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BACKGROUND: Follow-up studies of childhood ADHD have shown persistence of the disorder into adulthood, but no epidemiological data are yet available. METHOD: ADHD DSM-IV symptoms were obtained by self-report in an adult population-based sample of 1813 adults (aged 18-75 years), that was drawn from an automated general practitioner system used in Nijmegen, The Netherlands. The structure of ADHD symptoms was analysed by means of confirmatory factor analyses. Other data used in this report are the General Health Questionnaire (GHQ-28), information about the presence of three core symptoms of ADHD in childhood, and about current psychosocial impairment. RESULTS: The three-factor model that allowed for cross-loadings provided the best fit in the entire sample. This result was replicated across gender and age subsamples. Inattentive and hyperactivity symptom scores were significantly associated with measures of impairment, even after controlling for the GHQ-28. Subjects with four or more inattentive or hyperactive-impulsive symptoms were significantly more impaired than subjects with two, one and no symptoms. The prevalence of ADHD in adults was 1.0% (95% CI 0.6-1.6) and 2.5% (1.9-3.4) using a cutoff of six and four current symptoms respectively, and requiring the presence of all three core symptoms in childhood. CONCLUSIONS: These results support the internal and external validity of ADHD in adults between 18 and 75 years. ADHD is not merely a child psychiatric disorder that persists into young adulthood, but an important and unique manifestation of psychopathology across the lifespan. [ABSTRACT FROM AUTHOR]

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.

We recently showed that, after optimization, our methyl-CpG binding domain sequencing (MBD-seq) application approximates the methylome-wide coverage obtained with whole-genome bisulfite sequencing (WGB-seq), but at a cost that enables adequately powered large-scale association studies. A prior drawback of MBD-seq is the relatively large amount of genomic DNA (ideally >1 µg) required to obtain high-quality data. Biomaterials are typically expensive to collect, provide a finite amount of DNA, and may simply not yield sufficient starting material. The ability to use low amounts of DNA will increase the breadth and number of studies that can be conducted. Therefore, we further optimized the enrichment step. With this low starting material protocol, MBD-seq performed equally well, or better, than the protocol requiring ample starting material (>1 µg). Using only 15 ng of DNA as input, there is minimal loss in data quality, achieving 93% of the coverage of WGB-seq (with standard amounts of input DNA) at similar false/positive rates. Furthermore, across a large number of genomic features, the MBD-seq methylation profiles closely tracked those observed for WGB-seq with even slightly larger effect sizes. This suggests that MBD-seq provides similar information about the methylome and classifies methylation status somewhat more accurately. Performance decreases with <15 ng DNA as starting material but, even with as little as 5 ng, MBD-seq still achieves 90% of the coverage of WGB-seq with comparable genome-wide methylation profiles. Thus, the proposed protocol is an attractive option for adequately powered and cost-effective methylome-wide investigations using (very) low amounts of DNA.

Background: Previous genomewide association studies (GWASs) have identified a number of putative risk loci for alcohol dependence (AD). However, only a few loci have replicated and these replicated variants only explain a small proportion of AD risk. Using an innovative approach, the goal of this study was to generate hypotheses about potentially causal variants for AD that can be explored further through functional studies., Methods: We employed targeted capture of 71 candidate loci and flanking regions followed by next-generation deep sequencing (mean coverage 78X) in 806 European Americans. Regions included in our targeted capture library were genes identified through published GWAS of alcohol, all human alcohol and aldehyde dehydrogenases, reward system genes including dopaminergic and opioid receptors, prioritized candidate genes based on previous associations, and genes involved in the absorption, distribution, metabolism, and excretion of drugs. We performed single-locus tests to determine if any single variant was associated with AD symptom count. Sets of variants that overlapped with biologically meaningful annotations were tested for association in aggregate., Results: No single, common variant was significantly associated with AD in our study. We did, however, find evidence for association with several variant sets. Two variant sets were significant at the q-value <0.10 level: a genic enhancer for ADHFE1 (p = 1.47 × 10 -5 ; q = 0.019), an alcohol dehydrogenase, and ADORA1 (p = 5.29 × 10 -5 ; q = 0.035), an adenosine receptor that belongs to a G-protein-coupled receptor gene family., Conclusions: To our knowledge, this is the first sequencing study of AD to examine variants in entire genes, including flanking and regulatory regions. We found that in addition to protein coding variant sets, regulatory variant sets may play a role in AD. From these findings, we have generated initial functional hypotheses about how these sets may influence AD., (Copyright © 2017 by the Research Society on Alcoholism.)

The human ANKS1B gene encodes an activity-dependent effector of post-synaptic signaling. It was recently associated with neuropsychiatric phenotypes in genome-wide studies. While the biological function of ANKS1B has been partly elucidated, its role in behavior is poorly understood. Here, we breed and characterize a full knockout (KO) for murine Anks1b. We found that the homozygous KO genotype was partially lethal, showing significant deviation from expected segregation ratios at weaning. Behaviorally, KOs exhibited no difference in baseline acoustic startle response, but showed deficits in prepulse inhibition (PPI). KOs also exhibited locomotor hyperactivity and increased stereotypy at baseline. Administration of ketamine, a non-competitive NMDA-receptor antagonist, greatly exacerbated locomotor activity in the KOs at lower doses, but genotype groups were almost indistinguishable as dose increased. Stereotypy showed a complex response to ketamine in the KOs, with elevated stereotypy at lower doses and markedly less at high doses, compared to wild type. Our study is the first to probe the behavioral phenotypes associated with ablation of Anks1b. Deficits in PPI, locomotor hyperactivity, elevated stereotypy and altered response to NMDA receptor antagonism are murine behavioral outcomes with translational relevance for psychiatric disorders. These findings are also consistent with the role of Anks1b as an effector of glutamatergic signaling. As an intermediary between post-synaptic receptor stimulation and long-term changes to neuronal protein expression, further investigation of Anks1b is warranted., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Based on an extensive simulation study, McGregor and colleagues recently recommended the use of surrogate variable analysis (SVA) to control for the confounding effects of cell-type heterogeneity in DNA methylation association studies in scenarios where no cell-type proportions are available. As their recommendation was mainly based on simulated data, we sought to replicate findings in two large-scale empirical studies. In our empirical data, SVA did not fully correct for cell-type effects, its performance was somewhat unstable, and it carried a risk of missing true signals caused by removing variation that might be linked to actual disease processes. By contrast, a reference-based correction method performed well and did not show these limitations. A disadvantage of this approach is that if reference methylomes are not (publicly) available, they will need to be generated once for a small set of samples. However, given the notable risk we observed for cell-type confounding, we argue that, to avoid introducing false-positive findings into the literature, it could be well worth making this investment.Please see related Correspondence article: https://genomebiology.biomedcentral.com/articles/10/1186/s13059-017-1149-7 and related Research article: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0935-y.

