Schizophrenia Research 163 (2015) 1–8

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The importance of endophenotypes in schizophrenia research☆ David L. Braff ⁎ University of California San Diego, Department of Psychiatry, 9500 Gilman Drive, La Jolla, CA 92093-0804, United States

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Article history: Received 19 June 2014 Received in revised form 6 February 2015 Accepted 6 February 2015 Available online 18 March 2015

a b s t r a c t Endophenotypes provide a powerful neurobiological platform from which we can understand the genomic and neural substrates of schizophrenia and other common complex neuropsychiatric disorders. The Consortium on the Genetics of Schizophrenia (COGS) has conducted multisite studies on carefully selected key neurocognitive and neurophysiological endophenotypes in 300 families (COGS-1) and then in a follow up multisite case–control study of 2471 subjects (COGS-2). Endophenotypes are neurobiologically informed quantitative measures that show deficits in probands and their first degree relatives. They are more amenable to statistical analysis than are “fuzzy” qualitative clinical traits or confoundingly heterogeneous diagnostic categories. Endophenotypes are also viewed as uniquely informative in traditional diagnosis-based as well as emerging NIMH Research Domain (RDoC) contexts, offering a bridge between the two approaches to psychopathology classification and research. Endo- or intermediate phenotypes are heritable, and in the COGS-1 cohort their level of heritability is in the same range as is the heritability of schizophrenia itself, using the same statistical methods and subjects to assess both. Because we can demonstrate endophenotypes link to both gene networks and neural circuits on the one hand and also to real-life function, endophenotypes provide a critically important bridge for “connecting the dots” between genes, cells, circuits, information processing, neurocognition and functional impairment and personalized treatment selection in schizophrenia patients. By connecting schizophrenia risk genes with neurobiologically informed endophenotypes, and via the use of association, linkage, sequencing, stem cell and other strategies, we can provide our field with new neurobiologically informed information in our efforts to understand and treat schizophrenia. Evolving views, data and new analytic strategies about schizophrenia risk, pathology and treatment are described in this Viewpoint and in the accompanying Special Issue reports. Published by Elsevier B.V.

1. Introduction Endophenotypes have played a crucial role in advancing our understanding of the gene to phene knowledge gap of schizophrenia (e.g., Gottesman and Gould, 2003; Braff et al., 2007). The use of endophenotypes as neurobiologically informed quantitative measures is now rapidly increasing in schizophrenia research (cf. Fig. 1). Endophenotypes are quantitative laboratory based measures, hidden from the view of the “naked eye” of clinical observation. Endophenotype deficits are observed in groups of schizophrenia patients (SZ) relative to Healthy Control Subjects (HCS). First degree relatives of SZ probands show intermediate values. Gottesman and Shields' (1973)

☆ Grant Support: This work was supported by the National Institute of Mental Health (MH065571, MH042228 and MH093533) and the U.S. Department of Veterans Affairs (VISN 22 Mental Illness Research, Education, and Clinical Center) and the Niederhoffer Family Funds. ⁎ Tel.: +1 619 543 5570 (office); fax: +1 619 543 2493. E-mail address: [email protected].

http://dx.doi.org/10.1016/j.schres.2015.02.007 0920-9964/Published by Elsevier B.V.

transformative concept has yielded a plethora of highly informative endophenotype studies of schizophrenia, an accelerating harvest that still continues (cf. Gottesman and Gould, 2003; Braff et al., 2007; Tan et al., 2008; Glahn et al., 2014) as illustrated by Fig. 1. As quantitative measures, endophenotypes offer a significant statistical analytic advantage over the DSM-based qualitative and somewhat fuzzy clinical phenotypes. It is also crucial to point out that endophenotypes are viewed as crucial, strategically important measures in the NIMH Research Domain Criteria (RDoC) literature as indicated by the title of the commentary “Endophenotypes: Bridging genomic complexity and disorder heterogeneity” (Insel and Cuthbert, 2009). But are endophenotypes really simpler and more proximal to genes than diagnostic categories? I would posit that anyone who has interviewed several hundred schizophrenia patients and then administered the LNS working memory test (see Lee et al., 2015–in this issue) to the same patients would doubtlessly say that yes endophenotypes are simpler than the disorder. This admittedly face validity view is reinforced by multiple analytic strategies as discussed below (e.g., Gottesman and Gould, 2003; Braff et al., 2007; Glahn et al., 2014). Are

