Bioinformatic analyses and conceptual synthesis of evidence linking ZNF804A to risk for schizophrenia and bipolar disorder.

REVIEW ARTICLE Neuropsychiatric Genetics

Bioinformatic Analyses and Conceptual Synthesis of Evidence Linking ZNF804A to Risk for Schizophrenia and Bipolar Disorder Jonathan L. Hess,1 Thomas P. Quinn,1 Schahram Akbarian,2 and Stephen J. Glatt1* 1

Psychiatric Genetic Epidemiology & Neurobiology Laboratory (PsychGENe Lab), Departments of Psychiatry and Behavioral Sciences and Neuroscience and Physiology, SUNY Upstate Medical University, New York City, New York 2

Department of Psychiatry, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York City, New York

Manuscript Received: 15 May 2014; Manuscript Accepted: 14 November 2014

Advances in molecular genetics, fueled by the results of largescale genome-wide association studies, meta-analyses, and mega-analyses, have provided the means of identifying genetic risk factors for human disease, thereby enriching our understanding of the functionality of the genome in the post-genomic era. In the past half-decade, research on neuropsychiatric disorders has reached an important milestone: the identification of susceptibility genes reliably associated with complex psychiatric disorders at genome-wide levels of significance. This age of discovery provides the groundwork for follow-up studies designed to elucidate the mechanism(s) by which genetic variants confer susceptibility to these disorders. The gene encoding zinc-finger protein 804 A (ZNF804A) is among these candidate genes, recently being found to be strongly associated with schizophrenia and bipolar disorder via one of its non-coding mutations, rs1344706. Neurobiological, molecular, and bioinformatic analyses have improved our understanding of ZNF804A in general and this variant in particular; however, more work is needed to establish the mechanism(s) by which ZNF804A variants impinge on the biological substrates of the two disorders. Here, we review literature recently published on ZNF804A, and analyze critical concepts related to the biology of ZNF804A and the role of rs1344706 in schizophrenia and bipolar disorder. We synthesize the results of new bioinformatic analyses of ZNF804A with key elements of the existing literature and knowledge base. Furthermore, we suggest some potentially fruitful short- and long-term research goals in the assessment of ZNF804A. Ó 2014 Wiley Periodicals, Inc.

Key words: alternative splicing; bioinformatics; bipolar disorder; epigenetics; microarray; protein model; RNAsequencing; rs1344706; schizophrenia; ZNF804A

INTRODUCTION Schizophrenia (SZ) and bipolar disorder (BD) are debilitating psychiatric disorders with a worldwide prevalence of approximately 1%. The preponderance of evidence supports a neurodevelopmental origin of these disorders, which have a typical onset in late

Ó 2014 Wiley Periodicals, Inc.

How to Cite this Article: Hess JL, Quinn TP, Akbarian S, Glatt SJ. 2015. Bioinformatic Analyses and Conceptual Synthesis of Evidence Linking ZNF804A to Risk for Schizophrenia and Bipolar Disorder. Am J Med Genet Part B 168B:14–35.

adolescence to early adulthood. Although these disorders are reliably classified by their core symptoms and are distinguishable from other psychiatric disorders with similar features (i.e., schizoaffective disorder, brief psychotic disorder, etc.) or subclinical manifestations (i.e., schizotaxia [Tsuang et al., 2005]), much remains to be understood regarding the genetic, environmental, and biological risk factors for SZ and BD. Advances in genome-wide association studies (GWASs), metaanalyses, mega-analyses, and high-throughput sequencing and expression assays have helped uncover numerous genetic risk factors and biological pathways associated with SZ and BD. In a large-scale GWAS of SZ cases and non-mentally ill (NMI) control subjects, Ripke and colleagues (2013) estimated that 8,300 common single nucleotide polymorphisms (SNPs) in the genome account for 32% of the genetic liability for SZ (case n ¼ 21,246; control n ¼ 38,072). This most recent estimate for SZ liability explained by common SNPs is a considerable increase from the prior year’s Grant sponsor: Gerber Foundation; Grant sponsor: The Sidney R. Baer, Jr., Foundation; Grant sponsor: NARSAD: The Brain and Behavior Research Foundation; Grant sponsor: The U.S. National Institutes of Health. Correspondence to: Stephen J. Glatt, Ph.D., SUNY Upstate Medical University, 750 East Adams Street, Syracuse, NY, 13210. E-mail: [email protected] Article first published online in Wiley Online Library (wileyonlinelibrary.com) DOI 10.1002/ajmg.b.32284

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HESS ET AL. estimate by Lee and colleagues [2012a] of 23% (case n ¼ 9,087; control n ¼ 12,171)–potentially explained by the approximate three-fold increase in sample size between these studies. A recent estimate for BD was on par with SZ, with estimated heritability from common variants standing at 25% [Lee et al., 2013]. Together, these SNP-based estimates of genetic liability account for a substantial portion of the overall heritability of these disorders estimated from twin- and family-based studies (approximately 81% for SZ and 75% for BD) [Lee et al., 2013]. Functional genomic studies of SZ- and BD-associated variants, and the genes harboring them, promote an understanding of the biological impact of these variants in the context of pathophysiological pathways, and may help identify biological substrates to target pharmacologically. One such potential target is the putative transcription factor zinc finger protein 804 A (encoded by ZNF804A), which has garnered much attention in recent years given its reliable genetic association with SZ (P ¼ 1.61 107; odds ratio ¼ 1.12)[O’Donovan et al., 2008], and with SZ and BD in a combined sample (P ¼ 9.96 108; odds ratio ¼ 1.12)[O’Donovan et al., 2008]. The strongest association with these disorders among European subjects was observed for rs1344706, an intron 2 SNP (risk allele: A, forward strand). The association of the rs1344706 risk SNP with SZ has now been replicated beyond samples of European ancestry [Schwab et al., 2013] though inconsistent results have been observed in studies involving subjects of Han Chinese origin [Zhang et al., 2012; Li et al., 2013; Yang et al., 2013]. Since the initial genetic association studies implicating ZNF804A in SZ and BD, follow-up investigations have explored the functional impact of the alternate rs1344706 alleles on brain morphology through structural neuroimaging [Rasetti et al., 2011; Cousijn et al., 2012; Bergmann et al., 2013; Schultz et al., 2014], on cognition and working memory [Walters et al., 2010; Esslinger et al., 2011; Voineskos et al., 2011], and on expression of the ZNF804A transcript itself [Riley et al., 2010; Williams et al., 2011; Hill and Bray, 2012; Schultz et al., 2014]. ZNF804A has also been investigated for its impact on the downstream transcriptome using quantitative real time polymerase chain reaction (QRT–PCR) [Girgenti et al., 2012] and microarray-based assays [Hill et al., 2012; Umeda-Yano et al., 2013] of cells in culture. Research into ZNF804A has provided insights into the functional impact of this gene and the effect of its risk-conferring SNPs, though much remains to be known, including: i) how the riskassociated variant affects the constitution of the various ZNF804A transcripts and protein isoforms; ii) the various stages involved in regulating expression of ZNF804A in the brain; iii) the regulatory networks affected by each ZNF804A isoform in the brain (including potentially novel isoforms) and if these networks can be implicated in SZ and BD. In the present review, we provide a critical analysis of ZNF804A literature that has been published since our prior review [Hess and Glatt, 2014], and present data from novel bioinformatic analyses examining various features of ZNF804A genetics and biology. The novel analyses that we contributed to this review include: i) targeted co-expression analyses of genes implicated in the ZNF804A biological network; ii) an investigation of epigenetic modifications associated with the rs1344706 site; iii) a blood-brain comparison of ZNF804A exon expression from human exon-array data; iv) an analysis of ZNF804A epistasis through a pathway-based approach; v) estimates of ZNF804A isoform expression from next-generation

15 sequencing of mRNA; and vi) novel protein models of ZNF804A. We also propose new hypotheses based on a synthesis of our bioinformatic analyses of multiple facets of ZNF804A biology, which in turn allow us to suggest potential approaches for follow-up experimental studies. Through these collective activities, we aim to advance the fundamental understanding of ZNF804A and its risk-associated SNPs.

LITERATURE REVIEW Recently, we summarized genetic association studies and early functional analyses of ZNF804A extending from 2008 to early 2013, coupled with our preliminary bioinformatics analyses evaluating the impact of rs1344706 [Hess and Glatt, 2014]. Here, we provide an update on recent progress since the completion of that review. Our objective is to cohesively summarize the most recent findings in ZNF804A research, and, in turn, evaluate how these findings coalesce with the bigger picture linking the gene to risk for SZ and BD.

