight loci, including 1q21.1, 2p16.3 (NRXN1), 3q29, 7q11.2, 15q13.3, distal 16p11.2, proximal 16p11.2 and 22q11.2. Suggestive support was found for eight additional candidate susceptibility and protective loci, which consisted predominantly of CNVs mediated by nonallelic homologous recombination.
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http://dx.doi.org/10.1038/ng.3725DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5737772PMC
January 2017

A simple yet accurate correction for winner's curse can predict signals discovered in much larger genome scans.

Bioinformatics 2016 09 13;32(17):2598-603. Epub 2016 May 13.

Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics.

Motivation: For genetic studies, statistically significant variants explain far less trait variance than 'sub-threshold' association signals. To dimension follow-up studies, researchers need to accurately estimate 'true' effect sizes at each SNP, e.g. the true mean of odds ratios (ORs)/regression coefficients (RRs) or Z-score noncentralities. Naïve estimates of effect sizes incur winner's curse biases, which are reduced only by laborious winner's curse adjustments (WCAs). Given that Z-scores estimates can be theoretically translated on other scales, we propose a simple method to compute WCA for Z-scores, i.e. their true means/noncentralities.

Results: WCA of Z-scores shrinks these towards zero while, on P-value scale, multiple testing adjustment (MTA) shrinks P-values toward one, which corresponds to the zero Z-score value. Thus, WCA on Z-scores scale is a proxy for MTA on P-value scale. Therefore, to estimate Z-score noncentralities for all SNPs in genome scans, we propose F: DR I: nverse Q: uantile T: ransformation (FIQT). It (i) performs the simpler MTA of P-values using FDR and (ii) obtains noncentralities by back-transforming MTA P-values on Z-score scale. When compared to competitors, realistic simulations suggest that FIQT is more (i) accurate and (ii) computationally efficient by orders of magnitude. Practical application of FIQT to Psychiatric Genetic Consortium schizophrenia cohort predicts a non-trivial fraction of sub-threshold signals which become significant in much larger supersamples.

Conclusions: FIQT is a simple, yet accurate, WCA method for Z-scores (and ORs/RRs, via simple transformations).

Availability And Implementation: A 10 lines R function implementation is available at https://github.com/bacanusa/FIQT CONTACT: [email protected]

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btw303DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5013908PMC
September 2016

Dimensionality and Genetic Correlates of Problem Behavior in Low-Income African American Adolescents.

J Clin Child Adolesc Psychol 2017 Nov-Dec;46(6):824-839. Epub 2015 Oct 29.

c Department of Psychiatry , Virginia Commonwealth University.

Researchers have long observed that problem behaviors tend to cluster together, particularly among adolescents. Epidemiological studies have suggested that this covariation is due, in part, to common genetic influences, and a number of plausible candidates have emerged as targets for investigation. To date, however, genetic association studies of these behaviors have focused mostly on unidimensional models of individual phenotypes within European American samples. Herein, we compared a series of confirmatory factor models to best characterize the structure of problem behavior (alcohol and marijuana use, sexual behavior, and disruptive behavior) within a representative community-based sample of 592 low-income African American adolescents (50.3% female), ages 13 to 18. We further explored the extent to which 3 genes previously implicated for their role in similar behavioral dimensions (CHRM2, GABRA2, and OPRM1) independently accounted for variance within factors specified in the best-fitting model. Supplementary analyses were conducted to derive comparative estimates for the predictive utility of these genes in more traditional unidimensional models. Findings provide initial evidence for a bifactor structure of problem behavior among African American adolescents and highlight novel genetic correlates of specific behavioral dimensions otherwise undetected in an orthogonal syndromal factor. Implications of this approach include increased precision in the assessment of problem behavior, with corresponding increases in the reliability and validity of identified genetic associations. As a corollary, the comparison of primary and supplementary association analyses illustrates the potential for overlooking and/or overinterpreting meaningful genetic effects when failing to adequately account for phenotypic complexity.
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http://dx.doi.org/10.1080/15374416.2015.1070353DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4851603PMC
March 2018

JEPEGMIX: gene-level joint analysis of functional SNPs in cosmopolitan cohorts.