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900

Number of citations for the term endophenotype

800 700 600 500 400 300 200 100 0 1987-1991

1992-1996

1997-2001

2002-2006

2007-2013

Fig. 1. The growing importance of the endophenotype strategy in psychiatry, schizophrenia research as seen in the dramatic increase of the number of endophenotype citations from 1987 to 2013.

endophenotypes also closer to genomic substrates than fuzzy clinical diagnoses? Yes, endophenotypes do fill the gene to phene gap in our knowledge. Please see discussion below and related references (cf. Gottesman and Gould, 2003; Braff et al., 2007; Tan et al., 2008; Glahn et al., 2014). Beginning in 2003, the Consortium on the Genetics of Schizophrenia (COGS-1), characterized 300 families of schizophrenia probands with at least one unaffected sibling and both parents available for testing (cf. Calkins et al., 2007; Swerdlow et al., 2015–in this issue). COGS-2 is a follow-up study expanded to include almost 2500 case– control participants, using the 12 primary and some additional COGS1 endophenotypes, embedded in a carefully quality assured demographic, clinical and functional outcome database (e.g., cf. Calkins et al., 2007). Additional forthcoming genomic characterization and statistical genetic analyses partly using the Psychiatric Genomics Consortium (PGC) 550K PsychChip and other genomic platforms will follow. An interesting challenge about endophenotype deficits arises in family studies. Clinically unaffected first degree relatives of schizophrenia patients have at least some level of endophenotype deficits but these relatives are not affected by schizophrenia itself. Why are these endophenotype (and putatively gene variation) carriers not affected by the clinical phenotype of schizophrenia? Perhaps clinically unaffected relatives have a subthreshold summation of genetic and non-genetic risk factors and a below threshold genomic and endophenotype burden (e.g. Glahn et al., 2014). Alternatively, risk genes may interact with protective genes and/or protective environmental factors, reflecting the dynamic interplay of multiple risk and protective genetic (G) and environmental (E) observations (Gottesman and Gould, 2003; Braff et al., 2007; Braff, 2012; Glahn et al., 2014). This issue of gene burden thresholds and opposing protective factors is perhaps one of the most crucial yet relatively unexplored areas of schizophrenia research. The risk algorithm for schizophrenia is multifactorial and complex, and there have been long standing efforts to identify which “high risk” children with endophenotypic and genetic risk factors ultimately cross the threshold from risk and “convert” to developing a psychotic illness often after a normal or near normal appearing childhood. After a period of initial Jacobean revolutionary zeal (Braff and Braff, 2013) algorithms designed to identify which “high-risk” children do convert to having schizophrenia have undergone many iterations. In this context, some risk-associated endophenotypes such as mismatch negativity (MMN) (see Light et al., 2015, in this issue) can actually be measured in utero. Since many reports indicate that the onset of neural circuit disrupting events occurs before birth, the optimal time for effective early identification and intervention might ultimately be in the prenatal period

(Swerdlow, 2011). But, prenatal pharmacological intervention based on a risk algorithm is a strategy fraught with profound ethical and practical long term medical, scientific, legal and social challenges. It is more likely that benign (low side effect profile) cognitive and sensory training interventions will be utilized early in life in a high risk population of endophenotype deficit burdened children. In this broad context, what is our present and likely future state of knowledge and future strategies of endophenotype research in schizophrenia? 2. Present: the state of endophenotypes in schizophrenia research The present state of affairs includes the studies of endophenotypes presented in this Issue of Schizophrenia Research, based on the foundation of literally thousands of endophenotype-related articles partly referenced in this Special Issue. For example, there are over 3000 PubMed publication titles on prepulse inhibition (PPI) just one of the 12 main COGS endophenotypes. Of the many relevant studies, this Special Issue focuses on the COGS-2 case–control study of neurocognitive and neurophysiological quantitative endophenotypes (also see Swerdlow et al., 2014). It is important to note that these endophenotypes and associated risk genes were specifically selected a priori based upon the extant literature circa 2003 and then 2008. Thus, this was a strong inference derived a priori selection of both schizophrenia-related genes and endophenotypes. We showed that the 12 COGS-1 neurocognitive and neurophysiological endophenotypes (6 primary endophenotypes and 6 derived from the Penn Computerized Neurocognitive Battery) were heritable and first degree relatives of schizophrenia patients generally scored between the quantitatively endophenotype deficit laden schizophrenia patients and the carefully screened healthy control subjects (Greenwood et al., 2007). In addition, COGS-1 candidate gene, association, linkage and sequencing studies for identifying common heritable mutations bore much fruit for identifying the neurobiologically plausible common heritable as well as the gene burden associated endophenotypic deficits seen in these clinically unaffected COGS 1 relatives of schizophrenia probands (e.g. Greenwood et al., 2011, 2012, 2013). These findings served as a confirmation of the value of “clinically and neurobiologically informed psychiatric genomics” as opposed to very large scale agnostic studies which are valuable for loci and gene discovery but do not capitalize on years of neurobiological, genomic, and other strong inference based research findings (Braff, 2012; Braff and Braff, 2013). In this regard, Fig. 2 shows endophenotype–gene associations which survived a 25,000 bootstrap total significance test (TST) analysis, demonstrating a plausible neurobiological context of gene– endophenotype associations (cf. Greenwood et al., 2011).

