Abstract
Major depressive disorder (MDD) is recognized as a primary cause of disability worldwide, and effective management of this illness has been a great challenge. While genetic component is supposed to play pivotal roles in MDD pathogenesis, the genetic and phenotypic heterogeneity of the illness has hampered the discovery of its genetic determinants. In this study, in an independent Han Chinese sample (1824 MDD cases and 3031 controls), we conducted replication analyses of two genetic loci highlighted in a previous Chinese MDD genome-wide association study (GWAS), and confirmed the significant association of a single nucleotide polymorphism (SNP) rs12415800 near SIRT1. Subsequently, using hypothesis-free whole-brain analysis in two independent Han Chinese imaging samples, we found that individuals carrying the MDD risk allele of rs12415800 exhibited aberrant gray matter volume in the left posterior cerebellar lobe compared with those carrying the non-risk allele. Besides, in independent Han Chinese postmortem brain and peripheral blood samples, the MDD risk allele of rs12415800 predicted lower SIRT1 mRNA levels, which was consistent with the reduced expression of this gene in MDD patients compared with healthy subjects. These results provide further evidence for the involvement of SIRT1 in MDD, and suggest that this gene might participate in the illness via affecting the development of cerebellum, a brain region that is potentially underestimated in previous MDD studies.
Subject terms: Genomics, Depression
Introduction
Major depressive disorder (MDD), a clinically and genetically heterogeneous illness, has led to significant social and economic burden worldwide, and tremendous efforts have been invested to investigate its underlying pathological mechanisms in the past decades1. Convergent findings have pointed to the involvement of dendritic spine pathology, synaptic dysfunction, as well as aberrant structure and function of prefrontal cortex and hippocampus in the neurobiology of MDD2–6. Besides, accumulating data also highlights additional brain regions in its pathogenesis, such as cerebellum, which is engaged in emotional and cognitive processes7,8. In addition to these basic and preclinical findings, scientists have also obtained strong evidence supporting the unneglectable role of genetic susceptibility factors in the pathogenesis of MDD, whose heritability has been estimated to be ~37%9, and multiple genomic loci were found to be significantly associated with the illness in populations of European origin10,11. For example, a recent meta-analysis of genome-wide association study (GWAS) datasets in Europeans reported that 102 independent loci spanning 269 genes were significantly associated with depression, and many of them were linked to synaptic structure and neurotransmission11. Besides, studies dissecting the genetic architectures of MDD in other populations, e.g., Han Chinese, are also emerging in recent years. For instance, a Han Chinese sparse whole-genome sequencing study of 10,640 female subjects followed by independent replications in 6417 individuals of both sexes identified two single nucleotide polymorphisms (SNPs) conferring risk of MDD. One SNP (rs12415800) was near the Sirtuin 1 gene (SIRT1), and the other one (rs35936514) was in an intron of LHPP (this study was named CONVERGE GWAS)12. However, neither SNP showed evidence of association with risk of MDD in populations of European ancestry (rs12415800, p = 0.797; rs35936514, p = 0.293)10, and rs12415800 was even near monomorphic in Europeans (frequency of A-allele, 0.023 in Europeans versus 0.401 in Chinese, according to genotype data from 1000 Genomes Project13). Thus, this CONVERGE GWAS is believed to provide essential knowledge primarily regarding the genetic components of MDD in Han Chinese12.
However, statistical associations between genetic markers and clinical diagnosis in GWAS do not directly reveal their underlying mechanisms14–16, it is thus essential to translate genetic risk into neural mechanisms using biological approaches. Gene editing in murine models, which provides important clues for the function of MDD-risk genes (such as SIRT1)17,18, may be insufficient to fully characterize the disease mechanisms as human brains are more complicated than murine brains, and in vivo magnetic resonance imaging (MRI) analyses in living humans are believed to provide essential information. Recent MRI studies showed that MDD patients displayed abnormalities in subcortical brain structures compared with healthy controls19,20, and the relatives of MDD patients (individuals at increased genetic risk) exhibited similar deficits in phenotypes with less severity21,22. Therefore, these phenotypes likely reflect the biological pathways directly linked to the genetic risk factors of MDD14–16. A plausible strategy, to translate statistical associations between genetic loci and clinical diagnosis of MDD into potential neural mechanisms, is thus proposed to identify effects of genetic risk loci in the brain in virtue of such endophenotypic analyses23–30. To date, effects of the CONVERGE MDD GWAS loci on such phenotypes have been scarcely reported in Han Chinese, excepting a study showing associations between SIRT1 SNPs and regional cortical gray matter density in 92 healthy individuals from Eastern China31. Hence, to characterize the neural mechanisms underlying putative genetic risk loci in the CONVERGE MDD GWAS12, the first aim of the present study is to examine their effects on regional gray matter volumes (GMV) in Han Chinese individuals using structural MRI approaches.
Meanwhile, the biological impacts of most GWAS risk loci remain unclear as they mainly reside in the noncoding regions of the genome. Accumulating evidence suggests that these noncoding loci tend to affect mRNA expression of particular genes32. Indeed, altered expression of certain genes have been reported in the brain or peripheral blood of MDD patients compared with healthy controls33–35. For example, SIRT1 mRNA levels were previously found significantly reduced in the peripheral blood of MDD patients36–38. Nevertheless, whether such genes are relevant to the genetic susceptibility of MDD is unclear. Therefore, the second aim of the present study is to test whether the MDD risk SNP rs12415800 was associated with altered mRNA expression levels of certain genes in human brain and peripheral blood tissues.
Methods and materials
All the protocols and methods were approved by the institutional review board of Kunming Institute of Zoology, Chinese Academy of Sciences, and the ethics committees of all participating hospitals and universities. Informed consents were obtained from all participants prior to the study.
MDD case-control sample and statistical analysis in Chinese population
1824 MDD cases and 3031 controls of Chinese origin were recruited from Mainland China. Briefly, each MDD patient was diagnosed in mental health centers strictly following the DSM-IV guidelines in combination with clinical information collected through medical record review and family member interviews. Subjects affected by other psychiatric disorders or neurological disorders, being or planning to be pregnant, or breast-feeding at the time of study, were excluded. Control subjects were local volunteers with no self-reported history of mental illnesses. The samples we collected were not overlapped with the samples used in previous MDD CONVERGE GWAS12.
DNA samples were randomly distributed in plates and genotyped using the SNaPShot method, and all assays were performed blind to diagnosis and genotype. Logistic regression was utilized to analyze the associations between SNPs and MDD, with sex and residence of participants included in the covariates. For meta-analysis, we retrieved odds ratio (OR) and standard error (SE) values of each individual sample to calculate the inter-sample heterogeneity, pooled OR, and the overall 95% confidence intervals (CIs). We considered SNPs with a one-tailed p < 0.05 in our primary sample to be nominally significant; in the overall meta-analysis, SNPs with a two-tailed p < 5.00 × 10−8 was considered genome-wide statistically significant. Power analysis was conducted using the Power and Sample Size Program software39, and the reported OR of 1.150 in CONVERGE MDD GWAS12 was applied in the power analysis, which corresponds to a “weak” gene effect.