Bioinformatics 2016 Jan 1;32(2):295-7. Epub 2015 Oct 1.

Department of Psychiatry, Virginia Commonwealth University, Richmond, VA 23298, USA.

Motivation: To increase detection power, gene level analysis methods are used to aggregate weak signals. To greatly increase computational efficiency, most methods use as input summary statistics from genome-wide association studies (GWAS). Subsequently, gene statistics are constructed using linkage disequilibrium (LD) patterns from a relevant reference panel. However, all methods, including our own Joint Effect on Phenotype of eQTL/functional single nucleotide polymorphisms (SNPs) associated with a Gene (JEPEG), assume homogeneous panels, e.g. European. However, this renders these tools unsuitable for the analysis of large cosmopolitan cohorts.

Results: We propose a JEPEG extension, JEPEGMIX, which similar to one of our software tools, Direct Imputation of summary STatistics of unmeasured SNPs from MIXed ethnicity cohorts, is capable of estimating accurate LD patterns for cosmopolitan cohorts. JEPEGMIX uses this accurate LD estimates to (i) impute the summary statistics at unmeasured functional variants and (ii) test for the joint effect of all measured and imputed functional variants which are associated with a gene. We illustrate the performance of our tool by analyzing the GWAS meta-analysis summary statistics from the multi-ethnic Psychiatric Genomics Consortium Schizophrenia stage 2 cohort. This practical application supports the immune system being one of the main drivers of the process leading to schizophrenia.

Availability And Implementation: Software, annotation database and examples are available at http://dleelab.github.io/jepegmix/.

Contact: [email protected]

Supplementary Information: Supplementary material is available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btv567DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4708106PMC
January 2016

Integrating mRNA and miRNA Weighted Gene Co-Expression Networks with eQTLs in the Nucleus Accumbens of Subjects with Alcohol Dependence.

PLoS One 2015 18;10(9):e0137671. Epub 2015 Sep 18.

Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, United States of America; Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, United States of America; Center for Biomarker Research and Personalized Medicine, Virginia Commonwealth University, Richmond, VA, United States of America; Lieber Institute for Brain Development, Johns Hopkins University, Baltimore, MD, United States of America.

Alcohol consumption is known to lead to gene expression changes in the brain. After performing weighted gene co-expression network analyses (WGCNA) on genome-wide mRNA and microRNA (miRNA) expression in Nucleus Accumbens (NAc) of subjects with alcohol dependence (AD; N = 18) and of matched controls (N = 18), six mRNA and three miRNA modules significantly correlated with AD were identified (Bonferoni-adj. p≤ 0.05). Cell-type-specific transcriptome analyses revealed two of the mRNA modules to be enriched for neuronal specific marker genes and downregulated in AD, whereas the remaining four mRNA modules were enriched for astrocyte and microglial specific marker genes and upregulated in AD. Gene set enrichment analysis demonstrated that neuronal specific modules were enriched for genes involved in oxidative phosphorylation, mitochondrial dysfunction and MAPK signaling. Glial-specific modules were predominantly enriched for genes involved in processes related to immune functions, i.e. cytokine signaling (all adj. p≤ 0.05). In mRNA and miRNA modules, 461 and 25 candidate hub genes were identified, respectively. In contrast to the expected biological functions of miRNAs, correlation analyses between mRNA and miRNA hub genes revealed a higher number of positive than negative correlations (χ2 test p≤ 0.0001). Integration of hub gene expression with genome-wide genotypic data resulted in 591 mRNA cis-eQTLs and 62 miRNA cis-eQTLs. mRNA cis-eQTLs were significantly enriched for AD diagnosis and AD symptom counts (adj. p = 0.014 and p = 0.024, respectively) in AD GWAS signals in a large, independent genetic sample from the Collaborative Study on Genetics of Alcohol (COGA). In conclusion, our study identified putative gene network hubs coordinating mRNA and miRNA co-expression changes in the NAc of AD subjects, and our genetic (cis-eQTL) analysis provides novel insights into the etiological mechanisms of AD.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0137671PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4575063PMC
May 2016

Meta-analysis of Positive and Negative Symptoms Reveals Schizophrenia Modifier Genes.

