A substantial number of patients do not benefit from first line psychological therapies for the treatment of depression and anxiety. Currently, there are no clear predictors of treatment outcomes for these patients. The PROMPT project aims to establish an infrastructure platform for the identification of factors that predict outcomes following psychological treatment for depression and anxiety. Here we report on the first year of recruitment and describe the characteristics of our sample to date.

Extinction learning is an important mechanism in the successful psychological treatment of anxiety. Individual differences in response and relapse following Cognitive Behavior Therapy may in part be explained by variability in the ease with which fears are extinguished or the vulnerability of these fears to re-emerge. Given the role of the endocannabinoid system in fear extinction, this study investigates whether genetic variation in the endocannabinoid system explains individual differences in response to CBT. Children (N = 1,309) with a primary anxiety disorder diagnosis were recruited. We investigated the relationship between variation in the CNR1, CNR2, and FAAH genes and change in primary anxiety disorder severity between pre- and post-treatment and during the follow-up period in the full sample and a subset with fear-based anxiety disorder diagnoses. Change in symptom severity during active treatment was nominally associated (P < 0.05) with two SNPs. During the follow-up period, five SNPs were nominally associated with a poorer treatment response (rs806365 [CNR1]; rs2501431 [CNR2]; rs2070956 [CNR2]; rs7769940 [CNR1]; rs2209172 [FAAH]) and one with a more favorable response (rs6928813 [CNR1]). Within the fear-based subset, the effect of rs806365 survived multiple testing corrections (P < 0.0016). We found very limited evidence for an association between variants in endocannabinoid system genes and treatment response once multiple testing corrections were applied. Larger, more homogenous cohorts are needed to allow the identification of variants of small but statistically significant effect and to estimate effect sizes for these variants with greater precision in order to determine their potential clinical utility. © 2016 The Authors. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics Published by Wiley Periodicals, Inc.

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).

Osteoporosis is a common and debilitating bone disease that is characterised by a low bone mineral density (BMD), a highly heritable trait. Genome-wide association studies (GWAS) have proven to be very successful in identifying common genetic variants associated with BMD adjusted for age, gender and weight, however a large portion of the genetic variance for this trait remains unexplained. There is evidence to suggest significant genetic correlation between body size traits and BMD. It has also recently been suggested that unintended bias can be introduced as a result of adjusting a phenotype for a correlated trait. We performed a GWAS meta-analysis in two populations (total n = 6,696) using BMD data adjusted for only age and gender, in an attempt to identify genetic variants associated with BMD including those that may have potential pleiotropic effects on BMD and body size traits.

Anxiety disorders are common, and cognitive-behavioural therapy (CBT) is a first-line treatment. Candidate gene studies have suggested a genetic basis to treatment response, but findings have been inconsistent.

Single nucleotide polymorphisms (SNPs) within the MIR137 gene locus have been shown to confer risk for schizophrenia through genome-wide association studies (GWAS). The expression levels of microRNA-137 (miR-137) and its validated gene targets have also been shown to be disrupted in several neuropsychiatric conditions, including schizophrenia. Regulation of miR-137 expression is thus imperative for normal neuronal functioning. We previously characterized an internal promoter domain within the MIR137 gene that contained a variable number tandem repeat (VNTR) polymorphism and could alter the in vitro levels of miR-137 in a stimulus-induced and allele-specific manner. We now demonstrate that haplotype tagging-SNP analysis linked the rs1625579 GWAS SNP for schizophrenia to this internal MIR137 promoter through a proxy SNP rs2660304 located at this domain. We postulated that the rs2660304 promoter SNP may act as predisposing factor for schizophrenia through altering the levels of miR-137 expression in a genotype-dependent manner. Reporter gene analysis of the internal MIR137 promoter containing the common VNTR variant demonstrated genotype-dependent differences in promoter activity with respect to rs2660304. In line with previous reports, the major allele of the rs2660304 proxy SNP, which has previously been linked with schizophrenia risk through genetic association, resulted in downregulation of reporter gene expression in a tissue culture model. The genetic influence of the rs2660304 proxy SNP on the transcriptional activity of the internal MIR137 promoter, and thus the levels of miR-137 expression, therefore offers a distinct regulatory mechanism to explain the functional significance of the rs1625579 GWAS SNP for schizophrenia risk.