Replication of Association of SZ with rs1344706 Several studies recently re-examined the link between rs1344706 and SZ inpatients of Han Chinese descent through meta-analyses and contingency-based tests of allelic association. In a comprehensive meta-analysis of all available case-control GWAS data from subjects of Han Chinese descent (case n ¼ 8,982; control n ¼ 12,342), Li et al. [2013] reported no significant association of rs1344706 with SZ. In contrast, additional results have recently emerged to support an association of the rs1344706 risk allele among SZ patients of this ancestry [Yang et al., 2013]. In this new study by Yang et al. [2013] the authors detected a significant association of rs1344706(A) among SZ patients of Han Chinese descent (P ¼ 0.049; odds ratio ¼ 1.12, n ¼ 1,025) compared to healthy controls (n ¼ 975); however, no difference was found between SZ subtypes and healthy controls for rs1344706 genotype or allele frequencies [Yang et al., 2013]. Recent genome-wide analyses conducted by the Psychiatric Genomics Consortium (PGC) reported association signals with SZ for other variants near or within ZNF804A. The first report identified association of SZ with rs4380187 (7.7 kb from 30 end of ZNF804A) (P ¼ 5.66 108, OR ¼ 1.078, SE ¼ 0.014) in a sample of 21,246 cases and 38,072 controls. An expansion of the first study documented an association of SZ with rs11693094 (P ¼ 1.53 1012, OR ¼ 0.929, 95% CI: 0.910–0.948) in a sample of 38,131 cases and 114,674 controls. rs11693094 maps to intron 1 of ZNF804A and is in relatively tight linkage disequilibrium with rs1344706 in the European population based on Phase I of the 1,000 Genomes project (r2 ¼ 0.69, D0 ¼ 0.89).

rs1344706 Mediates ZNF804A Expression and Brain Morphology Two separate studies were published in 2013 with contrary findings regarding the molecular effects rs1344706 and the consequence of

16 the risk allele on morphometric features of the brain. The first study modeled the relationship of rs1344706 genotype with cortical thickness measured in patients diagnosed with SZ (n ¼ 82), BD (n ¼ 85) or psychosis not otherwise specified (NOS) (n ¼ 46) and NMI controls (n ¼ 152) using a generalized linear regression. No effect of rs1344706 on cortical thickness was revealed from live-brain imaging of participants with or without a psychiatric diagnosis [Bergmann et al., 2013]. Conversely, a separate study by Schultz et al. [2014] found that patients homozygous for the risk allele (n ¼ 20) at rs1344706 and controls homozygous for the risk allele (n ¼ 15) display opposite patterns of cortical thickness and folding in live-brain imaging, and ZNF804A expression from postmortem brain tissue [Schultz et al., 2014]. In fact, the authors suggested that rs1344706 was conferring a protective effect against aberrant brain morphologies in SZ patients; consequently, rs1344706 appeared as deleterious in healthy controls [Schultz et al., 2014]. Specifically, patients homozygous for the risk allele exhibited increased cortical thickness and preserved cortical folding inconsistent with typical deficits observed in SZ [Schultz et al., 2014]. Previous findings suggested that controls with the risk allele at rs1344706 (n ¼ 32) displayed increased ZNF804A expression in the prefrontal cortex (PFC) compared to controls with the reference allele in postmortem tissue (n ¼ 28) [Riley et al., 2010], a feature expected to generalize to SZ patients. However, Schultz et al. [2014] observed lower expression of ZNF804A in the PFC of patients with the risk allele (n ¼ 39) compared to those with the reference allele (n ¼ 25). More recently, increased ZNF804A expression was observed among psychiatric patients (SZ, BD, and major depressive disorder [MDD]) and controls heterozygous for rs1344706(A) (n ¼ 27) compared to homozygous subjects (n ¼ 19) [Guella et al., 2014]; this effect was replicated in a follow-up study that again compared allele-specific expression of a pooling of healthy controls and both patient groups heterozygous for rs1344706 (n ¼ 27) to rs1344706 homozygotes (n ¼ 17) [Guella and Vawter, 2014]. Note, the allele-specific effects reported by Riley et al. [2010], Guella et al. [2013], and Schultz et al. [2014] were observed only in the postmortem PFC. Given inconsistent reported effects of rs1344706 on ZNF804A expression, it is unlikely for allele-specific effects of this SNP on ZNF804A expression to be static across the human brain. Differences in the effects of variants embedded in cis-regulatory elements harbored by ZNF804A are expected in various cell types. An earlier report summarized differences in cis-regulatory elements contained in susceptibility genes for neuropsychiatric disorders, including ZNF804A [Buonocore et al., 2010]. The authors observed varying cis-regulatory effects in different regions of the human postmortem brain for subjects heterozygous at rs12476147, an exon 4 SNP in linkage disequilibrium with the non-coding rs1344706 SNP in the European ancestry (r2 ¼ 0.36, D0 ¼ 0.97) (HapMap v3, release 2). Spatiotemporal variations of cis-regulatory effects of such variants can be explained, in part, by the cross-tissue dissimilarity in the availability of regulatory factors (i.e., transcription factors, microRNAs, and splicing factors) that interact with cis-regulatory elements. In light of these considerations, Hill et al. evaluated allele-specific effects of rs1344706 on ZNF804A expression in postmortem fetal brain tissue, and noted significant decreases in ZNF804A expression associated with increased number of risk alleles [Hill and Bray, 2012].

AMERICAN JOURNAL OF MEDICAL GENETICS PART B Together, the findings from Schultz et al. [2014] and Hill et al. [2012] suggest the effects of rs1344706(A) on ZNF804A expression are consistent in the brain of second-trimester fetuses and adults. Note, no significant variation in rs1344706 allelic expression was observed in the first-trimester fetal brain [Hill and Bray, 2012]. Considering that opposite effects of rs1344706(A) on ZNF804A expression were observed in separate studies of the adult human brain [Riley et al., 2010; Guella et al., 2014], it is possible for cis-mediated effects of rs1344706(A) on ZNF804A expression to be inconsistent within larger groups due to dissimilar availability of regulatory factors, unmeasured haplotype effects or SNP–SNP interactions, or a case of rs1344706 displaying incomplete penetrance. An overarching caveat across the past studies of ZNF804A expression in the human postmortem brain [Riley et al., 2010; Williams et al., 2011; Hill and Bray, 2012; Guella et al., 2014; Schultz et al., 2014], is the uncertainty regarding the ZNF804A isoforms expressed, and whether rs1344706 mediates expression of particular isoforms. Currently, there are three alternative transcripts of ZNF804A annotated in the AceView database (https://www.ncbi. nlm.nih.gov/IEB/Research/Acembly/), among several other expected ZNF804A transcripts that have yet to be validated [Hess and Glatt, 2014]. We discuss these concepts at length in following sections, and highlight other uncertainties of ZNF804A requiring further investigation. New evidence has emerged in which the question of rs1344706(A) on ZNF804A isoform expression in the brain was address. A study by[Tao et al. 2014]reported expression of a novel isoform of ZNF804A in human postmortem brain. The authors profiled ZNF804A expression in the dorsolateral prefrontal cortex (DLPFC) throughout lifespan, and, described a novel ZNF804A isoform encoding only exon 3 and exon 4 referred to as ZNF804AE3E4; these observations were first identified by RNA-seq then validated by quantitative PCR. It was indicated that ZNF804AE3E4 is preferentially expressed at all ages and that ZNF804A isoforms were highest in expression during fetal ages [Tao et al., 2014], which is similar to our previous assessment of lifespan ZNF804A expression [Hess and Glatt, 2014]. Furthermore, lower expression of the ZNF804AE3E4 was associated with relevant features including: SZ diagnosis; the rs1344706 risk allele in fetal samples; and, BD patients with the rs1344706(AA) genotype [Tao et al., 2014]. A second caveat of the model suggested by Schultz et al. [2014] is the limited understanding of the consequence of rs1344706 in BD. Although a cross-disorder association of rs1344706 to SZ and BD is well supported [O’Donovan et al., 2008], evaluation of the effects of this variant on the expression of ZNF804A in the brain of BD patients, as well as its potential interaction with brain morphology in BD patients, is limited. As demonstrated by Schultz et al. [2014] it may not be appropriate to generalize observed effects across diagnostic boundaries. With this relationship in mind, it remains to be seen if rs1344706 confers a similar pathogenic role in BD as it may in SZ.

Deficits in Neuroendophenotypes of SZ Linked to rs1344706 In the realms of neurocognition and neurobiology, a recent study explored the role of ZNF804A in theory of mind (ToM), and evaluated whether the rs1344706 risk allele was linked to deficits in

HESS ET AL. subjects’ performance and brain activation in ToM-driven tasks [Mohnke et al., 2014]. Deficits in ToM were observed to manifest in both SZ and BD patients; patients and their unaffected first-degree relatives displayed impairments in ToM, so it is likely that heritable factors contribute to these disruptions [Bora et al., 2009; Bora and Pantelis, 2013]. Perceptual deficits in social cognition—a core feature of ToM—often emerge in patients with SZ. In the study by Mohnke et al. [2013] the consequences of rs1344706 genotype were assessed on ToM performance among non-mentally ill subjects. In a two-stage neuroimaging analysis, the authors detected an association between rs1344706 genotype and decreased activity in core brain regions thought to mediate ToM. These findings support those previously reported [Walter et al., 2011] regarding the impact of rs1344706 on distinct regions of the human brain involved in ToM: the left temporo-parietal junction (TPJ), right dorsomedial PFC (DMPFC), and, the left posterior cingulate cortex (PCC). [Mohnke et al.2013]surmised that disturbances to the ToM network mediated by ZNF804A might explain vulnerability for delusional thought patterns, a core feature of SZ. This study brings to light a role of rs1344706 in mediating the activation of ToMassociated brain regions, at least in NMI subjects. Further testing of the effect of rs1344706 on ToM performance among SZ patients is needed to elucidate the role of ZNF804A in mediating vulnerability for SZ symptoms. Recent neurophysiological studies indexed the genetic effect of rs1344706 on the P300 response in healthy subjects and psychiatric patients. These studies independently replicated an effect of rs1344706(A) on the modulation of the P300 response (a cognitive trait marker for neuronal dysfunction) and implicated rs1344706 (A) in preservation of neuronal functioning and cognitive performance [Del Re et al., 2014; O’Donoghue et al., 2014]. Though these viewpoints are supported elsewhere [Walters et al., 2010; Chen et al., 2012], most other evidence has implicated rs1344706(A) in neurobiological deficits or severity of mental illness [Hashimoto et al., 2010; Balog et al., 2011; Voineskos et al., 2011; Walter et al., 2011; Kuswanto et al., 2012; Wassink et al., 2012; Paulus et al., 2013]. From a pharmacotherapeutic standpoint, SZ patients homozygous for the risk allele at rs1344706 exhibited reduced improvement of positive symptoms during antipsychotic treatment compared to patients heterozygous for rs1344706(A) [Mo¨ssner et al., 2012]. These findings suggest a risk-conferring effect of rs1344706(A) and/or a role in clinical severity of SZ.

Interim Summary Additional support for an association of rs1344706(A) with SZ in Han Chinese individuals has been reported. Considerable evidence demonstrates an effect of rs1344706(A) on altered ZNF804A mRNA expression, including a novel 50 truncated isoform; effects of this risk SNP emerged in neuroimaging and neurocognitive analyses as well. rs1344706 is consistently implicated in a broad range of trait markers of SZ and BD, including molecular disruptions. Further studies are warranted on rs1344706; unraveling the contributions

17 of this variant and ZNF804A are key to understanding their pathophysiological contributions in SZ and BD.