Anxiety disorders (ADs), namely generalized AD, panic disorder and phobias, are common, etiologically complex conditions with a partially genetic basis. Despite differing on diagnostic definitions based on clinical presentation, ADs likely represent various expressions of an underlying common diathesis of abnormal regulation of basic threat-response systems. We conducted genome-wide association analyses in nine samples of European ancestry from seven large, independent studies. To identify genetic variants contributing to genetic susceptibility shared across interview-generated DSM-based ADs, we applied two phenotypic approaches: (1) comparisons between categorical AD cases and supernormal controls, and (2) quantitative phenotypic factor scores (FS) derived from a multivariate analysis combining information across the clinical phenotypes. We used logistic and linear regression, respectively, to analyze the association between these phenotypes and genome-wide single nucleotide polymorphisms. Meta-analysis for each phenotype combined results across the nine samples for over 18 000 unrelated individuals. Each meta-analysis identified a different genome-wide significant region, with the following markers showing the strongest association: for case-control contrasts, rs1709393 located in an uncharacterized non-coding RNA locus on chromosomal band 3q12.3 (P=1.65 × 10(-8)); for FS, rs1067327 within CAMKMT encoding the calmodulin-lysine N-methyltransferase on chromosomal band 2p21 (P=2.86 × 10(-9)). Independent replication and further exploration of these findings are needed to more fully understand the role of these variants in risk and expression of ADs., Competing Interests: The authors declare no conflicts of interest.

Mutated CpG sites (CpG-SNPs) are potential hotspots for human diseases because in addition to the sequence variation they may show individual differences in DNA methylation. We performed methylome-wide association studies (MWAS) to test whether methylation differences at those sites were associated with schizophrenia. We assayed all common CpG-SNPs with methyl-CpG binding domain protein-enriched genome sequencing (MBD-seq) using DNA extracted from 1408 blood samples and 66 postmortem brain samples (BA10) of schizophrenia cases and controls. Seven CpG-SNPs passed our FDR threshold of 0.1 in the blood MWAS. Of the CpG-SNPs methylated in brain, 94% were also methylated in blood. This significantly exceeded the 46.2% overlap expected by chance (P-value < 1.0×10(-8)) and justified replicating findings from blood in brain tissue. CpG-SNP rs3796293 in IL1RAP replicated (P-value = .003) with the same direction of effects. This site was further validated through targeted bisulfite pyrosequencing in 736 independent case-control blood samples (P-value < 9.5×10(-4)). Our top result in the brain MWAS (P-value = 8.8×10(-7)) was CpG-SNP rs16872141 located in the potential promoter of ENC1. Overall, our results suggested that CpG-SNP methylation may reflect effects of environmental insults and can provide biomarkers in blood that could potentially improve disease management., (© The Author 2015. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Introduction: Genome-wide association study meta-analyses have robustly implicated three loci that affect susceptibility for smoking: CHRNA5\CHRNA3\CHRNB4, CHRNB3\CHRNA6 and EGLN2\CYP2A6. Functional follow-up studies of these loci are needed to provide insight into biological mechanisms. However, these efforts have been hampered by a lack of knowledge about the specific causal variant(s) involved. In this study, we prioritized variants in terms of the likelihood they account for the reported associations., Methods: We employed targeted capture of the CHRNA5\CHRNA3\CHRNB4, CHRNB3\CHRNA6, and EGLN2\CYP2A6 loci and flanking regions followed by next-generation deep sequencing (mean coverage 78×) to capture genomic variation in 363 individuals. We performed single locus tests to determine if any single variant accounts for the association, and examined if sets of (rare) variants that overlapped with biologically meaningful annotations account for the associations., Results: In total, we investigated 963 variants, of which 71.1% were rare (minor allele frequency < 0.01), 6.02% were insertion/deletions, and 51.7% were catalogued in dbSNP141. The single variant results showed that no variant fully accounts for the association in any region. In the variant set results, CHRNB4 accounts for most of the signal with significant sets consisting of directly damaging variants. CHRNA6 explains most of the signal in the CHRNB3\CHRNA6 locus with significant sets indicating a regulatory role for CHRNA6. Significant sets in CYP2A6 involved directly damaging variants while the significant variant sets suggested a regulatory role for EGLN2., Conclusions: We found that multiple variants implicating multiple processes explain the signal. Some variants can be prioritized for functional follow-up., (© The Author 2015. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Genetic influence on DNA methylation is potentially an important mechanism affecting individual differences in humans. We use next-generation sequencing to assay blood DNA methylation at approximately 4.5 million loci, each comprising 2.9 CpGs on average, in 697 normal subjects. Methylation measures at each locus are tested for association with approximately 4.5 million single nucleotide polymorphisms (SNPs) to exhaustively screen for methylation quantitative trait loci (meQTLs)., Results: Using stringent false discovery rate control, 15 % of methylation sites show genetic influence. Most meQTLs are local, where the associated SNP and methylation site are in close genomic proximity. Distant meQTLs and those spanning different chromosomes are less common. Most local meQTLs encompass common SNPs that alter CpG sites (CpG-SNPs). Local meQTLs encompassing CpG-SNPs are enriched in regions of inactive chromatin in blood cells. In contrast, local meQTLs lacking CpG-SNPs are enriched in regions of active chromatin and transcription factor binding sites. Of 393 local meQTLs that overlap disease-associated regions from genome-wide studies, a high percentage encompass common CpG-SNPs. These meQTLs overlap active enhancers, differentiating them from CpG-SNP meQTLs in inactive chromatin., Conclusions: Genetic influence on the human blood methylome is common, involves several heterogeneous processes and is predominantly dependent on local sequence context at the meQTL site. Most meQTLs involve CpG-SNPs, while sequence-dependent effects on chromatin binding are also important in regions of active chromatin. An abundance of local meQTLs resulting from methylation of CpG-SNPs in inactive chromatin suggests that many meQTLs lack functional consequence. Integrating meQTL and Roadmap Epigenomics data could assist fine-mapping efforts.

