Introduction
Psychiatric disorders are a group of complex psychological symptoms, mainly characterized by clinically significant deficits in an individuals’ cognition, emotion regulation, and behavior [1]. The common psychiatric disorders include the attention deficiency/hyperactive disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), schizophrenia (SCZ), and major depressive disorder (MDD). Psychiatric disorders have been found commonly, with over a third of people in most countries reporting sufficient criteria to be diagnosed at some points of their lives [Reference Andrade, Caraveoanduaga, Berglund, Bijl, Kessler and Demler2]. Epidemiological research has shown that about 3–18% of children suffered psychiatric disorders causing significant functional impairment [Reference Costello, Egger and Angold3].
Psychiatric disorders are considered multifactorial and driven by a combination of biological, psychological, and environmental factors [Reference Arango, Diazcaneja, Mcgorry, Rapoport, Sommer and Vorstman4]. Multiple epidemiological [Reference Lichtenstein, Yip, Björk, Pawitan, Cannon and Sullivan5,Reference McGuffin, Rijsdijk, Andrew, Sham, Katz and Cardno6] and molecular biological [Reference Smoller, Kendler, Craddock, Lee, Neale and Nurnberger7,Reference McMahon, Akula, Schulze, Muglia and Tozzi8] studies have observed shared risk components among various psychiatric disorders. Similar environmental risk factors like physical abuse and neglect have also been found to underlie a range of psychiatric disorders, for instance SCZ and depression [Reference Keyes, Eaton, Krueger, Mclaughlin, Wall and Grant9]. Recently, there is a growing body of researches focus on the genetic mechanism of psychiatric disorders. The implication of genetic factors in the pathogenesis of psychiatric disorders has been well documented. For instance, the estimated heritability achieved 80% for SCZ [Reference McGuffin, Owen and Gottesman10] and >90% for classic autism [Reference Freitag11]. Multiple large-scale genetic studies of psychiatric disorders have been conducted and identified multiple susceptibility genes for psychiatric disorders [Reference Gratacòs, Costas, de Cid, Bayés, González and Baca-García12]. However, the etiology and molecular mechanism of psychiatric disorders remains elusive now.
Plasma proteins (also named blood proteins) are a group of proteins in blood plasma. More than 3,600 plasma proteins have been discovered, functionally implicated in signaling, transport, repair, and defense against infection [Reference Sun, Maranville, Peters, Stacey, Staley and Blackshaw13]. Altered plasma proteins have been found to be related to multiple human complex diseases including psychiatric disorders [Reference Chan, Gottschalk, Haenisch, Tomasik, Ruland and Rahmoune14,Reference Ganz, Heidecker, Hveem, Jonasson, Kato and Segal15]. As important intermediate phenotypes, plasma proteins are useful for early disease diagnosis, understanding human physiology, developing health biomarkers, and targeting to therapy [Reference Chan, Lucas, Hise, Schaefer, Xiao and Janini16,Reference Anderson17]. For instance, Hye et al. found that complement factor H and α-2-macroglobulin were specific markers of Alzheimer’s disease [Reference Hye, Lynham, Thambisetty, Causevic, Campbell and Byers18]. More recently, a study suggested that apolipoprotein A-1 could act as a serum marker for the response to lithium treatment in BD [Reference Sussulini, Dihazi, Banzato, Arruda, Stühmer and Ehrenreich19]. However, few efforts were paid to systematically explore the relationships between psychiatric disorders and plasma proteome.
