SUPPLEMENTARY INFORMATION
Table of Contents
1. Introduction
2. Supplementary information about samples from different databases
2.1. Study design
2.2. Brain collections
2.2.1. Stanley Medical Research Institute (SMRI): Bipolar disorder (BD), Schizophrenia (SCZ) and
control (CTRL) samples
2.2.1.1. Prefrontal cortex (PFC)
2.2.1.2. Parietal cortex (PCX)
2.2.2.3. Cerebellum (CB)
2.2.2. Victorian Brain Bank Network (VBBN): SCZ and CTRL samples
3. SMRI PCX and CB sample preparation
3.1. RNA extraction
3.2. Array processing
4. Gene expression data quality assessment
4.1. Probe level assessment
4.1.1. Affymetrix Gene Array filtering
4.1.2. Affymetrix U133 Array filtering
4.2. Sample level assessment
4.3. Batch effect adjustment
4.4. Summary
5. Gene expression data analyses
5.1. Identification of differential expression genes between SCZ and CTRL samples in PCX-SMRI
5.2. Weighted gene co-expression network analysis (WGCNA)
5.2.1. Network construction
5.2.2. Module detection
5.3. Characteristics of modules
5.4. Module preservation statistics
5.5. Pathway and functional analyses
6. Genotyping data from different consortiums
6.1. Genetic Association Information Network (GAIN)-SCZ
6.2. GAIN-BD
6.2. Translational Genomics Research Institute (TGen)-BD
7. Imputation of GWAS data
8. Integration analysis of expression and GWAS data
9. Supplementary references
10. Supplementary figures
Supplementary Fig. 1 | Cross-tabulation of results displays overlaps between Oldham modules and
PCX-CTRL modules.
Supplementary Fig. 2 | Topological overlap matrix (TOM) plot to detect modules in PCX
Supplementary Fig. 3 | Composite preservation statistics of SMRI PCX modules in SMRI PFC SCZ
and CTRL samples
Supplementary Fig. 4 | Composite preservation statistics of SMRI PCX modules in SMRI CB SCZ
and CTRL samples
Supplementary Fig. 5 | Composite preservation statistics of SMRI PCX modules in VBBN PFC SCZ
and CTRL samples
Supplementary Fig. 6 | Composite preservation statistics of SMRI PCX modules in SMRI PCX BD
and CTRL samples
Supplementary Fig. 7 | Composite preservation statistics of SMRI PCX modules in SMRI CB BD
and CTRL samples
Supplementary Fig. 8 | Manhattan Plot for M1A gene set enrichment in GAIN-BD genetic
association signals
Supplementary Fig. 9 | Manhattan Plot for M1A gene set enrichment in GAIN-SCZ genetic
association signals
Supplementary Fig. 10 | Manhattan Plot for M1A gene set enrichment in TGen-BD genetic
association signals
11. Supplementary tables
Supplementary Table 1 | Demography of SMRI PCX samples
Supplementary Table 2 | Demography of SMRI CB samples
Supplementary Table 3 | Demography of SMRI PFC samples
Supplementary Table 4 | Demography of VBBN PFC samples
Supplementary Table 5 | M1A gene list and intramodular connectivity
Supplementary Table 6 | M3A gene list and intramodular connectivity
Supplementary Table 7 | M1A gene list and top GO functions
Supplementary Table 8 | M3A gene list and top GO functions
1. Introduction
This file contains detailed information about sample sources and preparation (sections 2, 3), data
preprocessing and analysis (sections 4-8), and supplementary figures and tables. The sample sources
include the online resources from which we downloaded data and the data produced in our lab. The data
preprocessing and analysis includes quality control assessments and data analysis. In sections 5- 8, we
provide detailed information on gene expression network analysis, module preservation analysis, pathway
analysis, and genetic signals enrichment test. Additional tables and figures are presented at the end.
2. Supplementary information of samples from different databases
2.1. Study design
Parietal cortex tissues from Stanley Medical Research Institute (SMRI) SCZ and CTRL samples were
used as preliminary data in our analysis49. To validate our findings, we tested them in gene expression
data sets from three brain banks, three brain regions and two psychotic diseases.