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Fig. 2. Summary of the most significant p-value observed for each of the 46 genes with each of the 12 endophenotypes using a minimum p-value of b0.01 as a threshold. Note that not all associations with the same gene across endophenotypes reflect associations with the same SNP. Yellow indicates a p-value b0.01, blue indicates a p-value b10−3, and red indicates a pvalue b10−4. An asterisk (*) indicates that at least one SNP in the gene associated with the specified endophenotype has been previously associated with schizophrenia. Genes associated with 4 or more endophenotypes are indicated in bold. Greenwood and the COGS investigators. Analysis of 94 candidate genes and 12 endophenotypes for schizophrenia from the Consortium on the Genetics of Schizophrenia. Am. J. Psychiatry 168:930–46, 2011. (See also the partial replication in Greenwood et al., 2012).

In addition, the COGS-1 family study identified a 42-gene network which has an Ingenuity identified glutamate related NRG1– ERBB4 “hub” that was largely replicated in two independent samples (Greenwood et al., 2011, 2012). This gene network “makes sense,” since the NRG1 and ERBB4 are prominently involved in glutamate transmission and glutamate disruptions are strongly relevant to schizophrenia-related psychopathology (Barrett and Coyle, 2012; Javitt, 2012; Williams and Coyle, 2012; Greenwood et al., 2013; Goff,

2014; Umbricht et al., 2014). Also, related treatment trials of glutamate agonists identify decreased negative symptoms in schizophrenia patients (cf. Javitt, 2012; Goff, 2014; Umbricht et al., 2014). Still, schizophrenia is not a unitary disorder associated with one endophenotype deficit, nor does it result from a singular type of genomic variation or singular neural substrate. In fact, as shown in Fig. 3, the genes associated with endophenotype deficits create a coherent 42 gene network underling the 12 inter-related endophenotypes. A major current question is

Fig. 3. Genetic pathway(s) of COGS endophenotypes. Genetic network detailing the types of interactions that have been observed to occur between a subset of the 94 candidate genes on the COGS SNP Chip. Genes are represented as nodes, and the biological relationship between two nodes is represented as an edge (line or arrow) supported by at least one reference from the literature, a textbook, or canonical information derived from the human, mouse, and rat orthologs of the gene that are stored in the Ingenuity Pathways Knowledge Base. Solid and dashed lines/arrows indicate direct and indirect interactions, respectively. Protein–protein interactions are indicated in red, protein–DNA interactions in light blue, expression and activation in dark blue, and phosphorylation in green. Genes found to be associated (p b 0.01) with at least one endophenotype are highlighted in yellow, with an asterisk (*) indicating those genes associated with more than one endophenotype (Greenwood et al., 2011).