It is well known that gene expression is under genetic control [Reference Dimas, Deutsch, Stranger, Montgomery, Borel and Attar-Cohen20]. Extensive efforts have been paid to explore the genetic mechanism of gene expression regulation and identified a lot expression quantitative trait loci (eQTLs) [Reference Hernandez, Nalls, Moore, Chong, Dillman and Trabzuni21]. Recently, Foss et al. performed a large-scale genome-wide association study of more than 3,000 plasma proteins [Reference Foss, Radulovic, Shaffer, Ruderfer, Bedalov and Goodlett22]. They identified a group of significant protein quantitative trait loci (pQTLs) associated with plasma proteins levels [Reference Foss, Radulovic, Shaffer, Ruderfer, Bedalov and Goodlett22]. They also observed that the effects of eQTLs on transcript differed from that on protein levels, which emphasizes the importance of pQTLs studies [Reference Foss, Radulovic, Shaffer, Ruderfer, Bedalov and Goodlett22].
Recent studies demonstrated the generality of genetic correlations among complex human phenotypes. Linkage disequilibrium score (LDSC) regression is an efficient method and widely used for evaluating the genetic relationships among different human phenotypes [Reference Bulik-Sullivan, Loh, Finucane, Ripke and Yang23]. Utilizing genome-wide association study (GWAS) summary data, LDSC provides an easy and reliable way to simultaneously screen thousands of traits and find out the real genetic correlations among them [Reference Lee and Chow24]. Utilizing LDSC, Bulik-Sullivan et al. evaluated 276 genetic correlations among 24 traits, and observed significant genetic correlations between anorexia nervosa and SCZ, anorexia and obesity, and educational attainment and several diseases [Reference Bulik-Sullivan, Finucane, Anttila, Gusev, Day and Loh25]. Duncan et al. suggested that LDSC was an appropriate polygenic methods to estimate the overlapping genetic factors between post‐traumatic stress disorder (PTSD) and SCZ as well as bipolar and MDD [Reference Duncan, Ratanatharathorn, Aiello, Almli, Amstadter and Ashley-Koch26].
In this study, utilizing the latest GWAS data of blood proteins and five common psychiatric disorders from the Psychiatric Genomics Consortium (PGC), LDSC was used to systematically evaluate the genetic relationships between five common psychiatric disorders and human plasma proteome.
Materials and Methods
GWAS datasets of five psychiatric disorders
The latest GWAS summary data of ADHD (19,099 cases and 34,194 controls), ASD (7,387 cases and 8,567 controls), BD (20,129 cases and 21,524 controls), SCZ (33,426 cases and 32,541 controls), and MDD (135,458 cases and 344,901 controls) were downloaded from the Psychiatric GWAS Consortium (PGC) website (https://www.med.unc.edu/pgc/results-and-downloads) as discovery samples [27–Reference Wray, Ripke, Mattheisen, Trzaskowski, Byrne and Abdellaoui30]. Briefly, all study subjects were European whites and diagnosed using research standard diagnoses and expert clinical consensus diagnosis. Genotyping was performed using commercial platform such as Illumina 610K and Affymetrix SNP 6.0 chips. Imputation was conducted using IMPUTE2 against public reference panels such as the 1,000 Genomes Project Phase 2 and Phase 3. Association analysis was conducted using logistic regression model. Detailed description of sample characteristics, experimental design, and statistical analysis can be found in the published studies [27-30].
Cross-disorder GWAS replication data of psychiatric disorders
The latest cross-disorder GWAS of six common psychiatric disorders was derived from the Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH) [Reference Schork, Won, Appadurai, Nudel, Gandal and Delaneau31]. Briefly, Schork et al. conducted a cross-disorder GWAS of six common psychiatric disorders, including ADHD, anorexia, ASD, affective disorder, BP, and SCZ [Reference Schork, Won, Appadurai, Nudel, Gandal and Delaneau31]. The total sample size included 65,534 individuals with 46,008 cases and 19,526 controls [Reference Schork, Won, Appadurai, Nudel, Gandal and Delaneau31]. SNP genotying was performed by Inifinium PsychChip v1.0 array. Imputation was conducted using Impute2 with the 1,000 genomes project phase 3 reference. GWAS summary statistics were computed using logistic regression of the plink software. Age, gender, and 10 principle components of population structure were included as covariates. Detailed description of sample characteristics, genotyping, imputation, experimental design, and statistical analysis can be found in the published studies [Reference Schork, Won, Appadurai, Nudel, Gandal and Delaneau31].