2.2. Brain collections
2.2.1. Stanley Medical Research Institute (SMRI): BD, SCZ and CTRL samples
Brains came from the SMRI’s Neuropathology Consortium and Array collections, and included 50 SCZ
samples, 50 BD samples and 50 CTRL samples. The detailed information about age, sex, race,
postmortem interval, pH and side of brain is provided in the demographics table (Supplementary Table 1).
2.2.1.1. Parietal cortex (PCX) and Cerebellum (CB)
Our lab obtained the PCX and CB tissues from SMRI. The RNA was prepared in our lab, then sent to the
Yale core facility for the microarray experiments. The details of the sample preparation procedure are
listed in section 3.
2.1.1.2. Prefrontal cortex (PFC)
PFC gene expression data was downloaded from the Stanley Medical Research Institute’s online
genomics database (www.stanleygenomics.org). The Study ID is five and created by Seth E. Dobrin.
Brain region is frontalBA 46 and array type is Affymetrix hug133p.
2.2.2. Victorian Brain Bank Network (VBBN): SCZ and CTRL samples
VBBN expression data was downloaded from the Gene Expression Omnibus (GEO) database. The data
came from postmortem brain tissue (BA46) of 30 schizophrenic patients and 29 age- and sex-matched
CTRLs ( http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE21138 )50.
3. PCX and CB sample preparation
3.1. RNA extraction
RNA was extracted from the cerebellar cortex of 132 samples, and parietal cortex of 146 samples using
the RNeasy Mini kit (Qiagen, Valencia, CA). The concentration and A260/A280 ratio were measured on
the NanoDrop spectrophotometer. The 28S:18S rRNA ratio and RNA Integrity Number (RIN) were
measured using an RNA LabChip kit on the Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara,
CA). Only RNA samples with a RIN > 7 were used for the expression profiling.
3.2. Array processing
Affymetrix Human Gene 1.0 ST Array was used for whole genome transcriptome profiling at the
NIH Neuroscience Microarray Consortium facility at Yale University.
4. Gene expression data quality assessment
4.1. Probe level assessment
4.1.1. Filtering of Affymetrix gene array data
Single nucleotide polymorphisms (SNPs) in probe regions can affect probe hybridization
efficiency51. We created the list of probes containing SNPs, and removed those probes before
data preprocessing: 39,529 out of 805,481 probes were eliminated from the analysis. We
provided customized library files (http://bioinfo.psych.uic.edu/ArrayGenes/SNPsInProbes.jsp)
for the Robust Multichip Average (RMA) preprocessing steps: background correction, quantile
normalization and gene level summarization52. Afterwards, for convenience of comparison, only
genes with Entrez IDs were kept.
4.1.2. Filtering of Affymetrix U133 Array data
To reduce noise, we filtered out probe sets using the MAS 5.0 algorithm53; probe sets which
were called as present in more than 80% of the samples were retained. As for the PCX and CB
data, probe sets without detailed annotation were also removed.
4.2. Sample level assessment
For all data sets, we removed non-Europeans and outliers detected by Affymetrix Expression
Console and randomly chose one sample if the data set included replicates54.
4.3. Batch effect adjustment
ComBat, an efficient batch effect removal approach, was used to remove batch effects from these
data sets55,56.
4.4. Summary
The data collected in our lab from the SMRI PCX and CB tissues ultimately included CB data
from 39 schizophrenia patients, 36 bipolar disorder patients and 44 normal samples, as well as
PCX data from 45 schizophrenia patients, 42 bipolar disorder patients and 46 normal samples in
parietal cortex. For both these data sets, 19,984 genes were retained.
Downloaded SMRI PFC data came from 30 schizophrenia patients, 25 bipolar disorder patients
and 29 normal samples; for this data set, 14,988 probe sets were retained for further analysis.
The PFC data set from VBBN included 30 schizophrenia patients and 29 normal samples; 14,988
probe sets were retained for further analysis. The PFC data from CCHPC included 28
schizophrenia patients and 23 normal samples; 14,988 probe sets were retained for further
analysis.
5. Gene expression data analyses
5.1. Identification of genes differentially-expressed between SCZ and CTRL samples in PCX-SMRI
Multiple linear regression was used for each transcript, with age, sex and post-mortem interval as
covariates, to remove any effects from these potentially confounding factors. Differential gene
expression analysis between SCZ and CTRL samples was run after this correction.
