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. Author manuscript; available in PMC: 2019 Nov 1.
Published in final edited form as: Schizophr Res. 2018 Jun 12;201:393–399. doi: 10.1016/j.schres.2018.05.041

Polygenic Risk Score, Genome-wide Association, and Gene Set Analyses of Cognitive Domain Deficits in Schizophrenia

Soichiro Nakahara a,b, Sarah Medland c, Jessica A Turner e,f, Vince D Calhoun g,h,i, Kelvin O Lim h, Bryon A Mueller i, Juan R Bustillo h, Daniel S O’Leary j, Jatin G Vaidya j, Sarah McEwen k, James Voyvodic l, Aysenil Belger m, Daniel H Mathalon o, Judith M Ford o,q, Guia Guffanti p, Fabio Macciardi n, Steven G Potkin n, Theo GM van Erp a,*
PMCID: PMC6252137  NIHMSID: NIHMS976067  PMID: 29907492

Abstract

This study assessed genetic contributions to six cognitive domains, identified by the MATRICS Cognitive Consensus Battery as relevant for schizophrenia, cognition-enhancing, clinical trials. Psychiatric Genomics Consortium Schizophrenia polygenic risk scores showed significant negative correlations with each cognitive domain. Genome-wide association analyses identified loci associated with attention/vigilance (rs830786 within HNF4G), verbal memory (rs67017972 near NDUFS4), and reasoning/problem solving (rs76872642 within HDAC9). Gene set analysis identified unique and shared genes across cognitive domains. These findings suggest involvement of common and unique mechanisms across cognitive domains and may contribute to the discovery of new therapeutic targets to treat cognitive deficits in schizophrenia.

Keywords: schizophrenia, PRS, GWAS, neuropsychology, MCCB

1. Introduction

Schizophrenia is highly heritable (h2=0.8) (McGuffin et al., 1984). Moreover, cognitive impairments are core heritable features of schizophrenia (h2=0.20–0.80) (Blokland et al., 2017), yet contributing genes remain to be determined.

Psychiatric Genomics Consortium (PGC) case-control genome-wide association (GWA) studies have found 108 schizophrenia risk loci, including several in ZNF804A, NRGN, TCF4, MIR137, and major histocompatibility complex regions (Schizophrenia Psychiatric Genome-Wide Association Study (GWAS) Consortium, 2011; Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014).

PGC schizophrenia polygenic risk scores (PRS) have been associated with lower general cognitive ability (Lencz et al., 2014; McIntosh et al., 2013), speed of emotion identification and verbal reasoning (Germine et al., 2016), as well as verbal-numerical reasoning, reaction time, and memory (Hagenaars et al., 2016). Additionally, several control (Davies et al., 2015; Need et al., 2009) and schizophrenia GWAS using cognitive traits have been reported on (Hashimoto et al., 2013; Ohi et al., 2015; Ren et al., 2015; Sanchez-Roige et al., 2018; Smeland et al., 2017; Trampush et al., 2017). However, no PRS or GWA studies have assessed cognitive domains identified by the MATRICS Cognitive Consensus Battery (MCCB), developed for clinical trials of cognition-enhancing treatments for schizophrenia (Kern et al., 2008; Nuechterlein et al., 2008).

Here we report PRS, GWA, and gene set findings from genetic analyses with six MCCB cognitive domain scores (speed of processing, attention/vigilance, working memory, verbal learning, visual learning, and reasoning/problem solving) previously shown impaired in schizophrenia (Cohen’s d=−0.67 to d=−1.14) (van Erp et al., 2015).

2. Materials and Methods

2.1 Participants

This study includes data from 127 clinically stable individuals with schizophrenia (DSM-IV-TR, no medication changes within the last two months, no tardive dyskinesia) and 136 healthy volunteers (Table 1). Individuals with a history of major medical illness, drug dependence in the last 5 years (except nicotine), or current substance abuse disorder were excluded. Healthy volunteers with a history of major neurological or psychiatric illness or with a first-degree relative with an Axis-I psychotic disorder were also excluded. Participants’ cognitive domain scores, based on the Computerized Multiphasic Interactive Neurocognitive System (CMINDS®) neuropsychological test battery (O’Halloran et al., 2008), were published in a prior report (van Erp et al., 2015). Genotyping of blood samples from unrelated and mixed ethnicity subjects was performed using the Illumina MEGA+Psych chip (Illumina, SD, USA). All subjects signed written informed consent approved by institutional review boards.