Genome-wide association studies have identified multiple risk variants and loci that show robust association with schizophrenia. Nevertheless, it remains unclear how these variants confer risk to schizophrenia. In addition, the driving force that maintains the schizophrenia risk variants in human gene pool is poorly understood. To investigate whether expression-associated genetic variants contribute to schizophrenia susceptibility, we systematically integrated brain expression quantitative trait loci and genome-wide association data of schizophrenia using Sherlock, a Bayesian statistical framework. Our analyses identified ZNF323 as a schizophrenia risk gene (P = 2.22×10(-6)). Subsequent analyses confirmed the association of the ZNF323 and its expression-associated single nucleotide polymorphism rs1150711 in independent samples (gene-expression: P = 1.40×10(-6); single-marker meta-analysis in the combined discovery and replication sample comprising 44123 individuals: P = 6.85×10(-10)). We found that the ZNF323 was significantly downregulated in hippocampus and frontal cortex of schizophrenia patients (P = .0038 and P = .0233, respectively). Evidence for pleiotropic effects was detected (association of rs1150711 with lung function and gene expression of ZNF323 in lung: P = 6.62×10(-5) and P = 9.00×10(-5), respectively) with the risk allele (T allele) for schizophrenia acting as protective allele for lung function. Subsequent population genetics analyses suggest that the risk allele (T) of rs1150711 might have undergone recent positive selection in human population. Our findings suggest that the ZNF323 is a schizophrenia susceptibility gene whose expression may influence schizophrenia risk. Our study also illustrates a possible mechanism for maintaining schizophrenia risk variants in the human gene pool., (© The Author 2015. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Haloperidol is an effective antipsychotic drug for treatment of schizophrenia, but prolonged use can lead to debilitating side effects. To better understand the effects of long-term administration, we measured global metabolic changes in mouse brain following 3 mg/kg/day haloperidol for 28 days. These conditions lead to movement-related side effects in mice akin to those observed in patients after prolonged use. Brain tissue was collected following microwave tissue fixation to arrest metabolism and extracted metabolites were assessed using both liquid and gas chromatography mass spectrometry (MS). Over 300 unique compounds were identified across MS platforms. Haloperidol was found to be present in all test samples and not in controls, indicating experimental validity. Twenty-one compounds differed significantly between test and control groups at the p < 0.05 level. Top compounds were robust to analytical method, also being identified via partial least squares discriminant analysis. Four compounds (sphinganine, N-acetylornithine, leucine and adenosine diphosphate) survived correction for multiple testing in a non-parametric analysis using false discovery rate threshold < 0.1. Pathway analysis of nominally significant compounds (p < 0.05) revealed significant findings for sphingolipid metabolism (p = 0.015) and protein biosynthesis (p = 0.024). Altered sphingolipid metabolism is suggestive of disruptions to myelin. This interpretation is supported by our observation of elevated N-acetyl-aspartyl-glutamate in the haloperidol-treated mice (p = 0.004), a marker previously associated with demyelination. This study further demonstrates the utility of murine neurochemical metabolomics as a method to advance understanding of CNS drug effects.

Background: Methylome-wide association (MWAS) studies present a new way to advance the search for biological correlates for alcohol use. A challenge with methylation studies of alcohol involves the causal direction of significant methylation-alcohol associations. One way to address this issue is to combine MWAS data with genomewide association study (GWAS) data., Methods: Here, we combined MWAS and GWAS results for alcohol use from 619 individuals. Our MWAS data were generated by next-generation sequencing of the methylated genomic DNA fraction, producing over 60 million reads per subject to interrogate methylation levels at ~27 million autosomal CpG sites in the human genome. Our GWAS included 5,571,786 single nucleotide polymorphisms (SNPs) imputed with 1000 Genomes., Results: When combining the MWAS and GWAS data, our top finding was a region in an intron of CNTN4 (p = 2.55 × 10(-8) ), located between chr3: 2,555,403 and 2,555,524, encompassing SNPs rs1382874 and rs1382875. This finding was then replicated in an independent sample of 730 individuals. We used bisulfite pyrosequencing to measure methylation and found significant association with regular alcohol use in the same direction as the MWAS (p = 0.021). Rs1382874 and rs1382875 were genotyped and found to be associated in the same direction as the GWAS (p = 0.008 and p = 0.009). After integrating the MWAS and GWAS findings from the replication sample, we replicated our combined analysis finding (p = 0.0017) in CNTN4., Conclusions: Through combining methylation and SNP data, we have identified CNTN4 as a risk factor for regular alcohol use., (Copyright © 2015 by the Research Society on Alcoholism.)

The public health burden of alcohol is unevenly distributed across the life course, with levels of use, abuse, and dependence increasing across adolescence and peaking in early adulthood. Here, we leverage this temporal patterning to search for common genetic variants predicting developmental trajectories of alcohol consumption. Comparable psychiatric evaluations measuring alcohol consumption were collected in three longitudinal community samples (N=2,126, obs=12,166). Consumption-repeated measurements spanning adolescence and early adulthood were analyzed using linear mixed models, estimating individual consumption trajectories, which were then tested for association with Illumina 660W-Quad genotype data (866,099 SNPs after imputation and QC). Association results were combined across samples using standard meta-analysis methods. Four meta-analysis associations satisfied our pre-determined genome-wide significance criterion (FDR<0.1) and six others met our 'suggestive' criterion (FDR<0.2). Genome-wide significant associations were highly biological plausible, including associations within GABA transporter 1, SLC6A1 (solute carrier family 6, member 1), and exonic hits in LOC100129340 (mitofusin-1-like). Pathway analyses elaborated single marker results, indicating significant enriched associations to intuitive biological mechanisms, including neurotransmission, xenobiotic pharmacodynamics, and nuclear hormone receptors (NHR). These findings underscore the value of combining longitudinal behavioral data and genome-wide genotype information in order to study developmental patterns and improve statistical power in genomic studies.

Methyl-binding domain (MBD) enrichment followed by deep sequencing (MBD-seq), is a robust and cost efficient approach for methylome-wide association studies (MWAS). MBD-seq has been demonstrated to be capable of identifying differentially methylated regions, detecting previously reported robust associations and producing findings that replicate with other technologies such as targeted pyrosequencing of bisulfite converted DNA. There are several kits commercially available that can be used for MBD enrichment. Our previous work has involved MethylMiner (Life Technologies, Foster City, CA, USA) that we chose after careful investigation of its properties. However, in a recent evaluation of five commercially available MBD-enrichment kits the performance of the MethylMiner was deemed poor. Given our positive experience with MethylMiner, we were surprised by this report. In an attempt to reproduce these findings we here have performed a direct comparison of MethylMiner with MethylCap (Diagenode Inc, Denville, NJ, USA), the best performing kit in that study. We find that both MethylMiner and MethylCap are two well performing MBD-enrichment kits. However, MethylMiner shows somewhat better enrichment efficiency and lower levels of background "noise". In addition, for the purpose of MWAS where we want to investigate the majority of CpGs, we find MethylMiner to be superior as it allows tailoring the enrichment to the regions where most CpGs are located. Using targeted bisulfite sequencing we confirmed that sites where methylation was detected by either MethylMiner or by MethylCap indeed were methylated.