5.2. Weighted gene co-expression network analysis (WGCNA)
We identified genes with similar expression patterns using weighted gene co-expression network
analysis (WGCNA)57,58.
5.2.1. Network construction
The absolute values of Pearson correlation coefficients were calculated for all possible pairwise
genes. This correlation matrix, S= [ sij ], was weighted into an adjacency matrix A= [ aij ] by

power function, i.e. aij =power ( sij ,β)  sij . The parameter of power, β, was chosen to ensure
the adjacency matrix had an approximate scale-free topology.
5.2.2. Module detection
Gene module detection begun with transforming the adjacency matrix into a topological overlap
matrix (TOM) Ω= [  ij ], with 0< aij <1 implying 0<  ij <1. Next, TOM-based dissimilarity
between all possible pairwise genes was defined by d ij  1  ij . The Dynamic Tree cut
algorithm was used to detect network modules. WGCNA and Dynamic Tree cut algorithm were
implemented in R59.
5.3. Characteristics of modules
Association test based on singular value decomposition (SVD) on each module
We ran singular value decomposition (SVD) on each module’s expression matrix, and used the
resulting eigengene to characterize the entire module in the subsequent analysis. After multiple
linear regression on the eigengenes to remove the effects of sex, age, pH and PMI on SMRI and
VBBN samples, the corrected module values were used to test the disease association using
Pearson’s correlation test60.
5.4. Module preservation statistics
We utilized cross-tabulation and module preservation statistics to assess the module preservation
in different expression data sets61. The module preservation test has two advantages over the
traditional cross-tabulation test. First, it considers not only the number of overlapping genes, but
also the density and connectivity patterns of modules defined in the reference data set. Second,
network-based preservation statistics do not require modules to be identified in the test set for
comparison with the reference data set; this reduces the variation introduced to the analysis by
various parameter settings used to build the network.
Zsummary is the measurement statistic used to summarize evidence that a module is preserved
more significantly than a random sample of all network genes. Langfelder et al. proposed the
thresholds at follows: Zsummary < 2 implies no evidence for module preservation,
2 < Zsummary < 10 implies weak to moderate evidence, and Zsummary > 10 implies strong
evidence for module preservation62. When module size varied across data sets, we reported the
median rank statistic on our module preservation test, which is useful for comparing relative
preservation among modules because it does not depend on module size.
Before the preservation test, we converted the probe-level measurements into gene-level
measurements to make the data from different platform comparable. For each gene, only the
probe with the highest coefficient of variation was kept for further analysis. Overall, 8497 genes
were retained on our preservation calculation.
5.6. Pathway and functional analyses
The genes in the identified modules were analyzed through DAVID (DAVID,
http://david.abcc.ncifcrf.gov/tools.jsp)63, which provided existing knowledge about those genes,
on their functionality, potential relevance to neuropsychiatric diseases, and neuronal functions.
Also, we identified specific functional category, including canonical pathways, functional
categories, protein domain and Gene Ontology (GO) terms, that may be enriched for these genes.
M3A contains 106 genes; we used the whole gene list to run the test. M1A includes 490 genes;
due to its size, we selected the 200 genes most highly correlated with the module’s eigengene to
run the test.
6. Genotyping data from different consortia
6.1. SCZ GWAS data (GAIN)
SCZ genome-wide association data was downloaded from dbGaP
(http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000021.v3.p2). The
study web link is http://www.genome.gov/19518664 (Genetic Association Information Network,
GAIN)64. The consortium has collected 4591 cases and controls (1217 European-American cases,
1442 European-American controls, 953 African-American cases, 979 African-American
controls). Whole genome genotyping was done with the Affymetrix Genome-wide Human SNP
Array 6.0.
6.2. BD GWAS data (GAIN)
Genome-wide association study of BD data was downloaded from dbGaP
(http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000017.v3.p1). The
study web link is http://www.genome.gov/19518664 (Genetic Association Information Network,
GAIN)65. The consortium has collected 3261 cases and controls (1079 European-American cases,
1081 European-American controls, 415 African-American cases, 686 African-American
controls). Whole genome genotyping was done with the Affymetrix Genome-wide Human SNP
Array 6.0.