Structural imaging analysis in Chinese population
We used two independent structural imaging samples of Han Chinese population, the discovery sample (Beijing sample) and replication sample (Kunming sample).
Discovery sample
508 unrelated healthy controls (258 females, 250 males, mean age 24.5 ± 4.0 years) were recruited from the local community and screened using the Structured Clinical Interview for Diagnostic and the Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR) Axis I Disorders (SCID, non-patient edition). All participants were right-handed Han Chinese without any lifetime history or family history of psychiatric disorders. Magnetic resonance (MR) images were acquired using a 3.0T GE Discovery MR750 scanner at the Center for MRI Research, Peking University. T1-weighted high-resolution structural image was acquired in a sagittal orientation using an axial 3D fast, spoiled gradient recalled (FSPGR) sequence with the following parameters: repetition time = 6.66 ms, echo time = 2.93 ms, field of view = 256 × 256 mm2, slice thickness/gap = 1.0/0 mm, acquisition voxel size = 1 × 1 × 1 mm3, flip angle = 12°, 192 contiguous sagittal slices.
Replication sample
262 unrelated healthy Han Chinese subjects (174 females, 88 males, mean age 33.8 ± 12.0 years) were recruited for the current study. Healthy controls were recruited and interviewed to ensure that no one had lifetime history of psychiatric disorders, or received any treatment for psychiatric disorders. Structural MRI data were acquired using a Philips MRI scanner (Achieva Release 3.2.1.0) operating at 3 T, and high-resolution whole-brain T1-weighted images were acquired sagittally with an inversion-recovery prepared 3-D spoiled gradient echo (SPGR) pulse sequence (repetition time = 7.38 ms, echo time = 3.42 ms, flip angle = 8°, voxel dimensions = 1.04 ⨯ 1.04 ⨯ 1.80 mm3, slice thickness = 1.2 mm).
Statistical analysis
In both MRI samples, the structural images were processed with DPABI (http://rfmri.org/DPABI), a MatLab toolbox that calls for statistical parametric mapping 8 (SPM8, http://www.fil.ion.ucl.ac.uk/spm). Diffeomorphic Anatomical Registration Through Exponentiated Lie algebra (DARTEL) toolbox was also used to perform voxel-based morphometry (VBM) analysis with default parameters. All images were then normalized to the standard Montreal Neurological Institute (MNI) template, modulated to account for volume changes in the warping, and resampled to 1.5 ⨯ 1.5 ⨯ 1.5 mm3. Modulated gray matter images were smoothed with an 8 mm Gaussian kernel. An explicit mask was used from the SPM intracranial brain template so as to restrict which voxels should undergo statistical analysis. Results of different genotypic groups were compared using one-way ANOVA model with sex, age, and total GMV as covariates. We considered a whole-brain family-wise error (FWE) correction p < 0.05 with a cluster size>10 as an authentic significant effect.
Expression quantitative trait loci (eQTL) analysis of SIRT1 mRNA expression in Chinese brain samples
Discovery sample
Frozen amygdala tissues of 65 non-psychiatric individuals were obtained from the Chinese Brain Bank Center40,41. The RNA and DNA extractions, cDNA synthesis and quantitative real-time PCR (qRT-PCR) were performed as previously described40. In brief, total RNA was isolated from the amygdala tissues using TRIzol reagent (Life Technologies, USA). Gene expression levels were quantified using qRT-PCR with SYBR green mix (Roche, USA). The primer sequences used for human SIRT1 amplification were 5′-TCGCAACTATACCCAGAACATAGACA-3′ (forward) and 5′-CTGTTGCAAAGGAACCATGACA-3′ (reverse), and sequences of primers for the housekeeping gene RPS13 were 5′-CCCCACTTGGTTGAAGTTGA-3′ (forward) and 5′-CTTGTGCAACACCATGTGAA-3′ (reverse). The qRT-PCR assays were performed in triplicates, results were normalized to the expression of RPS13 and mean 2–ΔΔCt values (relative to one genotypic group) were calculated for each subject as the relative gene expression levels. Statistical test against genotypic groups was performed using one-way ANCOVA analysis, adjusting for age, gender and RNA integrity number (RIN).
Replication sample
Frozen amygdala tissues from 72 non-psychiatric donors were collected as the replication samples under the same criteria as those for the discovery sample. RNA isolation and gene expression quantification were then performed as described above.
Diagnostic analysis of SIRT1 mRNA expression in Chinese peripheral blood samples
Fifty unrelated first-episode drug-naive MDD patients (all were diagnosed following the DSM-V guidelines, and were not taking medications) and 52 healthy control subjects were recruited from the First people’s hospital of Yunnan province. MDD cases with substance abuse or other co-occurring mental disorders were excluded, and the 17-item Hamilton Rating Scale for Depression (HAMD17) was used to evaluate the depression level. Controls were local volunteers without physical or mental illnesses. Details of the sample information have been described in a recent study42. RNA extraction, cDNA synthesis and qRT-PCR were performed as described above. The relative gene expression was presented as the means of 2–ΔΔCt (relative to the control sample or one genotypic group), and one-way ANCOVA analysis was used to test if SIRT1 was significantly altered in MDD cases compared with controls, as well as between different genotypic groups.
Results
Rs12415800 is significantly associated with MDD
The previous Han Chinese MDD GWAS of 10,640 female individuals identified several SNPs showing genome-wide associations with MDD, and two of them (rs12415800 and rs35936514) were also significantly associated with MDD in an independent sample of both sexes (6417 subjects)12. In an attempt to further replicate the associations of rs12415800 and rs35936514 with the risk of MDD, we independently recruited 1824 MDD cases and 3031 controls from mainland China. There is no overlap between our primary MDD case-control sample and the samples utilized in previous CONVERGE GWAS12. Both SNPs were in Hardy–Weinberg Equilibrium in cases and controls (p > 0.05). Notably, the putative MDD risk allele (A) of rs12415800 showed a marginally significant overrepresentation in cases compared with controls (one-tailed p = 0.031, OR = 1.085, Table 1). This association signal and the direction of allelic effects were consistent with the previous GWAS12. We also conducted a power analysis of our primary MDD sample size using the following assumptions: 1824 MDD patients and 3031 controls, two-tailed p = 0.05, the frequency of rs12415800 A-allele in Chinese populations according to 1000 Genomes Project (0.401)13, and the reported OR of rs12415800 in CONVERGE GWAS (1.150)12. Our primary MDD sample size revealed a 64.3% power of detecting a significant association. Given that this primary MDD sample had a relatively lower statistical power, we performed a meta-analysis using data obtained from all available Han Chinese samples (i.e., discovery and replication samples in CONVERGE GWAS12), and observed a stronger association between rs12415800 and MDD (two-tailed p = 7.03 ⨯ 10−11, OR = 1.137, Table 2). However, rs35936514 was not associated with MDD in our primary sample (one-tailed p = 0.500), and was thus excluded from subsequent analyses. The genotype frequencies of the two SNPs are shown in Table S1.
