Whole genome association mapping of gene expression in the human prefrontal cortex

Variations in gene expression among individuals may have multiple downstream implications, including an effect on disease risk. “Genetical genomics” (or expression genetics) uses linkage and association methods to map gene expression phenotypes, connecting genetic variants to expression quantitative trait loci (eQTLs). It represents a promising approach to identifying novel expression regulatory elements in the genome. Studies of human lymphoblastoid cell lines¹, liver² and brain³ have also been reported. Meyers et al.³ studied 193 neuropathologically normal human brain samples from three cortical regions using the Affymetrix 500K Array for genotyping and the Illumina HumanRefseq-8 Expression Array for gene expression measurements. They assessed association between 366,140 SNPs and the expression of 14,078 transcripts, and identified 433 SNP-transcript pairs (99 transcripts) that showed significant cis-association (transcript-specific empirical P value ≤ 0.05); but only 25 of them (involving two genes, KIF1B and IPP) are significant after correcting for all the SNPs and phenotypes (transcripts) tested (Sidak multitranscript-corrected empirical P values ≤ 0.05). We would consider only the two genes truly significant cis- associations as they were the ones surviving correction for all the statistical tests.

There are several major limitations in the Myers et al. study, including sample heterogeneity (pooled samples from three different cortical regions: frontal, temporal, and parietal), expression data confounded by uncontrolled covariates, particularly brain pH value, and microarray batch effects. In any case, it would be reasonable to attempt a replication study. We performed a new brain eQTL mapping using psychiatric patient and control brains focusing, on prefrontal cortex, and with a statistical procedure optimized for covariates and microarray batch effects. We used Surrogate Variable Analysis (SVA)⁴ to remove covariate effects and ComBat⁵ to remove batch effects on gene expression before the SNP-expression association tests. These procedures, we hoped, would improve the power of detecting associations by removing sources of non-genetic variation from the data.

We obtained 164 brain samples from the Stanley Medical Research Institute (SMRI). These 164 samples came from two collections.^6–8 1) The Neuropathology Consortium has 60 brains, with 56 of the 60 samples Caucasian. The samples are from Schizophrenia, Bipolar Disorder, Major Depression patients, and healthy controls. 2) The SMRI Array Collection contains another set of 105 samples, with 103 of them Caucasian with Schizophrenia, Bipolar Disorder, or healthy controls. Diagnoses of the samples were made by two senior psychiatrists, using DSM-IV criteria and based on medical records, and, when possible, telephone interviews with family members. Diagnoses of unaffected controls were based on structured interviews by a senior psychiatrist with family member(s) to rule out Axis I diagnoses.

These two sets of samples have been studied for gene expression in the prefrontal cortex (Broadmann area 46, dorsolateral prefrontal cortex, possibly contains Broadmann area 10, frontal pole) by six investigators using five different microarray platforms. Data is available at SMRI Online Genomics Database (https://www.stanleygenomics.org). Altar’s group is the only one that studied both Consortium and Array samples using the same microarray platform (Affymetrix Human Genome U133A). We chose this dataset (Study 1 and 2 in the online database) as our expression data, and obtained the CEL files of the raw gene expression data. These include 87 Array and 40 Consortium Caucasian samples. All these expression data were normalized with the robust multi-array average (RMA) method using Partek software (http://www.partek.com). RMA expression values were calculated based on scaling to a target intensity of 100, transformed by Log2(x+20). The Affymetrix U133A array uses, on average, 11 probes of a probeset to assay expressions of 3’ of one transcript. The probeset is the expression measurement unit (phenotype) in this study. A total of 22,277 probesets were assayed in U133A. We selected 6,968 probesets that were coded as “present” by the Affymetrix Microarray Suite (MAS) call algorithm in ≥ 80% of samples.

We used Surrogate Variable Analysis (SVA)⁴ to identify known and unknown covariates influencing the gene expression data. The residuals from SVA were then used for ComBat⁵ to remove batch effects. The effects of known variables on the gene expression data were identified using linear regression pre- and post-SVA and ComBat. All samples include collection group, diagnosis, age, gender, race, postmortem interval (PMI), brain pH, smoking, alcohol use, suicide status, and psychotic feature data. We used these variables as covariates in the analysis. Drug and alcohol use were dichotomized into “Heavy” and “Not heavy” (as defined by SMRI). Age, PMI, pH, and lifetime antipsychotics data were analyzed as quantitative covariates. Other covariates were analyzed as binary covariates. Summary information about the sample demographic data and covariates can be found in the Supplementary Table ST1.