6.3. BD GWAS data (TGen)
Genome-wide association study of BD data was downloaded from the Translational Genomics
Research Institute (TGen). This study includes 1,190 newly genotyped BD cases from the
Bipolar Genome Study (BiGS) and 401 controls. Sample genotyping was conducted using
Affymetrix GeneChip Mapping 5.0K Array.
7. Imputation of GWAS data
BRLMM-p (Affymetrix) was used as the genotype-calling algorithm. SNPs with call rates less
than 99% were excluded from the analysis. SNPs showing departure from Hardy-Weinberg
equilibrium (HWE) were filtered out, as well (p < 0.001). Of the remaining SNPs, only SNPs
showing minor allele frequency (MAF) of at least 10% were carried forward for further analysis.
We used MaCH v1.0 for SNP imputation, to increase the density of interrogated SNPs66,67.
Overall, 2,593,107 SNPs in GAIN-SCZ, 3,281,319 SNPs in GAIN-BD, and 2,543,887 SNPs in
TGen-BD were included after imputation.
8. Integration analysis of gene expression and GWAS data
We utilized the following procedures to run the enrichment test20. First the max −log(P-value) of
a SNP located at 20kb upstream or downstream of a gene was assigned to represent the gene,
then M1A and M3A gene set enrichment scores (ES) were calculated based the gene’s rank. SNP
label level permutation was used to generate a distribution of the ES, and then the distribution
was normalized68. FDR q values were calculated if multiple gene sets were included in
enrichment test.
We tested the difference of gene length distribution between genes in the module M1A, which
was enriched with neuronal differentiation functions, and other genes in the genome. The result
was significant (p=5.47e-27). To test whether gene length bias existed in our genetic signals
enrichment test, we applied a permutation procedure to randomly-selected genes with similar
gene length distribution for verification. First, we randomly selected 490 genes (the number of
genes in the M1A module) not included in M1A, and compared those genes’ length distribution
to M1A’s length distribution. If the mean of randomly-selected genes’ length is longer than the
mean length of M1A genes, we ran the GWAS enrichment test for each selected gene set and
calculated the enrichment P value. Second, we repeated the above steps B times to obtain the null
statistic Pb as the background distribution P values. Finally, we computed the permutation pvalue for M1A GWAS enrichment as:
where B equals 1000 and PM1A is the M1A GWAS enrichment P value.
If the p value we calculated is less than 0.05, it means no bias was introduced by gene length, or
at least the effect of bias was not significant. Our results show that permutation p values are
0.089, 0.047, and 0.034 for GAIN-SCZ, GAIN-BD and TGen-BD GWAS signal enrichment
tests, respectively. This indicates that the significant enrichments we detected are not primarily a
product of gene length bias.
9. Supplementary References
49.
50.
51.
52.
53.
54.
55.
56.
57.
58.
59.
60.
61.
62.
63.
64.
65.
66.
67.
68.
Torrey, E.F., Webster, M., Knable, M., Johnston, N. & Yolken, R.H. The Stanley Foundation
brain collection and Neuropathology Consortium. Schizophrenia Research 44, 151-155 (2000).
Narayan, S. et al. Molecular profiles of schizophrenia in the CNS at different stages of illness.
Brain Research 1239, 235-248 (2008).
Benovoy, D., Kwan, T. & Majewski, J. Effect of polymorphisms within probe-target sequences
on olignonucleotide microarray experiments. Nucleic Acids Res 36, 4417-23 (2008).
Irizarry, R.A. et al. Exploration, normalization, and summaries of high density oligonucleotide
array probe level data. Biostatistics 4, 249-264 (2003).
Li, C. & Wong, W.H. Model-based analysis of oligonucleotide arrays: Expression index
computation and outlier detection. Proceedings of the National Academy of Sciences of the
United States of America 98, 31-36 (2001).
Affymetrix. Affymetrix Expression Console Software website. Vol. 2011.
Johnson, W.E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data
using empirical Bayes methods. Biostatistics 8, 118-127 (2007).
Chen, C. et al. Removing Batch Effects in Analysis of Expression Microarray Data: An
Evaluation of Six Batch Adjustment Methods. PLoS One 6(2011).
Zhang, B. & Horvath, S. A general framework for weighted gene co-expression network
analysis. Stat Appl Genet Mol Biol 4, Article17 (2005).
Horvath, S. & Langfelder, P. WGCNA: an R package for weighted correlation network analysis.