pQTL data of human plasma proteome
The GWAS summary data of human plasma proteome were derived from a recently published study [Reference Sun, Maranville, Peters, Stacey, Staley and Blackshaw13]. Briefly, Sun et al. quantify 3,622 plasma proteins in 3,301 healthy participants from the INTERVAL [Reference Angelantonio, Thompson, Kaptoge, Moore, Walker and Armitage32] study by using an expanded version of an aptamer-based multiplex protein assay (SOMAscan) [Reference Sun, Maranville, Peters, Stacey, Staley and Blackshaw13]. The genotyping protocol and quality control for the INTERVAL samples have been described previously in detail [Reference Astle, Elding, Jiang, Allen, Ruklisa and Mann33]. Briefly, genotyping was performed on the Affymetrix Axiom UK Biobank genotyping array. Imputation was performed via the Sanger Imputation Server by using a combined 1,000 Genomes Phase 3-UK10K reference panel. Simple linear regression using an additive genetic model was used to test genetic associations. After quality control, the GWAS summary data of 3,283 plasma proteins were used in following genetic correlation analysis. Detailed description of sample characteristics, experimental design, quality control, and statistical analysis can be found in the published studies [Reference Sun, Maranville, Peters, Stacey, Staley and Blackshaw13].
Genetic correlation scanning
Following the approach recommended by the developers [Reference Bulik-Sullivan, Loh, Finucane, Ripke and Yang23,Reference Bulik-Sullivan, Finucane, Anttila, Gusev, Day and Consortium34] and previous study [Reference Bulik-Sullivan, Finucane, Anttila, Gusev, Day and Loh25], LDSC software (v1.0.0; https://github.com/bulik/ldsc) were applied to the GWAS summary data for evaluating the genetic correlations between each of the five psychiatric disorders and each of the 3,283 plasma proteins at first. Using the same method, the significant genetic correlations were further validated using the cross-disorder GWAS replication data. The basic principle of the LDSC approach is to estimate directly from GWAS summary data using the deviation of the observed χ 2 test statistic for a SNP from its expected value under the null hypothesis of no association [Reference Shi, Kichaev and Pasaniuc35]. An SNP tagging more of its neighbors—and, thus, having a higher LD score—is more likely to tag one or more causal sites affecting the phenotype [Reference Shi, Kichaev and Pasaniuc35]. If genetic correlations are statistically and quantitatively significant, then we can determine that total phenotypic correlations cannot be attributed to fully environmental confounders [Reference Lee and Chow24]. In addition, Anney et al. have demonstrated that LD score regression can distinguish genuine polygenicity from the bias caused by population stratification and cryptic relatedness [Reference Anney, Ripke, Anttila, Grove, Holmans and Huang28]. The European LD scores, calculated from the 1,000 Genomes by the developers, were used in this study [Reference Anney, Ripke, Anttila, Grove, Holmans and Huang28].
Results
LDSC regression observed several genetic correlation signals between plasma proteins and psychiatric disorders with LDSC p values <0.05. For ADHD, genetic correlation signals were observed for lysosomal Pro-X carboxypeptidase (coefficient = 0.243, p value = 0.015), and alpha-2-antiplasmin (coefficient = 0.274, p value = 0.032).
For ASD, genetic correlations were observed for extracellular superoxide dismutase (Cu-Zn; coefficient = 0.530, p value = 0.023), hepatitis A virus cellular receptor 1 (coefficient = 0.405, p value = 0.031), chromogranin-A (coefficient = 0.409, p value = 0.034), pro-opiomelanocortin (POMC; coefficient = 0.523, p value = 0.041), cysteine-rich hydrophobic domain-containing protein 2 (coefficient = 0.263, p value = 0.043), and trypsin-1 (coefficient = 0.397, p value = 0.047).