Table 1.
CHR | SNP | Position | Allele | Frequency | Two-tailed p-value | One-tailed p-value | OR | 95%CIs | |
---|---|---|---|---|---|---|---|---|---|
Case | Control | ||||||||
10 | rs12415800 | 69624180 | A/G | 0.453 | 0.436 | 0.062 | 0.031 | 1.085 | 0.996-1.183 |
10 | rs35936514 | 126244970 | T/C | 0.261 | 0.263 | 0.999 | 0.500 | 1.000 | 0.909-1.100 |
CHR chromosome, SNP single nucleotide polymorphism, Allele effect allele/non-effect allele, Frequency frequency of effect allele, OR odds ratio, CIs confidence intervals
Test of Hardy–Weinberg Equilibrium for rs12415800: case, p = 0.836; control, p = 0.408
Test of Hardy–Weinberg Equilibrium for rs35936514: case, p = 0.066; control, p = 0.612
Table 2.
Sample | Case | Control | p-value | OR | 95%CIs |
---|---|---|---|---|---|
CONVERGE Discovery | 5303 | 5337 | 1.92 ⨯ 10−8 | 1.164 | 1.102–1.230 |
CONVERGE Replication | 3231 | 3186 | 7.71 ⨯ 10−4 | 1.130 | 1.053–1.213 |
Current study | 1824 | 3301 | 0.062 | 1.085 | 0.996–1.183 |
Meta-analysis | 10,358 | 11,824 | 7.03 ⨯ 10−11 | 1.137 | 1.094–1.182 |
OR odds ratio, CIs confidence intervals
Test of heterogeneity for meta-analysis: I2 = 0, p-value = 0.390
Rs12415800 is significantly associated with cerebellar gray matter volume
While the statistical association provided strong evidence for a putative role of rs12415800 in the pathogenesis of MDD, we further delved into potential underlying neural mechanisms. It was proposed that aberrant brain development might cause deficits in specific brain regions, leading to the onset of psychiatric illnesses including MDD3. We therefore examined whether rs12415800 was linked to alterations in the brain structure detected by in vivo MRI in two independent Han Chinese samples.
In our discovery imaging sample of 508 healthy subjects (acquired in Beijing), the whole-brain VBM analysis revealed significantly reduced GMV of the left posterior cerebellar lobe in the subjects carrying the MDD risk A-allele at both chromosomes compared with the other genotypic groups (peak voxel −16.5/−72/−33, F = 11.855, cluster size = 795, FWE corrected p = 0.015, Fig. 1a). Post-hoc analysis (removing subjects with extreme value which is 0.6115 that beyond mean ± 3*SD) with LSD correction indicated that the GMV in the left posterior cerebellar lobe was smaller in A/A than that in G/G genotype carriers (corrected p = 0.002, Fig. 1a) and that in A/G genotype carriers (corrected p = 0.048, Fig. 1a).
In an independent replication imaging sample including 262 healthy individuals (acquired in Kunming), the whole-brain VBM analysis also revealed significantly reduced GMV in the left posterior cerebellar lobe in the A/A carriers (MDD risk) compared with the other genotypic groups (peak voxel −49.5/−51/−40.5, F = 24.021, cluster size = 1647, FWE corrected p < 0.01, Fig. 1b), although the precise peak coordinates were not the same between the two samples. In the replication sample, we further analyzed the region of interest (ROI) from a sphere with a 10 mm radius centered at the peak voxel in discovery Beijing sample (−16.5/−72/−33) so as to directly replicate the results obtained from the discovery sample. The regional GMV in the left posterior cerebellar ROI (−16.5/−72/−33) was submitted to ANCOVA with genotype as between-subjects factor, and age, gender and the total GMV as covariates. Intriguingly, we again observed a significant inter-group difference in the omnibus test (F(2,255) = 3.503, corrected p = 0.032, Fig. 1b). Post-hoc analysis with LSD correction indicated that the GMV in the left posterior cerebellar lobe of G/G carriers was larger than that of the A/A (corrected p = 0.021, Fig. 1b) and A/G genotype subjects (corrected p = 0.031, Fig. 1b).
We also examined the effect of rs12415800 on GMV using data from imaging consortia such as ENIGMA and UK Biobank43,44. However, rs12415800 is almost monomorphic in European populations, and the data from these large consortia, which primarily analyzed European individuals, did not provide valuable information. Although the role of the left posterior cerebellar lobe region is yet to be characterized in mental illnesses, growing evidence has implied the involvement of cerebellar dysfunction in MDD45 in addition to its primary roles in motor control. Changes in the GMV of cerebellum have been identified in MDD46, and significant associations between cerebellar morphology and volume and cognitive performance were also reported8,47.
Rs12415800 is associated with brain SIRT1 mRNA expression
GWAS loci of complex diseases often exert their functions through affecting gene expression32,48,49. To understand whether rs12415800 was related to the expression of nearby genes, we conducted an eQTL analysis between the SNP and SIRT1 expression using qRT-PCR methods in two independent samples of Han Chinese amygdala tissues, a brain region engages in emotion processing and has been frequently found abnormal in MDD patients20,50. In our discovery amygdala sample (N = 65), although the risk allele [A] carriers tended to show decreased SIRT1 expression, the correlation was not statistically significant likely due to the small sample size in each genotypic group (one-tailed p = 0.0849). We then compared the mRNA expression of SIRT1 between the risk allele homozygous group [A/A] and the other genotypic groups [A/G + G/G], we found that the expression of SIRT1 was significantly lower in A/A group (MDD risk allele homozygotes) than A/G+G/G group (one-tailed p = 0.0391, Fig. 2a). In our replication amygdala sample (N = 72), the A/A genotype again indicated a lower expression of SIRT1 (one-tailed p = 0.0963, Fig. 2b). Despite the relatively small size of each Chinese amygdala sample, the consistent direction of allelic effects across samples suggested a tight link between rs12415800 and SIRT1 mRNA expression. To maximize the statistical power, we conducted a meta-analysis by combining the discovery and replication samples and observed a stronger association (one-tailed p = 0.0149). We have also queried the SNP rs12415800 in the public brain eQTL datasets, such as BrainSeq51, Brain xQTL52, CommonMind53, and PsychENCODE54, which primarily included individuals of European and African American ancestries. Unfortunately, rs12415800 or its linkage disequilibrium (LD) SNPs were not covered in these datasets likely due to the divergent allelic frequencies of this SNP in different populations (frequency of A-allele, 0.023 in Europeans versus 0.401 in Chinese, according to genotype data from 1000 Genomes Project13). Therefore, the eQTL associations of rs12415800 might be Han Chinese specific, and further analyses of this SNP in large Han Chinese cohorts are needed.
