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The large number of existing databases provides a freely available independent source of information with a considerable potential to increase the likelihood of identifying genes for complex diseases. We developed a flexible framework for integrating such heterogeneous databases into novel large scale genetic studies and implemented the methods in a freely-available, user-friendly R package called MIND. For each marker, MIND computes the posterior probability that the marker has effect in the novel data collection based on the information in all available data. MIND 1) relies on a very general model, 2) is based on the mathematical formulas that provide us with the exact value of the posterior probability, and 3) has good estimation properties because of its very efficient parameterization. For an existing data set, only the ranks of the markers are needed, where ties among the ranks are allowed. Through simulations, cross-validation analyses involving 18 GWAS, and an independent replication study of 6,544 SNPs in 6,298 samples we show that MIND 1) is accurate, 2) outperforms marker selection for follow up studies based on p-values, and 3) identifies effects that would otherwise require replication of over 20 times as many markers.AUTHOR SUMMARYThe large number of existing databases provides a freely available independent source of information with a considerable potential to increase the likelihood of identifying genes for complex diseases. We developed a flexible framework for integrating such heterogeneous databases into novel large scale genetic studies and implemented the methods in a freely-available, user-friendly R package called MIND. For each marker, MIND computes an estimate of the (posterior) probability that the marker has effect in the novel data collection based on the information in all available data. For an existing data set, only the ranks of the markers are needed to be known, where ties among the ranks are allowed. MIND 1) relies on a realistic model that takes confounding effects into account, 2) is based on the mathematical formulas that provide us with the exact value of the posterior probability, and 3) has good estimation properties because of its very efficient parameterization. Simulation, validation, and a replication study in independent samples show that MIND is accurate and greatly outperforms marker selection without using existing data sets.

Let A1,…,An be arbitrary events. The underlying problem is to give lower and upper bounds on the probability P(A1∪⋯∪An) based on \(P(A_{i_{1}}\cap\cdots\cap A_{i_{k}})\), 1≤i1

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-value0.10 (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.

Understanding individual differences in the development of extrapyramidal side effects (EPS) as a response to antipsychotic therapy is essential to individualize treatment.We performed genomewide association studies to search for genetic susceptibility to EPS. Our sample consisted of 738 schizophrenia patients, genotyped for 492K single nucleotide polymorphisms (SNPs). We studied three quantitative measures of antipsychotic adverse drug reactions-the Simpson-Angus Scale (SAS) for Parkinsonism, the Barnes Akathisia Rating Scale, and the Abnormal Involuntary Movement Scale (AIMS)-as well as a clinical diagnosis of probable tardive dyskinesia.Two SNPs for SAS, rs17022444 and rs2126709 with p = 1.2 x 10(-10) and p = 3.8 x 10(-7), respectively, and one for AIMS, rs7669317 with p = 7.7 x 10(-8), reached genomewide significance (Q value.1). rs17022444 and rs7669317 were located in intergenic regions and rs2126709 was located in ZNF202 on 11q24. Fourteen additional signals were potentially interesting (Q value.5). The ZNF202 is a transcriptional repressor controlling, among other genes, PLP1, which is the major protein in myelin. Mutations in PLP1 cause Pelizaeus-Merzbacher disease, which has Parkinsonism as an occurring symptom. Altered mRNA expression of PLP1 is associated with schizophrenia.Although our findings require replication and validation, this study demonstrates the potential of genomewide association studies to discover genes and pathways that mediate adverse effects of antipsychotics.

Motivation: During the past decade, we have seen an exponential growth of vast amounts of genetic data generated for complex disease studies. Currently, across a variety of complex biological problems, there is a strong trend towards the integration of data from multiple sources. So far, candidate gene prioritization approaches have been designed for specific purposes, by utilizing only some of the available sources of genetic studies, or by using a simple weight scheme. Specifically to psychiatric disorders, there has been no prioritization approach that fully utilizes all major sources of experimental data. Results: Here we present a multi-dimensional evidence-based candidate gene prioritization approach for complex diseases and demonstrate it in schizophrenia. In this approach, we first collect and curate genetic studies for schizophrenia from four major categories: association studies, linkage analyses, gene expression and literature search. Genes in these data sets are initially scored by category-specific scoring methods. Then, an optimal weight matrix is searched by a two-step procedure (core genes and unbiased P-values in independent genome-wide association studies). Finally, genes are prioritized by their combined scores using the optimal weight matrix. Our evaluation suggests this approach generates prioritized candidate genes that are promising for further analysis or replication. The approach can be applied to other complex diseases. Availability: The collected data, prioritized candidate genes, and gene prioritization tools are freely available at http://bioinfo.mc.vanderbilt.edu/SZGR/. Contact: zhongming.zhao@vanderbilt.edu Supplementary information: Supplementary data are available at Bioinformatics online.