Table 1.

Sample Demographics and Clinical Characteristics

Schizophrenia Patients (n=127) Healthy Volunteers (n=136) Statistic p-value
Mean Age (SD) 39.1 (11.2) 38.6 (11.4) t261=0.35 0.73
Sex (Male/Female) 106/21 98/38 χ21=4.91 0.03
Handednessa (bilateral/left/right) 3/10/114 2/6/128 FET 0.46
Subject Educationb (SD) 4.6 (1.0) 5.8 (0.9) t261=11.46 <0.0001
Parental Educationb (SD) 5.7 (1.8) 5.8 (1.5) t261=0.20 0.66
Race FET 0.31
 American Indian or Alaskan Native 2 2
 Asian 18 10
 Black or African American 20 17
 Native Hawaiian or Pacific Islander 1 1
 White 86 106
NAART 29.3 (12.8) 40.7 (11.4) t258= −7.55 <0.0001
Age at Onset 21.5 (6.6)
Duration of Illness 17.7 (11.2)
PANSS positive 15.4 (5.0)
PANSS negative 14.6 (5.5)
PANSS general 28.5 (7.5)
PANSS composite 0.8 (6.4)
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FET=Fisher’s Exact Test; NAART=North American Adult Reading Test; PANSS=Positive and Negative Syndrome Scale;

a

Based on the Edinburgh Handedness Inventory;

b

Based on the Hollingstead Socioeconomic Status Scale.

2.3 Polygenic Risk Score, Genome-wide Association, and Gene Set Analysis

Genotyping data were filtered to remove single-nucleotide polymorphisms (SNPs) with low minor allele frequency (MAF<0.01), deviations from Hardy-Weinberg Equilibrium (p<1×10−6), or poor genotyping call rate (<95%) using PLINK (Purcell et al., 2007). Filtered data were imputed to the 1000 Genomes Project reference panel (1000 Genomes Project Consortium et al., 2015) (phase 1, version 3) using the Michigan Imputation Server (Das et al., 2016). For each individual, a PRS was generated using the GWAS summary of Psychiatric Genomics Consortium (PGC)-schizophrenia meta-analysis (Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014), with linkage disequilibrium pruning parameters of R2=0.5 over 250 kb windows using 1000 Genomes Project reference panel (ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502). The current sample was not part of the PGC analysis on which the PRS was based. PRS was used to test for association with cognitive domain scores using Pearson’s correlations (two-tailed), statistically controlling for age, sex, and 4 multidimensional-scaling components (MDS). Genomewide linear regression analyses predicted each neuropsychological domain with each SNP, statistically controlling for diagnosis, age, sex, site, and four MDS. Fast and flexible gene- or set-based association tests, using GWAS summary data from the neuropsychological domain scores, were performed using Genome-wide Complex Trait Analysis (GCTA) (Bakshi et al., 2016; Yang et al., 2011). This method overcomes the limitations of the resampling-based methods by calculating the p-value for a set of SNPs (±50 Kb of a gene) from an approximated distribution of the sum of χ2-statistics over the SNPs using GWAS summary data and linkage disequilibrium correlations between SNPs from 1000 Genomes Project samples as a reference. We listed the top 40 identified genes and their p-values.

3. Results

The positive study results are that: 1) PGC-schizophrenia PRS showed significant negative correlations with each cognitive domain; 2) GWA identified significant associations for 3 out of 6 cognitive domains; and 3) gene-set analyses found unique and common contributing genes across the cognitive domains.

3.1 Polygenic Risk Score Analyses

PRS showed significant negative correlations with speed of processing (r260=−0.20, p=0.001), attention/vigilance (r258=−0.15, p=0.015), working memory (r261=−0.19, p=0.0018), verbal learning (r261=−0.19, p=0.0018), visual learning (r258=−0.28, p=2.8×10−6), reasoning/problem solving (r260=−0.21, p=0.0005), and the CMINDS composite (r256=−0.29, p=1.6×10−6).

3.2 Genome-wide Association Analyses

GWA analyses identified significant associations for attention/vigilance (rs830786 within HNF4G), verbal memory (rs67017972 100bp upstream of NDUFS4), and reasoning/problem solving (rs114499642, rs74412765 within LOC102724945, and rs76872642 within HDAC9) domain scores (p<5×10−8; Figure 1; Table 2).

Figure 1.