Nine plasma proteins were detected for BD such as alpha-N-acetylgalactosaminide alpha-2,6-sialyltransferase 6 (coefficient = 0.419, p value = 0.007), tumor necrosis factor receptor superfamily member 1B (coefficient = −0.383, p value = 0.012), guanine nucleotide exchange factor VAV3 (coefficient = −0.270, p value = 0.018), insulin-like growth factor-binding protein 6 (coefficient = −0.377, p value = 0.022), and rho guanine nucleotide exchange factor 10 (coefficient = −0.304, p value = 0.022).
For MDD, 12 blood plasma proteins were detected such as trefoil factor 1 (coefficient = −0.287, p value = 0.011), bone morphogenetic protein 7 (coefficient = 0.392, p value = 0.012), peregrin (coefficient = 0.361, p value = 0.013), beta-defensin 118 (coefficient = 0.328, p value = 0.014), and Neurensin-1 (coefficient = −0.359, p value = 0.026).
For SCZ, 15 blood plasma proteins were detected such as insulin-like growth factor-binding protein 6 (coefficient = −0.396, p value = 0.011), cathepsin Z (coefficient = −0.349, p value = 0.012), sphingosine kinase 2 (coefficient = 0.212, p value = 0.018), tropomyosin alpha-1 chain (coefficient = 0.264, p value = 0.021), CMP-N-acetylneuraminate-beta-galactosamide-alpha-2,3-sialyltransferase 1 (coefficient = 0.269, p value = 0.028), and protein nephroblastoma overexpressed (NOV) homolog (coefficient = 0.196, p value = 0.028; Table 1).
Table 1. Genetic correlations analysis results between psychiatric disorders and blood plasma protein (p value <0.05)
Abbreviations: ADHD, attention deficiency/hyperactive disorder; ASD, autism spectrum disorder; BD, bipolar disorder; MDD, major depressive disorder; SCZ, schizophrenia.
After comparing the LDSC results of the five psychiatric disorders, we also detected four common plasma proteins shared by BD and SCZ, including tumor necrosis factor receptor superfamily member 1B (p value = 0.012 for BD, p value = 0.011 for SCZ), insulin-like growth factor-binding protein 6 (p value = 0.022 for BD, p value = 0.030 for SCZ), rho guanine nucleotide exchange factor 10 (p value = 0.022 for BD, p value = 0.044 for SCZ), and normal mucosa of esophagus-specific gene 1 protein (p value = 0.030 for BD, p value = 0.045 for SCZ; Table 2).
Table 2. Common genetic correlations between psychiatric disorders and plasma protein (p < 0.05).
Abbreviations: BD, bipolar disorder; SCZ, schizophrenia.
The significant genetic correlations detected in the discovery GWAS datasets of five psychiatric disorders were further validated in the cross-disorder replication GWAS data. Two proteins identified in the discovery GWAS were further replicated in the cross-disorder replication GWAS data including multimerin-2 (coefficient = 0.471, p value = 0.032) and tumor necrosis factor receptor superfamily member 8 (coefficient = 0.388, p value = 0.033).
Discussion
To provide an atlas of genetic correlations between psychiatric disorders and plasma proteins, we conducted a large-scale genetic correlations between five common psychiatric disorders and 3,283 plasma proteins. We observed modest genetic correlations and identified several plasma proteins showing genetic correlation evidence with the five psychiatric disorders. Our study results provide novel clues for the pathogenetic and biomarkers studies of common psychiatric disorders.