There is an increasing interest in the use of two-stage case-control studies to reduce genotyping costs in the search for genes underlying common disorders. Instead of analyzing the data from the second stage separately, a more powerful test can be performed by combining the data from both stages. However, standard tests cannot be used because only the markers that are significant in the first stage are selected for the second stage and the test statistics at both stages are dependent because they partly involve the same data. Theoretical approximations are not available for commonly used test statistics and in this specific context simulations can be problematic because of the computational burden. We therefore derived a cost-effective, that is, accurate but fast in terms of central processing unit (CPU) time, approximation for the distribution of Pearson's statistic on 2 xm contingency tables in two-stage design with combined data. We included this approximation in an iterative method for designing optimal two-stage studies. Simulations supported the accuracy of our approximation. Numerical results confirmed that the use of two-stage designs reduces the genotyping burden substantially. Compared to not combining data, combining the data decreases the required sample sizes on average by 15% and the genotyping burden by 5%.

Abnormalities in the gamma-aminobutyric acid (GABA) neurotransmitter system have been noted in subjects with mood and anxiety disorders. Glutamic acid decarboxylase (GAD) enzymes synthesize GABA from glutamate, and, thus, are reasonable candidate susceptibility genes for these conditions. In this study, we examined the GAD1 and GAD2 genes for their association with genetic risk across a range of internalizing disorders. We used multivariate structural equation modeling to identify common genetic risk factors for major depression, generalized anxiety disorder, panic disorder, agoraphobia, social phobia and neuroticism (N) in a sample of 9270 adult subjects from the population-based Virginia Adult Twin Study of Psychiatric and Substance Use Disorders. One member from each twin pair for whom DNA was available was selected as a case or control based on scoring at the extremes of the genetic factor extracted from the analysis. The resulting sample of 589 cases and 539 controls was entered into a two-stage association study in which candidate loci were screened in stage 1, the positive results of which were tested for replication in stage 2. Several of the six single-nucleotide polymorphisms tested in the GAD1 region demonstrated significant association in both stages, and a combined analysis in all 1128 subjects indicated that they formed a common high-risk haplotype that was significantly over-represented in cases (P=0.003) with effect size OR=1.23. Out of 14 GAD2 markers screened in stage 1, only one met the threshold criteria for follow-up in stage 2. This marker, plus three others that formed significant haplotype combinations in stage 1, did not replicate their association with the phenotype in stage 2. Subject to confirmation in an independent sample, our study suggests that variations in the GAD1 gene may contribute to individual differences in N and impact susceptibility across a range of anxiety disorders and major depression.

The aim of this article was to derive an approximation of the distribution of Pearson's statistic for 2 x m contingency tables to calculate power for case-control studies accurately and efficiently in terms of computer time. We first prove that, rather than a non-central chi-square distribution, Pearson's statistic is asymptotically equivalent with the sum of squares of m independent normal random variables whose variances are typically different under the alternative hypothesis. Based on this asymptotically equivalent (AE) we derive a cost-effective approximation (CE). Numerical results show that CE was almost as precise as AE, but computationally more efficient. Although somewhat less costly in terms of CPU time, we show that a commonly used approximation using the non-central chi-square distribution can be very inaccurate and overestimate power in some scenarios and underestimate it in others. Simulations and exact distributions, on the other hand, are accurate but computationally very intensive compared to our CE. The CE reached its lowest accuracy under the null hypothesis where it has a chi-square distribution with m - 1 degree of freedom. This suggests that CE can be used to calculate power for tables where researchers would feel comfortable using Pearson's chi-square test.