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whether frontal glutamate transmission or other neurobiological processes reflect a common final pathway for gene networks (and related endophenotypic deficits) such as the COGS-data 42 gene network pictured in Fig. 3. It seems as if Bleuler (1911) was almost presciently correct that the “Group of Schizophrenias” reflects profound heterogeneity and this heterogeneity concept has stood the test of time and of changing ideas and technologies. Still, technologies evolve, we must be careful not to let the new technologies become the strategy (e.g. Michael Porter cited by Useem, 2014), but to use technological advances in the service of testing rational, strong inference based hypotheses based on the rich data we already have regarding the neurobiological and genomic basis of schizophrenia. The challenge of heterogeneity is also amenable to parsing patients into more homogenous groups via the use of quantitative endophenotypes that can be used to construct simpler, more homogenous subgroups and via the use of related statistical genetic strategies that identify genomic networks and neural circuit substrates of endophenotype characterized subjects (Gottesman and Gould, 2003; Schork et al., 2007). In addition, the heritability of the COGS-1 endophenotypes was in the range of .33 to .50 in the Greenwood et al. (2007) report and strikingly we have now found that the heritability of schizophrenia itself using the same methods for estimating heritability in the same COGS1 cohort is in about the same range in this cohort whose heritability of schizophrenia value was probably under downward pressure from the ascertainment strategy we utilized as described below (cf. Light et al., 2014). Perhaps “missing heritability” is less of a trenchant problem than is commonly assumed in schizophrenia research. The current era in which we identify heritable and de novo genomic variation and associated endophenotype quantitative biomarkers for neuropsychiatric disorders is just beginning (Braff, 2012; Braff and Braff, 2013). In this, and related areas, there is a need for recognizing that many alternative explanations are complementary and not mutually exclusive (cf. Owen et al., 2010 for a discussion of how both heritable and de novo mutations have a role in assessing schizophrenia risk). For now, all the genomic knowledge of the simple Mendelizing Huntington's Disease (HD) gene has led to some rather blunt tools of disease treatment and prevention: the “patient” can decide to be tested (or not) for a positive HD gene and with a positive result has a 100% chance of developing HD if she/he lives long enough and a 50% chance that an offspring will develop HD. For non-small-cell lung carcinoma, leukemia and breast cancer, genetic advances are clear. But treatment options remain a bit of a “Sophie's Choice”; i.e. to have bilateral breast amputation or not for a high genetic risk woman with a family history for breast cancer is the practical low tech and agonizing consequence of advanced genomic science. We can anticipate that in the future genetic counseling and family planning will be used exponentially more in common complex disorders such as schizophrenia. Below, other future options are examined. 3. Future Challenges and opportunities abound for endophenotype research in schizophrenia and are both exciting and daunting. Below is a synopsis of some of the many key issues in the field of endophenotypes, genomics and schizophrenia research. 3.1. The FDA now considers cognitive endophenotypes in schizophrenia to be a therapeutic treatment target As a result of the NIMH-sponsored Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) Initiative, FDA has agreed that cognitive impairment in schizophrenia is a valid treatment target. The gold-standard assessment for cognition is the MATRICS Consensus Cognitive Battery, which includes 4 domains that are also included in COGS endophenotype battery: working memory,