The raw microarray expression data demonstrated strong effects of brain pH (significant in 57% of probes) and batch effects (significant in 48% of probes). After SVA and ComBat processing, and assessing significance of covariates by permuting within batches, the proportions of genes showing significant pH and batch effects (p<0.05) were reduced to 2% and 5% respectively, which are close to chance expectation (Supplementary Table ST2).

The 6,968 residuals obtained from SVA/ComBat were used as phenotypes for association analysis. All residuals were standardized to have a mean of 0 and standard deviation of 1.

Genomic DNAs of the same individuals were extracted from frozen cerebellum tissues provided by the SMRI. A phenol/chloroform/isoamyl alcohol protocol⁹ was modified and followed. The DNAs were resuspended in 0.1 mM EDTA TE buffer. The genomic DNA was evaluated by NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE) for concentration, and by 1% agarose gel to validate the DNA integrity. We used the GeneChip Mapping 5.0 Array and Assay Kit (Affymetrix, Santa Clara, CA) for genotyping following the Affymetrix protocol. Genotypes were called using the BRLMM-p algorithm (Affymetrix) with all arrays simultaneously. SNP call rates ranged from 97.3% to 99.58%, average 98.9%. In the 156 Caucasian samples, 238,389 out of 443,816 SNPs have call rates ≥ 99%, minor allele frequency ≥ 10%, and Hardy-Weinberg Equilibrium (HWE) p ≥ 0.001. These 238,389 SNPs were used to test for correlations with gene expression.

We used the programs STRUCTURE,¹⁰ PLINK,¹¹ and EIGENSTRAT¹² to verify sample ethnic homogeneity, and PLINK¹¹ pairwise identity-by-state and identity-by-descent calculation to examine cryptic relatedness. The results confirmed that 127 selected samples are unrelated Caucasians, and these were used for genotype-expression association tests.

Gene expression regulation can be roughly divided into two types: cis-acting regulation by DNA elements in or adjacent to the transcripts, and trans-acting regulation by factors from the genomic regions distal from the transcripts, including from different chromosomes. We defined the SNPs within a region bounded by one Mb distance from both ends of each expression probeset as candidates for cis- analysis. All the other SNPs were analyzed for trans-acting associations of each gene. Forty expression probesets were excluded from the cis-analysis because of having multiple homologs in the genome. They were analyzed with the other trans-analyses using all SNPs as trans-candidate SNPs for them.

We used PLINK¹¹ to perform linear regression analysis to test for correlation between expression residuals and genotype (additive genetic model; the number of minor alleles at each SNP). From this analysis, an asymptotic p-value from the Wald statistic was obtained. All permutations were done permuting within mRNA microarray and genotyping batches clusters to control for batch effects. Permutations were performed by swapping sets of phenotypes between individuals. This preserves the relationship between genotypes (and controls for LD) and within the grouped phenotypes (thus controlling for any correlations between expression probes). Clusters of individuals within each mRNA microarray and genotype batch were defined. Permutations were performed within each cluster of individuals to control for batch effects. Two sets of permutations were done. Permutations for an expression-SNP combination were calculated with the adaptive perm option of PLINK, permuting up to 1 billion replicates (EMP_P). This corrects for possible non-normality of the phenotype distribution. Permutations correcting for multiple testing within a cis-region or whole genome scan were also performed, using the max(T) permutation option of PLINK (Regionwide_ P for cis-; Genomewide_P for trans-). For each phenotype, results were permutated 1,000 times, using the same seed to maintain the correlation between phenotypes. The most significant statistic per replicate was saved using the PLINK mperm-save option. To estimate phenotype-wide significance, the most significant statistic per replicate across all phenotypes was obtained (stat_best). Phenotype-wide corrected p-values were calculated as (R+1)/(N+1) where R is the number of times the stat_best exceeded the observed statistic and N is the number of permutations (1000).