Schizophr Bull 2016 Mar 27;42(2):279-87. Epub 2015 Aug 27.

Virginia Institute for Psychiatric and Behavioral Genetics, Department of Psychiatry, Virginia Commonwealth University School of Medicine, Richmond, VA; Mental Health Service Line, Washington VA Medical Center, Washington, DC; Department of Psychiatry, Georgetown University School of Medicine, Washington, DC.

Background: Evidence suggests that genetic factors may influence both schizophrenia (Scz) and its clinical presentation. In recent years, genome-wide association studies (GWAS) have demonstrated considerable success in identifying risk loci. Detection of "modifier loci" has the potential to further elucidate underlying disease processes.

Methods: We performed GWAS of empirically derived positive and negative symptom scales in Irish cases from multiply affected pedigrees and a larger, independent case-control sample, subsequently combining these into a large Irish meta-analysis. In addition to single-SNP associations, we considered gene-based and pathway analyses to better capture convergent genetic effects, and to facilitate biological interpretation of these findings. Replication and testing of aggregate genetic effects was conducted using an independent European-American sample.

Results: Though no single marker met the genome-wide significance threshold, genes and ontologies/pathways were significantly associated with negative and positive symptoms; notably, NKAIN2 and NRG1, respectively. We observed limited overlap in ontologies/pathways associated with different symptom profiles, with immune-related categories over-represented for negative symptoms, and addiction-related categories for positive symptoms. Replication analyses suggested that genes associated with clinical presentation are generalizable to non-Irish samples.

Conclusions: These findings strongly support the hypothesis that modifier loci contribute to the etiology of distinct Scz symptom profiles. The finding that previously implicated "risk loci" actually influence particular symptom dimensions has the potential to better delineate the roles of these genes in Scz etiology. Furthermore, the over-representation of distinct gene ontologies/pathways across symptom profiles suggests that the clinical heterogeneity of Scz is due in part to complex and diverse genetic factors.
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http://dx.doi.org/10.1093/schbul/sbv119DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4753595PMC
March 2016

DISTMIX: direct imputation of summary statistics for unmeasured SNPs from mixed ethnicity cohorts.

Bioinformatics 2015 Oct 9;31(19):3099-104. Epub 2015 Jun 9.

Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA 23298, USA.

Motivation: To increase the signal resolution for large-scale meta-analyses of genome-wide association studies, genotypes at unmeasured single nucleotide polymorphisms (SNPs) are commonly imputed using large multi-ethnic reference panels. However, the ever increasing size and ethnic diversity of both reference panels and cohorts makes genotype imputation computationally challenging for moderately sized computer clusters. Moreover, genotype imputation requires subject-level genetic data, which unlike summary statistics provided by virtually all studies, is not publicly available. While there are much less demanding methods which avoid the genotype imputation step by directly imputing SNP statistics, e.g. Directly Imputing summary STatistics (DIST) proposed by our group, their implicit assumptions make them applicable only to ethnically homogeneous cohorts.

Results: To decrease computational and access requirements for the analysis of cosmopolitan cohorts, we propose DISTMIX, which extends DIST capabilities to the analysis of mixed ethnicity cohorts. The method uses a relevant reference panel to directly impute unmeasured SNP statistics based only on statistics at measured SNPs and estimated/user-specified ethnic proportions. Simulations show that the proposed method adequately controls the Type I error rates. The 1000 Genomes panel imputation of summary statistics from the ethnically diverse Psychiatric Genetic Consortium Schizophrenia Phase 2 suggests that, when compared to genotype imputation methods, DISTMIX offers comparable imputation accuracy for only a fraction of computational resources.