The concept of (h, m) -hypermultitree (which is a special hypergraph) is introduced to present Bonferroni-type inequalities which are equalities for some families of sets. The main theorem is a common generalization of the earlier results of Hunter, Worsley, Tomescu and recent results of Prekopa and the author. The new bounds are based on significantly fewer probabilities than the same order Bonferroni bounds. This and some other properties explain the very high efficiency of the new bounds in many applications, e.g., estimate the values of multivariate normal distribution functions, which is demonstrated in the end of the paper. Mathematics subject classification (2000): 60C05.

In this article new lower and upper bounds are given for the probability of the union of events. For this purpose the new concept of hypercherry trees has been introduced. Earlier the concept of cherry tree and its application for bounding the probability of union of events was introduced by Bukszar and Prekopa. This, based on the cherry tree bound, is always an upper bound, and it can be regarded as a generalisation of the upper bound introduced by Hunter by means of maximum weight spanning trees. Later the Hunter bound was generalised by Tomescu. He used the concept of hypertrees in the framework of uniform hypergraphs and on the basis of these new hypergraph structures it became possible to define not only upper but also lower bounds on the probability of union of events. The new bounds of the paper are generalisations of Tomescu's bounds in the same sense as the upper bound by Bukszar and Prekopa was a generalisation of the Hunter bound. The efficiency of the new bounds is illustrated on some test pro...

A third order upper bound is presented on the probability of the union of a finite number of events, by means of graphs called cherry trees. These are graphs that we construct recursively in such a way that every time we pick a new vertex, connect it with two already existing vertices. If the latters are always adjacent, we call the cherry tree a t-cherry tree. A cherry tree has a weight that provides us with the upper bound on the union. Any Hunter-Worsley bound can be majorized by a t-cherry bound constructed by the use of the Hunter-Worsley tree. A cherry tree bound can be identified as a feasible solution to the dual of the Boolean probability bounding problem. A t-cherry tree bound can be identified as the objective function value of the dual vector corresponding to a dual feasible basis in the Boolean problem. This enables us to improve on the bound algorithmically, if we use the dual method of linear programming.

The problem of finding bounds for P(A1 ∪ ⋯ ∪ An) based on P(Ak1 ∩ ⋯ ∩ Aki) (1 ≤ k1 < ⋯ < ki ≤ n, i = 1,…,d) goes back to Boole (1854), (1868) and Bonferroni (1937). In this paper upper bounds are presented using methods in graph theory. The main theorem is a common generalization of the earlier results of Hunter, Worsley and recent results of Prékopa and the author. Algorithms are given to compute bounds. Examples for bounding values of multivariate normal distribution functions are presented.

Antidepressant response is likely influenced by genetic constitution, but the actual genes involved have yet to be determined. We have carried out a genomewide association study to determine whether common DNA variation influences antidepressant response.Our sample is derived from Level 1 participants in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study, all treated with citalopram. Association for the response phenotype included 883 responders and 608 nonresponders. For the remission phenotype, 743 subjects that achieved remission were compared with 608 nonresponders. We used a subset of single nucleotide polymorphisms (SNPs; n = 430,198) from the Affymetrix 500K and 5.0 Human SNP Arrays, and association analysis was carried out after correcting for population stratification.We identified three SNPs associated with response with p values less than 1 x 10(-5) near the UBE3C gene (rs6966038, p = 4.65 x 10(-7)), another 100 kb away from BMP7 (rs6127921, p = 3.45 x 10(-6)), and a third that is intronic in the RORA gene (rs809736, p = 8.19 x 10(-6)). These same SNPs were also associated with remission. Thirty-nine additional SNPs are of interest with p valuesor = .0001 for the response and remission phenotypes.Although the findings reported here do not meet a genomewide threshold for significance, the regions identified from this study provide targets for independent replication and novel pathways to investigate mechanisms of antidepressant response. This study was not placebo controlled, making it possible that we are also observing associations to nonspecific aspects of drug treatment of depression.