We found that POMC was correlated with autism, which was consistent with previous study [Reference Chamberlain and Herman36]. POMC is a precursor polypeptide with 241 amino acid residues, and cleave to give rise to multiple peptide hormones. Previous studies of adult individuals exhibiting self-injurious behavior suggested that the pro-opiomelanocortin system, especially the endogenous opioid system, was dysregulated in the subgroups of autistic patients [Reference Leboyer, Bouvard, Recasens, Philippe, Guilloudbataille and Bondoux37,Reference Sandman, Hetrick, Taylor and Chiczdemet38]. Cazzullo et al. have suggested that the concentration of plasma POMC fragments, especially opioid fragments, contributed to the symptoms of autism as well as the response to treatment [Reference Cazzullo, Musetti, Musetti, Bajo, Sacerdote and Panerai39]. A mutation in the opioid region of the POMC gene in an autistic individual indicated that a subgroup of patients will be identified who share a POMC genetic defect [Reference Sandman, Spence and Smith40,Reference Sandman, Touchette, Marion, Lenjavi and Chicz-Demet41].
Bone morphogenetic protein 7 (BMP7), a member of the transforming growth factor-β superfamily, is another notable finding of this study. BMP7 plays a critical role in the development of noradrenergic neurons. It has neurotrophic and neuroprotective effects on mature catecholaminergic neurons [Reference Tilleman, Hakim, Novikov, Liser, Nashelsky and Di Salvio42,Reference Goridis and Rohrer43]. Real-time polymerase chain reaction (PCR) of locus coeruleus tissue from 12 matched pairs of MDD subjects and psychiatrically normal control subjects revealed low levels of BMP7 gene expression in MDD [Reference Ordway, Szebeni, Chandley, Stockmeier, Xiang and Newton44]. Laser capture microdissection of noradrenergic neurons, astrocytes, and oligodendrocytes from the locus coeruleus revealed that the MDD-associated reduction in BMP7 gene expression was limited to astrocytes [Reference Ordway, Szebeni, Chandley, Stockmeier, Xiang and Newton44]. This suggests that reduced astrocyte support for pontine locus coeruleus neurons may contribute to pathology of brain noradrenergic neurons in MDD [Reference Ordway, Szebeni, Chandley, Stockmeier, Xiang and Newton44]. Rats exposed to chronic social defeat exhibited a similar reduction in BMP7 gene expression in the locus coeruleus [Reference Ordway, Szebeni, Chandley, Stockmeier, Xiang and Newton44].
The defects responsible for impaired sensorimotor gating in mice, a hallmark of SCZ, might include myelination dysregulation, which has been observed in some cases of human SCZ [Reference Braff, Grillon and Geyer45,Reference Hakak, Walker, Li, Wong, Davis and Buxbaum46]. Notably, sphingosine 1 phosphate (S1P) receptor expression in oligodendrocytes involves in the process of myelination in the rodent central nervous system and might contribute to glial differentiation, maturation, and myelination during development [Reference Kim, Miron, Dukala, Proia, Ludwin and Traka47]. Contos et al. suggested that constitutive knockout of S1P receptor 1 causes a behavioral phenotype reminiscent of SCZ [Reference Contos, Ishii, Fukushima, Kingsbury, Ye and Kawamura48]. The concentration of S1P is regulated by the activities of two kinases—sphingosine kinase 1 and 2, a number of broad specificity lipid phosphate phosphatases which have a selectivity toward S1P [Reference Maceyka, Harikumar, Milstien and Spiegel49]. In this study, we found that sphingosine kinase 2 was correlated with SCZ, which is consistent with previous conclusions.