In the cis-analysis, 3,951 SNP-expression probeset pairs, consisting of 3530 SNPs and 903 probesets (of 826 genes) were significantly correlated with region-wide permutated p (Regionwide_P) ≤ 0.05. We found that 562 associations (involving 106 genes) are significant after correcting for the 6,928 expression phenotypes that have been tested for cis-association (Phenotype-wide_P ≤ 0.05). 72 expression probesets had 3 to 20 different SNPs from each cis-region showing Phenotype-wide significant associations. They are eQTLs supported by multiple SNPs in the same region. The cis- associations show effect sizes R2 ranging from 0.05 to 0.67. The top 10 best signals by Wald P of these 106 genes are shown in Table 1. The complete cis-regulation list can be found in Supplementary Table ST3.

Table 1.

Top Ten Cis- Associations in Human Prefrontal Cortex

Probe	Gene Symbol	Best Correlated SNP	SNP Chr.	SNP Position	NMISS	Wald_P	EMP_P	Regionwide _P	Phenotype -wide_P	No. SNPs Tested
219683_at	FZD3	rs2874941	8	28429267	127	1.36E-31	<1.00E-09	<0.001	<0.001	216
205176_s_at	ITGB3BP	rs17391823	1	63759453	127	1.29E-30	<1.00E-09	<0.001	<0.001	218
217753_s_at	RPS26	rs1873914	12	54665694	127	1.17E-28	<1.00E-09	<0.001	<0.001	107
209316_s_at	HBS1L	rs4896128	6	135391449	127	4.82E-28	<1.00E-09	<0.001	<0.001	143
209472_at	CCBL2	rs3753683	1	89200159	127	9.09E-28	<1.00E-09	<0.001	<0.001	162
220122_at	MCTP1	rs10052066	5	93992505	126	1.44E-26	<1.00E-09	<0.001	<0.001	138
201922_at	TINP1	rs16872345	5	74185234	127	2.46E-25	<1.00E-09	<0.001	<0.001	187
205872_x_at	PDE4DIP	rs12124527	1	143607676	127	7.49E-25	<1.00E-09	<0.001	<0.001	16
208733_at	RAB2A	rs9437	8	61697971	127	3.70E-23	<1.00E-09	<0.001	<0.001	191
212582_at	OSBPL8	rs10862080	12	75453106	126	2.55E-22	<1.00E-09	<0.001	<0.001	182

NMISS: number of individuals with non-missing phenotype; Wald_P: Wald test asymptotic p-value; EMP_P: single point p estimated by permutation, which corrects for non-normality of phenotype; Regionwide_P, empirical p-value based on 1000 permutations with correction for the number of SNPs tested for cis-associations of each expression probeset; Phenotype-wide_P, empirical p-value based on 1,000 permutations, correcting for the number of SNPs tested for cis- associations of each expression probeset and the number of probeset (phenotypes) studied; Num SNP Tested: Number of SNPs that have been tested for each gene in the 2 Mb cis-region. Complete list of cis-associations can be found in Supplementary Table ST3.

In the trans-analysis, 241 SNP-transcript probeset pairs from 239 SNPs and 160 probesets (157 genes) had associations at permutation corrected Genomewide_P ≤ 0.05 (Supplementary Table ST4). But none is significant after further phenotype-wide correction.

Interestingly, pathway and functional analysis of the regulated genes in both cis- and trans-associations using Ingenuity Pathway Analysis (www.ingenuity.com) show that “protein degradation and protein synthesis” are the most enriched function groups. Protein ubiquitination is the most enriched canonical pathway (Supplement table ST5). This suggests that protein metabolism may be impacted by the genetic variants with detectable effects more than other biological systems.

We detected one SNP (rs17733118, upstream of ZFP64, a zinc finger protein homolog) showing associations with two distinct genes VPS8 and CTNNA1. ZFP64 is thus a potential master regulator that regulates expression of VPS8 and CTNNA1, though nothing is known about their interactions so far. Again worth noting, these trans- associations do not reach phenotype-wide significant level therefore with a good possibility of being false positive.