Objective—Examine the unique and congruent findings between multiple raters in a genomewide association studies (GWAS) in the context of understanding individual differences in treatment response during antipsychotic therapy for schizophrenia. Methods—We performed GWAS to search for genetic variation affecting treatment response. The analysis sample consisted of 738 patients with schizophrenia, successfully genotyped for ~492K SNPs from the Clinical Antipsychotic Trial of Intervention Effectiveness (CATIE). Outcomes included both clinician and patient report of illness severity on global impression scales, the CGI-S and PGI, 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 −8 , p =1.6×10 −7 , p =1.8×10 −7 ), mediated the effects of quetiapine on patient reported severity and an additional three SNPs in TJP1, 15q13.1 (p = 2.25×10 − 7, p = 4.86×10 −7 , p = 4.91×10 −7 ), mediated the effects of risperidone on patient reported severity. For clinician reported severity, two SNPs in PPA2, 4q24 (p = 3.68×10 −7 , p = 5.05×10 −7 ), were found to reach genome-wide significance. Conclusion—We found evidence of both novel and 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 demonstrates the potential of GWAS to discover genes that potentially mediate treatment response of antipsychotic medication.

Neurocognitive deficits are a core feature of schizophrenia and, therefore, represent potentially critical outcome variables for assessing antipsychotic treatment response. We performed genome-wide association studies (GWAS) with 492K single nucleotide polymorphisms (SNPs) in a sample of 738 patients with schizophrenia from the Clinical Antipsychotic Trials of Intervention Effectiveness study. Outcome variables consisted of a neurocognitive battery administered at multiple time points over an 18-month period, measuring processing speed, verbal memory, vigilance, reasoning, and working memory domains. Genetic mediation of improvements in each of these five domains plus a composite neurocognitive measure was assessed for each of five antipsychotics (olanzapine, perphenazine, quetiapine, risperidone, and ziprasidone). Six SNPs achieved genome-wide significance using a pre-specified threshold that ensures, on average, only 1 in 10 findings is a false discovery. These six SNPs were located within, or in close proximity to, genes EHF, SLC26A9, DRD2, GPR137B, CHST8, and IL1A. The more robust findings, that is those significant across multiple neurocognitive domains and having adjacent SNPs showing evidence for association, were rs286913 at the EHF gene (p-value 6.99 × 10(-8), q-value 0.034, mediating the effects of ziprasidone on vigilance), rs11240594 at SLC26A9 (p-value 1.4 × 10(-7), q-value 0.068, mediating the effects of olanzapine on processing speed), and rs11677416 at IL1A (p-value 6.67 × 10(-7), q-value 0.081, mediating the effects of olanzapine on working memory). This study has generated several novel candidate genes for antipsychotic response. However, our findings will require replication and functional validation. To facilitate replication efforts, we provide all GWAS p-values for download.

Understanding individual differences in the susceptibility to metabolic side effects as a response to antipsychotic therapy is essential to optimize the treatment of schizophrenia. Here, we perform genomewide association studies (GWAS) to search for genetic variation affecting the susceptibility to metabolic side effects. The analysis sample consisted of 738 schizophrenia patients, successfully genotyped for 492K single nucleotide polymorphisms (SNPs), from the genomic subsample of the Clinical Antipsychotic Trial of Intervention Effectiveness study. Outcomes included 12 indicators of metabolic side effects, quantifying antipsychotic-induced change in weight, blood lipids, glucose and hemoglobin A1c, blood pressure and heart rate. Our criterion for genomewide significance was a pre-specified threshold that ensures, on average, only 10% of the significant findings are false discoveries. A total of 21 SNPs satisfied this criterion. The top finding indicated that a SNP in Meis homeobox 2 (MEIS2) mediated the effects of risperidone on hip circumference (q=0.004). The same SNP was also found to mediate risperidone's effect on waist circumference (q=0.055). Genomewide significant finding were also found for SNPs in PRKAR2B, GPR98, FHOD3, RNF144A, ASTN2, SOX5 and ATF7IP2, as well as in several intergenic markers. PRKAR2B and MEIS2 both have previous research indicating metabolic involvement, and PRKAR2B has previously been shown to mediate antipsychotic response. Although our findings require replication and functional validation, this study shows the potential of GWAS to discover genes and pathways that potentially mediate adverse effects of antipsychotic medication.