Availability And Implementation: DISTMIX software, its reference population data, and usage examples are publicly available at http://code.google.com/p/distmix.

Contact: [email protected]

Supplementary Information: Supplementary Data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btv348DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4576696PMC
October 2015

Genome-wide gene pathway analysis of psychotic illness symptom dimensions based on a new schizophrenia-specific model of the OPCRIT.

Schizophr Res 2015 May 13;164(1-3):181-6. Epub 2015 Mar 13.

Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth, University School of Medicine, VA, USA; Washington Veterans Affairs Healthcare System, Washington D.C. USA; Georgetown University School of Medicine, Washington D.C. USA.

Empirically derived phenotypic measurements have the potential to enhance gene-finding efforts in schizophrenia. Previous research based on factor analyses of symptoms has typically included schizoaffective cases. Deriving factor loadings from analysis of only narrowly defined schizophrenia cases could yield more sensitive factor scores for gene pathway and gene ontology analyses. Using an Irish family sample, this study 1) factor analyzed clinician-rated Operational Criteria Checklist items in cases with schizophrenia only, 2) scored the full sample based on these factor loadings, and 3) implemented genome-wide association, gene-based, and gene-pathway analysis of these SCZ-based symptom factors (final N=507). Three factors emerged from the analysis of the schizophrenia cases: a manic, a depressive, and a positive symptom factor. In gene-based analyses of these factors, multiple genes had q<0.01. Of particular interest are findings for PTPRG and WBP1L, both of which were previously implicated by the Psychiatric Genomics Consortium study of SCZ; results from this study suggest that variants in these genes might also act as modifiers of SCZ symptoms. Gene pathway analyses of the first factor indicated over-representation of glutamatergic transmission, GABA-A receptor, and cyclic GMP pathways. Results suggest that these pathways may have differential influence on affective symptom presentation in schizophrenia.
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http://dx.doi.org/10.1016/j.schres.2015.02.013DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4409533PMC
May 2015

SWI/SNF chromatin remodeling regulates alcohol response behaviors in Caenorhabditis elegans and is associated with alcohol dependence in humans.

Proc Natl Acad Sci U S A 2015 Mar 23;112(10):3032-7. Epub 2015 Feb 23.

Departments of Pharmacology and Toxicology, Virginia Commonwealth University-Alcohol Research Center, Virginia Commonwealth University, Richmond, VA 23298

Alcohol abuse is a widespread and serious problem. Understanding the factors that influence the likelihood of abuse is important for the development of effective therapies. There are both genetic and environmental influences on the development of abuse, but it has been difficult to identify specific liability factors, in part because of both the complex genetic architecture of liability and the influences of environmental stimuli on the expression of that genetic liability. Epigenetic modification of gene expression can underlie both genetic and environmentally sensitive variation in expression, and epigenetic regulation has been implicated in the progression to addiction. Here, we identify a role for the switching defective/sucrose nonfermenting (SWI/SNF) chromatin-remodeling complex in regulating the behavioral response to alcohol in the nematode Caenorhabditis elegans. We found that SWI/SNF components are required in adults for the normal behavioral response to ethanol and that different SWI/SNF complexes regulate different aspects of the acute response to ethanol. We showed that the SWI/SNF subunits SWSN-9 and SWSN-7 are required in neurons and muscle for the development of acute functional tolerance to ethanol. Examination of the members of the SWI/SNF complex for association with a diagnosis of alcohol dependence in a human population identified allelic variation in a member of the SWI/SNF complex, suggesting that variation in the regulation of SWI/SNF targets may influence the propensity to develop abuse disorders. Together, these data strongly implicate the chromatin remodeling associated with SWI/SNF complex members in the behavioral responses to alcohol across phyla.
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http://dx.doi.org/10.1073/pnas.1413451112DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4364201PMC
March 2015
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