BIOINFORMATIC ANALYSES ZNF804A Gene Co-Expression with Putative Upstream Regulators To identify potential regulators of ZNF804A, we conducted an analysis of regulatory genes (i.e., transcription factors, splicing factors) predicted to act upstream of ZNF804A through the rs1344706-embedded motif. These factors may affect the transcription of ZNF804A and/or the alternative splicing efficiency of its premessenger RNA (mRNA) product. Strong mRNA co-expressivity (criterion: r |0.6| discerned in multiple tissues) between ZNF804A and its putative upstream regulators might indicate a functional connectedness of these regulatory molecules with ZNF804A. Several transcription factors [Hill and Bray, 2011; Voineskos et al., 2011; Hess and Glatt, 2014] and splicing factors [Hess and Glatt, 2014] have been reported or predicted to bind the rs1344706embedded DNA and RNA motifs. Two transcription factors (homez and hmx2) were previously evaluated for their binding potential to the rs1344706-embedded DNA motif in vitro [Hill and Bray, 2011]. From this study, no discernible interaction was found between homez or hmx2 and the rs1344706 motif. As of yet, the identity and affinity of nucleotide-binding protein(s) that interact(s) with the rs1344706 motif has not been experimentally determined, which poses a critical void in ZNF804A research. Determining the nucleotide-binding protein(s) that interact with the rs1344706 motif may help to elucidate the mechanisms by which this SNP effects ZNF804A expression. Coupling bioinformatics and statistical analysis is a feasible approach for evaluating the permissiveness of a SNP to particular nucleotide-binding proteins, and whether this permissiveness can be interpreted as a biological interaction. To perform this analysis, we collected gene expression data for ZNF804A and of its eight putative upstream regulators (identified either experimentally or from bioinformatic predictions) from the BrainSpan Atlas of the Developing Human Brain (http://www.brainspan.org/). We calculated correlation coefficients assuming linear relationships between genes using the normalized mRNA expression data. The correlation coefficients were then used to evaluate concomitant expression between ZNF804A and each of its putative upstream regulators (Supplementary Table 1). We included in our analysis all subjects aged eight post-conceptional weeks to 40 years. Based on our analysis, two genes encoding transcription factors were noted to have high co-expressivity with ZNF804A across four regions of the human postmortem brain. These transcription factors (myelin transcription factor 1 [MYT1L] and GATA binding protein 2 [GATA2]) were identified in separate in silico predictions [Voineskos et al., 2011; Hess and Glatt, 2014] as being a source of possible protein-nucleotide interaction with the rs1344706-embedded DNA motif (Supplementary Table 1). Minimal correlation was observed between the two splicing factors (SFs) predicted to bind rs1344706 differentially via SpliceAid analysis (Y-box binding protein 1 [YB-1] [Hess and Glatt, 2014] and serine/arginine-rich

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AMERICAN JOURNAL OF MEDICAL GENETICS PART B

splicing factor 20 [SRp20]); a biological connection of these SFs to ZNF804A in the brain appears unlikely given our findings, with the caveat that the expression data used in this analysis represented total ZNF804A mRNA; it is more likely for these SFs to correlate with the expression of particular ZNF804A isoforms. Beyond known transcription and splicing factors, a functional relationship between ZNF804A and the SZ-associated microRNA, miR-137 (encoded by MIR137) has been established through in vitro experiments [Kim et al., 2012]. Several GWAS hits for SZ were revealed to be likely targets of miR-137, suggesting the possibility that miR-137 contributes to regulatory networks involved in pathogenesis [Ripke et al., 2013]. With these relationships in mind, we evaluated if miR-137 and ZNF804A co-express in multiple brain tissues as an index of biological connectedness. As recent analyses did not find a significant correlation between miR-137 and ZNF804A in the postmortem DLPFC of adults [Guella et al., 2013], we attempted to uncover a biological relationship of miR-137 and ZNF804A in this brain region along with six other tissues. In total, seven brain tissues and four age windows were examined with the aim of finding co-expression of this gene pair in discrete spatiotemporal windows. We would expect a strongly negative correlation between miR-137 and its target(s) to suggest a biologically meaningful link, indicating the potential for microRNAs to down-regulate their targets. We observed strong correlations between mRNA levels of MIR137 and ZNF804A in the primary motor cortex (M1C) of first-trimester fetuses (r ¼ 0.93), and the ACC and ventrolateral PFC (VLPFC) in adolescent/adult subjects (r ¼ 0.72 and r ¼ 0.71, respectively) (Fig. 1). The observed correlation between MIR137 and ZNF804A in the DLPFC was strongest during adolescence/adulthood ages (r ¼ 0.63) (Fig. 1). Note, the correlations depicted reflect periods

of decreasing ZNF804A expression concurrent with increasing MIR137 expression in brain tissue. Though preliminary, our observations suggest miR-137 and ZNF804A interact in the human brain. Furthermore, an apparent shift in the co-expression of MIR137 and ZNF804A was observed between the fetal and adolescent/adult ages (Fig. 1), indicating that a biological link between these genes may be less likely in earlier ages. This relationship is unlikely to be mediated by rs1344706, as the miR-137 target site is located in the 30 untranslated region (UTR) of ZNF804A [Kim et al., 2012]. Overall, this analysis warrants further testing to validate the biological connection of miR137 and ZNF804A in human brain, and their impact on SZ pathogenesis. Together, these new preliminary results and prior in silico predictions suggest MYT1L and GATA2 are strong candidates for regulating ZNF804A expression via rs1344706. Interestingly, the two transcription factors (homez and hmx2) previously tested for a potential interaction with rs1344706, to which no direct interaction was found, had the lowest gene expression correlation to ZNF804A in three of the four postmortem brain regions analyzed (Supplementary Table 1). Together with in silico evidence, our findings also indicate a biological connectedness of ZNF804A with MYT1L, GATA2, and miR-137. Interestingly, several genetic variants in MYT1L are associated with SZ [Li et al., 2012; Lee et al., 2012b]; also, abnormal expression of GATA2 in postmortem brain tissue of SZ patients has been reported [Miller et al., 2012]. Regulation of ZNF804A expression by these particular SZ-susceptibility genes is a provocative finding; aberrant expression of MYT1L or GATA2 may be precursor events in the pathophysiological process that cause dysregulation of ZNF804A expression via rs1344706. Abnormal expression of ZNF804A may then disrupt its biological pathways, thereby increasing risk for SZ. Experimental

FIG. 1. MIR137 and ZNF804A co-expression measured from RNA-sequencing data collected from human postmortem brain tissue. Missing bars reflect tissues with no observed MIR137 expression. Abbreviations: anterior cingulate cortex (ACC); dorsolateral prefrontal cortex (DLPFC); hippocampus (HIP); primary motor cortex (M1C); orbital frontal cortex (OFC); striatum (STR); vetrolateral prefrontal cortex (VLPFC).

HESS ET AL. testing is warranted to validate these findings, and to evaluate the impact these interactions might have on expression of ZNF804A and its isoforms.

Interim Summary MYT1L and GATA2 were predicted to bind rs1344706 in silico; the mRNA of these TFs strongly co-expressed with ZNF804A in multiple brain tissues, suggesting a biological interaction may exist. ZNF804A is reported to harbor consensus binding sites for miR137, and experimental evidence has validated this interaction. Our gene co-expression analysis of postmortem human brain tissue suggests miR-137 and ZNF804A may interact with spatial and temporal specificity.

Epigenetic Landscape at rs1344706 Post-translational modifications of the nucleosomal histones (a nucleosome, comprised of 146bp DNA wrapped around an octamer of the core histone proteins H2A, H2B, H3, H4, is considered the elementary unit of chromatin) potentially inform about the epigenomic architectures (“active” vs. “repressed” chromatin, etc.) at a given locus. We examined data previously generated by the NIH Roadmap Project (Release 9) via chromatin immunoprecipitation followed by next-generation sequencing (ChIP-seq), which measured histone modifications present throughout the human genome. The Genboree Workbench [Bernstein et al., 2010] was used to examine processed ChIP-seq data referred to as “tracks” for any histone-tail modifications assayed in the original experiments. Analyses were restricted to postmortem brain samples extracted from fetal (Genboree donors: n ¼ 4) and adult subjects (Roadmap Epigenome Project donors: n ¼ 2). In total, density plot tracks and enrichment peak tracks for seven histone-tail modifications were compared at the rs1344706 position as follows: histone 3 lysine 4 methylation (H3K4me1); histone 3 lysine 36 trimethylation (H3K36me3); histone 3 lysine 9 trimethylation (H3K9me3); histone 3 lysine 27 trimethylation (H3K27me3); histone 3 lysine 9 acetylation (H3K9ac); histone 3 lysine 27 acetylation (H3K27ac); and histone 3 lysine 4 trimethylation (H3K4me3). While an exhaustive discussion for each of these histone modifications may be beyond the scope of this review (we refer the interested reader to the following references: [Taverna et al., 2007; Zhou et al., 2011; Houston et al., 2013]), H3K4me3 in brain is mostly organized as sharp peaks extending over 1–2 Kb at transcription start sites and additional CpG rich regulatory sequences, while the related mark, H3K4me1, broadly tags transcription start sites, and many regulatory elements linked to enhancer and repressor function, H3K36me3 is enriched across the body (incl. exons) of actively expressed genes. These three histone lysine methylation marks, like H3K9ac, H3K27ac and other types of histone acetylation, show on a genome-wide scale a significant correlation with active gene expression. In contrast, H3K27me3 and H3K9me3 are histone marks typically enriched at sites of repressed genes, with H3K27me3 as product of the Polycomb repressive