verbal memory and learning, attention/vigilance, and social cognition (Green et al., 2004; Nuechterlein et al., 2008). In fact, several of the MATRICS tests are essentially the same (verbal working memory (LNS) and attention/vigilance (CPT)) or very similar (verbal memory and learning) between the MATRICS and COGS Batteries. This relatively recent consideration is often not considered when the usefulness of endophenotypes is discussed (e.g., Karayiorgou et al., 2012). In concert, the exciting development of neuroplasticity-based cognitive and sensory training interventions to improve neurocognitive deficits in schizophrenia is also a very important advance but is agnostic as to its genomic and exact neural circuit basics (Fisher et al., 2010). COGS-1 and -2 have identified a heritable series of quantitative, neurocognitive endophenotypes, and some of these domains are related to functional capacity and outcome and are thus considered to be prime treatment targets, as discussed in these Special Issue papers (e.g., Turetsky et al., 2015–in this issue). In addition, the usefulness of isomorphic cross species neurophysiological endophenotypes has already been validated using PPI and P50 suppression quantitative neurophysiological endophenotypes with their well delineated predictive therapeutic validity (e.g., Geyer et al., 2001; Swerdlow et al., 2001). In therapeutic approaches based on endophenotypes, we are not necessarily trying to restore longstanding underlying genomic and neural circuit dysfunction via new treatments. Rather, both biological and sensory/cognitive treatments may, at least partially,“normalize” neural circuits and activate collateral circuits (Swerdlow, 2011) but also may have effects even after the treatment is discontinued, as is seen for neurocognitive measures long lasting (e.g. 6 months), post-treatment discontinuation improvement in targeted cognitive treatment (TCT) therapies (Fisher et al., 2010). As it turns out, both psychosocial and biological therapeutic effects may act to restore impaired neural circuits or may stimulate collateral and intact neural circuits that may “take over” in restoring neurocognitive and neurophysiological functional domains from the longstanding impaired neural circuits whose genesis may be found in very early, even in utero, neurodevelopmental events in disorders such as schizophrenia and autism (Swerdlow, 2011; Stoner et al., 2014). 3.2. Large N, small effect sizes in large populations: are “highly significant” results also important? Endophenotype gene networks help in integrating genomic results Many GWAS studies utilize very large numbers of subjects for identifying common genetic variation of small effect size for characterizing heritable traits such as height or BMI and for exploring the genetics of common complex disorders such as schizophrenia. But schizophrenia, highly heritable, is profoundly difficult to map genetically. In a recent report, cumulative totals of 21,246 cases and 26,005 control subjects were examined and 13 risk alleles were identified, but they accounted for a total of 32% of schizophrenia risk. Ultimately, according to Ripke et al. (2013) up to 8300 alleles may account for “at least” 32% of the variance in schizophrenia risk. In their follow-up PGC study, they identified 108 loci associated with schizophrenia, a monumental effort of the Schizophrenia Working Group of the Psychiatric Genomics Consortium (SWGPGC) (2014). But, the effect size of each allele is small. That is not a criticism — that is the reality. So follow-up studies will be needed to identify genes, gene networks and gene–gene as well as gene–environment interactions to more fully understand schizophrenia. The PGC overall mapping effort is also extremely important in order for us to have a complete genomic profile of schizophrenia risk as the “buildings” of the schizophrenia Manhattan plot of risk increase in size and number. This pattern of findings makes sense if, for example, these loci are parts of gene networks such as those identified as underlying endophenotypic deficits in schizophrenia patients (see Fig. 3). Still, it is striking that both the PGC and COGS studies converge in identifying glutamate as a key player in schizophrenia pathology (e.g. SWG-PGC-2014, Greenwood et al., 2013; Ripke et al., 2013). With

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informed and integrative neurobiology, the small effect sizes can be more fully understood. This state of affairs has been termed the “Large N, Highly Significant P Value, Small Effect Size Conundrum” in schizophrenia research (Braff, 2012). It is important to note that endophenotypes are already associated with strong inference, biologically based gene networks for identifying and integrating risk genes and filling in knowledge of the gene to phene pathway (Fig. 3). Also, these endophenotype deficits are already important in understanding schizophrenia since they are associated with real world function deficits (e.g. Green et al., 2011; Light and Braff, 2005; other papers, This Issue). 3.3. Heritability of endophenotypes and heritability of schizophrenia The heritability and genomic substrates of many neurocognitive and neurophysiological endophenotypes have been established via twin and family studies and recently the genomic basis of neurophysiological endophenotypes has been enhanced. Measures such as EEG power, ERPs, P300, startle modulation and antisaccade have been extensively explored in the Minnesota Twin and Family Study Cohort (MTFSC) for healthy subjects (Special Issue Psychophysiology, Iacono Editor: e.g., Braff, 2014; Malone et al., 2014; Vaidyanathan et al., 2014a,b). It is important to note that COGS-1 & 2 endophenotypes and SNPs were originally selected on the basis of their prior identification in schizophrenia patients and their first degree relatives and their heritability, and we have now identified underlying common genetic variation associated with the COGS-1 endophenotypes (e.g., Greenwood et al., 2011, 2012, 2013). Of course, there also seems to be a silo of non-heritable de novo genetic events that are sometimes highly penetrant and therefore powerful in their effects, and these can confer both schizophrenia risk and may be associated with endophenotype dysfunction in the COGS-1 families (c.f. Gulsuner et al., 2013) (see Fig. 4). The heritability of COGS endophenotypes parallels that of the MTFSC cohort. The heritability of schizophrenia itself in COGS-1 is in the range of the