The second-generation antipsychotic olanzapine is effective in reducing psychotic symptoms but can cause extreme weight gain in human patients. We investigated the role of the gut microbiota in this adverse drug effect using a mouse model. First, we used germ-free C57BL/6J mice to demonstrate that gut bacteria are necessary and sufficient for weight gain caused by oral delivery of olanzapine. Second, we surveyed fecal microbiota before, during, and after treatment and found that olanzapine potentiated a shift towards an "obesogenic" bacterial profile. Finally, we demonstrated that olanzapine has antimicrobial activity in vitro against resident enteric bacterial strains. These results collectively provide strong evidence for a mechanism underlying olanzapine-induced weight gain in mouse and a hypothesis for clinical translation in human patients.

Schizophrenia (SZ) genome-wide association studies (GWASs) have identified common risk variants in >100 susceptibility loci; however, the contribution of rare variants at these loci remains largely unexplored. One of the strongly associated loci spans MIR137 (miR137) and MIR2682 (miR2682), two microRNA genes important for neuronal function. We sequenced ∼6.9 kb MIR137/MIR2682 and upstream regulatory sequences in 2,610 SZ cases and 2,611 controls of European ancestry. We identified 133 rare variants with minor allele frequency (MAF) <0.5%. The rare variant burden in promoters and enhancers, but not insulators, was associated with SZ (p = 0.021 for MAF < 0.5%, p = 0.003 for MAF < 0.1%). A rare enhancer SNP, 1:g.98515539A>T, presented exclusively in 11 SZ cases (nominal p = 4.8 × 10(-4)). We further identified its risk allele T in 2 of 2,434 additional SZ cases, 11 of 4,339 bipolar (BP) cases, and 3 of 3,572 SZ/BP study controls and 1,688 population controls; yielding combined p values of 0.0007, 0.0013, and 0.0001 for SZ, BP, and SZ/BP, respectively. The risk allele T of 1:g.98515539A>T reduced enhancer activity of its flanking sequence by >50% in human neuroblastoma cells, predicting lower expression of MIR137/MIR2682. Both empirical and computational analyses showed weaker transcription factor (YY1) binding by the risk allele. Chromatin conformation capture (3C) assay further indicated that 1:g.98515539A>T influenced MIR137/MIR2682, but not the nearby DPYD or LOC729987. Our results suggest that rare noncoding risk variants are associated with SZ and BP at MIR137/MIR2682 locus, with risk alleles decreasing MIR137/MIR2682 expression.

Major depressive disorder is heritable and a leading cause of disability. Cognitive behavior therapy is an effective treatment for major depression. By quantifying genetic risk scores based on common genetic variants, the aim of this report was to explore the utility of psychiatric and cognitive trait genetic risk scores, for predicting the response of 894 adults with major depressive disorder to cognitive behavior therapy. The participants were recruited in a psychiatric setting, and the primary outcome score was measured using the Montgomery Åsberg Depression Rating Scale-Self Rated. Single-nucleotide polymorphism genotyping arrays were used to calculate the genomic risk scores based on large genetic studies of six phenotypes: major depressive disorder, bipolar disorder, attention-deficit/hyperactivity disorder, autism spectrum disorder, intelligence, and educational attainment. Linear mixed-effect models were used to test the relationships between the six genetic risk scores and cognitive behavior therapy outcome. Our analyses yielded one significant interaction effect (B = 0.09, p < 0.001): the autism spectrum disorder genetic risk score correlated with Montgomery Åsberg Depression Rating Scale-Self Rated changes during treatment, and the higher the autism spectrum disorder genetic load, the less the depressive symptoms decreased over time. The genetic risk scores for the other psychiatric and cognitive traits were not related to depressive symptom severity or change over time. Our preliminary results indicated, as expected, that the genomics of the response of patients with major depression to cognitive behavior therapy were complex and that future efforts should aim to maximize sample size and limit subject heterogeneity in order to gain a better understanding of the use of genetic risk factors to predict treatment outcome.