19 chromatin remodeling complex being highly regulated at promoters, and H3K9me3 also associated with broader domains of heterochromatization, including repeat DNA. However,there is additional complexity because H3K9me3 is also implicated in alternative splicing at some of the actively expressed genes [Saint-Andre´ et al., 2011]. At rs1344706, differences in the abundance of many of these histone-tail modifications were found between fetal and adult subjects and across brain regions (Fig. 2). However, enrichment peaks for only H3K9m3 were observed at rs1344706 in fetal brain and several adult brain tissues. The five brain regions in adult samples displaying enrichment for H3K9me3 at rs1344706 included anterior caudate, cingulate gryus, middle frontal lobe, hippocampus middle, and inferior temporal lobe. These brain regions are often implicated in SZ and BD. Given that the distribution of histone markers observed at rs1344706 was not identical in all brain regions, it is plausible for the SNP to be susceptible to spatiotemporal differences in the human brain with regards to epigenetic regulation of ZNF804A. Evidence from the second-trimester fetal brain suggested it as a potential critical period of rs1344706mediated effects on ZNF804A expression [Hill and Bray, 2012]. Although the effect of rs1344706 seems best explained by altered transcription-factor binding [Hill and Bray, 2011; Voineskos et al., 2011] or splice-factor binding [Hess and Glatt, 2014], it is plausible that this SNP affects ZNF804A expression via epigenetic mechanisms. It is possible that an absent histone-tail peak suggests either labile interaction or is a consequence of the dynamic epigenetic mechanisms for cell-type specificity. These preliminary findings suggest an important relationship between both epigenetic modifications and the rs1344706 variant; changes in this apparently normal interaction may impact ZNF804A expression. It is important to note that the histone methylation profiles were generated with tissue homogenates as input; given the considerable cellular heterogeneity within each brain region, it is unclear whether the H3K9me3 modifications epigenetically decorate the ZNF804A locus similarly in different types of neurons, glia and other nonneuronal cells. The question remains as to whether the epigenetic landscape is susceptible to changes due to rs1344706 and to what extent the epigenetic landscape impacts expression of ZNF804A. Though rs1344706 was not found to be directly responsible for ZNF804A gene expression in an expression quantitative trait loci (eQTL) analysis [Williams et al., 2011], rs1344706 might still exert an indirect effect on ZNF804A gene expression via several mechanisms regulating transcriptional activity and transcript processing. These possible avenues might include allele-specific disruptions to the binding efficiency of transcription factors [Hill and Bray, 2011; Voineskos et al., 2011; Hess and Glatt, 2014], altering pre-mRNA splicing of ZNF804A [Hess and Glatt, 2014], or indirectly affecting the accessibility of the DNA to DNA-binding proteins via epigenetic modifications. Further investigation is needed to elucidate the mechanisms by which rs1344706 contributes to ZNF804A expression as a short-term consequence, and whether interfering with these mechanisms triggers a biological cascade affecting pathogenesis as a long-term consequence.

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FIG. 2. A comparison of the epigenetic landscape at rs1344706 surveyed by ChIP-seq across postmortem human brain samples. Data for fetal brain (A) and several brain regions from adult subjects (B–H) was available to analyze, including: inferior temporal lobe (B); substantia nigra (C); cingulate gyrus (D); germinal matrix (E); middle frontal lobe (F); anterior caudate (G); and hippocampus middle (H). The original data was generated by the NIH Roadmap Project. Green density plots ¼ modifications detected at rs1344706, black bars ¼ peaks of highly enriched modifications.

HESS ET AL.

Interim Summary Signal peaks for seven histone-tail modifications at rs1344706 were mined from postmortem brain experiments. H3K9me3 (marker of repressed chromatin and alternative splicing) was enriched at the rs1344706 site in the fetal brain and five brain regions assayed in adult subjects.

Alternative Splicing of ZNF804A in the Blood and Brain We previously summarized our analyses of ZNF804A exon expression in the postmortem brain of SZ subjects and NMI controls [Hess and Glatt, 2014], noting remarkable variability in the expression of exon 2 and exon 4 between these groups, indicating contrasts in alternative splicing of ZNF804A. Here we summarize results from analyses of exon-based microarray data extracted from separate studies on human peripheral blood cells, postmortem brain tissue, and neurons differentiated from induced pluripotent stem cells. The main hypothesis of our analysis was as follows: variations in ZNF804A exon expression would be detected in various tissues and diagnostic groups, and that deviations from the normal pattern observed in NMI controls might indicate aberrant mechanisms of alternative splicing in SZ subjects.

21 We analyzed ZNF804A exon expression in the following tissues and diagnostic groups: (1) peripheral blood mononuclear cells from BD, SZ, and NMI control donors (GEO Accession: GSE18312); (2) postmortem cerebellum from SZ, BD, and depression (MDD) subjects and NMI controls (GEO Accession: GSE35974); (3) postmortem parietal cortex from SZ, BD, and MDD subjects and NMI controls (GEO Accession: GSE35977); and (4) 6-week old neurons differentiated from induced pluripotent stem cells derived from fibroblasts of SZ cases and NMI controls (GEO Accession: GSE25673). All samples were assessed for RNA quality by the initial investigators. In one study this was determined by RNA gel electrophoresis to evaluate samples [Brennand et al., 2011], whereas the remaining studies provided numerical indicators of RNA quality (RIN). Samples with integrity scores >6.0 [Bousman et al., 2010] or 7.0 [Chen et al., 2013] were retained by these investigators for microarray profiling, and are thus included in our analysis. An analysis of variance (ANOVA) model was used to compare exon expression of ZNF804A for diagnostic groups in each data set. In peripheral blood, we detected nominal down-regulation in two separate exon 4 probes among patients (Fig. 3) (SZ vs. NMI: P ¼ 0.046, fold change ¼ 1.60; BD vs. NMI: P ¼ 0.02, fold change ¼ 1.86). Improved study design and larger sample groups may reveal stronger differences in ZNF804A exon expression in the blood of affected patients. Though speculative at this

FIG. 3. ZNF804A exon-expression in a blood-based microarray of patients with bipolar disorder (BD) or schizophrenia (SZ) and non-mentally ill (NMI) comparisons. Spot intensities for samples with a RNA integrity number > 6.0 were previously recorded by a microarray scanner, the output was downloaded as .CEL files from the Gene Expression Omnibus (Accession: GSE18312) and imported into Partek Genomics Suite v6.6 (Partek, Inc.) for processing and analysis. All samples were subjected to robust multi-array average, log-2 transformation, and quantile normalization. Diagnostic groups were compared using a one-way analysis of variance model (ANOVA). *SZ (n ¼ 12) versus NMI controls (n ¼ 7); P ¼ 0.046, fold change ¼ 1.60. **BD (n ¼ 9) versus NMI controls; P ¼ 0.02, fold change ¼ 1.86.

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stage, it seems possible that expression of ZNF804A isoforms in peripheral blood might be worthwhile to examine for inclusion in poly-omic diagnostic classifiers of mental illness. In contrast to peripheral blood, we detected significant up-regulation of exon 2 and exon 4 probes in SZ patients compared to NMI controls in the postmortem cerebellum (exon 2: P ¼ 0.00033, fold change ¼ 1.45; exon 4: P ¼ 0.027, fold change ¼ 1.14) (Fig. 4a). No significant contrast in ZNF804A exon expression was detected between diagnostic groups in the postmortem parietal cortex (Fig. 4b). Nevertheless, more reliable differences may be detectable by augmenting sample size or by implementing technical improvements, such as performing microarrays on single cell types isolated from bulk tissue, or specific cortical layers. The contrasts described above are reflective of regional averages, that is, the differential exon expression of multiple cell types in a single brain region, which negatively impacts the signal:noise ratio. Lastly, we observed up-regulation of both exon 2 (P ¼ 0.006, fold change ¼ 2.12) and exon 4 of ZNF804A in neurons differentiated from induced pluripotent stem cells derived from SZ patients and NMI controls (fifth ZNF804A probe: ps ¼ 3.21 105, fold change ¼ 1.81; sixth ZNF804A probe: P ¼ 0.009, fold change ¼ 2.35) (Fig. 5). We also detected significant expression differences between groups at the 30 end of exon 1 (P ¼ 0.00 025) (Fig. 5). Note, each of the significance levels described above were not adjusted for multiple testing, but given the consistency of the effects observed in exon 2 and exon 4 expression across three independent data sets and our previous observations [Hess and Glatt, 2014], a chance result seems less likely. Furthermore, ZNF804A exon expression in the induced pluripotent stem cell mimics that of the adult postmortem brain, which is an important observation warranting future ZNF804A research in cultures of induced pluripotent stem cells. Though continuously being evaluated, several poly-omic domains (e.g., proteome, transcriptome, methylome, etc.) are

viewed as somewhat comparable across the blood and brain; however, cross-tissue evaluation of transcriptome data from these tissue types revealed imperfect correlations [Tylee et al., 2013]. Therefore, we reason that the opposing effects of exon 4 expression observed in our analysis of blood versus postmortem brain data of SZ patients may be explained by dissimilarities in the poly-omic architecture of these tissue types; this same reasoning may be applied to the differential effects observed in blood versus induced pluripotent stem cells. Alternatively, the incongruity of effects between brain and blood for ZNF804A may be explained by the restricted types of brain tissue examined, including a lack of celltype specific mRNA profiling in the original studies. Bearing in mind that exon-array data are limited in the interpretability of direct comparisons of exons, we acknowledge that any observed differences in exon expression between cases and controls may be affected by other features independent of alternative splicing, such as sequence variation, hybridization efficiencies of exon probes, or artifacts introduced by mRNA instability. The modest differences we observed in the exon-array analyses could be a circumstance of generally lowered ZNF804A expression related to the tissue source, diagnosis, or genetic background of the samples assayed. Further experimentation is warranted to definitively conclude whether exon dysregulation in cases is a consequence of alternative splicing mechanisms.