In the previous study on brain samples.³ Myers et al. identified 433 SNP-transcript pairs (99 transcripts) showing region-wide cis-association (corrected for all the SNPs tested in each cis-region) but only identified two genes showing phenotype-wide significant cis-association. We analyzed 366 genes, which showed region-wide significant cis-association in our study, in the 46 frontal cortex subset samples from Myers’ study using our SVA-ComBat procedure. To maximize power, missing data was imputed using nearest neighbor averaging prior to SVA analyses. Information was available for sex, age, transcripts detected rate (TDR), sample collection institution and batch dates (but not brain pH). Effects of these covariates were analyzed using regression in the pre- and post- SVA + ComBat data. In the preprocessed data, batch effects, institution, and TDR were significant in 40%, 15%, and 20% of the probes. In the postprocessed data and permuting within batch cluster, batch effects, institution, and TDR were significant in 5%, 6%, and 0% of the probes. Association analyses performed in the same manner as with the SMRI data. Only the SNPs that were significant in our study were tested. Thus region-wide significant refers to significance after correction for the number of SNPs actually analyzed rather than for the whole cis-region. Of the 826 genes showing associations with a Regionwide_wide P < 0.05 in the SMRI data, only 366 genes could be tested in the Myers data. Defining replication to be only association with the same SNP in the same direction (same allele increases or decreases gene expression), 103 associations involving 45 genes are region-wide significant. Among them 26 associations involving seven genes are phenotype-wide significant in the replicate sample (Table 2 shows the best association for each of the 45 genes).

Table 2.

Cis-Associations that Are Region-wide and/or Phenotype-wide Significant in both SMRI and Myers' Samples in the Same Association Direction (Best Association for Each Gene)

