Elsevier

Journal of Psychiatric Research

Volume 83, December 2016, Pages 168-175
Journal of Psychiatric Research

Identification of SLC25A37 as a major depressive disorder risk gene

https://doi.org/10.1016/j.jpsychires.2016.09.011Get rights and content

Abstract

Major depressive disorder (MDD) is one of the most prevalent and disabling mental disorders, but the genetic etiology remains largely unknown. We performed a meta-analysis (14,543 MDD cases and 14,856 controls) through combining the GWAS data from the Major Depressive Disorder Working Group of the Psychiatric GWAS Consortium and the CONVERGE consortium and identified seven SNPs (four of them located in the downstream of SCL25A37) that showed suggestive associations (P < 5.0 × 10−7) with MDD. Systematic integration (Sherlock integrative analysis) of brain eQTL and GWAS meta-analysis identified SCL25A37 as a novel MDD risk gene (P = 2.22 × 10−6). A cis SNP (rs6983724, ∼28 kb downstream of SCL25A37) showed significant association with SCL25A37 expression (P = 1.19 × 10−9) and suggestive association with MDD (P = 1.65 × 10−7). We validated the significant association between rs6983724 and SCL25A37 expression in independent expression datasets. Finally, we found that SCL25A37 is significantly down-regulated in hippocampus and blood of MDD patients (P = 3.49 × 10−3 and P = 2.66 × 10−13, respectively). Our findings implicate that the SCL25A37 is a MDD susceptibility gene whose expression may influence MDD risk. The consistent down-regulation of SCL25A37 in MDD patients in three independent samples suggest that SCL25A37 may be used as a potential biomarker for MDD diagnosis. Further functional characterization of SCL25A37 may provide a potential target for future therapeutics and diagnostics.

Introduction

Major depressive disorder (MDD) is a complex mental disorder characterized by persistent and pervasive low mood, including low self-esteem, loss of interest or pleasure, and feelings of personal worthlessness. The lifetime prevalence of MDD is around 15% (Hasin et al., 2005, Kessler et al., 2003), which makes it one of the most prevalent mental disorders. MDD has a high mortality and significant long-term morbidity (Angst et al., 2002, Lopez et al., 2006). Persons with MDD have a high risk for suicide and approximately 11% MDD patients die from suicide (Wulsin et al., 1999). The economic and social burden of MDD is particularly great (Ferrari et al., 2013, Olchanski et al., 2013). For example, the cost of MDD in USA (including direct costs such as outpatient, inpatient, drugs, and long-term care) and non-health care costs (such as law enforcement, reduced workplace productivity, and unemployment) was estimated to be $173.2 billion in 2005 (Greenberg et al., 2015). However, this number was rapidly increased to $210.5 billion in 2010 (rose by 21%) (Greenberg et al., 2015). With the continuously increase of cost, MDD poses a major global health and economy challenge.

Though MDD affects millions of people and is a leading cause of disability worldwide, the pathophysiology of MDD remains largely unknown. Accumulating evidence indicated that both genetic and environmental factors are involved in the pathogenesis of MDD (CONVERGE consortium∗, 2015, Flint and Kendler, 2014, Lesch, 2004, Subbarao et al., 2008, Sullivan et al., 2012). The heritability of MDD was estimated around 0.32 (Lubke et al., 2012, Sullivan et al., 2000), indicating that genetic factors play a role in MDD. To elucidate the genetic basis of MDD, numerous genetic studies have been conducted in different human populations (Kohli et al., 2011, Lewis et al., 2010, Muglia et al., 2010, Rietschel et al., 2010, Ripke et al., 2012, Shi et al., 2011, Wray et al., 2011). Nevertheless, only very limited risk genetic variants or susceptibility genes have been identified and validated (CONVERGE consortium∗, 2015). The advent of genome-wide association studies (GWAS) provides a chance to decipher the genetic mechanisms of MDD. Due to the dramatic increase in sample size and genotyping throughput, GWAS can identify genetic variants with small effect (CONVERGE consortium∗, 2015). In 2012, the Major Depressive Disorder Working Group of the Psychiatric GWAS Consortium (Ripke et al., 2012) conducted a large GWAS analysis of MDD. In the first stage, more than 1.2 million SNPs were analyzed in 9240 MDD cases and 9519 controls. In the second stage, 554 SNPs (P < 0.001) from the first stage were replicated in independent samples, including 6783 MDD cases and 50,695 controls. Despite the fact that 76,237 individuals (including 16,023 MDD cases) were included (Ripke et al., 2012), no genome-wide significant SNP was identified, strongly suggest that the genetic architecture of MDD is much complex than we had thought.