Interim Summary We report additional preliminary evidence of ZNF804A exon dysregulation in postmortem human brain tissue, peripheral blood, and induced pluripotent stem cells. Exon 2 and exon 4 of ZNF804A were consistently up-regulated in SZ patients in postmortem cerebellum and induced pluripotent

FIG. 4. ZNF804A exon expression collected for four diagnostic groups (bipolar disorder [BD], depression [DEP], schizophrenia [SZ], nonmentally ill comparisons [NMI]) from postmortem cerebellum (A) (accession: GSE35974; SZ: n ¼ 44; BD: n ¼ 37; DEP: n ¼ 13; CT: n ¼ 50) and parietal cortex (B) (accession: GSE35977; SZ: n ¼ 51; BD: n ¼ 45; DEP: n ¼ 14; CT: n ¼ 50). Spot intensities for samples with a RNA integrity number 7.0 were previously recorded by a microarray scanner; these intensities were downloaded as .CEL files from the Gene Expression Omnibus. Files were then imported into Partek Genomics Suite v6.6 (Partek, Inc.) for processing and analysis. All samples were subjected to robust multi-array average, log-2 transformation, and quantile normalization. Diagnostic groups were compared using a oneway analysis of variance model (ANOVA). *SZ versus NMI control: P < 0.05; **SZ versus NMI control: P < 0.0005.

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FIG. 5. ZNF804A exon expression in 6-week old neurons differentiated from induced pluripotent stem cells (iPSCs) from schizophrenia (SZ) patients (n ¼ 12) and non-mentally ill (NMI) comparisons (n ¼ 12). RNA integrity was evaluated by gel electrophoresis by the handlers. Spot intensities recorded by a microarray scanner were downloaded as .CEL files was from the Gene Expression Omnibus (Accession: GSE25673) and imported into Partek Genomics Suite v6.6 (Partek, Inc.) for processing and analysis. All samples were subjected to robust multi-array average, log-2 transformation, and quantile normalization. Diagnostic groups were compared using a one-way analysis of variance model (ANOVA). **P < 0.01; ***P < 0.0005; and ****P < 0.00005.

stem cells; down-regulation was observed for exon 4 in the blood of SZ and BD subjects. Exon dysregulation may be a consequence of alternative splicing or generally reduced expression of ZNF804A in SZ or BD subjects.

Spatiotemporal Pattern of ZNF804A Exon and Isoform Expression in the Human Brain We next sought to extend these observations to isoform-level data estimated from RNA-sequencing (RNA-seq) experiments from fetal and adult postmortem brains. We acquired, processed, and analyzed data from two relevant data series involving RNA-seq of the transcriptome in the postmortem human fetal neocortex (Accession: E-GEOD-38805) and the superior temporal gyrus (STG) of a SZ case-control data set (Accession: E-MTAB-1030). Briefly, raw

sequence data were collated as FASTQ files from ArrayExpress and imported into Partek Flow software (Partek, Inc.) for alignment to the human reference genome (hg19). Alignment was performed via the Bowtie2 algorithm to generate sorted and aligned reads (.BAM file). BAM files for each sample were then imported into Partek Genomics Suite v6.6 (Partek, Inc.) for analysis using the manufacturer-designed RNA-seq workflow. The read fragments previously sequenced were assembled into transcripts using the AceView annotation [Thierry-Mieg and Thierry-Mieg, 2006] and quantified as normalized units of expression for RNA-seq: reads per kilobase per million mapped reads (RPKM). Standard measures of RNA integrity (i.e., RIN, RQI) were reported in these studies; all samples were indicated as having high quality for RNA-seq (RQI 6.0 [Wu et al., 2012], RIN > 8.5 [Fietz et al., 2012]). In both data series, we targeted the expression readouts from all known ZNF804A isoforms (Fig. 6, bottom) to survey the isoforms that are potentially expressed in the human brain, and to estimate the abundance of

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FIG. 6. ZNF804A isoform expression assessed from raw RNA-sequencing data collated from the ArrayExpress database. (A) Expression of ZNF804A isoforms averaged across four neocortical germinal zones from human fetal postmortem samples (RIN > 8.5, n ¼ 22; mean standard error) (Accession: EGEOD-38805). (B) Expression of ZNF804A isoforms in the adult postmortem superior temporal gyrus (RQI > 6.0, case: n ¼ 9; control: n ¼ 7). (C) The ZNF804A locus is depicted (exons ¼ shaded boxes; introns ¼ solid lines), in addition to its three mRNA isoforms: ZNF804A_aAug10//NM_194250.1 (solid arrow); ZNF804A_cAug10 (dashed arrow); and ZNF804A_bAug10//AK055275.1 (dotted arrow).

each using an isoform quantification procedure available through the Partek software. We estimated the abundance of three known ZNF804A isoforms in the fetal postmortem neocortex and adult postmortem STG using RPKM in Figure 6. Specifically, readouts suggested that ZNF804A_bAug10//AK055275.1 isoform was preferentially expressed in the adult postmortem STG, followed by the ZNF804A_aAug10//NM_194250.1 isoform (Fig. 6, top right). In contrast, the full-length ZNF804A_aAug10//NM_194250.1 isoform appeared over-represented in fetal postmortem neocortex (Fig. 6, top left). We did not detect expression of ZNF804A_cAug10 isoform in any of the fetal or adult subjects. The apparent preferential expression of ZNF804A_bAug10//AK055275.1 in adult brain tissue (Fig. 6, top right) may be a feature of alternative exon splicing as we speculated from data presented in Figures 3–5; we hypothesize that dysregulation of exon 2 and exon 4 in SZ subjects indicates aberrant RNA processing of ZNF804A and aberrant expression of ZNF804A isoforms. Although ZNF804A isoform expression did not differ significantly in SZ subjects compared to NMI controls, improved design for sequencing and isoform quantification, and increased sample size, may improve the statistical power needed to measure deviations in isoform expression if they indeed exist. Next we provide a complete expression profile of all four ZNF804A exons generated by RNA-seq of four postmortem brain tissues distributed across various periods of human development (from eight post-conceptional weeks to approximately 40 years).

These data are meant to illustrate the potential biases caused by read coverage distribution affecting exon-level measurements in RNAseq [Jiang et al., 2011; Zhao et al., 2014]. All data presented in Figure 7 were extracted, processed, and analyzed from the meta-set of RNAseq data provided by the BrainSpan Atlas of the Developing Human Brain. All samples used in our analysis had a documented RIN 6.5 from quality assessment. The brain regions we analyzed include the DLPFC, striatum (STR), hippocampus (HIP), and anterior cingulate cortex (ACC); these regions are often implicated in SZ and BD. A simple comparison of exon intensities across lifespan as shown in Figure 7 demonstrates a possible preferential expression of the 30 end of the transcript at all ages and brain regions. Exon 4 showed relatively consistent over-representation relative to the other exons across the four brain regions analyzed. We found similar over-representation of 30 exons in an analysis of exon-level intensities of five randomly selected genes from the same meta-dataset (data not shown). We speculated that differential exon expression may be a proxy of alternative splicing of ZNF804A transcripts as we previously described [Hess and Glatt, 2014]. However, higher expression of the 30 exons of ZNF804A may be explained by non-physiological circumstances such as systematic bias introduced by poly(A)þ capture of mRNA for sequencing. As such, further examination of isoform expression warrants precise techniques and statistical procedures to adjust for these potential biases. It is noteworthy that the RNA-seq results presented here are estimates from publicly available data and are potentially limited by various shortcomings. The data selected for analysis reflect the

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FIG. 7. ZNF804A exon expression in four regions of the human postmortem brain across development. All samples were indicated as having high RNA quality for RNA-seq (RIN 6.0). Age range begins at 8 postconceptional weeks (pcw) to approximately 40 years (yrs). Exon expression is depicted as log-scale of reads per kilobase per million mapped reads (log2[RPKM]). Expression readouts were downloaded from the metadata set from the Developing Transcriptome study archived in the BrainSpan Atlas (http://brainspan.org/).

closest approximations of isoform expression in the human brain that we could generate based on the availability of data and procedures of commercial software used for quantification. We cannot be certain if our analysis of RNA-seq data has discerned a genuine difference in expression of ZNF804A isoforms in the postmortem brain. In light of these remarks, we are encouraged by the recently published study that profiled ZNF804A expression in samples of postmortem human brain tissue [Tao et al., 2014]— the main findings of which were summarized in the Literature Review section above. Principally, a novel transcript described as missing exons 1 and 2, with an alternative start codon harbored in exon 4, was preferentially expressed in the DLPFC [Tao et al., 2014]. Our speculations appear partially congruent with the evidence presented in this latest study. Nevertheless, there are clear indications that exon 1 and exon 2 of ZNF804A can be alternatively skipped, and that isoforms characterized by a 50 truncation are expressed in the human brain and may be preferentially expressed at certain in brain regions of relevance to SZ and BD. It is increasingly important to determine the functional impact of each isoform to determine if changes in the ratio of ZNF804A isoform expression are coordinated with the regulation of neurodevelopmental pathways in fetal brain development and/or maintenance of processes related to neuronal stability, signaling, and

architecture in the adult brain. Exploring these relationships will also shine a light on the role of ZNF804A in SZ and BD, such as resolving if the rs1344706 risk SNP induces a change in the ratio of ZNF804A isoforms in the fetal brain, and if aberrant isoform expression of ZNF804A influences pathogenesis.

Interim Summary Mining RNA-seq data helped to distinguish isoforms of ZNF804A expressed in the human brain and isoforms that may be preferentially expressed in the fetal and adult brain. Dysregulation of exon 2 and exon 4 in SZ cases in various tissues suggests aberrant splicing of ZNF804A is linked to SZ; however, our analysis of RNA-seq data from a case-control experiment lacked statistical power to detect differences in isoform expression between SZ subjects and NMI controls.