		SMRI data				Myers data
GENE	SNP	Affymetrix probe	Wald_P	Region wide_Corr_P	Phenotype- wide_P	Illumina probe	Wald_P	Region wide_Corr _P	Phenotype- wide_P
ITGB3BP	rs10789138	205176_s_at	2.46E-30	<0.001	<0.001	GI_27597074	1.83E-08	<0.001	<0.001
PEX6	rs2274517	320_at	1.50E-13	<0.001	<0.001	GI_21361243	5.19E-09	<0.001	<0.001
RPS26	rs2069408	217753_s_at	4.31E-12	<0.001	<0.001	GI_15011935	8.93E-07	<0.001	<0.001
UBA52	rs2314664	221700_s_at	3.32E-09	<0.001	0.005	GI_15451941	1.87E-10	<0.001	<0.001
HMGN1	rs2735306	200943_at	3.89E-08	<0.001	0.027	GI_34147704	2.81E-05	0.004	0.027
SPATA7	rs1048190	219583_s_at	3.89E-06	<0.001	0.926	GI_13384599	4.02E-05	<0.001	0.039
HBS1L	rs4646871	209316_s_at	3.40E-19	<0.001	<0.001	GI_24431963	4.32E-05	<0.001	0.044
TOMM7	rs2286498	201812_s_at	2.34E-09	<0.001	0.004	GI_9506858	1.13E-04	<0.001	0.105
TINP1	rs6453086	201922_at	5.10E-08	<0.001	0.035	GI_21359901	2.02E-04	<0.001	0.208
RBM6	rs2240327	201967_at	1.72E-06	<0.001	0.713	GI_5032032	3.37E-04	0.003	0.339
CD53	rs2885805	203416_at	7.21E-09	<0.001	0.007	GI_21237756	3.61E-04	0.007	0.360
DGCR6	rs418623	208024_s_at	2.88E-05	0.004	1.000	GI_15208653	5.59E-04	0.003	0.482
NFE2L2	rs4893911	201146_at	2.97E-04	0.038	1.000	GI_20149575	5.94E-04	<0.001	0.512
STX7	rs11154682	212632_at	1.07E-04	0.018	1.000	GI_4507294	8.90E-04	0.004	0.643
EIF2S1	rs17248895	201143_s_at	4.14E-07	<0.001	0.257	GI_34147492	1.14E-03	0.028	0.731
SLC38A1	rs2241960	218237_s_at	4.64E-11	<0.001	<0.001	GI_21361928	1.41E-03	0.004	0.794
PCM1	rs9325823	214937_x_at	2.69E-05	0.008	1.000	GI_34878901	1.59E-03	0.010	0.839
ASRGL1	rs2513077	218857_s_at	1.40E-09	<0.001	0.002	GI_23308566	1.60E-03	0.016	0.839
RPL36AL	rs2985697	207585_s_at	1.61E-04	0.020	1.000	GI_34335143	1.92E-03	0.016	0.885
GSTM5	rs1887547	205752_s_at	6.14E-09	<0.001	0.007	GI_23065562	3.36E-03	0.016	0.970
CDC25B	rs4815610	201853_s_at	3.90E-05	0.007	1.000	GI_11641410	3.52E-03	0.002	0.974
ALDH8A1	rs12661423	220148_at	4.74E-09	<0.001	0.005	GI_25952151	3.68E-03	0.020	0.974
CHPT1	rs7963747	221675_s_at	4.96E-06	0.002	0.963	GI_9910383	5.49E-03	0.026	0.994
RAD23B	rs716004	201223_s_at	4.34E-06	0.002	0.948	GI_19924138	4.95E-03	0.035	0.994
LOC400642	rs990072	217506_at	2.99E-10	<0.001	<0.001	GI_42661344	4.95E-03	0.044	0.994
ZNF148	rs9850300	203319_s_at	1.53E-11	<0.001	<0.001	GI_11415035	1.16E-02	0.003	0.999
POLR1D	rs534150	218258_at	3.13E-08	<0.001	0.025	GI_7705739	9.23E-03	0.019	0.999
SCRG1	rs17325472	205475_at	1.89E-15	<0.001	<0.001	GI_6005869	9.72E-03	0.021	0.999
RNF10	rs651627	208632_at	1.69E-05	0.003	1.000	GI_34452680	9.93E-03	0.026	0.999
MTRR	rs327575	203200_s_at	7.65E-06	0.003	0.991	GI_4505278	1.09E-02	0.029	0.999
DDT	rs2000467	202929_s_at	4.80E-05	0.007	1.000	GI_5453630	1.11E-02	0.047	0.999
RPL3	rs4821940	212039_x_at	8.94E-04	0.048	1.000	GI_16507968	1.21E-02	0.048	0.999
DGUOK	rs6546923	209549_s_at	7.53E-04	0.050	1.000	GI_18426962	1.43E-02	0.013	1.000
MAP2K1	rs17228212	202670_at	1.94E-04	0.021	1.000	GI_14589898	1.35E-02	0.014	1.000
TRAPPC4	rs9645664	217959_s_at	1.56E-05	0.003	1.000	GI_7706666	4.07E-02	0.026	1.000
S100A13	rs913859	202598_at	3.85E-06	<0.001	0.924	GI_41117409	1.40E-02	0.028	1.000
SLC35A1	rs242264	203306_s_at	1.32E-04	0.018	1.000	GI_20149579	1.77E-02	0.029	1.000
FUS	rs17708876	200959_at	1.88E-03	0.041	1.000	GI_4826733	1.43E-01	0.032	1.000
NME2	rs4794220	201268_at	1.52E-04	0.012	1.000	GI_4505408	2.21E-02	0.036	1.000
PEPD	rs10422643	202108_at	3.54E-04	0.028	1.000	GI_4557834	1.39E-02	0.037	1.000
SRP9	rs360093	201273_s_at	3.06E-04	0.039	1.000	GI_4507216	1.34E-02	0.039	1.000
DNM1L	rs1971911	203105_s_at	2.39E-05	0.005	1.000	GI_6996006	2.37E-02	0.045	1.000
CTSB	rs17810889	213275_x_at	2.40E-04	0.021	1.000	GI_22538429	2.88E-02	0.046	1.000
RAB36	rs5751592	211471_s_at	9.41E-05	0.010	1.000	GI_31795534	6.17E-02	0.047	1.000
STAT6	rs10506347	201331_s_at	1.31E-04	0.004	1.000	GI_23397677	1.32E-02	0.048	1.000

Wald_P: Wald test asymptotic p-value; Regionwide_P, empirical p-value based on 1000 permutations with correction for the number of SNPs tested for cis-associations of each expression probeset; Phenotype-wide_P, empirical p-value based on 1000 permutations, correcting for the number of SNPs tested for cis-associations of each expression probeset and the number of probeset (phenotypes) studied.

The relatively low level of replication is not surprising and lack of replication does not invalidate either set of findings. The replicate sample size is quite very small (only 46 samples). Normally the replicate sample size should be larger than the initial study to have sufficient power to reproduce the findings from the initial study. Also there is a brain region difference (frontal cortex in Myers’ study; prefrontal cortex in our study), demographic data differences (Myers’ samples average age 81; in this study average age 45).

Myers et al. reported RPS26 gene association with SNP rs11171739 as an example of replication of Cheung et al.’s finding in lymphoblastoid cells.¹ We observed this association as well. Another study of liver also identified RPS26- rs2292239 correlation as one of its strongest associations². RPS26 seems to be one of the most strongly genetically regulated genes in the human genome.