Additionally, we observed genetic correlation evidence between guanine nucleotide exchange factor 3 VAV3 (VAV3) and BD. A study including 199 participants from the Mayo Clinic Bipolar Disorder Biobank suggested that several SNPs of VAV3 gene was associated with the response to antiepileptic drugs-mood stabilizers in BD patients [Reference Ho, Nassan, Balwinder, Colby, McElroy and Frye50]. Previous molecular biological studies have found multiple shared risk components between BD and SCZ [Reference Lichtenstein, Yip, Björk, Pawitan, Cannon and Sullivan5,Reference Smoller, Kendler, Craddock, Lee, Neale and Nurnberger7]. It is interesting that we found common proteins shown significant genetic correlations between BD and SCZ in this study, for instance tumor necrosis factor receptor superfamily member 1B (TNFRSF1B). TNFRSF1B, also known as tumor necrosis factor receptor 2 (TNFR2), is a membrane receptor that binds tumor necrosis factor-alpha. TNFRSF1B is expressed in glia and neurons [Reference Mitsuhiro, Annie-Claire, Isabelle and Nicolas de51]. It has been reported that TNFRSF1B mediated trophic or protective role in neuronal survival [Reference Fontaine, Mohandsaid, Hanoteau, Fuchs, Pfizenmaier and Eisel52]. TNFRSF1B knockout studies in mice suggested a role of TNFRSF1B in protecting neurons from apoptosis by stimulating antioxidative pathways [Reference Bruce, Boling, Kindy, Peschon, Kraemer and Carpenter53]. Till et al. suggested that the polymorphism of TNFRSF1B gene resulted in a lower capability to induce NF-kB activation, leading to an enhancement of TNFR1-induced apoptosis [Reference Till, Rosenstiel, Krippnerheidenreich, Mascheretticroucher, Croucher and Schafer54]. SCZ patients with 676G allele of TNFRSF1B have a decreased neuron survival, dendritic branching, and capacity of remyelination [Reference Thabet, Ben Nejma, Zaafrane, Gaha, Ben Salem and Romdhane55]. Compared with healthy control subjects, SCZ and BD patients have higher plasma soluble TNFRSF1B levels [Reference Hoseth, Ueland, Dieset, Birnbaum, Shin and Kleinman56]. This could be interpreted as the increasing in soluble TNFRSF1B levels to reduce apoptosis and modulate TNF activity in the euthymic period in BD [Reference Doganavsargil-Baysal, Cinemre, Aksoy, Akbas, Metin and Fettahoglu57]. Based on the previous and our study results, it is reasonable to infer that the observed negative genetic correlations between TNFRSF1B and BD/SCZ may partially be explained by the protective effects of TNFRSF1B on neurons. Further studies are needed to confirm the role of TNFRSF1B in the pathological mechanisms of BD and SCZ.
To the best of our knowledge, this is the first large-scale genetic correlation analysis of plasma proteome and psychiatric disorders. Because of using GWAS genetic data, our study results should be less susceptible to environmental confounding factors. Notably, two limitations of our approach should also be noted. First, it should be noted that the objective of this study is to evaluate the genetic correlations between plasma proteome and psychiatric disorders, and to scan novel candidate plasma proteins related to psychiatric disorders. Further functional studies are needed to confirm our findings and clarify the potential biological mechanisms of observed associations between plasma proteins and psychiatric disorders in this study. Second, the GWAS summary data of this study are all from European ancestry. Therefore, it should be careful to apply our study results to other ethnic groups.
Conclusions
In summary, by utilizing LDSC approach, we conducted a large-scale analysis to investigate the genetic correlations between blood plasma proteome and psychiatric disorders. Our study identified a set of candidate plasma proteins showing association signals with psychiatric disorders. We hope that our findings will provide novel insights into the future pathogenetic studies of psychiatric disorders and serve as a fundamental resource for understanding the genetic mechanisms of the effects of plasma proteome on psychiatric disorders.
Funding.
This study is supported by the National Natural Scientific Foundation of China (81472925, 81673112), the Key Projects of International Cooperation Among Governments in Scientific and Technological Innovation (2016YFE0119100), the Natural Science Basic Research Plan in Shaanxi Province of China (2017JZ024), and the Fundamental Research Funds for the Central Universities.
Conflicts of Interest.
The authors have stated that they have no conflict of interest.
Authorship Contributions.
S.C. and F.Z. conceived and designed the study, wrote the manuscript, collected the data, and carried out the statistical analyses; F.G., M.M., L.Z., B.C., X.Q., C.L., P.L., O.P.K., and Y.W. made preparations for the manuscript at first. All authors reviewed and approved the final manuscript.
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