Correlation of ZNF804A Gene Expression to its Putative Targets Here we implemented a gene co-expression analysis of ZNF804A with its putative targets collated from independent studies [Girgenti

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et al., 2012; Hill et al., 2012; Umeda-Yano et al., 2013] that modeled abnormal expression of ZNF804A in cell culture and measured the effects on downstream gene expression. Our analysis was based on the same hypothesis of co-expressed genes discussed in our previous section on ZNF804A correlates to its upstream regulators. First, we tested if any of the genes reported to interact with ZNF804A or genes differentially expressed due to abnormal ZNF804A expression also display strong gene co-expression with ZNF804A. We collated a list of 197 genes from the literature with evidence of direct or indirect regulation by ZNF804A; the list of genes was derived from three independent studies [Girgenti et al., 2012; Hill et al., 2012; Umeda-Yano et al., 2013]. Next, we quantified the expression of each gene in the human postmortem brain from published RNA-seq experiments and determined correlation coefficients of each gene (referred to as “putative target”) with ZNF804A. The goal was to index co-expressivity within and across multiple regions of the postmortem human brain. We analyzed mRNA expression data taken from the RNA-seq experiments archived in the BrainSpan Atlas of the Developing Human Brain (Developmental Transcriptome data set: RNA-Seq Gencode v10). We extracted expression values for each putative target reported in the above-cited papers from this meta-data set. Expression values were normalized to standard format for RNA-seq data (RPKM). In total, mRNA expression for 146 of the 197 putative targets of ZNF804A published in the literature were collected for several tissues of the postmortem human brain, that is, a portion of the putative targets were absent in BrainSpan’s catalog. Then we subset the mRNA expression for each of the 146 genes for five brain

regions implicated in SZ: HIP [Zaidel et al., 1997], temporal neocortex (TCx) [Marsh et al., 1994], STR [Bertolino et al., 1999], ACC [Velakoulis et al., 2002], and DLPFC [Bertolino et al., 1999]. We summarized our findings for the nine genes displaying a consistently high co-expression with ZNF804A across multiple brain tissues in Figure 8. The coefficient of correlation for the nine genes ranged |0.6|–|0.8| in multiple brain tissues, suggesting likely biological interaction with ZNF804A. Note, several of these nine genes are related by biological functions indexed by the Gene Ontology Consortium, such as regulation of post-translational modifications (IKBKAP, PARP2, UBE3C) and involvement in metabolic processes in the cell (HMGCR, COMT, IKBKAP, PARP2, TDP1, UBE3C). At least four of the nine genes had been reported on in the context of psychiatry or neurobiology, example, COMT [Ira et al., 2013], HMGRCR [Fernø et al., 2006], UBE3C [Garriock et al., 2010],and FAM107A [Shao and Vawter, 2008]. A total of 27 genes displayed significant evidence of co-expression with ZNF804A in at least two brain tissues after correction for multiple comparisons (Supplementary Tables 2–6). Given our data, we expect ZNF804A to either regulate the expression of these nine genes in the human brain, or to be jointly co-regulated with them. In addition to genes displaying reliable correlation to ZNF804A expression in all five brain regions, other SZ susceptibility genes were observed to strongly correlate with ZNF804A expression in single brain regions. We identified a positive correlation of DRD2 expression with ZNF804A expression (r ¼ 0.69) in the HIP, as well as negative correlations of COMT expression to ZNF804A across

FIG. 8. Correlation of mRNA expression for ZNF804A to nine putative targets that displayed coefficients 0.6 in more than one brain tissue. Normalized gene expression values (RPKM) were extracted from the RNA-sequencing meta-data set archived by the BrainSpan database (Developmental Transcriptome study). Statistical analysis was performed with StataMP 11. Abbreviations: temporal neocortex (TCx); striatum (STR); hippocampus (HIP); anterior cingulate cortex (ACC); dorsolateral prefrontal cortex (DLPFC). Missing bars reflect genes with no observed expression.

HESS ET AL. four brain regions (DLPFC, HIP, STR, TCx; min ¼ 0.38, max ¼ 0.66). Our findings provide additional support to the role of ZNF804A in dopamine metabolism and signaling. As previously suggested [Girgenti et al., 2012], ZNF804A is possibly involved in biological pathways related to psychosis via regulation of dopaminergic pathways. We provide a complete summary of the correlation coefficients in the supplementary materials. To further evaluate the gene expression correlation of DRD2 and ZNF804A, we modified our correlation analysis to account for the isoforms of ZNF804A and DRD2. We collected mRNA expression data from two RNA-seq data sets published in the ArrayExpress database and estimated the expression of ZNF804A and DRD2 isoforms. The first data set was derived from an RNA-seq experiment targeting STG in SZ subjects and NMI controls (Accession ID: E-MTAB-1030), whereas the second data set was from a study that profiled the germinal zones in the human fetal neocortex (Accession ID: E-GEOD-38805). Briefly, raw sequencing reads contained in FASTQ file formats were imported into Partek Flow software (Partek, Inc.) for alignment to the human reference genome (hg19) using the Bowtie2 algorithm. The aligned files (.BAM) for each sample were imported into Partek Genomics Suite v6.6 (Partek, Inc.) for sorting and indexing. Using the RNA-seq workflow in Partek Genomics Suite, the short reads that aligned to the reference human genome were quantified as assembled transcripts using the AceView annotation [Thierry-Mieg and ThierryMieg, 2006] and normalized to RPKM. This method allowed us to estimate isoform expression for the transcriptome. Next, we calculated correlation coefficients on the three known isoforms of ZNF804A and eight known DRD2 isoforms. For the E-MTAB-1030 data set, we separated our statistical analyses for SZ subjects and NMI controls to determine whether differences or overlap in ZNF804A and DRD2 expression of individual isoforms existed between these patients in the assayed brain region (i.e., STG). Raw RNA-seq data for a total of seven NMI comparisons and nine SZ cases for the adult STG data series were included. We did not observe any significant correlation coefficient for ZNF804A isoforms and DRD2 isoforms among the adult SZ cases and NMI comparison subjects analyzed (though sample size was limited) (Supplementary Tables 7–8). We did however uncover a reliable correlation for specific pairs of isoforms in regions of the human fetal postmortem brains. We observed a positive correlation between ZNF804A_bAug10//AK055275.1 and the full-length DRD2 isoform (DRD2_aAug10) (r ¼ 0.5, unadjusted P ¼ 0.018, n ¼ 22) (Supplementary Table 9). We also observed negative gene expression correlations between the full-length ZNF804A_aAug10// NM_194250.1 and two DRD2 isoforms (DRD2_fAug10 and DRD2_eAug10) (r ¼ 0.43, unadjusted P ¼ 0.047; r ¼ 0.43, unadjusted P ¼ 0.047, respectively) (Supplementary Table 9). Given the high degree of ZNF804A and DRD2 co-expressivity and reported effect of ZNF804A on DRD2 expression [Girgenti et al., 2012], it is possible that ZNF804A may mediate the expression of specific DRD2 transcripts; this interaction may change according to the developmental stage of the brain and/or as a consequence of pathogenesis. Our understanding of the relationship between ZNF804A and DRD2 may benefit from investigations to determine if ZNF804A affects DRD2 expression in the human postmortem brain or in cell culture, including an effect on the DRD2long:

27 DRD2short ratio [Zhang et al., 2007; Bertolino et al., 2009]. Interestingly, our observation of a putative connection between ZNF804A and DRD2 in the fetal postmortem brain is partially supported by previous findings of dysregulation of DRD2 mRNA caused by experimental enhancement of ZNF804a expression in a rat neural progenitor cell line of embryonic origin [Girgenti et al., 2012].

Interim Summary Genes reported to be dysregulated by ZNF804A typically correlated with ZNF804A expression in postmortem human brain tissue. A total of 27 genes correlated with ZNF804A expression in at least two brain regions and survived multiple-testing corrections. Nine of the 27 genes that survived multiple-testing adjustments correlated strongly with ZNF804A (r |0.6|) in at least two brain tissues: C10orf54, COMT, FAM107A, HMGCR, IKBKAP, PARP2, RPRD2, TDP1, and UBE3C. Co-expression analysis of RNA-seq data detected DRD2 isoforms that may be regulated by ZNF804A.

Epistasis in the ZNF804A pathway We examined genetic data for 1,170 cases and 1,378 controls collected as part of the Genetic Association Information Network (GAIN) study of SZ (dbGaP accession ID: phs000021.v1.p1). The genomes of these subjects were profiled on the Affymetrix 6.0 SNP array providing genotype information for 729,454 DNA variants. We conducted a targeted epistasis analysis to test potential SNPSNP interactions between ZNF804A and genes that we and others have ascribed to its biological pathway. Specifically, we included: i) a set of 67 genes that displayed significant correlation coefficients with ZNF804A expression from our co-expression analyses were included in this analysis; and ii) regulatory molecules associated with ZNF804A expression from experimental validation (MIR137) or bioinformatic analysis (POU3F1, GATA2, MYT1L). We collated tag SNPs for these 71 genes from data available in HapMap release 24 (http://hapmap.ncbi.nlm.nih.gov/). We identified 614 tag SNPs for 61 of the 71 genes included; the remaining genes had no tag SNPs (POU3F1, CYTH2, CES2, CLUAP1, COX8C, NOTCH2, RASD1, SNORA7B, ZIK1, and MIR137). Four hundred and thirty of the 614 tag SNPs were not represented on the Affymetrix 6.0 array, requiring us to identify proxy SNPs in strong to perfect LD with the un-typed tags (r2 0.9, 1,000 Genomes distribution of CEU ancestry). In all, 338 suitable tags or tag proxies representative of 55 genes were employed for this analysis. A total of 332 valid pair-wise tests for epistasis between the ZNF804A rs1344706 SNP and each tag or tag proxy SNP were performed using Plink [Purcell et al., 2007]. We applied post-test procedures to account for all pair-wise tests; a false discovery rate (FDR) of 10% was used. All results with a P 0.05 before post-test are reported (Table I). In the case-control analysis, 23 nominally significant interactions involving rs1344706 were detected before post-test corrections were

SNP rs7134738 rs7582833 rs7038554 rs8105469 rs11214501 rs2850303 rs4936263 rs11022340 rs6783333 rs7259187 rs6776877 rs10188804 rs7657160 rs9880436 rs13087711 rs9809615 rs1836796 rs1807939 rs12984458 rs10771265 rs11126220 rs11685645 rs942453