Recent genome-wide association studies (GWAS) have made substantial progress in identifying disease loci. The next logical step is to design functional experiments to identify disease mechanisms. This step, however, is often hampered by the large size of loci identified in GWAS that is caused by linkage disequilibrium between SNPs. In this study, we demonstrate how integrating methylome-wide association study (MWAS) results with GWAS findings can narrow down the location for a subset of the putative casual sites. We use the disease schizophrenia as an example. To handle "data analytic" variation, we first combined our MWAS results with two GWAS meta-analyses (N = 32,143 and 21,953), that had largely overlapping samples but different data analysis pipelines, separately. Permutation tests showed significant overlapping association signals between GWAS and MWAS findings. This significant overlap justified prioritizing loci based on the concordance principle. To further ensure that the methylation signal was not driven by chance, we successfully replicated the top three methylation findings near genes SDCCAG8, CREB1 and ATXN7 in an independent sample using targeted pyrosequencing. In contrast to the SNPs in the selected region, the methylation sites were largely uncorrelated explaining why the methylation signals implicated much smaller regions (median size 78 bp). The refined loci showed considerable enrichment of genomic elements of possible functional importance and suggested specific hypotheses about schizophrenia etiology. Several hypotheses involved possible variation in transcription factor-binding efficiencies.

Anxiety disorders (ADs) are common mental disorders caused by a combination of genetic and environmental factors. Since ADs are highly comorbid with each other, partially due to shared genetic basis, studying AD phenotypes in a coordinated manner may be a powerful strategy for identifying potential genetic loci for ADs. To detect these loci, we performed genome-wide association studies (GWAS) of ADs. In addition, as a complementary approach to single-locus analysis, we also conducted gene- and pathway-based analyses. GWAS data were derived from the control sample of the Molecular Genetics of Schizophrenia (MGS) project (2,540 European American and 849 African American subjects) genotyped on the Affymetrix GeneChip 6.0 array. We applied two phenotypic approaches: (1) categorical case-control comparisons (CC) based upon psychiatric diagnoses, and (2) quantitative phenotypic factor scores (FS) derived from a multivariate analysis combining information across the clinical phenotypes. Linear and logistic models were used to analyse the association with ADs using FS and CC traits, respectively. At the single locus level, no genome-wide significant association was found. A trans-population gene-based meta-analysis across both ethnic subsamples using FS identified three genes (MFAP3L on 4q32.3, NDUFAB1 and PALB2 on 16p12) with genome-wide significance (false discovery rate (FDR] <5%). At the pathway level, several terms such as transcription regulation, cytokine binding, and developmental process were significantly enriched in ADs (FDR <5%). Our approaches studying ADs as quantitative traits and utilizing the full GWAS data may be useful in identifying susceptibility genes and pathways for ADs.

Clozapine is a particularly effective antipsychotic medication but its use is curtailed by the risk of clozapine-induced agranulocytosis/granulocytopenia (CIAG), a severe adverse drug reaction occurring in up to 1% of treated individuals. Identifying genetic risk factors for CIAG could enable safer and more widespread use of clozapine. Here we perform the largest and most comprehensive genetic study of CIAG to date by interrogating 163 cases using genome-wide genotyping and whole-exome sequencing. We find that two loci in the major histocompatibility complex are independently associated with CIAG: a single amino acid in HLA-DQB1 (126Q) (P=4.7 × 10(-14), odds ratio (OR)=0.19, 95% confidence interval (CI)=0.12-0.29) and an amino acid change in the extracellular binding pocket of HLA-B (158T) (P=6.4 × 10(-10), OR=3.3, 95% CI=2.3-4.9). These associations dovetail with the roles of these genes in immunogenetic phenotypes and adverse drug responses for other medications, and provide insight into the pathophysiology of CIAG.

Importance: Epigenetic studies present unique opportunities to advance schizophrenia research because they can potentially account for many of its clinical features and suggest novel strategies to improve disease management., Objective: To identify schizophrenia DNA methylation biomarkers in blood., Design, Setting, and Participants: The sample consisted of 759 schizophrenia cases and 738 controls (N = 1497) collected in Sweden. We used methyl-CpG-binding domain protein-enriched genome sequencing of the methylated genomic fraction, followed by next-generation DNA sequencing. We obtained a mean (SD) number of 68 (26.8) million reads per sample. This massive data set was processed using a specifically designed data analysis pipeline. Critical top findings from our methylome-wide association study (MWAS) were replicated in independent case-control participants using targeted pyrosequencing of bisulfite-converted DNA., Main Outcomes and Measures: Status of schizophrenia cases and controls., Results: Our MWAS suggested a considerable number of effects, with 25 sites passing the highly conservative Bonferroni correction and 139 sites significant at a false discovery rate of 0.01. Our top MWAS finding, which was located in FAM63B, replicated with P = 2.3 × 10-10. It was part of the networks regulated by microRNA that can be linked to neuronal differentiation and dopaminergic gene expression. Many other top MWAS results could be linked to hypoxia and, to a lesser extent, infection, suggesting that a record of pathogenic events may be preserved in the methylome. Our findings also implicated a site in RELN, one of the most frequently studied candidates in methylation studies of schizophrenia., Conclusions and Relevance: To our knowledge, the present study is one of the first MWASs of disease with a large sample size using a technology that provides good coverage of methylation sites across the genome. Our results demonstrated one of the unique features of methylation studies that can capture signatures of environmental insults in peripheral tissues. Our MWAS suggested testable hypotheses about disease mechanisms and yielded biomarkers that can potentially be used to improve disease management.

The central importance of epigenetics to the aging process is increasingly being recognized. Here we perform a methylome-wide association study (MWAS) of aging in whole blood DNA from 718 individuals, aged 25-92 years (mean = 55). We sequenced the methyl-CpG-enriched genomic DNA fraction, averaging 67.3 million reads per subject, to obtain methylation measurements for the ∼27 million autosomal CpGs in the human genome. Following extensive quality control, we adaptively combined methylation measures for neighboring, highly-correlated CpGs into 4 344 016 CpG blocks with which we performed association testing. Eleven age-associated differentially methylated regions (DMRs) passed Bonferroni correction (P-value < 1.15 × 10(-8)). Top findings replicated in an independent sample set of 558 subjects using pyrosequencing of bisulfite-converted DNA (min P-value < 10(-30)). To examine biological themes, we selected 70 DMRs with false discovery rate of <0.1. Of these, 42 showed hypomethylation and 28 showed hypermethylation with age. Hypermethylated DMRs were more likely to overlap with CpG islands and shores. Hypomethylated DMRs were more likely to be in regions associated with polycomb/regulatory proteins (e.g. EZH2) or histone modifications H3K27ac, H3K4m1, H3K4m2, H3K4m3 and H3K9ac. Among genes implicated by the top DMRs were protocadherins, homeobox genes, MAPKs and ryanodine receptors. Several of our DMRs are at genes with potential relevance for age-related disease. This study successfully demonstrates the application of next-generation sequencing to MWAS, by interrogating a large proportion of the methylome and returning potentially novel age DMRs, in addition to replicating several loci implicated in previous studies using microarrays.