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COGS endophenotypes. This lower than commonly cited 80% is 1) more in line with Gottesman's recent analyses (Wray and Gottesman, 2012); and 2) probably reflects downward pressure on the heritability of schizophrenia in COGS-1 accruing from the ascertainment scheme of examining schizophrenia families via the requirement of an affected proband and at least one unaffected sibling (discordant sib-pair) with exclusions for schizophrenia occurring in both maternal and paternal family lines (cf. Light et al., 2014). The likely “mutation replenishing” effect of de novo events in hot spot breeding grounds may partially account for resistance to downward selection pressure that would (but does not necessarily) diminish fitness and decrease schizophrenia prevalence over time (Owen et al., 2010). These nonheritable sequencing derived de novo mutations interact in as yet an unquantified way with heritable common, vertically transmitted mutations which underlie some of the heritable endophenotype dysfunction in the group of schizophrenias. 3.4. Biologically informed genomic and endophenotype strategies augment GWAS results Is the agnosticism of very large scale GWAS approaches in schizophrenia research: A) A good idea, B) a very challenging idea or C) both? The correct answer, I believe, is C. Actually, the neurobiological agnosticism of GWAS studies is both good and challenging. It is good because it allows for using a rigorous 5 × 10−8 whole genome threshold for identifying the significance of small effect size common genetic variation that may be implicated in both endophenotype and schizophrenia risk detection and etiology. Thus the value of the PGC approach is undoubtedly quite impressive (e.g., Ripke et al. 108 loci paper). The downside of this strategy is that for p values in genomics “apparent precision masks high uncertainty” (Lazzeroni et al., 2014) and such agnosticism necessarily a priori ignores the wealth of data from candidate gene and other studies based on neurobiological and clinical knowledge of the relationships of genomic variation and associated strong

Consortium on the Genetics of Schizophrenia (COGS) Endophenotypes and Schizophrenia Reaching Across Many Domains Genes and Function (Gregory Light, Ph.D.; David Braff, M.D.)

3 CONSORTIA CONSORTIUM: COGS PAARTNERS (African-Americans) MGI (Multiplex Families)

Translational Studies Cross Discipline Cross Species (David Braff, M.D.; Mark Geyer, Ph.D.; Neal Swerdlow, M.D., Ph.D.)

COGS 1: 7 site 300 family Study

Copy-Number Variation (CNV) Exomic Sequencing (Mary Claire King, Ph.D.; Jack McClellan, M.D.)*

COGS 2: 5 site 3,000 Subject Case-Control Total N= 5,071

Methylation Events (Andrew Feinberg, M.D., MPH)*

Veterans Health System, VISN 22 Mental Illness Research, Education & Clinical Centers (MIRECC) Cognition & Genomics Project Connectome (Lilia Iakoucheva, Ph.D.)

SENSORY-COGNITIVE REMEDIATION (MIRECC Targeted Cognitive Training (TCT) (Gregory Light, Ph.D.; David Braff, M.D.)

NEW STRATEGIES (e.g. Stem Cells) (Rusty Gage, Ph.D., Kristen Brennand, Ph.D.) MIRECC: Advanced Neurophysiology EEG Studies and Brain Initiative (Gregory Light, Ph.D.; Scott Makeig, Ph.D.; Terry Sejnowski, Ph.D.; David Braff, M.D.)

GENOTYPING PGC Chip 500 SNPS , 250K GWAS (Steve McCarroll, Ph.D.; Pamela Sklar, M.D., Ph.D.)*

Gene Expression (Laura Almasy, Ph.D.; ) Statistical Genetic Advances, e.g. GAMOvA (Schorket al.) Total Sig Test (Lazzeroni et al.)

Fig. 4. The COGS Project has led to many collaborations as is partially illustrated in this figure. This reflects the far ranging versatility of the endophenotype approach to schizophrenia research.