Gene SSPN ANTXR1 QSOX2 GNG7 NCAM1 NCAM1 NCAM1 PARVA OSBPL10 GNG7 FAM107A ANTXR1 FHDC1 OSBPL10 OSBPL10 OSBPL10 NCAM1 NCAM1 ZNF585A SSPN ANTXR1 MYT1L C1ORF54

OR 0.739 0.7575 0.7632 0.7602 1.262 0.7977 1.241 0.7743 0.7818 0.8088 1.226 1.217 0.8199 0.8248 1.212 0.8068 0.8355 1.202 0.8355 1.203 1.186 1.216 1.283

STAT 11.15 10.45 8.857 7.641 7.613 7.214 6.706 6.391 6.079 5.814 5.714 5.31 5.233 5.189 5.116 5 4.575 4.483 4.466 4.399 4.18 4.174 4.173

P 8.39E-04 1.23E-03 2.92E-03 5.71E-03 5.79E-03 7.23E-03 9.61E-03 0.011 0.014 0.016 0.017 0.021 0.022 0.023 0.024 0.025 0.032 0.034 0.035 0.036 0.041 0.041 0.041 FDR 0.204 0.204 0.323 0.385 0.385 0.4 0.456 0.476 0.505 0.508 0.508 0.525 0.525 0.525 0.525 0.526 0.593 0.593 0.593 0.593 0.593 0.593 0.593

# Probes 1 6 1 1 2 2 2 2 2 1 1 6 1 2 2 2 2 2 3 1 6 2 2

Proxy SNP (r2) — — rs12338076 (0.93) rs6510696 (1.0) rs4492854 (1.0) rs584427 (.934) rs4937993 (0.9) — rs7635910 (0.92) rs2159917 (0.82) rs3792400 (0.71) — rs7664303 (1.0) — — rs7633447 (0.96) — rs1821693 (0.97) rs4239587 (1.0) — — — — Coef 0.1081327 0.0114438 0.0120861 0.0074364 0.0284875 0.1166647 0.0162557 0.053683 0.0679993 0.0287175 0.1085936 0.0378087 0.1529644 0.0221879 0.0002577 0.0430199 0.1581364 0.0921678 0.0158387 — 0.0392718 — 0.0946149

SE 0.095153 0.063444 0.043999 0.090973 0.065225 0.069128 0.062517 0.058337 0.048624 0.091443 0.101579 0.055899 0.160563 0.041281 0.042512 0.04551 0.067777 0.067179 0.032276 — 0.058493 — 0.088093

t 1.14 0.18 0.27 0.08 0.44 1.69 0.26 0.92 1.4 0.31 1.07 0.68 0.95 0.54 0.01 0.95 2.33 1.37 0.49 — 0.67 — 1.07

P* 0.257 0.857 0.784 0.935 0.663 0.093 0.795 0.358 0.163 0.754 0.286 0.499 0.342 0.591 0.995 0.345 0.02 0.171 0.624 — 0.503 — 0.284

Quantitative effect of SNP-SNP interactions examined in BrainCloud MA SNP set

KEY: MA þ SNP ¼ microarray and genome-wide SNP data from BrainCloud database. * *p-value for pair-wise SNP–SNP that controlled for main effects of ZNF804A expression and each SNP separately in conjunction with covariates (age, sex, and age*sex). If “# Probes” >1, median was used to summarize intensity. rs1366840 was used as a proxy for rs1344706(ZNF804A) in the SNP-SNP interaction analysis on gene expression.

Chr 12 2 9 19 11 11 11 11 3 19 3 2 4 3 3 3 11 11 19 12 2 2 1

SNP-SNP interactions detected in GAIN-SZ

TABLE I. SNP-SNP Interactions in the ZNF804A Pathway (Pair-Wise to rs1344706)

28 AMERICAN JOURNAL OF MEDICAL GENETICS PART B

HESS ET AL. included. No interactions remained significant with FDR 0.8 with their corresponding tag SNP. We used a linear regression model including SNP genotype and ZNF804A mRNA expression as predictors in conjunction with a SNP-SNP interaction variable to predict variance in mRNA expression of each gene implicated in SNP-SNP interactions with ZNF804A. We controlled for effects of age, sex, and age*sex factors in our statistical analysis. Using this approach, we detected a significant interaction effect of rs1344706(ZNF804A)-rs1836796

29 (NCAM1) on NCAM1 expression (P ¼ 0.02). Our findings demonstrate the potential phenotypic changes influenced by SNP-SNP interactions with ZNF804A. One caveat to this analysis is the lack of a directly typed rs1344706, or proxy in perfect LD with rs1344706. Overall, our pathway-based analysis of ZNF804A epistatic interactions showed promising results that highlight possible genetic relationships between ZNF804A and genes potentially involved in its network. Our analysis provides additional support to the importance of ZNF804A to cell adhesion molecules, which was previously demonstrated through ZNF804A knock-down in neural stem cell culture [Hill et al., 2012]. Multiple genes involved in cell adhesion displayed nominal associations with ZNF804A via epistasis analysis. Moreover, expression of NCAM1 may be influenced by the rs1344706-rs1836796 interaction. Our findings provide additional support to the putative interaction between ZNF804A and the MYT1L transcription factor, which we previously highlighted from in silico analysis of rs1344706 and co-expression analysis of ZNF804A and MYT1L mRNA using human postmortem brain samples [Hess and Glatt, 2014].

In Silico Protein Models of ZNF804A Isoforms First, we modeled the ZNF804A proteins generated by various ZNF804A mRNA isoforms including the full-length 1209 amino-acid isoform (ZNF804_aAug10//NM_194250.1 [Thierry-Mieg and Thierry-Mieg, 2006]), the 602 amino-acid isoform with a truncated 50 -end (ZNF804A_bAug10//AK055275.1 [Thierry-Mieg and Thierry-Mieg, 2006]), and the 54 amino-acid isoform with a truncated 30 -end (ZNF804A_cAug10 [Thierry-Mieg and Thierry-Mieg, 2006]). Each model was generated by the Protein Homology/analogy Recognition Engine V2.0 (Phyre2) protein-folding server (Kelley and Sternberg, 2009) using the “intensive” modeling algorithm. Several images of each modeled isoform were captured, a selection of which are displayed in Figures 9 and 10.

FIG. 9. Images of the three-dimensional model for the full length (1209 amino acids) ZNF804A isoform captured in the JMOL browser applet. In the left panel, a complete view of the structure is shown. An enhanced view of the boxed region marking the N-terminal section of the protein is shown in the right panel. The ribbon models are colored by secondary structure (pink ¼ a-helix; blue ¼ b-turn; gray ¼ disordered).

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As revealed by the structural images of the full-length isoform, the ZNF804A protein is estimated to be predominately disorganized; approximately 86% of the full-length model displays random coiling as predicted by the Phyre2 output, consistent with our prior estimates based on the secondary structure modeling of ZNF804A [Hess and Glatt, 2014]. By generating these interactive models, we inspected the N-terminus of the ZNF804A_cAug10 isoform to determine if the residues were predicted to form a typical C2H2 zinc-finger motif. We expected the putative motif region in ZNF804A to fold as two betasheets that harbor cysteine residues; spatially, these residues should be proximal to an alpha-helical loop harboring two histidine residues. This spatial orientation is consistent with “typical” C2H2-zinc finger motifs [Iuchi, 2001; Hayes et al., 2008], as this would allow the cysteine and histidines to coordinate a zinc ion. As depicted in Figure 10, the N-terminal region of ZNF804A is predicted to fold into a compact structure in which two b-sheets come into close contact with two a-helices, each separated by a short linker region resembling that of a typical C2H2-motif. Based on its predicted structure, it is plausible for ZNF804A to express a functional zinc-finger motif. However, some deviations were noted; two of the residues expected to fold into secondary structures were found in short linker regions. This may be explained by limitations of the folding algorithm; conversely, the deviations observed may be reflective of an “atypical” zinc-finger in ZNF804A. Based on our previous reports of the two-dimensional structure of the ZNF804A_bAug10//AK055275.1 isoform [Hess and Glatt, 2014] we expected the predicted model of this isoform to be extensively disordered. As expected, the model for the ZNF804A_bAug10//AK055275.1 isoform is approximately 95% intrinsically disordered (data not shown). We anticipate that such extensive structural disorder would interfere with the protein’s functionality. No work yet has characterized the functions of the ZNF804A_bAug10//AK055275.1 isoform, and determining the functionality of this isoform may be challenging given its lack of a putative zinc-finger motif and its highly disordered structure. We reasoned two postulates regarding its putative functionality: 1) the

ZNF804A_bAug10//AK055275.1 isoform functions as a putative transcription factor and/or RNA splicing factor independent of a zinc-finger motif; alternatively, 2) ZNF804A_bAug10//AK055275.1 otherwise requires the zinc-finger motif to properly function, however the loss of the motif-encoding segment in exon 2 produces a functionally deficient protein isoform of ZNF804A. Distinguishing the functional effect of the ZNF804A_bAug10//AK055275.1 isoform and the exact mechanism by which it operates should be of attention in the next stages of ZNF804A research.

Interim Summary The three-dimensional protein models of some ZNF804A isoforms appear to display “typical” features of C2H2-zinc finger configuration. Extensive disorder within three-dimensional models is consistent with previous estimates from secondary structure twodimensional predictions. It is uncertain if the intrinsic disorder in the ZNF804A protein (especially ZNF804A_bAug10//AK055275.1 protein) affects its functionality.