In addition to warming temperatures, Arctic ecosystems are responding to climate change with earlier snowmelt and soil thaw. Earlier snowmelt has been examined infrequently in field experiments, and we lack a comprehensive look at belowground responses of the soil biogeochemical system that includes plant roots, decomposers, and soil nutrients. We experimentally advanced the timing of snowmelt in factorial combination with an open-top chamber warming treatment over a 3-year period and evaluated the responses of decomposers and nutrient cycling processes. We tested two alternative hypotheses: (a) Early snowmelt and warming advance the timing of root growth and nutrient uptake, altering the timing of microbial and invertebrate activity and key nutrient cycling events; and (b) loss of insulating snow cover damages plants, leading to reductions in root growth and altered biological activity. During the 3 years of our study (2010-2012), we advanced snowmelt by 4, 15, and 10 days, respectively. Despite advancing aboveground plant phenology, particularly in the year with the warmest early-season temperatures (2012), belowground effects were primarily seen only on the first sampling date of the season or restricted to particular years or soil type. Overall, consistent and substantial responses to early snowmelt were not observed, counter to both of our hypotheses. The data on soil physical conditions, as well interannual comparisons of our results, suggest that this limited response was because of the earlier date of snowmelt that did not coincide with substantially warmer air and soil temperatures as they might in response to a natural climate event. We conclude that the interaction of snowmelt timing with soil temperatures is important to how the ecosystem will respond, but that 1- to 2-week changes in timing of snowmelt alone are not enough to drive season-long changes in soil microbial and nutrient cycling processes.

Drug addiction is a costly and recurring healthcare problem, necessitating a need to understand risk factors and mechanisms of addiction, and to identify new biomarkers. To date, genome-wide association studies (GWAS) for heroin addiction have been limited; moreover they have been restricted to examining samples of European and African-American origin due to difficulty of recruiting samples from other populations. This is the first study to test a Han Chinese population; we performed a GWAS on a homogeneous sample of 370 Han Chinese subjects diagnosed with heroin dependence using the DSM-IV criteria and 134 ethnically matched controls. Analysis using the diagnostic criteria of heroin dependence yielded suggestive evidence for association between variants in the genes CCDC42 (coiled coil domain 42; p = 2.8x10-7) and BRSK2 (BR serine/threonine 2; p = 4.110-6). In addition, we found evidence for risk variants within the ARHGEF10 (Rho guanine nucleotide exchange factor 10) gene on chromosome 8 and variants in a region on chromosome 20q13, which is gene-poor but has a concentration of mRNAs and predicted miRNAs. Gene-based association analysis identified genome-wide significant association between variants in CCDC42 and heroin addiction. Additionally, when we investigated shared risk variants between heroin addiction and risk of other addiction-related and psychiatric phenotypes using polygenic risk scores, we found a suggestive relationship with variants predicting tobacco addiction, and a significant relationship with variants predicting schizophrenia. Our genome wide association study of heroin dependence provides data in a novel sample, with functionally plausible results and evidence of genetic data of value to the field.

Recent heritability analyses have indicated that genome-wide association studies (GWAS) have the potential to improve genetic risk prediction for complex diseases based on polygenic risk score (PRS), a simple modelling technique that can be implemented using summary-level data from the discovery samples. We herein propose modifications to improve the performance of PRS. We introduce threshold-dependent winner's-curse adjustments for marginal association coefficients that are used to weight the single-nucleotide polymorphisms (SNPs) in PRS. Further, as a way to incorporate external functional/annotation knowledge that could identify subsets of SNPs highly enriched for associations, we propose variable thresholds for SNPs selection. We applied our methods to GWAS summary-level data of 14 complex diseases. Across all diseases, a simple winner's curse correction uniformly led to enhancement of performance of the models, whereas incorporation of functional SNPs was beneficial only for selected diseases. Compared to the standard PRS algorithm, the proposed methods in combination led to notable gain in efficiency (25-50% increase in the prediction R2) for 5 of 14 diseases. As an example, for GWAS of type 2 diabetes, winner's curse correction improved prediction R2 from 2.29% based on the standard PRS to 3.10% (P = 0.0017) and incorporating functional annotation data further improved R2 to 3.53% (P = 2×10-5). Our simulation studies illustrate why differential treatment of certain categories of functional SNPs, even when shown to be highly enriched for GWAS-heritability, does not lead to proportionate improvement in genetic risk-prediction because of non-uniform linkage disequilibrium structure.

Anorexia nervosa (AN) and obsessive-compulsive disorder (OCD) are often comorbid and likely to share genetic risk factors. Hence, we examine their shared genetic background using a cross-disorder GWAS meta-analysis of 3495 AN cases, 2688 OCD cases, and 18,013 controls. We confirmed a high genetic correlation between AN and OCD (rg = 0.49 ± 0.13, p = 9.07 × 10-7) and a sizable SNP heritability (SNP h2 = 0.21 ± 0.02) for the cross-disorder phenotype. Although no individual loci reached genome-wide significance, the cross-disorder phenotype showed strong positive genetic correlations with other psychiatric phenotypes (e.g., rg = 0.36 with bipolar disorder and 0.34 with neuroticism) and negative genetic correlations with metabolic phenotypes (e.g., rg = -0.25 with body mass index and -0.20 with triglycerides). Follow-up analyses revealed that although AN and OCD overlap heavily in their shared risk with other psychiatric phenotypes, the relationship with metabolic and anthropometric traits is markedly stronger for AN than for OCD. We further tested whether shared genetic risk for AN/OCD was associated with particular tissue or cell-type gene expression patterns and found that the basal ganglia and medium spiny neurons were most enriched for AN-OCD risk, consistent with neurobiological findings for both disorders. Our results confirm and extend genetic epidemiological findings of shared risk between AN and OCD and suggest that larger GWASs are warranted.