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inference based neural circuit dysfunction associated with schizophrenia. But as with the case in examining heritable vs. de novo mutations of schizophrenia risk, the two approaches should be seen as complementary, not competing (cf. Owen et al., 2010). In addition, Goldstein (e.g., Goldstein, 2009) has warned against continually “turning the crank” on GWAS (and then sequencing) approaches absent strong inference based hypotheses. GWAS in schizophrenia patients with high endophenotypic (and theoretically) high genomic loads may be a uniquely productive way to apply GWAS findings (Tan et al., 2008; Braff, 2012; Glahn et al., 2012, 2014). Also the typical GWAS p value for determining a significance of 5 × 10−8 is somewhat draconian when there is reason to believe a priori that loci are implicated in disease pathology. This may lead to false negative results especially if there is a biological explanation for using a directional hypothesis and thereby constraining the space being examined (Lazzeroni and Ray, 2010; Flint et al., 2014; Lazzeroni et al., 2014). Clearly, in the COGS experience, association, linkage, and sequencing studies have shown that there is a complex picture of both common and rare de novo mutations in these cohorts contributing to schizophrenia risk (Greenwood et al., 2011, 2012, 2013; Gulsuner et al., 2013). Interestingly, this risk involves glutamate in both the hypothesis driven COGS (Greenwood et al., 2011, 2012, 2013) and agnostically derived PGC samples (Schizophrenia Working Group of the Psychiatric Genomics Consortium). A major problem is that we do not yet know the relative contributions of common small effect size versus rare large effect size, highly penetrant often de novo events that occur in schizophrenia and other common complex disorders. This is a challenge for the future. A related challenge is to not allow “the new technology to become the strategy” (Porter cited by Useem, 2014). As GWAS and sequencing studies evolve, we must resist the temptation to just keep turning the crank with new technologies absent careful, strong inference based strategies. Thus, it is important to use both common variation and de novo strategies and to see them as complementary rather than competing (Braff and Braff, 2013; Owen et al., 2010). The information on COGS-2 endophenotypes in this issue contributes to our understanding of the magnitude of endophenotype deficits in schizophrenia and of modulatory factors in endophenotype expression. This information is important for future studies. These factors may also be informative as we select genomic statistical genotyping and statistical strategies in the future (Schork et al., 2007). 3.5. The “Knowledge to Wisdom Gap” in schizophrenia genomics and endophenotype research Recently, the National Academy of Sciences report on “Precision Medicine” made the point that in this era of commodity priced genome scale measurements, we now can envision systematic reclassification of human pathobiology on the population scale (National Research Council, 2011). There is a virtual explosion of interest in the new knowledge of endophenotype deficits in schizophrenia (see Fig. 1). But it is important to differentiate between knowledge and wisdom. Knowledge is the accumulation of individual facts, and we are doing relatively well in this area in the translational neuropsychiatric genomic revolution (Braff, 2012; Braff and Braff, 2013). Not surprisingly, we seem to lag in the very difficult integration of these facts in order to create a comprehensive understanding of the maladaptive developing brain and the expression of the heterogeneous signs and symptoms of schizophrenia and schizophrenia-related endophenotypes to fill in this “Knowledge to Wisdom Gap”. This endeavor is “incremental” in nature, a term sometimes used disparagingly. But one could easily argue that except for Darwin, Mendel and Watson & Crick (Watson and Crick, 1953a,b; Braff, 2014) all of genomics has been incremental — not a bad thing at all. When the “Book of Life” was opened in 2000 and the Human Genome Project identified the 3 billion base pairs (bps) contained in human DNA and the 1 to 2% of bps that actually produced proteins via genes, there was an optimistic