Expression of SIRT1 is significantly reduced in MDD patients compared with healthy controls
Previous studies have reported lower SIRT1 mRNA expression in the peripheral blood of MDD patients than in that of healthy controls in Chinese and European populations36–38. To validate this result, we collected peripheral blood tissues from an independent Han Chinese cohort (50 MDD cases and 52 controls) and tested their SIRT1 mRNA expression using qRT-PCR. Notably, the cases in this sample were first-episode MDD patients who had not received any medication by the time of the blood collection. We found that SIRT1 mRNA expression was decreased by 12.3% in the peripheral blood of MDD patients compared with controls (one-tailed p = 0.00284, Fig. 2c), which was consistent with previous results36–38. Therefore, reduced mRNA expression of SIRT1 is likely a risk factor for MDD.
We then stratified the blood samples according to the risk genotypes. Since the genotyping results of rs12415800 are not directly available in this sample, we examined a SNP (rs4746720) in strong LD with rs12415800 in Han Chinese (r2 = 0.961, D′ = 1.000, according to genotype data from 1000 Genomes Project13). Interestingly, rs4746720 was also associated with SIRT1 expression in the 102 Han Chinese blood tissues, with the C/C genotype carriers (which is linked with the risk allele homozygous group [A/A] at rs12415800) showing lower SIRT1 mRNA levels compared with other groups (one-tailed p = 0.0213, Fig. 2d). In a further analysis of solely MDD cases or solely the controls, the C/C genotype of rs4746720 also predicted reduced SIRT1 expression (one-tailed p = 0.0334 in MDD cases and one-tailed p = 0.181 in healthy controls, Fig. 2d). Notably, the above analyses results did not achieve the conventional significance level, likely due to the small sample size.
Discussion
Due to the moderate heritability, great etiological and phenotypic heterogeneity, and limited knowledge of genotype–phenotype relationships, the genetic foundation of MDD remains difficult to elucidate in the past few years. Despite multiple GWASs in European populations, GWAS of MDD in Han Chinese populations has only been conducted once by the CONVERGE consortium12, in which they identified two GWAS loci of interest. The gene near one of these risk loci, SIRT1, has 9 exons and spans 33,715 bp at the chromosome 10q21.3 region. The association between MDD and this gene was successfully replicated in our independent Han Chinese samples and remained genome-wide statistically significant in the overall meta-analysis. This gene was also associated with MDD in Japanese55, although the risk SNPs were different between Japanese and Han Chinese. However, SIRT1 was not highlighted as a MDD risk gene in European populations. This inconsistency between populations might be explained by several possibilities. A most likely explanation is that there might be fundamental differences of the genetic architecture at this locus between populations. Specifically, the allele frequencies at rs12415800 between Han Chinese and Europeans are largely divergent, and the LD structures linked with rs12415800 were also sharply distinct between the two populations (we observed a series of SNPs in strong LD with rs12415800 among Han Chinese, few of which were highly linked in Europeans (Fig. S1)). These distinctions could be attributed to the natural selection and different population histories, and their contributions to the inconsistent genetic risk factors for psychiatric illnesses between continental populations are widely supported by previous studies56,57.
SIRT1 encodes sirt1, a nicotinamide-adenine dinucleotide- (NAD+−) dependent HDAC, and deacetylates multiple substrates including transcription factors, histones, and enzymes58. This gene has been implicated it MDD in multiple recent studies. For example, Libert et al. and Lei et al. respectively, found that mice lacking sirt1 in the brain exhibited depression-related behaviors17,18. In addition, Abe-Higuchi et al. found that chronic stress reduced the activity of sirt1 in the dentate gyrus (DG) of murine hippocampus, thereby contributing to the onset of depression-like behaviors59. When sirt1 activation was rescued in these mice, the depression-related phenotypes were significantly alleviated, while pharmacological inhibition of hippocampal sirt1 function resulted in increased depression-like behaviors59. Likewise, Lo Iacono et al. examined the sirt1 mRNA expression in an adult “depressed” mice model established with juvenile isolation stress, and found significant reduction of sirt1 expression in both the brain and peripheral blood mononuclear cells60. In the present study, we observed significant association of SIRT1 risk allele with lower mRNA of this gene in human tissues, which was consistent with the diagnostic analysis which found decreased SIRT1 expression in MDD patients. Our results are thus in line with the above studies in murine models, and reduced level/activity of sirt1 is therefore a potential risk factor for MDD. However, it should be noted that animal model studies have also obtained varied results. For instance, Ferland et al. reported that rats exposed to chronic stress had higher protein levels and activities of sirt1 in the hippocampal CA3 and DG regions61. Kim et al. observed increased sirt1 expression in the nucleus accumbens (NAc) of stressed mice62. While these data may seem inconsistent, possible explanations have been raised, including varied genetic backgrounds of studied animals and different stress exposure protocols58. Besides, previous studies also reported significant impact of circadian control machineries on sirt1 activity59, it is therefore speculated that discrepancies in the time of experiment conduction might have contributed to the varied results, especially that of the behavioral studies58.
Majority of the MDD studies involving sirt1 focused on mPFC, hippocampus, and NAc, the brain regions well-known to facilitate emotion control and cognition. Using neuroimaging results obtained from human subjects, we also expanded the understanding of potential MDD mechanisms underlying the genetic risk conferred by SIRT1 to an additional brain region. We show that SIRT1 is likely involved in cerebellar structure and development, especially in the left posterior cerebellar lobe. While the precise function of SIRT1 in this brain region is unclear, the significant association of SIRT1 with GMV in the left posterior cerebellar lobe after multiple corrections in two independent samples is unlikely observed by chance. In agreement with this, sirt1 has been reported to act as an upstream regulator of Sonic hedgehog (SHH) pathway in normal and oncogenic neural development63, and SHH signaling plays a vital role in the cerebellar development64, providing hints for appropriate neurodevelopment in MDD. On the other hand, sirt1 is involved in mitochondrial biogenesis65, the process likely related to cerebellar development and MDD pathogenesis66–68. We thus hypothesize that rs12415800 may confer risk of MDD via reducing SIRT1 expression and therefore abnormal cerebellar development. Indeed, evidence for the involvement of cerebellum in the neurobiology of MDD and cognition has emerged8,45–47. Compared with other brain areas, cerebellum has a longer developmental timeline, making it vulnerable to a series of internal and external risk factors69,70. Additionally, there are extensive connections between cerebellum and cerebral cortex7,71, the brain area consistently highlighted in recent genome-wide meta-analysis of depression11. It is thus reasonable to assume that cerebellar abnormalities may lead to deficits in cortical developmental72,73, and thereby contributing to depression. In summary, the gray matter reduction in the left posterior cerebellar lobe might affect the prefrontal–cerebellar circuit and results in the emotional and cognitive deficits in MDD. However, it is also possible that the association of rs12415800 with cerebellum may reflect a pleiotropic effect of SIRT1 in complex traits and human health. For example, SIRT1 expression has been reported to decrease in patients with autistic spectrum disorder (ASD) compared to healthy controls (https://cells.ucsc.edu/?ds=autism)74, and reduced cerebellar gray matter in the ASD patient has also been reported75. These data suggest that the association of rs12415800 with cerebellum may be shared in many psychiatric conditions.