BMC Bioinformatics 9(2008).
Langfelder, P., Zhang, B. & Horvath, S. Defining clusters from a hierarchical cluster tree: the
Dynamic Tree Cut package for R. Bioinformatics 24, 719-720 (2008).
Langfelder, P. & Horvath, S. Eigengene networks for studying the relationships between coexpression modules. Bmc Systems Biology 1(2007).
Langfelder, P., Luo, R., Oldham, M.C. & Horvath, S. Is My Network Module Preserved and
Reproducible? PLoS Computational Biology 7(2011).
Langfelder, P. et al. A systems genetic analysis of high density lipoprotein metabolism and
network preservation across mouse models. Biochim Biophys Acta.
Huang, D.W., Sherman, B.T. & Lempicki, R.A. Systematic and integrative analysis of large gene
lists using DAVID bioinformatics resources. Nature Protocols 4, 44-57 (2009).
Stefansson, K. et al. Common variants conferring risk of schizophrenia. Nature 460, 744-747
(2009).
Dick. Genomewide linkage analyses of bipolar disorder: A new sample of 250 pedigrees from
the national institute of mental health genetics initiative (vol 73, pg 107, 2003). American
Journal of Human Genetics 73, 979-979 (2003).
Li, Y., Willer, C., Sanna, S. & Abecasis, G. Genotype Imputation. Annual Review of Genomics
and Human Genetics 10, 387-406 (2009).
Li, Y., Willer, C.J., Ding, J., Scheet, P. & Abecasis, G.R. MaCH: Using Sequence and Genotype
Data to Estimate Haplotypes and Unobserved Genotypes. Genetic Epidemiology 34, 816-834
(2010).
Wang, J., Zhang, K.L., Cui, S.J., Chang, S.H. & Zhang, L.Y. i-GSEA4GWAS: a web server for
identification of pathways/gene sets associated with traits by applying an improved gene set
enrichment analysis to genome-wide association study. Nucleic Acids Research 38, W90-W95
(2010).
10. Supplementary Figures
Supplementary Fig. 1 | Cross-tabulation of results displays overlaps between Oldham modules and
PCX-CTRL modules
Colors indicate the significance of the overlap in gene composition between particular modules, specifically,
the -log of the p-values from the hypergeometric distribution test.
Supplementary Fig. 2 | Topological overlap matrix (TOM) plot to detect modules in PCX SCZ+CTRL
samples
Each line in hierarchical clustering dendrogram indicated one gene. Modules correspond to branches of the
dendrogram, displayed as various colors in horizontal and vertical bars. The color in the topological overlap
matrix plot indicated module membership in each module.
Supplementary Fig. 3 | Composite preservation statistics of SMRI PCX modules in SMRI PFC SCZ and
CTRL samples.
Summary preservation statistic, Zsummary, was used to test whether SMRI PCX modules were preserved in
SMRI PFC data. M1A is the brown module, and M3A is the green module. X-axis represents the number of
genes in each module; Y-axis represents Zsummary in the second data set. The blue and green dashed lines in the
figure indicate the thresholds Z=2 and Z=10, respectively. Zsummary < 2 implies no evidence for module
preservation, 2 < Zsummary < 10 implies weak to moderate evidence, and Zsummary > 10 implies strong evidence
for module preservation. M1A is well-preserved and M3A is moderately-preserved in SMRI PFC.
Supplementary Fig. 4 | Composite preservation statistics of SMRI PCX modules in SMRI CB SCZ and
CTRL samples.
X-axis represents the number of genes in each module; Y-axis represents Zsummary in the second data set. The
blue and green dashed lines in the figure indicate the thresholds Z=2 and Z=10, respectively. Zsummary < 2
implies no evidence for module preservation, 2 < Zsummary < 10 implies weak to moderate evidence,
and Zsummary > 10 implies strong evidence for module preservation. Each point represents a module in SMRI
PCX, labeled by color. M1A is the brown module, and M3A is green module.
Supplementary Fig. 5 | Composite preservation statistics of PCX modules in VBBN PFC SCZ and
CTRL samples.