Sequencing of gene-coding regions (the exome) is increasingly used for studying human disease, for which copy-number variants (CNVs) are a critical genetic component. However, detecting copy number from exome sequencing is challenging because of the noncontiguous nature of the captured exons. This is compounded by the complex relationship between read depth and copy number; this results from biases in targeted genomic hybridization, sequence factors such as GC content, and batching of samples during collection and sequencing. We present a statistical tool (exome hidden Markov model [XHMM]) that uses principal-component analysis (PCA) to normalize exome read depth and a hidden Markov model (HMM) to discover exon-resolution CNV and genotype variation across samples. We evaluate performance on 90 schizophrenia trios and 1,017 case-control samples. XHMM detects a median of two rare (<1%) CNVs per individual (one deletion and one duplication) and has 79% sensitivity to similarly rare CNVs overlapping three or more exons discovered with microarrays. With sensitivity similar to state-of-the-art methods, XHMM achieves higher specificity by assigning quality metrics to the CNV calls to filter out bad ones, as well as to statistically genotype the discovered CNV in all individuals, yielding a trio call set with Mendelian-inheritance properties highly consistent with expectation. We also show that XHMM breakpoint quality scores enable researchers to explicitly search for novel classes of structural variation. For example, we apply XHMM to extract those CNVs that are highly likely to disrupt (delete or duplicate) only a portion of a gene.

MIR137 has been identified as a candidate gene for schizophrenia from genome-wide association studies via association with an intronic single nucleotide polymorphism (SNP), rs1625579. The location of the SNP suggests one mechanism in which transcriptional or posttranscriptional regulation of miR-137 expression could underlie schizophrenia. We identified and validated a novel promoter of the MIR137 gene adjacent to miR-137 itself which can direct the expression of distinct mRNA isoforms encoding miR-137. Analysis of both endogenous gene expression and reporter gene assays determined that this internal promoter is regulated by repressor element-1 silencing transcription factor (REST), which has previously been associated with pathways linked to schizophrenia. Distinct isoforms of REST mediate differential expression at this locus, suggesting the relative levels of these isoforms are important for miR-137 expression profiles. The internal promoter contains a variable number tandem repeat (VNTR) domain adjacent to the pre-miR-137 sequence. The reporter gene activity directed by this promoter was modified by the genotype of the VNTR. Differential expression was also observed in response to cocaine, which is known to regulate the REST pathway in SH-SY5Y cells. Our data support the hypothesis that a "gene × environment" interaction could modify the level of miR-137 expression via this internal promoter and that the genotype of the VNTR could modulate transcriptional responses. We demonstrate that this promoter region is not in disequilibrium with rs1625579 and therefore would supply a distinct pathway to potentially alter miR-137 levels in response to environmental cues.

Understanding the consequences of regulatory variation in the human genome remains a major challenge, with important implications for understanding gene regulation and interpreting the many disease-risk variants that fall outside of protein-coding regions. Here, we provide a direct window into the regulatory consequences of genetic variation by sequencing RNA from 922 genotyped individuals. We present a comprehensive description of the distribution of regulatory variation--by the specific expression phenotypes altered, the properties of affected genes, and the genomic characteristics of regulatory variants. We detect variants influencing expression of over ten thousand genes, and through the enhanced resolution offered by RNA-sequencing, for the first time we identify thousands of variants associated with specific phenotypes including splicing and allelic expression. Evaluating the effects of both long-range intra-chromosomal and trans (cross-chromosomal) regulation, we observe modularity in the regulatory network, with three-dimensional chromosomal configuration playing a particular role in regulatory modules within each chromosome. We also observe a significant depletion of regulatory variants affecting central and critical genes, along with a trend of reduced effect sizes as variant frequency increases, providing evidence that purifying selection and buffering have limited the deleterious impact of regulatory variation on the cell. Further, generalizing beyond observed variants, we have analyzed the genomic properties of variants associated with expression and splicing and developed a Bayesian model to predict regulatory consequences of genetic variants, applicable to the interpretation of individual genomes and disease studies. Together, these results represent a critical step toward characterizing the complete landscape of human regulatory variation.