We identified the associations between MDD risk allele and SIRT1 mRNA expression in human brain and blood tissues, which is in agreement with the hypothesis that noncoding risk loci of complex diseases tend to affect gene expression in relevant tissues32. This hypothesis has been validated in various European samples. However, owing to the difficulties of brain tissue collection, genome-wide transcriptome analysis in Chinese brain samples has not been extensively published yet. Here, using candidate gene qRT-PCR analysis, we found that the MDD risk allele might contribute to SIRT1 mRNA variation in Chinese human brains. Although our Chinese sample size is much smaller than those of published European studies, we believe that this brain sample still provide valuable information that promotes our understanding of the molecular mechanisms of MDD and other psychiatric disorders. However, the association of rs12415800 with SIRT1 expression is not robust either in brain or blood tissues, it is thus possible that there exist additional variants in LD with rs12415800 showing stronger associations with SIRT1 expression. This speculation is warranted especially considering that rs12415800 is located in the intergenic region, and functional predictions using HaploReg v4.1 suggested that it was unlikely a functional SNP76. Besides, although we identified the eQTL associations between rs12415800 and expression of SIRT1 in the brain and blood tissues, we noticed that SNPs in high LD with rs12415800 also spanned additional genes, such as CTNNA3, DNAJC12, HERC4, and MYPN. Although the functions of those genes in MDD pathogenesis are less investigated compared with SIRT1, we cannot exclude the possibilities that those genes may also be relevant to MDD genetic risk and participate in its pathogenesis. For example, CTNNA3 has been reported to preferably expressed in the cerebellum, although there were no overt cerebellar morphological changes in CTNNA3 knockout mice compared with wild-type mice77. Further investigations of these genes are also necessary.
The non-significant association of the other COMVERGE MDD GWAS SNP rs35936514 in our Han Chinese sample is not unexpected. There are several explanations for this failure of replication. First, our sample was not as large as the CONVERGE sample, and a resultant lower statistical power of our MDD sample might cause this inconsistency. Moreover, there are several studies demonstrating the population stratification between different regional Han Chinese samples, and some genomic loci exhibit differential association statuses with diseases or traits between regional Han Chinese populations, which might affect the replication of results between different studies78,79. In fact, failures in replications of GWAS loci for psychiatric disorders in Han Chinese have already been reported several times80,81.
Notably, there are several limitations and we are cautious in the interpretation of the present results. First, we noticed that p-value of the association between rs12415800 and MDD in our sample did not achieve the genome-wide level of statistical significance (p = 5.00 × 10–8), which was likely caused by the limited sample size and the "winner’s curse" effect that the genetic effects of new association findings tend to be overestimated in the discovery study82. Second, despite the MDD samples we utilized have been reported previously and demonstrated to be effective in identifying the authentic genetic risk effects, it is acknowledged that the analysis of population stratification in this sample is lacking because the genome-wide SNP genotypes are unavailable at present. Although the cases and controls were randomly selected, further analysis after removing the effects of population substructure might further strengthen the conclusions. Third, whether the expression of SIRT1 was also altered in the left posterior cerebellar lobe of MDD subjects, the brain region highlighted in our imaging analyses, remains unclear. Further analyses of gene expression analyses and functional studies involving with cerebellar tissues would strengthen the present study. Finally, although we observed strong statistical associations between cerebellum structure and MDD risk alleles, the mechanisms for this link remains opaque, and future investigations are needed to characterize the function of cerebellum in MDD.
In conclusion, we have confirmed a MDD risk gene SIRT1 in Han Chinese population, and have identified a novel neural and molecular mechanism underlying genetic risk associations. In addition, we report the novel finding that individuals carrying MDD risk alleles show shifts in cerebellar structure even in healthy populations, and the cerebellum therefore might be relevant to the MDD risk linked to aberrant SIRT1 expression. These results together provide new insights into the pathogenesis of MDD.
Supplementary information
Acknowledgements
The authors sincerely acknowledge with appreciation all the individuals with major depressive disorders and healthy controls whose contributions made this work possible. The authors are deeply grateful to all the participants as well as to the physicians working on this project. The authors sincerely appreciate Prof. Bing Su (Kunming Institute of Zoology) and staffs in his lab for providing the Kunming imaging sample. This work was supported by grants from National Natural Science Foundation of China (81722019 to M.L., 31701133 to X.X., 81825009 to W.Y., 81571313 to W.Y.); Yunnan Applied Basic Research Projects (2018FB051 to X.X.); National Key R&D Program of China (2016YFC1307000 to W.Y.); the medical and health science and technology project in Zhejiang (2018KY721 to D.-S.Z.); the Major Science and Technology Projects in Ningbo, Zhejiang Province, China (2017C510012); the Medical Science and Technology Project in Ningbo, Zhejiang Province, China (2017A10). X.X. was also supported by the Chinese Academy of Sciences Western Light Program, and Youth Innovation Promotion Association, CAS. M.L. was also supported by CAS Pioneer Hundred Talents Program and the 1000 Young Talents Program. Data were generated as part of the PsychENCODE Consortium, supported by: U01MH103392, U01MH103365, U01MH103346, U01MH103340, U01MH103339, R21MH109956, R21MH105881, R21MH105853, R21MH103877, R21MH102791, R01MH111721, R01MH110928, R01MH110927, R01MH110926, R01MH110921, R01MH110920, R01MH110905, R01MH109715, R01MH109677, R01MH105898, R01MH105898, R01MH094714, P50MH106934, U01MH116488, U01MH116487, U01MH116492, U01MH116489, U01MH116438, U01MH116441, U01MH116442, R01MH114911, R01MH114899, R01MH114901, R01MH117293, R01MH117291, R01MH117292 awarded to: Schahram Akbarian (Icahn School of Medicine at Mount Sinai), Gregory Crawford (Duke University), Stella Dracheva (Icahn School of Medicine at Mount Sinai), Peggy Farnham (University of Southern California), Mark Gerstein (Yale University), Daniel Geschwind (University of California, Los Angeles), Fernando Goes (Johns Hopkins University), Thomas M. Hyde (Lieber Institute for Brain Development), Andrew Jaffe (Lieber Institute for Brain Development), James A. Knowles (University of Southern California), Chunyu Liu (SUNY Upstate Medical University), Dalila Pinto (Icahn School of Medicine at Mount Sinai), Panos Roussos (Icahn School of Medicine at Mount Sinai), Stephan Sanders (University of California, San Francisco), Nenad Sestan (Yale University), Pamela Sklar (Icahn School of Medicine at Mount Sinai), Matthew State (University of California, San Francisco), Patrick Sullivan (University of North Carolina), Flora Vaccarino (Yale University), Daniel Weinberger (Lieber Institute for Brain Development), Sherman Weissman (Yale University), Kevin White (University of Chicago), Jeremy Willsey (University of California, San Francisco), and Peter Zandi (Johns Hopkins University). Data were generated as part of the CommonMind Consortium supported by funding from Takeda Pharmaceuticals Company Limited, F. Hoffman-La Roche Ltd and NIH grants R01MH085542, R01MH093725, P50MH066392, P50MH080405, R01MH097276, RO1-MH-075916, P50M096891, P50MH084053S1, R37MH057881 and R37MH057881S1, HHSN271201300031C, AG02219, AG05138 and MH06692. Brain tissue for the study was obtained from the following brain bank collections: the Mount Sinai NIH Brain and Tissue Repository, the University of Pennsylvania Alzheimer’s Disease Core Center, the University of Pittsburgh NeuroBioBank and Brain and Tissue Repositories and the NIMH Human Brain Collection Core. CMC Leadership: Pamela Sklar, Joseph Buxbaum (Icahn School of Medicine at Mount Sinai), Bernie Devlin, David Lewis (University of Pittsburgh), Raquel Gur, Chang-Gyu Hahn (University of Pennsylvania), Keisuke Hirai, Hiroyoshi Toyoshiba (Takeda Pharmaceuticals Company Limited), Enrico Domenici, Laurent Essioux (F. Hoffman-La Roche Ltd), Lara Mangravite, Mette Peters (Sage Bionetworks), Thomas Lehner, Barbara Lipska (NIMH).