Motivation: A limitation of current methods used to declare significance in genome-wide association studies (GWAS) is that they do not provide clear information about the probability that GWAS findings are true of false. This lack of information increases the chance of false discoveries and may result in real effects being missed. Results: We propose a method to estimate the posterior probability that a marker has (no) effect given its test statistic value, also called the local false discovery rate (FDR), in the GWAS. A critical step involves the estimation the parameters of the distribution of the true alternative tests. For this, we derived and implemented the real maximum likelihood function, which turned out to provide us with significantly more accurate estimates than the widely used mixture model likelihood. Actual GWAS data are used to illustrate properties of the posterior probability estimates empirically. In addition to evaluating individual markers, a variety of applications are conceivable. For instance, posterior probability estimates can be used to control the FDR more precisely than Benjamini–Hochberg procedure. Availability: The codes are freely downloadable from the web site http://www.people.vcu.edu/∼jbukszar. Contact: jbukszar@vcu.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Context Neuroticism is a trait that reflects a tendency toward negative mood states. It has long been linked to internalizing psychiatric conditions, such as anxiety and depression, and it accounts for much of the substantial comorbidity seen between these disorders. Objective To identify common genetic variants that affect neuroticism to better understand (the comorbidity between) a broad range of psychiatric disorders and to develop effective treatments. Design, Setting, and Participants More than 420 000 genetic markers were tested for their association with neuroticism in a genomewide association study (GWAS). The GWAS sample consisted of 1227 healthy individuals ascertained from a US national sampling frame and available from the National Institute of Mental Health genetics repository. The most promising markers were subsequently tested in a German replication sample comprising 1880 healthy individuals. Main Outcome Measures A strict definition of replication (same marker, same direction of effects, and same measure) combined with a threshold we proposed previously for declaring significance in genetic studies that ensures a mean probability of producing false-positive findings of less than 10%. Results The most promising results in the GWAS and replication samples were single-nucleotide polymorphisms (SNPs) in the gene MAMDC1 . These SNPs all tagged the same 2 haplotypes and had P values of 10 −5 to 10 −6 in the GWAS sample and of .006 to .02 in the replication sample. Furthermore, the replication involved the same SNPs and the same direction of effects. In a combined analysis of all data, several SNPs were significant according to the threshold that allows for 10% false-positive findings. Conclusions The small effect sizes may limit the prognostic, diagnostic, and therapeutic use of SNP markers such as those in MAMDC1 . However, the present study demonstrates the potential of a GWAS to discover potentially important pathogenic pathways for which clinically more powerful (bio)markers may eventually be developed.

Knowledge about the proportion of markers without effects (p( 0 )) and the effect sizes in large scale genetic studies is important to understand the basic properties of the data and for applications such as the control of false discoveries and designing adequately powered replication studies. Many p(0) estimators have been proposed. However, high dimensional data sets typically comprise a large range of effect sizes and it is unclear whether the estimated p(0) is related to the whole range, including markers with very small effects, or just the markers with large effects. In this article we develop an estimation procedure that can be used in all scenarios where the test statistic distribution under the alternative can be characterized by a single parameter (e.g. non-centrality parameter of the non-central chi-square or F distribution). The estimation procedure starts with estimating the largest effect in the data set, then the second largest effect, then the third largest effect, etc. We stop when the effect sizes become so small that they cannot be estimated precisely anymore for the given sample size. Once the individual effect sizes are estimated, they can be used to calculate an interpretable estimate of p(0). Thus, our method results in both an interpretable estimate of p(0) as well as estimates of the effect sizes present in the whole marker set by repeatedly estimating a single parameter. Simulations suggest that the effects are estimated precisely with only a small upward bias. The R codes that compute the effect estimates are freely downloadable from the website: http://www.people.vcu.edu/~jbukszar/.

It has recently become possible to screen thousands of markers to detect genetic causes of common diseases. Along with this potential comes analytical challenges, and it is important to develop new statistical tools to identify markers with causal effects and accurately estimate their effect sizes. Knowledge of the proportion of markers without true effects (p0) and the effect sizes of markers with effects provides information to control for false discoveries and to design follow-up studies. We apply newly developed methods to simulated Genetic Analysis Workshop 15 genome-wide case-control data sets, including a maximum likelihood (ML) and a quasi-ML (QML) approach that incorporate the test statistic distribution and estimates effect size simultaneously with p0, and two conservative estimators of p0 that do not rely on the test statistic distribution under the alternative. Compared with four existing commonly used estimators for p0, our results illustrated that all of our estimators have favorable properties in terms of the standard deviation with which p0 is estimated. On average, the ML method performed slightly better than the QML method; the conservative method performed well and was even slightly more precise than the ML estimators, and can be more robust in less optimal conditions (small sample sizes and small number of markers). Further improvements and extensions of the proposed methods are conceivable, such as estimating the distribution of effect sizes and taking population stratification into account when obtain estimates of p0 and effect size.