Behavioral sensitization has been widely studied in animal models and is theorized to reflect neural modifications associated with human psychostimulant addiction. While the mesolimbic dopaminergic pathway is known to play a role, the neurochemical mechanisms underlying behavioral sensitization remain incompletely understood. In this study, we conducted the first metabolomics analysis to globally characterize neurochemical differences associated with behavioral sensitization. Methamphetamine (MA)-induced sensitization measures were generated by statistically modeling longitudinal activity data for eight inbred strains of mice. Subsequent to behavioral testing, nontargeted liquid and gas chromatography-mass spectrometry profiling was performed on 48 brain samples, yielding 301 metabolite levels per sample after quality control. Association testing between metabolite levels and three primary dimensions of behavioral sensitization (total distance, stereotypy and margin time) showed four robust, significant associations at a stringent metabolome-wide significance threshold (false discovery rate, FDR <0.05). Results implicated homocarnosine, a dipeptide of GABA and histidine, in total distance sensitization, GABA metabolite 4-guanidinobutanoate and pantothenate in stereotypy sensitization, and myo-inositol in margin time sensitization. Secondary analyses indicated that these associations were independent of concurrent MA levels and, with the exception of the myo-inositol association, suggest a mechanism whereby strain-based genetic variation produces specific baseline neurochemical differences that substantially influence the magnitude of MA-induced sensitization. These findings demonstrate the utility of mouse metabolomics for identifying novel biomarkers, and developing more comprehensive neurochemical models, of psychostimulant sensitization., (© 2013 John Wiley & Sons Ltd and International Behavioural and Neural Genetics Society.)

Most psychiatric disorders are moderately to highly heritable. The degree to which genetic variation is unique to individual disorders or shared across disorders is unclear. To examine shared genetic etiology, we use genome-wide genotype data from the Psychiatric Genomics Consortium (PGC) for cases and controls in schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorders (ASD) and attention-deficit/hyperactivity disorder (ADHD). We apply univariate and bivariate methods for the estimation of genetic variation within and covariation between disorders. SNPs explained 17-29% of the variance in liability. The genetic correlation calculated using common SNPs was high between schizophrenia and bipolar disorder (0.68 ± 0.04 s.e.), moderate between schizophrenia and major depressive disorder (0.43 ± 0.06 s.e.), bipolar disorder and major depressive disorder (0.47 ± 0.06 s.e.), and ADHD and major depressive disorder (0.32 ± 0.07 s.e.), low between schizophrenia and ASD (0.16 ± 0.06 s.e.) and non-significant for other pairs of disorders as well as between psychiatric disorders and the negative control of Crohn's disease. This empirical evidence of shared genetic etiology for psychiatric disorders can inform nosology and encourages the investigation of common pathophysiologies for related disorders.

Aim: As the primary relevant tissue (brain) for psychiatric disorders is commonly not available, we aimed to investigate whether blood can be used as a proxy in methylation studies on the basis of two models. In the 'signature' model methylation-disease associations occur because a disease-causing factor affected methylation in the blood. In the 'mirror-site' model the methylation status in the blood is correlated with the corresponding disease-causing site in the brain. MATERIALS, METHODS & RESULTS: Methyl-binding domain enrichment and next-generation sequencing of the blood, cortex and hippocampus from four haloperidol-treated and ten untreated C57BL/6 mice revealed high levels of correlation in methylation across tissues. Despite the treatment inducing a large number of methylation changes, this correlation remains high., Conclusion: Our results show that, consistent with the signature model, factors that affect brain processes (i.e., haloperidol) leave biomarker signatures in the blood and, consistent with the mirror-site model, the methylation status of many sites in the blood mirror those in the brain.

Importance: Schizophrenia (SCZ) is a devastating psychiatric condition. Identifying the specific genetic variants and pathways that increase susceptibility to SCZ is critical to improve disease understanding and address the urgent need for new drug targets., Objective: To identify SCZ susceptibility genes., Design: We integrated results from a meta-analysis of 18 genome-wide association studies (GWAS) involving 1,085,772 single-nucleotide polymorphisms (SNPs) and 6 databases that showed significant informativeness for SCZ. The 9380 most promising SNPs were then specifically genotyped in an independent family-based replication study that, after quality control, consisted of 8107 SNPs., Setting: Linkage meta-analysis, brain transcriptome meta-analysis, candidate gene database, OMIM, relevant mouse studies, and expression quantitative trait locus databases., Patients: We included 11,185 cases and 10,768 control subjects from 6 databases and, after quality control 6298 individuals (including 3286 cases) from 1811 nuclear families., Main Outcomes and Measures: Case-control status for SCZ., Results: Replication results showed a highly significant enrichment of SNPs with small P values. Of the SNPs with replication values of P.01, the proportion of SNPs that had the same direction of effects as in the GWAS meta-analysis was 89% in the combined ancestry group (sign test, P < 2.20 x 10(-16) and 93% in subjects of European ancestry only (P < 2.20 < 10(-16)). Our results supported the major histocompatibility complex region showing a3.7-fold overall enrichment of replication values of P < .01 in subjects from European ancestry. We replicated SNPs in TCF4 (P = 2.53 x 10(-10)) and NOTCH4 (P = 3.16 x 10(-7)) that are among the most robust SCZ findings. More novel findings included POM121L2 (P = 3.51 x 10(-7)), AS3MT (P = 9.01 x 10(-7)), CNNM2 (P = 6.07 = 10(-7)), and NT5C2(P = 4.09 x 10(-7)). To explore the many small effects, we performed pathway analyses. The most significant pathways involved neuronal function (axonal guidance, neuronal systems, and L1 cell adhesion molecule interaction)and the immune system (antigen processing, cell adhesion molecules relevant to T cells, and translocation to immunological synapse)., Conclusions and Relevance: We replicated novel SCZ disease genes and pathogenic pathways. Better understanding the molecular and biological mechanisms involved with schizophrenia may improve disease management and may identify new drug targets., Competing Interests: Disclosures: None reported.