sense that this would lead quickly to a genomic revolution of personalized neuropsychiatric medicine. In some areas, such as non-small-cell lung carcinoma, leukemia, and breast cancer risk, detection and treatment, we have developed some therapeutic interventions based on the genomic knowledge of simple diseases with accessible tissue. But the brain, encased in a cranial vault with perhaps 75,000,000,000 neurons and up to thousands of connections per neuron represents a totally different quantitative, developmentally-linked and spatial challenge. It is easy to see that it might take 20 to 30 to 40 years before we and our students and their students develop efficacious tools to offer early detection and intervention and especially prevention for neural circuit disorders encapsulated in the relatively inaccessible cranial vault with their genesis occurring as early as in the in utero epoch of brain development; or perhaps breakthroughs may occur at a much faster pace. We simply cannot predict the likely time course of these life-enhancing advances (see below). For some approaches, we may ultimately be able to change the expression of the human genome at its earliest stages. Our knowledge of schizophrenia-derived stem cells has led to attempts to grow “hippocampal neurons” and even neural circuits and to reverse deleterious events and active neural processes such as cell and lesions and migration with antipsychotic medications in vitro as illustrated by our COGS collaborators (e.g. Brennand et al., 2012; Fig. 4). This path too will undoubtedly be long and challenging although COGS is collaborating in complementary studies in order to construct a thorough multi-level understanding of schizophrenia and its endophenotypes (Fig. 4). 3.6. Pathways for the future Pathways for future advances in our genomic and endophenotype neuropsychiatric revolution, are going to be exciting and challenging. Current ongoing COGS studies are outlined in Fig. 4 and in the following large scale endophenotype studies of this Special Issue. With time and effort and incremental advances as well as possible serendipitous findings, our ability to advance the unfolding of new effective and efficacious treatments of schizophrenia will be increased. There are a number of possible pathways for advances in the future, (Braff and Freedman, 2008; Braff and Braff, 2013). Perhaps there will be: 1) Slow but steady incremental advances in our knowledge of endophenotypes and schizophrenia genomics, pathogenesis and treatment strategies. Incremental research is powerful and builds on existing facts to create new ideas to advance our field. It is a source for advancing the use of endophenotypes in understanding and treating schizophrenia. 2) A scientifically revolutionary “Kuhnian” advance (Kuhn, 1962) whereby investigators from one or more fields “put the picture together” in a unique, paradigm shifting way and find truly revolutionary analytic and treatment strategies (Braff and Braff, 2013). 3) Serendipitous events such as what was seen in the discovery of the therapeutic uses of chlorpromazine and antidepressants. This may involve endophenotype biomarkers for predicting and assessing treatment success (Fisher et al., 2010; Roffman et al., 2013; Goff, 2014; Umbricht et al., 2014). It may also involve the reversal of endophenotype deficits in schizophrenia patients via atheoretical but possibly paradigm shifting drug repurposing strategies. In the context of this Special Issue, in the near future, the COGS-1 family data and COGS-2 case control endophenotype data will be further characterized and examined by use of the Psychiatric Genomics Consortium (PGC) 550K PsychChip and then hopefully be sequenced. First, we will examine SNPs and genes we have already identified as being associated with endophenotype deficits in schizophrenia. Then we will conduct a GWAS of endophenotypes using the full PGC 550K SNP array of a 250,000 allele GWAS backbone, 250,000 additional alleles and an additional 50,000 carefully selected genetic variations. This will facilitate extending and replicating our identified genes and gene

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networks of relevance to schizophrenia and its endophenotypes. Presently, quantitative neurocognitive endophenotypes are used in assessments of cognition that are used for both pharmacological and sensory/cognitive training interventions. Neurophysiological endophenotypes are useful for screening for antipsychotic efficacy in animal models as well as human testing (Braff et al., 2001; Geyer et al., 2001), and are being genotyped in the Minnesota Twin Study sample reported in the recent Special Issue of Psychophysiology (W. Iacono, Editor, Psychophysiology: Braff, 2014; Malone et al., 2014; Vaidyanathan et al., 2014a,b). Thus, endophenotype based genomic and behavioral knowledge and interventions are already advancing our understanding of schizophrenia and informing its treatment. The papers in this Special Issue are hallmark characterizations of neurocognitive and neurophysiological endophenotypes in a definitively large cohort of schizophrenia patients and healthy control subjects. These endophenotypes are also very useful in shaping our understanding of the relationship between traditional diagnostic categories and the emerging NIMH Research Domain (RDoC) literature with its explicit citation of the importance of endophenotypes in psychopathology research (Insel and Cuthbert, 2009). Endophenotypes are important blocks of information upon which we can build a better understanding of the cognitive, neurophysiological, genetic and neural circuit based genomics and treatment of patients in the group of schizophrenias, as we advance our science from bench to bedside to help our patients and their families enjoy a better future. Role of funding source Other than providing support, the NIH had no further role in this manuscript. Contributors Dr. Braff wrote this manuscript. Drs. Michael Green, Raquel Gur, Gregory Light and Neal Swerdlow provided valuable edits to the manuscript. Dr. Braff played a leadership role in the organization and operation of the Consortium on the Genetics of Schizophrenia (COGS) for both COGS-1 and COGS-2 studies. Conflict of interest Dr. Braff reports no financial relationships with commercial interests. Acknowledgments This study was supported by grants R01-MH065571, R01-MH065588, R01MH065562, R01-MH065707, R01-MH065554, R01-MH065578, R01-MH065558, R01MH86135, K01-MH087889, R01-MH042228 and R01-MH093533 from the National Institute of Mental Health. The author acknowledges the outstanding assistance of Ms. Joyce Sprock and Ms. Maria Bongiovanni in the preparation of this manuscript, and the COGS Investigators, Staff and Valued Research Participants.

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