Sample size, genetic and phenotypic heterogeneity (there are several subtypes of MDD) may be the possible reasons for the failure of identification of genome-wide significant variants for MDD. To minimize the genetic and phenotypic heterogeneity, the CONVERGE consortium collected a relatively homogenous Chinese sample (5303 MDD cases and 5337 controls) and performed whole-genome sequencing at low coverage (CONVERGE consortium∗, 2015). Two genome-wide significant loci (one near the SIRT1 gene and the other one in an intron of the LHPP gene) were identified by the CONVERGE consortium. Though the CONVERGE consortium has successfully identified two MDD risk loci, much of the heritability of MDD remains unknown. More importantly, the identified MDD risk variants reside in non-coding regions, with limited annotation and no obvious functional consequence. Therefore, how these susceptibility variants contribute to MDD risk remains elusive. In addition, many loci may have small effects and fail to reach the genome-wide significance in a single GWAS study. Thus, mining the potential effect of the weak GWAS associations (e.g., variants with P < 10−5) may help to uncover the missing heritability of MDD.

Here we carried out a meta-analysis by combining the GWAS data from the Major Depressive Disorder Working Group of the Psychiatric GWAS Consortium (Ripke et al., 2012) and the CONVERGE consortium (CONVERGE consortium∗, 2015). We then systematically integrated genetic association signals from the meta-analysis and brain expression quantitative trait loci (eQTL) data through using a Bayesian statistical framework (Sherlock), and identified SLC25A37 as a novel MDD risk gene. To further characterize the potential role of SLC25A37 in MDD etiology, we attempted to validate our results in independent gene expression datasets, and investigated the expression of SLC25A37 in MDD patients and healthy controls in independent samples. Our consistent and convergent results suggest that SLC25A37 may have a role in the etiology of MDD.

Section snippets

MDD GWAS data and meta-analysis

Sample size is an important factor for GWAS of MDD. New MDD risk variants (or loci) may be identified with the increase of sample size. To this aim, we combined two large-scale GWAS datasets of MDD. The first GWAS dataset was from the Major Depressive Disorder Working Group of the Psychiatric GWAS Consortium (Ripke et al., 2012). In brief, this study represents a mega-analysis of genome-wide association studies for major depressive disorder. The SNP associations (a total of 1,235,109 SNPs) from

Meta-analysis of MDD GWAS

Meta-analysis of GWAS datasets from the Major Depressive Disorder Working Group of the Psychiatric GWAS Consortium (Ripke et al., 2012) and the CONVERGE consortium (CONVERGE consortium∗, 2015) revealed that no SNPs reached genome-wide significant level (P < 5.0 × 10−8). However, we detected several interesting SNPs that have the same direction of effect in both European and Chinese samples. Table 1 lists the SNPs that have P-values less than 5 × 10−7 in the combined sample (14,543 MDD cases and

Discussion

MDD is one of the most prevalent mental disorders. Though the heritability of MDD is relatively high (around 0.32) (Lubke et al., 2012, Sullivan et al., 2000), only very limited MDD risk variants (or genes) have been identified by recent large-scale GWAS. The conundrum of genetic studies suggest the complex genetic architecture of MDD. As stated in the work of Ripke et al. (Ripke et al., 2012), sample size may be an important factor and new risk loci may be identified with the increase of

Contributors

XJL designed and directed the research. XJL, YXH, LH, DFZ, and CZ performed the research. XJL, YXH, and CZ analyzed the data. XJL, CZ, YRF, and YGY wrote the paper. All authors read and approved the final manuscript.