Polygenic risk scores (PRSs) have successfully summarized genome-wide effects of genetic variants in schizophrenia with significant predictive power. In a clinical sample of first-episode psychosis (FEP) patients, we estimated the ability of PRSs to discriminate case-control status and to predict the development of schizophrenia as opposed to other psychoses.

Over 100 genetic loci harbor schizophrenia-associated variants, yet how these variants confer liability is uncertain. The CommonMind Consortium sequenced RNA from dorsolateral prefrontal cortex of people with schizophrenia (N = 258) and control subjects (N = 279), creating a resource of gene expression and its genetic regulation. Using this resource, ∼20% of schizophrenia loci have variants that could contribute to altered gene expression and liability. In five loci, only a single gene was involved: FURIN, TSNARE1, CNTN4, CLCN3 or SNAP91. Altering expression of FURIN, TSNARE1 or CNTN4 changed neurodevelopment in zebrafish; knockdown of FURIN in human neural progenitor cells yielded abnormal migration. Of 693 genes showing significant case-versus-control differential expression, their fold changes were ≤ 1.33, and an independent cohort yielded similar results. Gene co-expression implicates a network relevant for schizophrenia. Our findings show that schizophrenia is polygenic and highlight the utility of this resource for mechanistic interpretations of genetic liability for brain diseases.

Structural variation is an important class of genetic variation in mammals. High-throughput sequencing (HTS) technologies promise to revolutionize copy-number variation (CNV) detection but present substantial analytic challenges. Converging evidence suggests that multiple types of CNV-informative data (e.g. read-depth, read-pair, split-read) need be considered, and that sophisticated methods are needed for more accurate CNV detection. We observed that various sources of experimental biases in HTS confound read-depth estimation, and note that bias correction has not been adequately addressed by existing methods. We present a novel read-depth-based method, GENSENG, which uses a hidden Markov model and negative binomial regression framework to identify regions of discrete copy-number changes while simultaneously accounting for the effects of multiple confounders. Based on extensive calibration using multiple HTS data sets, we conclude that our method outperforms existing read-depth-based CNV detection algorithms. The concept of simultaneous bias correction and CNV detection can serve as a basis for combining read-depth with other types of information such as read-pair or split-read in a single analysis. A user-friendly and computationally efficient implementation of our method is freely available.

Copy number variants (CNVs) at chromosome 16p13.11 have been associated with a range of neurodevelopmental disorders including autism, ADHD, intellectual disability and schizophrenia. Significant sex differences in prevalence, course and severity have been described for a number of these conditions but the biological and environmental factors underlying such sex-specific features remain unclear. We tested the burden and the possible sex-biased effect of CNVs at 16p13.11 in a sample of 10,397 individuals with a range of neurodevelopmental conditions, clinically referred for array comparative genomic hybridisation (aCGH); cases were compared with 11,277 controls. In order to identify candidate phenotype-associated genes, we performed an interval-based analysis and investigated the presence of ohnologs at 16p13.11; finally, we searched the DECIPHER database for previously identified 16p13.11 copy number variants. In the clinical referral series, we identified 46 cases with CNVs of variable size at 16p13.11, including 28 duplications and 18 deletions. Patients were referred for various phenotypes, including developmental delay, autism, speech delay, learning difficulties, behavioural problems, epilepsy, microcephaly and physical dysmorphisms. CNVs at 16p13.11 were also present in 17 controls. Association analysis revealed an excess of CNVs in cases compared with controls (OR = 2.59; p = 0.0005), and a sex-biased effect, with a significant enrichment of CNVs only in the male subgroup of cases (OR = 5.62; p = 0.0002), but not in females (OR = 1.19, p = 0.673). The same pattern of results was also observed in the DECIPHER sample. Interval-based analysis showed a significant enrichment of case CNVs containing interval II (OR = 2.59; p = 0.0005), located in the 0.83 Mb genomic region between 15.49-16.32 Mb, and encompassing the four ohnologs NDE1, MYH11, ABCC1 and ABCC6. Our data confirm that duplications and deletions at 16p13.11 represent incompletely penetrant pathogenic mutations that predispose to a range of neurodevelopmental disorders, and suggest a sex-limited effect on the penetrance of the pathological phenotypes at the 16p13.11 locus.