X-axis represents the number of genes in each module; Y-axis represents Zsummary in the second data set. The
blue and green dashed lines in the figure indicate the thresholds Z=2 and Z=10, respectively. Zsummary < 2
implies no evidence for module preservation, 2 < Zsummary < 10 implies weak to moderate evidence,
and Zsummary > 10 implies strong evidence for module preservation. Each point represents a module in PCX,
labeled by color. M1A is the brown module, and M3A is green module.
Supplementary Fig. 6 | Composite preservation statistics of PCX modules in SMRI PCX BD and CTRL
samples.
X-axis represents the number of genes in each module; Y-axis represents Zsummary in the second data set. The
blue and green dashed lines in the figure indicate the thresholds Z=2 and Z=10, respectively. Zsummary < 2
implies no evidence for module preservation, 2 < Zsummary < 10 implies weak to moderate evidence,
and Zsummary > 10 implies strong evidence for module preservation. Each point represents a module in PCX,
labeled by color. M1A is the brown module, and M3A is green module.
Supplementary Fig. 7 | Composite preservation statistics of PCX modules in SMRI CB BD and CTRL
samples.
X-axis represents the number of genes in each module; Y-axis represents Zsummary in the second data set. The
blue and green dashed lines in the figure indicate the thresholds Z=2 and Z=10, respectively. Zsummary < 2
implies no evidence for module preservation, 2 < Zsummary < 10 implies weak to moderate evidence,
and Zsummary > 10 implies strong evidence for module preservation. Each point represents a module in PCX,
labeled by color. M1A is the brown module, and M3A is green module.
Supplementary Fig. 8 | Manhattan Plot for M1A gene set enrichment with GAIN BD genetic association
signals.
GWAS of BD includes 2,662,182 SNPs with corresponding P-values. The lowest –log(p) SNP located in 20kb
upstream and downstream was used to present the gene. Totally 1,352,922 variants were used and 18,316
genes were mapped for enrichment analysis.
Supplementary Fig. 9 | Manhattan Plot for M1A gene set enrichment with GAIN SCZ genetic
association signals.
GWAS of SCZ includes 2,593,107 SNPs with corresponding P-values. The lowest –log(p) SNP located in
20kb upstream and downstream was used to present the gene. Totally 1,345,474 variants were used and 17,542
genes were mapped for enrichment analysis.
Supplementary Fig. 10 | Manhattan Plot for M1A gene set enrichment with TGen BD genetic association
signals.
GWAS of BD includes 2,542,706 SNPs with corresponding P-values. The lowest –log(p) SNP located in 20kb
upstream and downstream was used to present the gene. Totally 1,322,654 variants were used and 17,607
genes were mapped for enrichment analysis.
11. Supplementary Tables
Supplementary Table 1 | Demography of SMRI PCX samples
Schizophrenia
Bipolar disorder
Age(years)
42.76(20-60)
44.23(20-65)
Sex(M/F)
35/14
25/21
Race(Euro/Non-Euro)
45/4
44/2
PMI(hours)
32.20(9-80)
35.65(12-84)
Brain pH
6.39(5.8-6.93)