Conflict of interest
The authors declare that they have no conflict of interest.
Footnotes
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
These authors contributed equally: Weipeng Liu, Hao Yan, Danyang Zhou, Xin Cai
Contributor Information
Weihua Yue, Email: dryue@bjmu.edu.cn.
Ming Li, Email: limingkiz@mail.kiz.ac.cn.
Xiao Xiao, Email: xiaoxiao2@mail.kiz.ac.cn.
Supplementary information
Supplementary information accompanies this paper at (10.1038/s41398-019-0675-3).
References
- 1.Kupfer DJ, Frank E, Phillips ML. Major depressive disorder: new clinical, neurobiological, and treatment perspectives. Lancet. 2012;379:1045–1055. doi: 10.1016/S0140-6736(11)60602-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Penzes P, Cahill ME, Jones KA, VanLeeuwen JE, Woolfrey KM. Dendritic spine pathology in neuropsychiatric disorders. Nat. Neurosci. 2011;14:285–293. doi: 10.1038/nn.2741. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Forrest MP, Parnell E, Penzes P. Dendritic structural plasticity and neuropsychiatric disease. Nat. Rev. Neurosci. 2018;19:215–234.. doi: 10.1038/nrn.2018.16. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Duman RS, Aghajanian GK. Synaptic dysfunction in depression: potential therapeutic targets. Science. 2012;338:68–72. doi: 10.1126/science.1222939. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Duman RS, Aghajanian GK, Sanacora G, Krystal JH. Synaptic plasticity and depression: new insights from stress and rapid-acting antidepressants. Nat. Med. 2016;22:238–249. doi: 10.1038/nm.4050. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Kang HJ, et al. Decreased expression of synapse-related genes and loss of synapses in major depressive disorder. Nat. Med. 2012;18:1413–1417. doi: 10.1038/nm.2886. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Sathyanesan A, et al. Emerging connections between cerebellar development, behaviour and complex brain disorders. Nat. Rev. Neurosci. 2019;20:298–313. doi: 10.1038/s41583-019-0152-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Moberget T, et al. Cerebellar gray matter volume is associated with cognitive function and psychopathology in adolescence. Biol. Psychiatry. 2019;86:65–75. doi: 10.1016/j.biopsych.2019.01.019. [DOI] [PubMed] [Google Scholar]
- 9.Sullivan PF, Neale MC, Kendler KS. Genetic epidemiology of major depression: review and meta-analysis. Am. J. Psychiatry. 2000;157:1552–1562. doi: 10.1176/appi.ajp.157.10.1552. [DOI] [PubMed] [Google Scholar]
- 10.Wray NR, et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat. Genet. 2018;50:668–681. doi: 10.1038/s41588-018-0090-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Howard DM, et al. Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nat. Neurosci. 2019;22:343–352. doi: 10.1038/s41593-018-0326-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Converge consortium. Sparse whole-genome sequencing identifies two loci for major depressive disorder. Nature. 2015;523:588–591. doi: 10.1038/nature14659. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Genomes Project Consortium et al. A global reference for human genetic variation. Nature. 2015;526:68–74. doi: 10.1038/nature15393. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Meyer-Lindenberg A, Weinberger DR. Intermediate phenotypes and genetic mechanisms of psychiatric disorders. Nat. Rev. Neurosci. 2006;7:818–827. doi: 10.1038/nrn1993. [DOI] [PubMed] [Google Scholar]
- 15.Hasler G, Northoff G. Discovering imaging endophenotypes for major depression. Mol. Psychiatry. 2011;16:604–619. doi: 10.1038/mp.2011.23. [DOI] [PubMed] [Google Scholar]
- 16.Gottesman II, Gould TD. The endophenotype concept in psychiatry: etymology and strategic intentions. Am. J. Psychiatry. 2003;160:636–645. doi: 10.1176/appi.ajp.160.4.636. [DOI] [PubMed] [Google Scholar]
- 17.Libert S, et al. SIRT1 activates MAO-A in the brain to mediate anxiety and exploratory drive. Cell. 2011;147:1459–1472. doi: 10.1016/j.cell.2011.10.054. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Lei, Y. et al. SIRT1 in forebrain excitatory neurons produces sexually dimorphic effects on depression-related behaviors and modulates neuronal excitability and synaptic transmission in the medial prefrontal cortex. Mol. Psychiatry10.1038/s41380-019-0352-1 (2019). [DOI] [PMC free article] [PubMed]
- 19.Schmaal L, et al. Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the ENIGMA Major Depressive Disorder Working Group. Mol. Psychiatry. 2017;22:900–909. doi: 10.1038/mp.2016.60. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Schmaal L, et al. Subcortical brain alterations in major depressive disorder: findings from the ENIGMA Major Depressive Disorder working group. Mol. Psychiatry. 2016;21:806–812. doi: 10.1038/mp.2015.69. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Liu CH, et al. Resting-state brain activity in major depressive disorder patients and their siblings. J. Affect. Disord. 2013;149:299–306. doi: 10.1016/j.jad.2013.02.002. [DOI] [PubMed] [Google Scholar]
- 22.MacKenzie LE, Uher R, Pavlova B. Cognitive performance in first-degree relatives of individuals with vs. without major depressive disorder: a meta-analysis. JAMA Psychiatry. 2019;76:297–305. doi: 10.1001/jamapsychiatry.2018.3672. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Li M, Ge T, Feng J, Su B. SLC6A15 rs1545843 and depression: implications from brain imaging data. Am. J. Psychiatry. 2013;170:805. doi: 10.1176/appi.ajp.2013.12111458. [DOI] [PubMed] [Google Scholar]
- 24.Inkster B, et al. Association of GSK3beta polymorphisms with brain structural changes in major depressive disorder. Arch. Gen. Psychiatry. 2009;66:721–728. doi: 10.1001/archgenpsychiatry.2009.70. [DOI] [PubMed] [Google Scholar]