Figure 1

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GWAS Manhattan and QQ Plots for each Cognitive Domain. The Manhattan plots display the association p-value for each SNP in the genome (displayed as −log10 of the p-value). Red and blue lines display p=1×10−6 line and p=1×10−5 respectively. Quantile-quantile plot for the empirical and theoretical distributions are shown as black and red lines, respectively.

Table 2.

Genetic variants associated with cognitive domains.

rsID CHR BP A1 BETA STAT p-value Gene
Speed of Processing rs1149530 7 16848375 T 0.77 5.1 6.4×10−7
rs818800 7 16846521 G 0.73 4.9 1.4×10−6
rs11763030 7 16887616 A 0.62 4.9 1.9×10−6
Attention/Vigilance rs830786 8 76355875 T −1.5 −5.6 4.9×10−8 HNF4G
rs75131442 8 76831979 T −1.8 −5.3 3.3×10−7
rs79963003 8 76874205 A −1.6 −5.2 4.0×10−7
Working Memory rs17511050 4 133842651 T −1.4 −5.3 2.8×10−7
rs148396385 2 151173721 G −1.0 −5.3 2.9×10−7
rs17396139 4 162285366 C −0.42 −5.2 4.0×10−7
Verbal Memory rs67017972 5 53071288 A 0.60 5.7 4.3×10−8 NDUFS4
rs7164861 15 89125815 T −1.3 −5.4 1.6×10−7
rs78096325 9 20207963 A −1.2 −5.2 3.3×10−7
Visual Memory rs776010265 9 126073408 G −0.49 −5.0 1.0×10−6
rs2900031 14 48879362 T 0.59 5.0 1.4×10−6
rs7156750 14 48885146 T 0.48 4.9 1.7×10−6
Reasoning/Problem Solving rs74412765 14 34522904 T −1.7 −5.7 3.2×10−8 LOC102724945
rs76872642 7 18669403 A −3.4 −5.6 5.3×10−8 HDAC9
rs114499642 13 104628385 G −2.6 −5.6 6.0×10−8
CMINDS Composite Score rs150466277 19 50957600 C −2.8 −5.4 1.4×10−7
rs80284955 18 41042547 G −1.1 −5.3 2.5×10−7
rs185342442 13 98503979 G −3.1 −5.2 3.9×10−7
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rsID=SNP ID; CHR=chromosome; BP=base pair position; A1=minor allele; BETA=regression coefficient; STAT=t-statistic; Gene=lists a known gene near SNPs that are significant at about p<5.0×10−8.

3.3 Gene Set Analyses

Gene set analyses identified unique and shared genes associated with cognitive domain scores (Table 3).

Table 3.

Gene set analysis based on GWAS results identified unique (non-shaded) and shared genes (shaded) associated with cognitive domain scores