6.39(5.8-6.97)
Left Brain(Fixed/Frozen)
23/26
22/24
Normal Controls
45.41(30-70)
34/15
48/1
27.22(8-58)
6.50(5.8-7.03)
25/24
Supplementary Table 2 | Demography of SMRI CB samples
Schizophrenia
Bipolar disorder
Age(years)
43.21(20-70)
44.72(20-65)
Sex(M/F)
28/11
19/17
Race(Euro/Non-Euro)
39/0
36/0
Brain pH
6.43(5.80-6.93)
6.43(5.92-6.97)
Left Brain(Fixed/Frozen)
19/20
20/16
Normal Controls
45.11(30-70)
31/13
44/0
6.53(5.80-7.03)
23/21
Supplementary Table 3 | Demography of SMRI PFC samples
Schizophrenia
Bipolar disorder
Age(years)
43.18(19-62)
44.4(19-64)
Sex(M/F)
35/15
26/24
Race(Euro/Non-Euro)
46/4
47/3
PMI(hours)
32.80(9-80)
36.6(12-84)
Brain pH
6.38(5.8-6.9)
6.36(5.76-6.9)
Brain region(Left/Right)
24/26
23/27
Normal Controls
45.3(29-68)
35/15
49/1
27.68(8-58)
6.51(5.8-7.1)
26/24
Supplementary Table 4 | Demography of VBBN PFC samples
Schizophrenia
Age(years)
43.40(19-81)
Sex(M/F)
24/6
PMI(hours)
39.13(17-68)
Brain pH
6.24(5.6-6.64)
Normal Controls
44.72(21-80)
24/5
40.47(12-69)
6.31(5.82-6.56)
Supplementary Table 5 | M1A gene list (top 25) and correlation with module eigengenes
Gene
NOTCH2
SLC4A4
SLC25A18
PREX2
ACBD7
METTL7A
SLC39A12
GRAMD1C
GJA1
GPC5
DOCK7
ATP13A4
ACSS3
SLC1A3
GPAM
AMOT
SLC15A2
GOLIM4
MSI2
GPR98
BMPR1B
SLC1A2
MID1
ATP1A2
MLC1
Entrez ID
4853
8671
83733
80243
414149
25840
221074
54762
2697
2262
85440
84239
79611
6507
57678
154796
6565
27333
124540
84059
658
6506
4281
477
23209
Chr
chr1
chr4
chr22
chr8
chr10
chr12
chr10
chr3
chr6
chr13
chr1
chr3
chr12
chr5
chr10
chrX
chr3
chr3
chr17
chr5
chr4
chr11
chrX
chr1
chr22
Start
Stop
position
position
120454176 120612276
72053003
72437799
18043183
18073651
68864353
69143897
15117474
15130775
51318534
51326300
18240821
18332218
113557680 113666021
121756788 121770873
92050882
93519490
62920397
63153969
193119866 193272696
81471809
81649582
36606689
36688436
113909622 113943521
112017731 112084043
121613287 121660458
167727231 167813669
55333931
55757299
89854617
90460087
95679128
96076167
35272753
35441524
10413596
10851773
160085548 160113381
50497820
50524358
Correlation
0.966282
0.955201
0.951395
0.949843
0.948906
0.94853
0.947239
0.946852
0.944748
0.944504
0.944176
0.943363
0.943127
0.942854
0.942621
0.941833
0.940898
0.939817
0.939664
0.936887
0.936801
0.935932
0.935091
0.934725
0.934619
P value
7.32E-52
1.02E-46
3.03E-45
1.11E-44
2.4E-44
3.25E-44
9.07E-44
1.23E-43
6.12E-43
7.34E-43
9.37E-43
1.7E-42
2.02E-42
2.46E-42
2.91E-42
5.12E-42
9.89E-42
2.09E-41
2.32E-41
1.48E-40
1.57E-40
2.75E-40
4.7E-40
5.92E-40
6.33E-40
Supplementary Table 6 | M3A gene list (top 25) and correlation with module eigengenes
Gene
MT1X
TRAF3IP2
MT2A
RFX4
MT1M
MGST1
PLOD2
MT1L
MT1DP
SLC7A2
GPR56
CYP4F11
GLIS3
LRIG1
YAP1
FNDC3B
FAM189A2
IL6ST
IL33
PPFIA1
MT1P3
MT1JP
ITPR2
HGF
LPIN1
Entrez ID
4501
10758
4502
5992
4499
4257
5352
4500
326343
6542
9289
57834
169792
26018
10413
64778
9413
3572
90865
8500
140851
4498
3709
3082
23175
Chr
chr16
chr6
chr16
chr12
chr16
chr12
chr3
chr16
chr16
chr8
chr16
chr19
chr9
chr3
chr11
chr3
chr9
chr5
chr9