Background Catechol- O -methyltransferase (COMT) has been investigated for its possible role in a wide range of psychiatric phenotypes. In particular, several studies support association of this gene with panic disorder and other anxiety-related traits. Methods We examined the COMT gene for association with genetic risk across a range of anxiety spectrum phenotypes. We used multivariate structural equation modeling to select twin pairs scoring at the extremes of a latent genetic risk factor shared by neuroticism, several anxiety disorders, and major depression from a large population-based twin sample. With one member from each of these pairs, the resulting sample of 589 cases and 539 control subjects were entered into a two-stage association study in which genetic markers were screened in stage 1, the positive results of which were tested for replication in stage 2. Results The functional val158met polymorphism (rs4680) plus nine other single nucleotide polymorphism markers selected to capture the major allelic variation across the COMT locus were analyzed for differences between cases and control subjects. Although the val (G) allele of rs4680 showed marginally significant association in our combined stage 1 plus stage 2 sample, a high-risk haplotype of this allele with the A allele of rs165599 was significantly over-represented in cases ( p = 1.97e-5, odds ratio=1.95). This haplotype also predicted individual differences in neuroticism and risk for several anxiety disorders and major depression. Consistent with prior studies, our findings are female-specific. Conclusions Variations in the COMT gene contribute to genetic risk shared across a range of anxiety-related phenotypes.

Considerable controversy exists concerning the positive and negative syndrome scale (PANSS), one of the most widely used instruments in schizophrenia research. In this article we revisited the factor structure and external validity of the PANSS in a sample of 500 participants with DSM IV diagnoses of schizophrenia. We found that a model with six latent factors provided a relatively good fit, considered adequate by two rules of thumb. Five factors corresponded closely to those typically derived in other studies: Negative, Positive, Excited/Activation, Anxious-Depressed/Dysphoric, and Disorganized/Autistic preoccupation. The sixth factor seemed to have face validity and was labeled Withdrawn. With the exception of Anxious-Depressed/Dysphoric, Cronbach's Alpha ranged from 0.70 to 0.85 suggesting an acceptable internal consistency. External validity was studied through correlations with socio-demographic variables, DSM IV (subtype) diagnoses, clinical characteristics, and drug use. The many significant correlations suggested that the six PANSS scales measure meaningful aspects of schizophrenia. Furthermore, the pattern of correlations varied, providing evidence that the scales assessed partly different aspects of the disease. Our analyses also suggested that some of the controversy about the PANSS can possibly be attributed to methodological factors where the substantial cross-loadings of some PANSS items may play an important role.

Daniel E. Adkinsa,*,†, Amit N. Khachanea,†, Joseph L. McClaya, Karolina Aberga, Jozsef Bukszara, Patrick F. Sullivanb,c, and Edwin J.C.G. van den Oorda aCenter for Biomarker Research and Personalized Medicine, School of Pharmacy, Virginia Commonwealth University, Richmond VA, USA bDepartments of Genetics, Psychiatry, & Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill NC, USA cDepartment of Medical Epidemiology & Biostatistics, Karolinska Institutet, Stockholm, Sweden

Lower and upper bounds on the probability of the union of some events are presented. The bounds are based on special hypergraphs called (h, m,)-hyper-multitrees. A new probability bounding technique based on the bounds is described and it is applied to estimate the value of multivariate normal distribution functions.

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.To identify SCZ susceptibility genes.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.Linkage meta-analysis, brain transcriptome meta-analysis, candidate gene database, OMIM, relevant mouse studies, and expression quantitative trait locus databases.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.Case-control status for SCZ.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, P2.20 x 10(-16) and 93% in subjects of European ancestry only (P2.2010(-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).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.

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 (q0.1), ensuring that, on average, only 10% of significant findings are false discoveries. In total, 12 additional SNPs demonstrated suggestive associations (q0.5). 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 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.