Speed of Processing p-value Attention/Vigilance p-value Working Memory p-value Verbal Memory p-value Visual Memory p-value Reasoning/Problem Solving p-value CMINDS Composite Score p-value
AGR2 0.0002 ACD 0.00077 ACD 0.00017 C10orf25 0.00095 ADH1A 0.00115 APIP 0.00044 ACD 0.00041
AGR3 0.0004 ABCB1 0.00076 BRF2 0.00010 DTYMK 0.00057 AGAP11 0.00109 BAG4 0.00068 ABCA11P 0.00069
ANGEL1 0.0009 ACTR3 0.00092 C5orf22 2.31E-005 FAM200B 0.00092 ALOX12 0.00011 CATSPER2 0.00052 ALOX12 0.00032
ANGEL2 0.0010 C16orf86 0.00077 C16orf86 0.00017 GCSAML 0.00097 ARL6 0.00064 CHN1 0.00048 C16orf86 0.00041
ATF1 0.0016 C19orf48 0.00014 CCDC42B 0.00029 GTF2H1 0.00011 C17orf49 0.00058 CKMT1B 0.00048 C17orf49 0.00078
BTD 0.0010 CBWD6 0.00074 CTCF 0.00033 HOXD-AS1 0.00035 CAMK2N2 0.00064 CLTA 0.00082 BLVRA 0.00105
CDH1 0.0002 CEBPA 0.00084 DDX54 0.00045 HOXD-AS2 0.00048 COL28A1 0.00039 CYP2A13 0.00033 BRINP2 0.00061
CDH3 0.0002 CEBPA-AS1 0.00065 E2F2 0.00029 HOXD1 0.00080 DACT2 5.89E-005 CYP2F1 0.00058 ATP6V1B1 0.00110
DIO3 0.0017 ENKD1 0.00077 ENKD1 0.00017 HOXD3 0.00017 DDOST 0.00100 DDHD2 0.00031 ENKD1 0.00041
DIO3OS 0.0007 EVPLL 0.00063 FAM102A 0.00048 HOXD4 0.00010 DNALI1 9.88E-005 FGFR1 0.00011 CCDC116 0.00033
FLJ43681 0.0017 FKBP9 0.00086 GPR124 0.00016 HOXD8 0.00094 ECE2 0.00061 FKBP9 0.00015 DCLK3 0.00034
HIF1AN 0.0011 FOXD4L6 0.00075 GFOD2 0.00025 HSPA13 0.00044 FAM25A 0.00044 HIC1 0.00065 GFOD2 0.00083
HSPA8 0.0016 HSPA8 0.00029 IQCD 0.00039 ING5 0.00034 GLUD1 0.00123 KLRG2 0.00069 EIF4E3 0.00093
HYAL1 0.0015 HAX1 0.00059 IRF4 0.00048 LDHA 0.00039 GNL2 0.00043 LETM2 0.00012 C9orf92 8.59E-005
LINC00687 0.0012 KLK1 4.38E-006 KLK1 0.00040 LDHC 0.00075 LOC101927780 0.00060 MEPCE 0.00057 FARP2 0.00053
LOC101927881 0.0014 KLK15 3.01E-006 KLK15 0.00030 LOC101928869 0.00052 LINC00656 0.00061 MIR132 0.00074 GSTM2P1 0.00071
NDUFB8 0.0007 KLK3 1.66E-006 KLK3 0.00014 LOC102800310 0.00033 LOC101928035 0.00105 MIR1343 0.00067 LOC100289361 0.00068
LOC442497 0.0013 KLK2 3.07E-006 LOC100506178 0.00045 MATR3 0.00025 LINC00458 4.16E-005 MIR212 0.00075 LINC00458 0.00068
LRRC74 0.0014 KLKP1 6.88E-006 LOC401320 0.00039 MIR10B 0.00017 LOC100506713 0.00011 MIR6840 0.00057 LOC100506713 0.00039
LOC341056 0.0012 LOC341056 0.00082 MIR6762 0.00048 MIR1228 0.00093 LOC101929420 4.04E-006 OR5A1 0.00083 LOC101927750 0.00068
MIR4311 0.0011 LARS2-AS1 0.00100 MIR7106 0.00039 MIR4781 0.00062 MIR1470 0.00024 OR5A2 0.00036 MIR130B 0.00067
MIR6872 0.0012 LINC01210 0.00066 MPZ 0.00045 MZB1 0.00088 MIR195 0.00071 OR5AN1 0.00069 MIR195 0.00094
MYF6 0.0012 LOC100499194 0.00031 NEUROD6 3.34E-005 NDUFA4L2 0.00044 MIR497 0.00071 PILRB 0.00050 MIR497 0.00094
MCEMP1 0.0012 LAMA4 0.00056 R3HCC1L 0.00035 NOMO3 0.00049 MIR497HG 0.00071 PMS2P1 0.00035 MIR497HG 0.00094
NHEG1 0.0004 LOC440896 0.00075 PPM1B 0.00011 NXPH4 0.00073 PPM1B 0.00024 PPAPDC1B 0.00029 PPM1B 0.00012
NPBWR2 0.0010 MGC45922 8.75E-006 PTN 0.00013 OR10S1 0.00030 PAQR9 0.00073 PPP1R35 0.00081 PAQR9 0.00074
OPRL1 0.0017 PARD6A 0.00077 PARD6A 0.00017 OR2G2 0.00030 MN1 9.86E-005 PVRIG2P 0.00069 PARD6A 0.00041
PCP2 0.0017 OR5AR1 0.00052 RASAL1 0.00026 OR4D5 0.00014 PPIL2 0.00013 RP9P 0.00028 PPIL2 0.00018
RETN 0.0010 OR5M11 0.00080 RNU6-83P 0.00018 OR6T1 9.57E-005 PINK1-AS 0.00114 RNU6-83P 0.00012 MIR301B 0.00067
RPS6KC1 0.0006 OR5M3 0.00061 RLTPR 0.00044 OR8D4 0.00026 MIR5581 0.00116 SERF2 0.00078 RLTPR 0.00069
SEC31B 0.0009 RUNDC3B 0.00057 RUNDC3B 0.00011 PAIP2 0.00095 PROK2 0.00047 SLC4A4 0.00063 RNASE10 0.00071
SEMA3B-AS1 0.0015 OR5M9 0.00082 SDHC 0.00038 RPF2 0.00021 RNASEK 0.00046 SMG6 0.00018 RNASEK 0.00066
SLC35D3 0.0011 OR9G1 0.00084 SLC25A40 0.00033 SHMT2 0.00062 RNASEK-C17orf49 0.00056 SPDYE3 0.00021 RNASEK-C17orf49 0.00074
STXBP2 0.0014 OR9G9 0.00084 SPRR2B 0.00047 SLC23A1 0.00077 SNIP1 0.00023 SRSF10 0.00030 SDF2L1 0.00038
TFDP2 0.0007 MIR6778 0.00097 SPRR2E 0.00036 SNHG4 0.00032 TFAMP1 0.00105 STAG3L5P 0.00043 STK25 0.00089
TMEM225 0.0015 PGM5P2 0.00075 SPRR2F 0.00032 TMEM225 0.00055 TFAP2B 0.00071 STAG3L5P-PVRIG2P-PILRB 0.00040 TMEM225 0.00044
WNT8B 0.0011 OR5M8 0.00035 THAP11 0.00049 SPRN 0.00088 UBE2L3 0.00087 STRC 0.00072 UBE2L3 0.00099
TUSC2 0.0016 SNORD88C 0.00030 TPCN1 0.00048 STAC3 0.00036 YPEL1 0.00033 TBX21 0.00090 YPEL1 0.00033
TSNAXIP1 0.0012 SOX14 0.00086 TSNAXIP1 0.00045 SNORA74A 0.00036 WIZ 0.00039 WHSC1L1 0.00011 YDJC 0.00058
ZNF596 0.0017 TOP3A 0.00084 ZNF596 0.00032 TMEM59 0.00082 ZNF121 0.00105 ZCWPW1 0.00069 VAX2 0.00012
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The shaded genes are associated with two or more cognitive domains presented on the same row.