The potential importance of DNA methylation in the etiology of complex diseases has led to interest in the development of methylome-wide association studies (MWAS) aimed at interrogating all methylation sites in the human genome. When using blood as biomaterial for a MWAS the DNA is typically extracted directly from fresh or frozen whole blood that was collected via venous puncture. However, DNA extracted from dry blood spots may also be an alternative starting material. In the present study, we apply a methyl-CpG binding domain (MBD) protein enrichment-based technique in combination with next generation sequencing (MBD-seq) to assess the methylation status of the ~27 million CpGs in the human autosomal reference genome. We investigate eight methylomes using DNA from blood spots. This data are compared with 1,500 methylomes previously assayed with the same MBD-seq approach using DNA from whole blood. When investigating the sequence quality and the enrichment profile across biological features, we find that DNA extracted from blood spots gives comparable results with DNA extracted from whole blood. Only if the amount of starting material is ≤ 0.5µg DNA we observe a slight decrease in the assay performance. In conclusion, we show that high quality methylome-wide investigations using MBD-seq can be conducted in DNA extracted from archived dry blood spots without sacrificing quality and without bias in enrichment profile as long as the amount of starting material is sufficient. In general, the amount of DNA extracted from a single blood spot is sufficient for methylome-wide investigations with the MBD-seq approach.

Prior genome-wide association studies (GWAS) of major depressive disorder (MDD) have met with limited success. We sought to increase statistical power to detect disease loci by conducting a GWAS mega-analysis for MDD. In the MDD discovery phase, we analyzed more than 1.2 million autosomal and X chromosome single-nucleotide polymorphisms (SNPs) in 18 759 independent and unrelated subjects of recent European ancestry (9240 MDD cases and 9519 controls). In the MDD replication phase, we evaluated 554 SNPs in independent samples (6783 MDD cases and 50 695 controls). We also conducted a cross-disorder meta-analysis using 819 autosomal SNPs with P<0.0001 for either MDD or the Psychiatric GWAS Consortium bipolar disorder (BIP) mega-analysis (9238 MDD cases/8039 controls and 6998 BIP cases/7775 controls). No SNPs achieved genome-wide significance in the MDD discovery phase, the MDD replication phase or in pre-planned secondary analyses (by sex, recurrent MDD, recurrent early-onset MDD, age of onset, pre-pubertal onset MDD or typical-like MDD from a latent class analyses of the MDD criteria). In the MDD-bipolar cross-disorder analysis, 15 SNPs exceeded genome-wide significance (P<5 × 10(-8)), and all were in a 248 kb interval of high LD on 3p21.1 (chr3:52 425 083-53 822 102, minimum P=5.9 × 10(-9) at rs2535629). Although this is the largest genome-wide analysis of MDD yet conducted, its high prevalence means that the sample is still underpowered to detect genetic effects typical for complex traits. Therefore, we were unable to identify robust and replicable findings. We discuss what this means for genetic research for MDD. The 3p21.1 MDD-BIP finding should be interpreted with caution as the most significant SNP did not replicate in MDD samples, and genotyping in independent samples will be needed to resolve its status.

Objective: Quantitative trait loci identified in animal models provide potential candidate susceptibility loci for human disorders. In this study, we investigated whether internalizing disorders (anxiety disorders, major depression, and neuroticism) were associated with a region on human chromosome 1 syntenic with a quantitative trait locus for rodent emotionality., Methods: We genotyped 31 single-nucleotide polymorphisms in genes located on chromosome 1q31.2 in a two-stage association study of 1128 individuals chosen for a high or a low genetic risk for internalizing disorders from the Virginia Adult Twin Study of Psychiatric and Substance Use Disorders., Results: None of the individual single-nucleotide polymorphisms showed consistent association across stages. A four-marker haplotype in the regulator of G-protein signaling 1 gene (RGS1) was significantly associated with decreased internalizing risk in both stages, whereas another showed a nominal association with a higher risk., Conclusion: Our data suggest that markers in the RGS1 gene might be in linkage disequilibrium with a protective allele that reduces the risk of anxiety and depressive disorders.

Methamphetamine (MA) is an illegal stimulant drug of abuse with serious negative health consequences. The neurochemical effects of MA have been partially characterized, with a traditional focus on classical neurotransmitter systems. However, these directions have not yet led to novel drug treatments for MA abuse or toxicity. As an alternative approach, we describe here the first application of metabolomics to investigate the neurochemical consequences of MA exposure in the rodent brain. We examined single exposures at 3 mg/kg and repeated exposures at 3 mg/kg over 5 days in eight common inbred mouse strains. Brain tissue samples were assayed using high-throughput gas and liquid chromatography mass spectrometry, yielding quantitative data on >300 unique metabolites. Association testing and false discovery rate control yielded several metabolome-wide significant associations with acute MA exposure, including compounds such as lactate ( p = 4.4 × 10 -5 , q = 0.013), tryptophan ( p = 7.0 × 10 -4 , q = 0.035) and 2-hydroxyglutarate ( p = 1.1 × 10 -4 , q = 0.022). Secondary analyses of MA-induced increase in locomotor activity showed associations with energy metabolites such as succinate ( p = 3.8 × 10 -7 ). Associations specific to repeated (5 day) MA exposure included phosphocholine ( p = 4.0 × 10 -4 , q = 0.087) and ergothioneine ( p = 3.0 × 10 -4 , q = 0.087). Our data appear to confirm and extend existing models of MA action in the brain, whereby an initial increase in energy metabolism, coupled with an increase in behavioral locomotion, gives way to disruption of mitochondria and phospholipid pathways and increased endogenous antioxidant response. Our study demonstrates the power of comprehensive MS-based metabolomics to identify drug-induced changes to brain metabolism and to develop neurochemical models of drug effects.

Background: In methylome-wide association studies (MWAS) there are many possible differences between cases and controls (e.g. related to life style, diet, and medication use) that may affect the methylome and produce false positive findings. An effective approach to control for these confounders is to first capture the major sources of variation in the methylation data and then regress out these components in the association analyses. This approach is, however, computationally very challenging due to the extremely large number of methylation sites in the human genome., Result: We introduce MethylPCA that is specifically designed to control for potential confounders in studies where the number of methylation sites is extremely large. MethylPCA offers a complete and flexible data analysis including 1) an adaptive method that performs data reduction prior to PCA by empirically combining methylation data of neighboring sites, 2) an efficient algorithm that performs a principal component analysis (PCA) on the ultra high-dimensional data matrix, and 3) association tests. To accomplish this MethylPCA allows for parallel execution of tasks, uses C++ for CPU and I/O intensive calculations, and stores intermediate results to avoid computing the same statistics multiple times or keeping results in memory. Through simulations and an analysis of a real whole methylome MBD-seq study of 1,500 subjects we show that MethylPCA effectively controls for potential confounders., Conclusions: MethylPCA provides users a convenient tool to perform MWAS. The software effectively handles the challenge in memory and speed to perform tasks that would be impossible to accomplish using existing software when millions of sites are interrogated with the sample sizes required for MWAS.