Funding

X.J.L was supported by the 100 Talents Program (BaiRenJiHua) of the Kunming Institute of Zoology, Chinese Academy of Sciences, and Project of Thousand Youth Talents of China. Y.G.Y was supported by the Strategic Priority Research Program (B) of the Chinese Academy of Sciences (XDB02020003). C.Z. was supported by the National Natural Science Foundation of China (81471358) and the Shanghai Municipal Education Commission—Gaofeng Clinical Medicine Grant Support (20152530).

Competing financial interests

The authors reported no biomedical financial interests or potential conflicts of interest.

Acknowledgements

We thank Di Huang for her technical support. We also thank the research participants for their contributions.

References (65)

  • N. Olchanski et al.

    The economic burden of treatment-resistant depression

    Clin. Ther.

    (2013)
  • M. Rietschel et al.

    Genome-wide association-, replication-, and neuroimaging study implicates HOMER1 in the etiology of major depression

    Biol. Psychiatry

    (2010)
  • Y. Wang et al.

    Abnormal mitoferrin-1 expression in patients with erythropoietic protoporphyria

    Exp. Hematol.

    (2011)
  • G.R. Abecasis et al.

    A map of human genome variation from population-scale sequencing

    Nature

    (2010)
  • Y. Benjamini et al.

    Controlling the false discovery rate: a practical and powerful approach to multiple testing

    J. R. Stat. Soc. Ser. B

    (1995)
  • R. Bernard et al.

    Altered expression of glutamate signaling, growth factor, and glia genes in the locus coeruleus of patients with major depression

    Mol. Psychiatry

    (2010)
  • J. Bryois et al.

    Cis and trans effects of human genomic variants on gene expression

    PLoS Genet.

    (2014)
  • B.G. Bunney et al.

    Circadian dysregulation of clock genes: clues to rapid treatments in major depressive disorder

    Mol. Psychiatry

    (2015)
  • C.C. Chang et al.

    Mitochondria DNA change and oxidative damage in clinically stable patients with major depressive disorder

    PLoS One

    (2015)
  • W. Chen et al.

    Abcb10 physically interacts with mitoferrin-1 (Slc25a37) to enhance its stability and function in the erythroid mitochondria

    Proc. Natl. Acad. Sci. U. S. A.

    (2009)
  • CONVERGE consortium*

    Sparse whole-genome sequencing identifies two loci for major depressive disorder

    Nature

    (2015)
  • V. Duric et al.

    A negative regulator of MAP kinase causes depressive behavior

    Nat. Med.

    (2010)
  • A.J. Ferrari et al.

    Burden of depressive disorders by country, sex, age, and year: findings from the global burden of disease study 2010

    PLoS Med.

    (2013)
  • A. Gardner et al.

    Beyond the serotonin hypothesis: mitochondria, inflammation and neurodegeneration in major depression and affective spectrum disorders

    Prog. Neuropsychopharmacol. Biol. Psychiatry

    (2010)
  • Z. Gerhart-Hines et al.

    Metabolic control of muscle mitochondrial function and fatty acid oxidation through SIRT1/PGC-1alpha

    EMBO J.

    (2007)
  • P.E. Greenberg et al.

    The economic burden of adults with major depressive disorder in the United States (2005 and 2010)

    J. Clin. Psychiatry

    (2015)
  • D.S. Hasin et al.

    Epidemiology of major depressive disorder: results from the national epidemiologic survey on alcoholism and related conditions

    Arch. Gen. Psychiatry

    (2005)
  • L.A. Hindorff et al.

    Potential etiologic and functional implications of genome-wide association loci for human diseases and traits

    Proc. Natl. Acad. Sci. U. S. A.

    (2009)
  • L.A. Hindorff et al.

    Potential etiologic and functional implications of genome-wide association loci for human diseases and traits

    Proc. Natl. Acad. Sci. U. S. A.

    (2009)
  • C.L. Hyde et al.

    Identification of 15 genetic loci associated with risk of major depression in individuals of European descent

    Nat. Genet.

    (2016)
  • R. Jansen et al.

    Gene expression in major depressive disorder

    Mol. Psychiatry

    (2016)
  • R.C. Kessler et al.

    The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication (NCS-R)

    JAMA

    (2003)
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