- 25.Igata R, et al. PCLO rs2522833-mediated gray matter volume reduction in patients with drug-naive, first-episode major depressive disorder. Transl. Psychiatry. 2017;7:e1140. doi: 10.1038/tp.2017.100. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Bigos KL, et al. Genetic variation in CACNA1C affects brain circuitries related to mental illness. Arch. Gen. Psychiatry. 2010;67:939–945. doi: 10.1001/archgenpsychiatry.2010.96. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Wedenoja J, et al. Replication of association between working memory and Reelin, a potential modifier gene in schizophrenia. Biol. Psychiatry. 2010;67:983–991. doi: 10.1016/j.biopsych.2009.09.026. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Xiao X, et al. The gene encoding protocadherin 9 (PCDH9), a novel risk factor for major depressive disorder. Neuropsychopharmacology. 2018;43:1128–1137. doi: 10.1038/npp.2017.241. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Chang H, et al. The protocadherin 17 gene affects cognition, personality, amygdala structure and function, synapse development and risk of major mood disorders. Mol. Psychiatry. 2018;23:400–412. doi: 10.1038/mp.2016.231. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Chang H, Xiao X, Li M. The schizophrenia risk gene ZNF804A: clinical associations, biological mechanisms and neuronal functions. Mol. Psychiatry. 2017;22:944–953. doi: 10.1038/mp.2017.19. [DOI] [PubMed] [Google Scholar]
- 31.Rao, S., Luo, N., Sui, J., Xu, Q., Zhang, F. Effect of the SIRT1 gene on regional cortical grey matter density in the Han Chinese population. Br. J. Psychiatry 1–5 (2018) 10.1192/bjp.2018.270. [DOI] [PubMed]
- 32.Edwards SL, Beesley J, French JD, Dunning AM. Beyond GWASs: illuminating the dark road from association to function. Am. J. Hum. Genet. 2013;93:779–797. doi: 10.1016/j.ajhg.2013.10.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Jansen R, et al. Gene expression in major depressive disorder. Mol. Psychiatry. 2016;21:339–347. doi: 10.1038/mp.2015.57. [DOI] [PubMed] [Google Scholar]
- 34.Labonte B, et al. Sex-specific transcriptional signatures in human depression. Nat. Med. 2017;23:1102–1111. doi: 10.1038/nm.4386. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Xiao X, et al. The cAMP responsive element-binding (CREB)-1 gene increases risk of major psychiatric disorders. Mol. Psychiatry. 2018;23:1957–1967. doi: 10.1038/mp.2017.243. [DOI] [PubMed] [Google Scholar]
- 36.Luo XJ, Zhang C. Down-regulation of SIRT1 gene expression in major depressive disorder. Am. J. Psychiatry. 2016;173:1046. doi: 10.1176/appi.ajp.2016.16040394. [DOI] [PubMed] [Google Scholar]
- 37.McGrory CL, Ryan KM, Kolshus E, Finnegan M, McLoughlin DM. Peripheral blood SIRT1 mRNA levels in depression and treatment with electroconvulsive therapy. Eur. Neuropsychopharmacol. 2018;28:1015–1023. doi: 10.1016/j.euroneuro.2018.06.007. [DOI] [PubMed] [Google Scholar]
- 38.Abe N, et al. Altered sirtuin deacetylase gene expression in patients with a mood disorder. J. Psychiatr. Res. 2011;45:1106–1112. doi: 10.1016/j.jpsychires.2011.01.016. [DOI] [PubMed] [Google Scholar]
- 39.Dupont WD, Plummer WD., Jr. Power and sample size calculations. A review and computer program. Control Clin. Trials. 1990;11:116–128. doi: 10.1016/0197-2456(90)90005-M. [DOI] [PubMed] [Google Scholar]
- 40.Zhao L, et al. Replicated associations of FADS1, MAD1L1, and a rare variant at 10q26.13 with bipolar disorder in Chinese population. Transl. Psychiatry. 2018;8:270. doi: 10.1038/s41398-018-0337-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Li H, et al. Integrative analyses of major histocompatibility complex loci in the genome-wide association studies of major depressive disorder. Neuropsychopharmacology. 2019;44:1552–1561. doi: 10.1038/s41386-019-0346-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Zhong J, et al. Integration of GWAS and brain eQTL identifies FLOT1 as a risk gene for major depressive disorder. Neuropsychopharmacology. 2019;44:1542–1551. doi: 10.1038/s41386-019-0345-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Elliott LT, et al. Genome-wide association studies of brain imaging phenotypes in UK Biobank. Nature. 2018;562:210–216. doi: 10.1038/s41586-018-0571-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Hibar DP, et al. Common genetic variants influence human subcortical brain structures. Nature. 2015;520:224–229. doi: 10.1038/nature14101. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Depping MS, Schmitgen MM, Kubera KM, Wolf RC. Cerebellar contributions to major depression. Front Psychiatry. 2018;9:634. doi: 10.3389/fpsyt.2018.00634. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Shen Z, et al. Changes of grey matter volume in first-episode drug-naive adult major depressive disorder patients with different age-onset. Neuroimage Clin. 2016;12:492–498. doi: 10.1016/j.nicl.2016.08.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Hogan MJ, et al. Cerebellar brain volume accounts for variance in cognitive performance in older adults. Cortex. 2011;47:441–450. doi: 10.1016/j.cortex.2010.01.001. [DOI] [PubMed] [Google Scholar]
- 48.Li M, et al. A human-specific AS3MT isoform and BORCS7 are molecular risk factors in the 10q24.32 schizophrenia-associated locus. Nat. Med. 2016;22:649–656. doi: 10.1038/nm.4096. [DOI] [PubMed] [Google Scholar]
- 49.Yang, Z. et al. The genome-wide risk alleles for psychiatric disorders at 3p21.1 show convergent effects on mRNA expression, cognitive function and mushroom dendritic spine. Mol. Psychiatry10.1038/s41380-019-0592-0 (2019). [DOI] [PubMed]
- 50.Hamilton JP, Siemer M, Gotlib IH. Amygdala volume in major depressive disorder: a meta-analysis of magnetic resonance imaging studies. Mol. Psychiatry. 2008;13:993–1000. doi: 10.1038/mp.2008.57. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Jaffe AE, et al. Developmental and genetic regulation of the human cortex transcriptome illuminate schizophrenia pathogenesis. Nat. Neurosci. 2018;21:1117–1125. doi: 10.1038/s41593-018-0197-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52.Ng B, et al. An xQTL map integrates the genetic architecture of the human brain's transcriptome and epigenome. Nat. Neurosci. 2017;20:1418–1426. doi: 10.1038/nn.4632. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 53.Fromer M, et al. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat. Neurosci. 2016;19:1442–1453. doi: 10.1038/nn.4399. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 54.PsychEncode Consortium, et al. The PsychENCODE project. Nat. Neurosci. 2015;18:1707–1712. doi: 10.1038/nn.4156. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 55.Kishi T, et al. SIRT1 gene is associated with major depressive disorder in the Japanese population. J. Affect. Disord. 2010;126:167–173. doi: 10.1016/j.jad.2010.04.003. [DOI] [PubMed] [Google Scholar]