Background: Methylation studies are a promising complement to genetic studies of DNA sequence. However, detailed prior biological knowledge is typically lacking, so methylome-wide association studies (MWAS) will be critical to detect disease relevant sites. A cost-effective approach involves the next-generation sequencing (NGS) of single-end libraries created from samples that are enriched for methylated DNA fragments. A limitation of single-end libraries is that the fragment size distribution is not observed. This hampers several aspects of the data analysis such as the calculation of enrichment measures that are based on the number of fragments covering the CpGs., Results: We developed a non-parametric method that uses isolated CpGs to estimate sample-specific fragment size distributions from the empirical sequencing data. Through simulations we show that our method is highly accurate. While the traditional (extended) read count methods resulted in severely biased coverage estimates and introduces artificial inter-individual differences, through the use of the estimated fragment size distributions we could remove these biases almost entirely. Furthermore, we found correlations of 0.999 between coverage estimates obtained using fragment size distributions that were estimated with our method versus those that were "observed" in paired-end sequencing data., Conclusions: We propose a non-parametric method for estimating fragment size distributions that is highly precise and can improve the analysis of cost-effective MWAS studies that sequence single-end libraries created from samples that are enriched for methylated DNA fragments.

Objective: To examine the unique and congruent findings between multiple raters in a genome-wide association study (GWAS) in the context of understanding individual differences in treatment response during antipsychotic therapy for schizophrenia., Materials and Methods: We performed GWAS to search for genetic variation affecting treatment response. The analysis sample included 738 patients with schizophrenia, successfully genotyped for ∼492k single nucleotide polymorphisms (SNPs) from the Clinical Antipsychotic Trial of Intervention Effectiveness. Outcomes included both clinician and patient report of illness severity on global impression scales, the clinical global impression severity scale and patient global impression, respectively. Our criterion for genome-wide significance was a prespecified threshold ensuring that, on average, only 10% of the significant findings are false discoveries., Results: Thirteen SNPs reached genome-wide significance. The top findings indicated three SNPs in PDE4D, 5q12.1 (P=4.2×10, 1.6×10, 1.8×10), mediating the effects of quetiapine on patient-reported severity and an additional three SNPs in TJP1, 15q13.1 (P=2.25×10, 4.86×10, 4.91×10), mediating the effects of risperidone on patient-reported severity. For clinician-reported severity, two SNPs in PPA2, 4q24 (P=3.68×10, 5.05×10), were found to reach genome-wide significance., Conclusion: We found evidence of both a novel and a consistent association when examining the results from the patient and clinician ratings, suggesting that different raters may capture unique facets of schizophrenia. Although our findings require replication and functional validation, this study shows the potential of GWAS to discover genes that potentially mediate treatment response of antipsychotic medication.

Only a subset of patients will typically respond to any given prescribed drug. The time it takes clinicians to declare a treatment ineffective leaves the patient in an impaired state and at unnecessary risk for adverse drug effects. Thus, diagnostic tests robustly predicting the most effective and safe medication for each patient prior to starting pharmacotherapy would have tremendous clinical value. In this article, we evaluated the use of genetic markers to estimate ancestry as a predictive component of such diagnostic tests. We first estimated each patient's unique mosaic of ancestral backgrounds using genome-wide SNP data collected in the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) (n = 765) and the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) (n = 1892). Next, we performed multiple regression analyses to estimate the predictive power of these ancestral dimensions. For 136/89 treatment-outcome combinations tested in CATIE/STAR*D, results indicated 1.67/1.84 times higher median test statistics than expected under the null hypothesis assuming no predictive power (p<0.01, both samples). Thus, ancestry showed robust and pervasive correlations with drug efficacy and side effects in both CATIE and STAR*D. Comparison of the marginal predictive power of MDS ancestral dimensions and self-reported race indicated significant improvements to model fit with the inclusion of MDS dimensions, but mixed evidence for self-reported race. Knowledge of each patient's unique mosaic of ancestral backgrounds provides a potent immediate starting point for developing algorithms identifying the most effective and safe medication for a wide variety of drug-treatment response combinations. As relatively few new psychiatric drugs are currently under development, such personalized medicine offers a promising approach toward optimizing pharmacotherapy for psychiatric conditions.

Aim: We studied the use of methyl-CpG binding domain (MBD) protein-enriched genome sequencing (MBD-seq) as a cost-effective screening tool for methylome-wide association studies (MWAS)., Materials & Methods: Because MBD-seq has not yet been applied on a large scale, we first developed and tested a pipeline for data processing using 1500 schizophrenia cases and controls plus 75 technical replicates with an average of 68 million reads per sample. This involved the use of technical replicates to optimize quality control for multi- and duplicate-reads, an in silico experiment to identify CpGs in loci with alignment problems, CpG coverage calculations based on multiparametric estimates of the fragment size distribution, a two-stage adaptive algorithm to combine data from correlated adjacent CpG sites, principal component analyses to control for confounders and new software tailored to handle the large data set., Results: We replicated MWAS findings in independent samples using a different technology that provided single base resolution. In an MWAS of age-related methylation changes, one of our top findings was a previously reported robust association involving GRIA2. Our results also suggested that owing to the many confounding effects, a considerable challenge in MWAS is to identify those effects that are informative about disease processes., Conclusion: This study showed the potential of MBD-seq as a cost-effective tool in large-scale disease studies.

Genetic factors underlying trait neuroticism, reflecting a tendency towards negative affective states, may overlap genetic susceptibility for anxiety disorders and help explain the extensive comorbidity amongst internalizing disorders. Genome-wide linkage (GWL) data from several studies of neuroticism and anxiety disorders have been published, providing an opportunity to test such hypotheses and identify genomic regions that harbor genes common to these phenotypes. In all, 11 independent GWL studies of either neuroticism (n=8) or anxiety disorders (n=3) were collected, which comprised of 5341 families with 15 529 individuals. The rank-based genome scan meta-analysis (GSMA) approach was used to analyze each trait separately and combined, and global correlations between results were examined. False discovery rate (FDR) analysis was performed to test for enrichment of significant effects. Using 10 cM intervals, bins nominally significant for both GSMA statistics, P(SR) and P(OR), were found on chromosomes 9, 11, 12, and 14 for neuroticism and on chromosomes 1, 5, 15, and 16 for anxiety disorders. Genome-wide, the results for the two phenotypes were significantly correlated, and a combined analysis identified additional nominally significant bins. Although none reached genome-wide significance, an excess of significant P(SR)P-values were observed, with 12 bins falling under a FDR threshold of 0.50. As demonstrated by our identification of multiple, consistent signals across the genome, meta-analytically combining existing GWL data is a valuable approach to narrowing down regions relevant for anxiety-related phenotypes. This may prove useful for prioritizing emerging genome-wide association data for anxiety disorders.