Seven SNPs in a 125 Kb genomic region showed cis-association with two different genes ALDH8A1 and HBS1L at the phenotype-wide significant level (Table 3). They may be considered as co-regulated transcripts. ALDH8A1 and HBS1L are transcribed in the same direction, with increased expression associated with the same SNP allele. They might be derived from a polycistronic transcript, though polycistronic transcription, except for microRNA cluster, has rarely been reported or studied in humans so far.¹³

Table 3.

HBS1L and ALDH8A1 share cis- associations

probe	SNP Chr.	SNP	SNP Position	Beta	R2	Regionwide_P	Phenotype- wide_P	Gene
220148_at	6	rs4646871	135307310	−0.671	0.213	<0.001	0.032	ALDH8A1
209316_s_at	6	rs4646871	135307310	−1.049	0.475	<0.001	<0.001	HBS1L
220148_at	6	rs1014021	135376293	0.681	0.222	<0.001	0.020	ALDH8A1
209316_s_at	6	rs1014021	135376293	0.999	0.435	<0.001	<0.001	HBS1L
220148_at	6	rs12661423	135391263	0.716	0.241	<0.001	0.005	ALDH8A1
209316_s_at	6	rs12661423	135391263	1.024	0.449	<0.001	<0.001	HBS1L
220148_at	6	rs1590975	135393781	0.681	0.222	<0.001	0.020	ALDH8A1
209316_s_at	6	rs1590975	135393781	0.999	0.435	<0.001	<0.001	HBS1L
220148_at	6	rs9321481	135394341	0.667	0.213	<0.001	0.036	ALDH8A1
209316_s_at	6	rs9321481	135394341	0.997	0.430	<0.001	<0.001	HBS1L
220148_at	6	rs7741515	135416061	0.728	0.245	<0.001	0.005	ALDH8A1
209316_s_at	6	rs7741515	135416061	1.012	0.431	<0.001	<0.001	HBS1L
220148_at	6	rs2150681	135416925	0.733	0.244	<0.001	0.005	ALDH8A1
209316_s_at	6	rs2150681	135416925	1.011	0.423	<0.001	<0.001	HBS1L

Beta: Regression coefficient; R2: the proportion of phenotypic variance due to the SNP; Regionwide_P, empirical p-value based on 1000 permutations with correction for the number of SNPs tested for cis-associations of each expression probeset; Phenotype-wide_P, empirical p-value based on 1000 permutations, correcting for the number of SNPs tested for cis-associations of each expression probeset and the number of probeset (phenotypes) studied.

Since we had psychiatric disorder patient samples in this study, we were interested in knowing whether the sample composition influenced the detected regulation elements. In the covariate analysis of the expression data, we found that disease diagnoses contribute very little to the global variations of gene expression level before and after the SVA/ComBat adjustment, comparing with many other factors, including PMI, brain pH (Supplementary Table 2). After regressing out factors including affection status, we found that affection status has little effect on the eQTL mapping results in this study.

It is conceivable that genetic variants would have stronger and direct impact on regional cis-regulation of gene expression, while distant trans-regulation would involve more factors and thus show less genetic effects. We identified an exceedingly large amount of cis-associations that can stand the strict statistical correction for multiple testing. No trans-associations are significant after correction for the number of SNPs and phenotypes analyzed. Other eQTL studies have claimed detection of trans- regulations with region-wide and occasionally phenotype-wide significance but there is little consistency between studies^14–17. The difficulty of replicating trans- eQTLs has been previously observed¹⁵. We are advocating the use of phenotype-wide significance, which might help to reduce the false positives that would be more difficult to replicate.

We note that this study focuses on genes that have observable relatively large variation in expression, and on SNPs that have common minor allele frequencies, in order to have well-powered SNP-expression pairs for eQTL mapping study. A considerable number of important neuropsychiatric disease candidate genes, including 5-HTT (SLC6A4), DRD1, DRD2, DRD3, DRD4, DRD5, GRIA1, GRIA3, GRIA4, GRIN1, GRIN2B, GRIN2C, GRIN2D, PER1, CRY1, CRY2, and others, were not assessed in this study because their expression probes were filtered out due to low detection levels.