chr11
chr20
chr16
chr12
chr7
chr2
Start
Stop
position
position
Correlation
56716393
56718108
0.91599
111877657
111927449
0.913403
56642496
56643409
0.907505
106976685
107156581
0.903227
56642568
56667898
0.895629
16500076
16517344
0.892384
145787227
145879282
0.881285
56651373
56652727
0.876324
56677599
56679162
0.875918
17396304
17428025
0.875673
57653958
57698944
0.867166
16023181
16045676
0.867138
3824127
4300035
0.860373
66429144
66551435
0.856017
101981279
102104149
0.836262
171757418
172118487
0.836008
71940348
72007371
0.835066
55230923
55290772
0.828274
6215809
6257983
0.819621
70116815
70230502
0.81249
33805758
33806127
0.803921
56669651
56670998
0.800318
26488285
26986131
0.788552
81328322
81399454
0.773948
11886740
11967620
0.768599
P value
1.81E-35
6.24E-35
9.05E-34
5.65E-33
1.19E-31
4.09E-31
2.09E-29
1.07E-28
1.22E-28
1.32E-28
1.83E-27
1.84E-27
1.31E-26
4.41E-26
6.76E-24
7.18E-24
8.97E-24
4.29E-23
2.86E-22
1.27E-21
7.01E-21
1.40E-20
1.23E-19
1.51E-18
3.60E-18
Supplementary Table 7 | M1A’s gene list and top GO functions
Category
Term
%
PValue
FDR
GOTERM_BP_FAT GO:0030182~neuron differentiation
11 7.50E-08 1.27E-04
EGFR, PARD3, TUBB2B, PTPRZ1, CLU, SOX2, EMX2, PAX6, GJA1, DOCK7, NR2E1, GLI3, GPR98,
TGFB2, SLC1A3, S1PR1, CRB1, LHX2, NTRK2, OPHN1, BMP7, BMPR1B
GOTERM_BP_FAT GO:0048666~neuron development
8.5 3.63E-06 0.006141
EGFR, PARD3, PTPRZ1, CLU, PAX6, GJA1, DOCK7, NR2E1, GPR98, TGFB2, SLC1A3, CRB1, LHX2,
NTRK2, OPHN1, BMPR1B, BMP7
GOTERM_CC_FAT GO:0044459~plasma membrane part
26.5 5.69E-06 0.007388
RHOJ, CADM1, GPR125, SLC15A2, FERMT2, GJA1, AQP4, TLR4, SDC4, SDC2, IL17RB, SLC1A4,
GPC5, EDNRB, SLC1A2, S1PR1, SLC1A3, APOE, SLC4A4, EGFR, GABRG1, SLC9A3R1, NTSR2,
ARHGAP31, CYBRD1, OPHN1, ADD3, PARD3, FGFR3, PHKA1, CLDN10, GNG12, EZR, FAT1, P2RY1,
DTNA, SLC6A11, GPR75, PTPRZ1, MAOA, AXL, GPR137B, ATP1A2, GJB6, NOTCH2, RAB31,
TMEM47, KCNN3, NTRK2, AMOT, MERTK, BMPR1B, CD302
GOTERM_BP_FAT GO:0000902~cell morphogenesis
8.5 6.76E-06 0.011446
EGFR, PARD3, PTPRZ1, CLU, PAX6, GJA1, DOCK7, SOX9, NR2E1, TGFB2, EZR, SLC1A3, CRB1, LHX2,
OPHN1, BMPR1B, BMP7
GOTERM_BP_FAT GO:0007423~sensory organ development
6.5 2.26E-05 0.038226
EGFR, SOX2, PAX6, GJB6, NR2E1, GLI3, GPR98, TGFB2, EYA1, CRB1, NTRK2, BMPR1B, BMP7
GOTERM_BP_FAT
GO:0032989~cellular component
8.5 2.61E-05 0.044204
morphogenesis
EGFR, PARD3, PTPRZ1, CLU, PAX6, GJA1, DOCK7, SOX9, NR2E1, TGFB2, EZR, SLC1A3, CRB1, LHX2,
OPHN1, BMPR1B, BMP7
Supplementary Table 8 | M3A’s gene list and top GO functions
Category
Term
SP_PIR_KEYWORDS metal-thiolate cluster
MT1X, MT2A, MT1M, MT1JP, MT1L, MT1E, MT1P0
GOTERM_BP_FAT
GO:0051270~regulation of cell motion
IGF1R, NRP1, LYN, IL6ST, F3, ABHD2, FGF2
SP_PIR_KEYWORDS chelation
MT1L, MT1M, MT1E, MT1JP, MT1P3, MT1X
SP_PIR_KEYWORDS cadmium
MT1L, MT1M, MT1E, MT1JP, MT1P3, MT1X
GOTERM_BP_FAT
GO:0042493~response to drug
ABCC9, TNFRSF11B, LYN, IL1B, XRCC1, MGST1, ABCG2
%
3.88
PValue
2.41E-05
FDR
0.031
6.80
6.01E-04
0.942
2.91
6.49E-04
0.822
2.91
6.49E-04
0.822
6.80
0.00108
1