- 56.Li M, et al. Allelic differences between Europeans and Chinese for CREB1 SNPs and their implications in gene expression regulation, hippocampal structure and function, and bipolar disorder susceptibility. Mol. Psychiatry. 2014;19:452–461. doi: 10.1038/mp.2013.37. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 57.Li M, et al. Recent positive selection drives the expansion of a schizophrenia risk nonsynonymous variant at SLC39A8 in Europeans. Schizophr. Bull. 2016;42:178–190. doi: 10.1093/schbul/sbv070. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 58.Lu G, et al. Role and possible mechanisms of Sirt1 in depression. Oxid. Med. Cell. Longev. 2018;2018:8596903. doi: 10.1155/2018/8596903. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 59.Abe-Higuchi N, et al. Hippocampal sirtuin 1 signaling mediates depression-like behavior. Biol. Psychiatry. 2016;80:815–826. doi: 10.1016/j.biopsych.2016.01.009. [DOI] [PubMed] [Google Scholar]
- 60.Lo Iacono L, et al. Adversity in childhood and depression: linked through SIRT1. Transl. Psychiatry. 2015;5:e629. doi: 10.1038/tp.2015.125. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 61.Ferland CL, et al. Sirtuin activity in dentate gyrus contributes to chronic stress-induced behavior and extracellular signal-regulated protein kinases 1 and 2 cascade changes in the hippocampus. Biol. Psychiatry. 2013;74:927–935. doi: 10.1016/j.biopsych.2013.07.029. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 62.Kim HD, et al. SIRT1 mediates depression-like behaviors in the nucleus accumbens. J. Neurosci. 2016;36:8441–8452. doi: 10.1523/JNEUROSCI.0212-16.2016. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 63.Tiberi L, et al. A BCL6/BCOR/SIRT1 complex triggers neurogenesis and suppresses medulloblastoma by repressing Sonic Hedgehog signaling. Cancer Cell. 2014;26:797–812. doi: 10.1016/j.ccell.2014.10.021. [DOI] [PubMed] [Google Scholar]
- 64.De Luca A, et al. Sonic hedgehog patterning during cerebellar development. Cell. Mol. Life Sci. 2016;73:291–303. doi: 10.1007/s00018-015-2065-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 65.Gerhart-Hines Z, et al. Metabolic control of muscle mitochondrial function and fatty acid oxidation through SIRT1/PGC-1alpha. EMBO J. 2007;26:1913–1923. doi: 10.1038/sj.emboj.7601633. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 66.Rezin GT, et al. Inhibition of mitochondrial respiratory chain in brain of rats subjected to an experimental model of depression. Neurochem. Int. 2008;53:395–400. doi: 10.1016/j.neuint.2008.09.012. [DOI] [PubMed] [Google Scholar]
- 67.Cai N, et al. Molecular signatures of major depression. Curr. Biol. 2015;25:1146–1156.. doi: 10.1016/j.cub.2015.03.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 68.Wang Q, Dwivedi Y. Transcriptional profiling of mitochondria associated genes in prefrontal cortex of subjects with major depressive disorder. World J. Biol. Psychiatry. 2017;18:592–603. doi: 10.1080/15622975.2016.1197423. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 69.Wang VY, Zoghbi HY. Genetic regulation of cerebellar development. Nat. Rev. Neurosci. 2001;2:484–491. doi: 10.1038/35081558. [DOI] [PubMed] [Google Scholar]
- 70.ten Donkelaar HJ, Lammens M, Wesseling P, Thijssen HO, Renier WO. Development and developmental disorders of the human cerebellum. J. Neurol. 2003;250:1025–1036. doi: 10.1007/s00415-003-0199-9. [DOI] [PubMed] [Google Scholar]
- 71.Ramnani N. The primate cortico-cerebellar system: anatomy and function. Nat. Rev. Neurosci. 2006;7:511–522. doi: 10.1038/nrn1953. [DOI] [PubMed] [Google Scholar]
- 72.Limperopoulos C, et al. Injury to the premature cerebellum: outcome is related to remote cortical development. Cereb. Cortex. 2014;24:728–736. doi: 10.1093/cercor/bhs354. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 73.Wang SS, Kloth AD, Badura A. The cerebellum, sensitive periods, and autism. Neuron. 2014;83:518–532. doi: 10.1016/j.neuron.2014.07.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 74.Bu X, et al. Role of SIRT1/PGC-1alpha in mitochondrial oxidative stress in autistic spectrum disorder. Neuropsychiatr. Dis. Treat. 2017;13:1633–1645. doi: 10.2147/NDT.S129081. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 75.Fatemi SH, et al. Consensus paper: pathological role of the cerebellum in autism. Cerebellum. 2012;11:777–807. doi: 10.1007/s12311-012-0355-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 76.Ward LD, Kellis M. HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res. 2012;40:D930–D934. doi: 10.1093/nar/gkr917. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 77.Folmsbee SS, et al. αT-catenin in restricted brain cell types and its potential connection to autism. J. Mol. Psychiatry. 2016;4:2. doi: 10.1186/s40303-016-0017-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 78.Liu S, et al. Genomic analyses from non-invasive prenatal testing reveal genetic associations, patterns of viral infections, and Chinese population history. Cell. 2018;175:347–359 e14. doi: 10.1016/j.cell.2018.08.016. [DOI] [PubMed] [Google Scholar]
- 79.Xu S, et al. Genomic dissection of population substructure of Han Chinese and its implication in association studies. Am. J. Hum. Genet. 2009;85:762–774. doi: 10.1016/j.ajhg.2009.10.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 80.Xiao X, Li M. Replication of Han Chinese GWAS loci for schizophrenia via meta-analysis of four independent samples. Schizophr. Res. 2016;172:75–77. doi: 10.1016/j.schres.2016.02.019. [DOI] [PubMed] [Google Scholar]
- 81.Ma L, et al. Evaluating risk loci for schizophrenia distilled from genome-wide association studies in Han Chinese from Central China. Mol. Psychiatry. 2013;18:638–639. doi: 10.1038/mp.2012.63. [DOI] [PubMed] [Google Scholar]
- 82.Xiao R, Boehnke M. Quantifying and correcting for the winner's curse in genetic association studies. Genet. Epidemiol. 2009;33:453–462. doi: 10.1002/gepi.20398. [DOI] [PMC free article] [PubMed] [Google Scholar]
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