DNA from Epstein-Barr virus-transformed lymphocyte cell lines (LCLs) has proven useful for studies of genetic sequence polymorphisms. Whether LCL DNA is suitable for methylation studies is less clear. We conduct a genome-wide methylation investigation using an array set with 45 million probes to investigate the methylome of LCL DNA and technical duplicates of WB DNA from the same 10 individuals. We focus specifically on methylation sites that show variation between individuals and, therefore, are potentially useful as biomarkers. The sample correlations for the methylation variable probes ranged from 0.69 to 0.78 for the WB duplicates and from 0.27 to 0.72 for WB vs LCL. To compare the pattern of the methylation signals, we grouped adjacent probes based on their inter-correlations. These analyses showed ∼29 000 and ∼14 000 blocks in WB and LCL, respectively. Merely 31% of the methylated regions detected in WB were detectable in LCLs. Furthermore, we observed significant differences in mean difference between WB and LCL as compared with duplicates of WB (P-value =2.2 × 10(-16)). Our study shows that there are substantial differences in the DNA methylation patterns between LCL and WB. Thus, LCL DNA should not be used as a proxy for WB DNA in methylome-wide studies.

Affecting about 1 in 12 Americans annually, depression is a leading cause of the global disease burden. While a range of effective antidepressants are now available, failure and relapse rates remain substantial, with intolerable side effect burden the most commonly cited reason for discontinuation. Thus, understanding individual differences in susceptibility to antidepressant therapy side effects will be essential to optimize depression treatment. Here we perform genome-wide association studies (GWAS) to identify genetic variation influencing susceptibility to citalopram-induced side effects. The analysis sample consisted of 1762 depression patients, successfully genotyped for 421K single-nucleotide polymorphisms (SNPs), from the Sequenced Treatment Alternatives to Relieve Depression (STAR(*)D) study. Outcomes included five indicators of citalopram side effects: general side effect burden, overall tolerability, sexual side effects, dizziness and vision/hearing side effects. Two SNPs met our genome-wide significance criterion (q<0.1), ensuring that, on average, only 10% of significant findings are false discoveries. In total, 12 additional SNPs demonstrated suggestive associations (q<0.5). The top finding was rs17135437, an intronic SNP within EMID2, mediating the effects of citalopram on vision/hearing side effects (P=3.27 × 10(-8), q=0.026). The second genome-wide significant finding, representing a haplotype spanning ∼30 kb and eight genotyped SNPs in a gene desert on chromosome 13, was associated with general side effect burden (P=3.22 × 10(-7), q=0.096). Suggestive findings were also found for SNPs at LAMA1, AOX2P, EGFLAM, FHIT and RTP2. Although our findings require replication and functional validation, this study demonstrates the potential of GWAS to discover genes and pathways that potentially mediate adverse effects of antidepressant medications.

Background: Understanding individual differences in susceptibility to antidepressant therapy side-effects is essential to optimize the treatment of depression., Method: We performed genome-wide association studies (GWAS) to search for genetic variation affecting the susceptibility to side-effects. The analysis sample consisted of 1439 depression patients, successfully genotyped for 421K single nucleotide polymorphisms (SNPs), from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study. Outcomes included four indicators of side-effects: general side-effect burden, sexual side-effects, dizziness and vision/hearing-related side-effects. Our criterion for genome-wide significance was a prespecified threshold ensuring that, on average, only 10% of the significant findings are false discoveries., Results: Thirty-four SNPs satisfied this criterion. The top finding indicated that 10 SNPs in SACM1L mediated the effects of bupropion on sexual side-effects (p = 4.98 × 10(-7), q = 0.023). Suggestive findings were also found for SNPs in MAGI2, DTWD1, WDFY4 and CHL1., Conclusions: Although our findings require replication and functional validation, this study demonstrates the potential of GWAS to discover genes and pathways that could mediate adverse effects of antidepressant medication.

Pharmacogenomics is yet to fulfill its promise of manifestly altering clinical medicine. As one example, a predictive test for tardive dyskinesia (TD) (an adverse drug reaction consequent to antipsychotic exposure) could greatly improve the clinical treatment of schizophrenia but human studies are equivocal. A complementary approach is the mouse-then-human design in which a valid mouse model is used to identify susceptibility loci, which are subsequently tested in human samples. We used inbred mouse strains from the Mouse Phenome Project to estimate the heritability of haloperidol-induced activity and orofacial phenotypes. In all, 159 mice from 27 inbred strains were chronically treated with haloperidol (3 mg kg(-1) per day via subdermal slow-release pellets) and monitored for the development of vacuous chewing movements (VCMs; the mouse analog of TD) and other movement phenotypes derived from open-field activity and the inclined screen test. The test battery was assessed at 0, 30, 60, 90 and 120 days in relation to haloperidol exposure. As expected, haloperidol caused marked changes in VCMs, activity in the open field and extrapyramidal symptoms (EPS). Unexpectedly, factor analysis demonstrated that these measures were imprecise assessments of a latent construct rather than discrete constructs. The heritability of a composite phenotype was ∼0.9 after incorporation of the longitudinal nature of the design. Murine VCMs are a face valid animal model of antipsychotic-induced TD, and heritability estimates from this study support the feasibility of mapping of susceptibility loci for VCMs.

QT prolongation is associated with increased risk of cardiac arrhythmias. Identifying the genetic variants that mediate antipsychotic-induced prolongation may help to minimize this risk, which might prevent the removal of efficacious drugs from the market. We performed candidate gene analysis and five drug-specific genome-wide association studies (GWASs) with 492K single-nucleotide polymorphisms to search for genetic variation mediating antipsychotic-induced QT prolongation in 738 schizophrenia patients from the Clinical Antipsychotic Trial of Intervention Effectiveness study. Our candidate gene study suggests the involvement of NOS1AP and NUBPL (P-values=1.45 × 10(-05) and 2.66 × 10(-13), respectively). Furthermore, our top GWAS hit achieving genome-wide significance, defined as a Q-value <0.10 (P-value=1.54 × 10(-7), Q-value=0.07), located in SLC22A23, mediated the effects of quetiapine on prolongation. SLC22A23 belongs to a family of organic ion transporters that shuttle a variety of compounds, including drugs, environmental toxins and endogenous metabolites, across the cell membrane. This gene is expressed in the heart and is integral in mouse heart development. The genes mediating antipsychotic-induced QT prolongation partially overlap with the genes affecting normal QT interval variation. However, some genes may also be unique for drug-induced prolongation. This study demonstrates the potential of GWAS to discover genes and pathways that mediate antipsychotic-induced QT prolongation.