4. Discussion

All correlations between the PGC schizophrenia PRS and the cognitive domains scores were negative, consistent with the interpretation that higher schizophrenia genetic risk is associated with worse cognitive performance, corroborating prior findings (Germine et al., 2016; Hagenaars et al., 2016; Lencz et al., 2014; McIntosh et al., 2013).

With regard to the GWAS locus associated with attention/vigilance, HNF4G is expressed in the brain (http://www.brain-map.org). HNF4G is regulated by miR-194, which is dysregulated in individuals with 22q11.2 deletion syndrome who have a 20–30 fold increased risk for psychosis (Sellier et al., 2014). Additionally, mouse Hnf4g was found to be upregulated after toxoplasma gondii infection (He et al., 2016) which is a putative risk factor for schizophrenia (Webster et al., 2013). With regard to reasoning/problem solving, little is known about the LOC102724945 and rs114499642 loci. However, HDAC9, histone deacetylase 9, is expressed in brain and has previously been associated with schizophrenia (Kebir et al., 2014; Tam et al., 2010). HDAC9 is involved in transcriptional regulation, cell cycle progression, and neuronal development and transmission. Rs67017972, associated with verbal memory, is located 100bp upstream of NDUFS4. Lower prefrontal cortex and hippocampal NDUFS4 expression has been found in schizophrenia (Altar et al., 2005; Arion et al., 2015), and Ndufs4 cKO mice show impaired cognitive function and increased anxiety-like behavior (Choi et al., 2017).