Conclusions and Future Directions The extensive literature on ZNF804A reveals several aspects in which this candidate gene was implicated in SZ and BD pathophysiology. Numerous genetic analyses initially associated ZNF804A to these diagnoses. Subsequently, ZNF804A has been implicated in aberrant brain morphologies, neurocognition, and responses to antipsychotic medication in numerous human studies. Molecular studies of ZNF804A provided insight to putative biological pathways involving this gene, as well as the effect of the rs1344706 risk SNP on expression of ZNF804A mRNA and protein. In all, a better understanding of the basis of association

FIG. 10. Images of the ZNF804A.cAug10 model viewed in Swiss PDB Viewer. Residues corresponding to the predicted C2H2-zinc finger motif are shown and labeled accordingly. A hypothetical zinc ion is depicted in (A). Dotted lines represent the zinc ion being coordinated by two histidine residues (His41 and His47) and two cysteines (Cys25 and Cys28). Distances (in Angstroms) between these hypothetical zinc-ion coordinating residues are included in (B).

HESS ET AL. between ZNF804A and SZ and BD is gradually coming to fruition. However, many fundamental aspects of this relationship remain unclear. No conclusive evidence related to the neurobiological activity of ZNF804A is available, nor is there complete consensus of the true functional risk SNP(s). Though rs1344706 has been associated very reliably in numerous investigations it is not necessarily the causative SNP itself. Indeed, association signals among multiple SNPs in or near ZNF804A have been detected, which we have described above. It is noteworthy that rs1344706 was not associated as an eQTL with ZNF804A in previous analyses; therefore it is possible that this SNP does not have a biological effect. Such lack of definitive functional linkages is a pervasive issue in the follow-up of genetic associations of SNPs with complex phenotypes. It is noteworthy that common SNPs by themselves are not powerfully indicators of disorder risk. Only in aggregate may such variants have a sufficient influence on complex traits. This, in turn, is a basis of heterogeneity and a major complication to identifying risk factors for SZ and BD. Thus far, ZNF804A is regarded as a putative transcription factor operating through a zinc-finger motif located in its exon 2 coding segment. Based on our preliminary three-dimensional models of ZNF804A, the region corresponding to the zinc-finger motif appears to have some of the features of a typical C2H2-zinc finger motif including its folding pattern and expected organization of key histidines and cysteines proximal to one another, suggesting a potential role in coordinating a zinc ion. However, there are features of ZNF804A that distinguish it from other typical zincfinger bearing proteins–namely the fact that ZNF804A only has one zinc-finger motif and a disordered protein structure. Generally, proteins of the C2H2-zinc-finger class display multiple zinc-finger motifs in their structure ranging from 3 to 30 [Iuchi, 2001]. If these zinc-finger proteins interact with DNA, they do so by binding a specific triplet sequence, a stretch of three nucleotides [Iuchi, 2001]. Increasing the number of zinc-fingers within a DNAbinding protein can exponentially increase its specificity for other molecules. Given these concepts, it seems unlikely for the single predicted zinc-finger motif in ZNF804A to garner much specificity for DNA or RNA targets or protein, unless ZNF804A does not behave as a typical C2H2-zinc finger protein. Yet, other work suggests that high specificity with a single zinc-finger is a possibility. In the body of zinc-finger literature, we found exemplary cases of two proteins, ZNF593 and GAGA factor, which bear single C2H2-zinc finger motifs that have very specific functions in the cell. ZNF593 modulates the binding of the Oct-2 transcription factor to DNA [Hayes et al., 2008], and is relevant to ZNF804A due to its high degree of shared homology and structural resemblance to ZNF593. Remarkably, approximately 65% of the ZNF593 protein is disordered, as confirmed by NMR spectroscopy [Hayes et al., 2008]. What we might otherwise expect to be a significant barrier to the functionality of ZNF804A (expected 85% disordered state) may in fact be acceptable for this apparent sub-class of zinc-finger proteins. However, ZNF593 apparently functions as a regulator via proteinprotein interaction, whereas the preponderance of evidence for ZNF804A suggests its role as a transcription factor. The second example, GAGA factor, is a protein that harbors a single zinc-finger motif yet is capable of recognizing a stretch of DNA larger than the expected triplet sequence. The GAGA factor, encoded

31 by the Trl gene in Drosophilia melanogaster, interacts with a core 7nucleotide DNA sequence and exerts a dynamic effect on gene expression, chromatin organization, and molecular interaction [Pedone et al., 1996; Adkins et al., 2006]. There are three known functional domains contained in the GAGA factor: (1) a C2H2-zinc finger motif; (2) a BTB/POZ domain; and (3) a poly Q domain. Together, these distinct domains exert a concerted impact on transcription and may facilitate oligomerization. Though ZNF804A has only one predicted functional motif, detailed biochemical study of its protein structure and interactions may elucidate multiple potential domains beyond the canonical zinc-finger motif. Determining the exact role of ZNF804A will be a significant advance for both neuropsychiatric and zinc-finger research as, to our knowledge, there are no other strong examples in the literature of a zinc-finger transcription factor with features quite like ZNF804A. To improve our understanding of ZNF804A and its interactions within the cell, it may be advantageous to examine the co-expression network of ZNF804A using transcriptome data from human postmortem brain from cases and controls. Our co-expression analysis suggested a biological connection is likely to exist between ZNF804A and three of its predicted upstream regulators (miR-137, GATA2, and MYT1L), in addition to nine putative targets, in the human brain. A caveat to this study is the use of transcriptomearray data, which may introduce artifacts that are not biologically relevant. Sophisticated methods of gene co-expression analysis have been proposed to correct for potential transcriptome artifacts and over-fitting of data, such as a weighted correlation network analysis [Langfelder and Horvath, 2008]. Replication of our findings via transcriptome-wide analysis may be a fruitful approach for uncovering details of the ZNF804A network that might not otherwise be uncovered through our stringent approach of relying on coefficient thresholds and a priori evidence. Our strategy for co-expression analysis was tailored by in silico data and structured hypotheses; as such, adjusting with statistical weights may have marginally benefited our analysis. Conversely, defining a confidence threshold for gene co-expression can be useful, as recently outlined in an investigation that successfully detected gene co-expression modules related to autism spectrum disorder [Willsey et al., 2013] distributed in space and time within the human brain. The report by Willsey et al. [2013] demonstrated the success of a sophisticated gene co-expression analysis and the insight that can be extracted from co-expression network enriched by spatiotemporal modules attributable to changing biological impact for a single gene or a network of genes. Note, Willsey et al. [2013] coupled data-mining with experimental testing, allowing for reliable interactions to be distinguished based on co-expression analysis; our current study does not include separate experimental testing to validate the statistical analyses performed, though such empirical work is warranted. Our analysis of RNA-seq data from pre-existing postmortem brain data revealed that full-length and 50 truncated isoforms of ZNF804A are potentially expressed in the human brain throughout the lifespan. Our findings were partially congruent with findings from a recent study that profiled ZNF804A mRNA in a larger sampling of postmortem human brain across several groups of psychiatric patients; this particular report demonstrated similar findings, yet with important differences in the reliability of their methods and evidence of

32


genetic effect of rs1344706 on isoform expression [Tao et al., 2014]. There are no conclusive findings as to the mechanisms of ZNF804A isoform production or the biological effect of rs1344706 on this process. Our bioinformatic analyses revealed candidate transcription and splicing factors that may be affected by rs1344706; we hypothesized these mechanisms to be important for affecting isoform expression. Further neurobiological testing of these isoforms is warranted to determine their importance for SZ and BD. Our analysis of epistasis among genes implicated in the ZNF804A pathway highlighted possible genetic connections mediating the relationship of ZNF804A with genes encoding cell adhesion molecules. The importance of ZNF804A to the class of cell adhesion genes was previously indicated from knock-down of ZNF804A mRNA in neural stem cells [Hill et al., 2012]. Additionally, we found a nominal association of ZNF804A with the MYT1L transcription factorencoding gene, which is consistent with the relationship between ZNF804A and MYT1L we previously detected from bioinformatic and co-expression analysis of rs1344706 [Hess and Glatt, 2014]. Our analysis of SNP-SNP interactions involving rs1344706 was not a genome-wide assessment; therefore, we cannot make definitive conclusions as to the importance of our epistasis results. Our analysis identified promising candidate interactions, which warrant additional testing with larger sample sizes and improved tag-SNP coverage. Overall, the models and concepts that we present here illustrate the complexity of ZNF804A and the rs1344706 variant. We presented several hypotheses warranting follow-up analysis, including several avenues for examining the mechanism by which rs1344706 affects expression of ZNF804A. As of yet, research of ZNF804A is still in its infancy; although genetic analyses have sufficiently replicated the genetic role of rs1344706 in SZ and BD, much remains to be understood regarding the biological function of this gene and its risk SNP. However, molecular studies and cognitive analyses tend to agree that rs1344706 has a definitive impact on ZNF804A expression, and that this risk SNP impacts cognitive functioning and brain morphology. The element that remains disputed is if rs1344706 has a global deleterious effect on neurobiological pathways leading to SZ or BD, or if its effect emerges only in particular disorder subtypes. Given the genetic and biological heterogeneity implicit with SZ and B, one explanation may not be sufficient. SZ and BD are rich with intrinsic complexity; numerous combinations of predisposing factors and neurobiological disturbances likely contribute to one’s susceptibility for these disorders. It is little surprise for ZNF804A and its risk SNP to demonstrate similar depths of complexity—a richness that is common among a variety of risk genes for neuropsychiatric disorders. Additional follow-up analyses beyond those presented here might unravel the biological pathways affected by ZNF804A in the human brain, and when these pathways are most susceptible to effects of the risk SNP. As such, we anticipate future directions of ZNF804A research to benefit from distinguishing the role of ZNF804A, and unraveling its regulatory network in the cell and contributions to neurobiological pathways in the brain.

Behavior Research Foundation, and the U.S. National Institutes of Health.

ACKNOWLEDGMENTS

Colantuoni C, Lipska BK, Ye T, Hyde TM, Tao R, Leek JT, Colantuoni EA, Elkahloun AG, Herman MM, Weinberger DR, Kleinman JE. 2011. Temporal dynamics and genetic control of transcription in the human prefrontal cortex. Nature 478:519–523.

This work was supported by grants from the Gerber Foundation, the Sidney R. Baer Jr., Foundation, NARSAD: The Brain and

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