Gene set analyses, based on the GWAS results, found that several genes contribute to multiple cognitive domains. For example, PPM1B is associated with working memory, visual memory, and CMINDS composite. PPM1B is a member of the PP2C family of Ser/Thr protein phosphatases, and is expressed in brain. PP2C family members are known negative regulators of cell stress response pathways, and are involved in neuroprotection and neurodegeneration (Klumpp et al., 2006, 2002). Protein interactions between PPM1B, NRG1, and DTNBP1, putative schizophrenia susceptibility genes, have also been reported (Tsuang, 2000). HSPA8, associated with attention/vigilance and speed of processing, was previously identified as a schizophrenia risk locus (Bozidis et al., 2014). HSPA8, Heat shock 70 kDa protein 8, is known to contribute to many biological processes, including signal transduction, apoptosis, autophagy, protein homeostasis, and cell growth and differentiation. PLCB3-PARD3-PARD6A complex, associated with the CMINDS composite and working memory, was found to be associated with schizophrenia in BA22 RNA-Seq study (Huang et al., 2014). PARD6A, partitioning defective 6 homolog alpha, is involved in asymmetrical cell division and cell polarization processes. ALOX12, associated with CMINDS composite and visual learning, had been identified in a Korean schizophrenia study (Kim et al., 2010). MIR497, related to the CMINDS composite and visual learning, was differentially expressed in the prefrontal cortex exosome in schizophrenia and bipolar disorder (Delgado-Morales, 2017). Finally, MIR195, associated with visual memory and CMINDS composite, was found to be upregulated in the superior temporal gyrus of individuals with schizophrenia (Beveridge et al., 2010, 2008). MIR195 regulates numerous schizophrenia-related genes, such as BDNF, RELN, Visinin-like 1, 5-hydroxytryptamine (serotonin) receptor 2a, and glutamate receptor, ionotropic, N-methyl-d-aspartate 3A. Each of the cognitive domains, especially the CMINDS composite, shares several genes with another cognitive domain. Given that the CMINDS composite is an average across all 6 cognitive domains, overlap in genes with the CMINDS composite is expected.

Study strengths are the use of cognitive domain scores that are considered important targets for cognition-enhancing treatments for schizophrenia. Study limitations include sample size and lack of a replication sample, though the observed negative correlations between the schizophrenia PRS and the cognitive domain scores strengthen the confidence in our GWA and gene set findings. Nevertheless, replication of the findings in larger cohorts is warranted.

In conclusion, we found that the PGC-based schizophrenia PRS was significantly negatively correlated with CMINDS cognitive domain performance. In addition, we identified novel loci associated with cognitive domain performance. Finally, gene-based analysis revealed that cognitive domains share contributing genes. These findings suggest involvement of novel unique and common biological mechanisms in cognitive domain deficits in schizophrenia and may contribute to the discovery of new therapeutic targets to treat cognitive deficits in schizophrenia, which do not respond well to traditional antipsychotic treatments (Kahn and Keefe, 2013).

Acknowledgments

Funding/Support: This work was supported by National Institutes of Health grant numbers: NIH 1 U24 RR021992 (Function Biomedical Informatics Research Network), NIH 1 U24 RR025736-01 (Biomedical Informatics Research Network Coordinating Center), 5R01MH094524, P20GM103472, and UL1 TR000153.

Footnotes

Author Contributions: Drs. Nakahara and Van Erp had full access to all of the data in the study, conducted the statistical analysis, and take responsibility for the integrity of the data and the accuracy of the data analysis. Drs. Nakahara and Van Erp wrote the first draft of the manuscript. All authors critically reviewed the manuscript, provided comments, and approved the manuscript for publication.

Previous Presentation: This study has not been previously presented.

Additional Contributions: We are thankful to Mrs. Liv McMillan, BS for overall study coordination, Harry Mangalam, PhD, Joseph Farran, BS, and Adam Brenner, BS, for administering the University of California, Irvine High-Performance Computing cluster, and to the research subjects for their participation.

Financial Disclosure: Dr. Soichiro Nakahara’s effort was supported by Astellas Pharma Inc. while he was a visiting scholar in University of California, Irvine. Dr. Bustillo consulted with Novartis and Otsuka Pharmaceuticals. Dr. Mathalon consulted for Boerhinger Ingelheim and Takeda. Dr. Preda consulted for Boehringer-Ingelheim. Dr. Potkin has financial interests in Bristol-Myers Squibb, Eisai, Inc., Eli Lilly, Forest Laboratories, Genentech, Janssen Pharmaceutical, Lundbeck, Merck, Novartis, Organon, Pfizer, Roche, Sunovion, Takeda Pharmaceutical, Vanda Pharmaceutical, Novartis, Lundbeck, Merck, Sunovion and has received grant funding from Amgen, Baxter, Bristol-Myers Squibb, Cephalon, Inc., Eli Lilly, Forest Laboratories, Genentech, Janssen Pharmaceutical, Merck, Otsuka, Pfizer, Roche, Sunovion, Takeda Pharmaceutical, Vanda Pharmaceutical, NIAAA, NIBIB, NIH/NCRR, University of Southern California, UCSF, UCSD, Baylor College of Medicine. The remaining authors declare no potential conflict of interest.

Role of the Sponsor: The funding sources had no role in the study design, conduct of the study, data collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript.

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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