Download
  1. SNP data
  2. Gene data
  3. Co-expression data
  4. eQTL data
  5. Pascal data
  6. PPI data
  7. Differential expression data
  8. BrainSpan expression pattern data
  9. BrainCloud expression pattern data
  10. WGS data of monozygotic twins discordant for SCZ
  11. Reference

SNP data

SNP data was downloaded from PGC2 which contains 9,444,230 SNPs.[1] The sample size included in the GWAS meta-analysis was 82,315, including 35,476 schizophrenia cases and 46,839 controls. RegulomeDB[2] score was introduced to annotate the SNPs. Non-synonymous (missense) variants were also annotated by SIFT[3,4] and PolyPhen-2.[5] The total data can be downloaded from PGC2

Gene data

Gene data was prioritized by the following items: (1) genes identified by GWAS;[6-23] (2)genes affected by CNVs;[24] (3)genes identified by linkage and association study;[25-27] (4)genes identified by convergent functional genomics (CFG);[28] (5)genes identified by Sherlock integrative analysis;[29-30] (6)genes identified by Pascal gene based test;[31] (7)genes expressed differentially in schizophrenia.[32-37]The prioritization of schizophrenia risk genes can be downloaded from here

Co-expression data

we performed gene co-expression analyses using RNA-seq-based expression data from the BrainSpan: Atlas of the Developing Human Brain (http://www.brainspan.org/). BrainSpan atlas were divided into four anatomic regions and fifteen developmental stages as described in studies of Willsey et al.[38] and Gilman et al.68 The four anatomic regions are: (1) V1C-STC cluster (V1C, ITC, IPC, A1C, and STC); (2) Prefrontal and primary motor-somatosensory cortex or M1C-S1C cluster (M1C, S1C, VFC, MFC, DFC, and OFC); (3) STR-AMY cluster (STR, HIP, AMY); (4) MD-CBC cluster (MD and CBC). The fifteen developmental stages are: (1) Embryonic, from 4 to 8 postconception weeks (PCW); (2) Early fetal, from 8 to 10 PCW; (3) Early fetal, from 10 to 13 PCW; (4) Early mid-fetal, from 13 to 16 PCW; (5) Early mid-fetal, from 16 to 19 PCW; (6) Late mid-fetal, from 19 to 24 PCW; (7) Late fetal, from 24 to 38 PCW; (8) Neonatal & early infancy, from 0 to 6 months (M); (9) Late infancy from 6 to 12 M; (10) Early childhood, from 1 to 6 years (Y); (11) Middle and late childhood, from 6 to 12 Y; (12) Adolescence, from 12 to 20 Y; (13) Young adulthood, from 20 to 40 Y; (14) Middle adulthood, from 40 to 60 Y; (15) Late adulthood, more than 60 years old. Pearson correlation coefficients were calculated in each brain region across developmental stages as described previously.[38-39] Only gene pair whose Pearson correlation coefficient equal or bigger than 0.8 were retained. The whole data can be downloaded from here.

eQTL data

We downloaded genome-wide expression and genotype data from the study of Myers et al., which included 193 neuropathologically normal human brain samples.[40] The association between each SNP and transcript was assessed using PLINK,[41] as described in the study of Myers et al.[40] In total, we assessed correlations among 366,140 SNPs and the expression of 14,078 detected transcripts. Because the eQTL data is quite large, if users want to require it, please cantact us!

Pascal data

Gene-based tests were performed for association by using Pascal.[31] SNP P-value data was downloaded from PGC2 as desciribed in "SNP data". Pascal data can be downloaded from here.

PPI data

We downloaded PPI data from InWeb, a well-characterized PPI databased developed by Lage et al.[42,43]

Differential expression data

We downloaded 5 microarray datasets (which used brain tissues from schizophrenia patients and controls) from gene expression omnibus (GEO) including GSE53987 (contains three brain tissues: pre-frontal cortex, striatum and hippocampus; a total of 114 samples),[32] GSE12649 (prefrontal cortex; a total of 69 postmortem brains samples),[33] GSE21138 (prefrontal cortex; a total of 59 postmortem brains samples),[34] GSE35978 (cerebellum and parietal cortex brain; a total of 195 samples),[35] and GSE62191 (frontal cortex; a total of 59 samples).[36] We re-analyzed these microarray datasets and top 1 differentially expressed genes in schizophrenia cases and controls were integrated into SZDB. In addition, differentially expressed genes identified by RNA sequencing[37] were also integrated into SZDB. Differential expression data can be downloaded from here

BrainSpan expression pattern data

As desciribed in "Co-expression data", BrainSpan atlas were divided into four anatomic regions and fifteen developmental stages.

BrainCloud expression pattern data

BrainCloud[44] PFC expression data was also divided into fifteen developmental stages, as described in "Co-expression data".

WGS data of monozygotic twins discordant for SCZ

de novo mutations data identified by whole-genome sequencing of monozygotic twins discordant for schizophrenia can be download from here. More details please refer to the original aricle Tang J, et al,. 2017. Journal of Genetics and Genomics[45].

Reference

  1. Schizophrenia Working Group of the Psychiatric Genomics Consortium*. Biological insights from 108 schizophrenia-associated genetic loci. Nature 2014; 511: 421-427.
  2. Xie D, Boyle AP, Wu L, Zhai J, Kawli T, Snyder M. Dynamic trans-acting factor colocalization in human cells. Cell 2013; 155: 713-724.
  3. Ng PC, Henikoff S. SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Res 2003; 31: 3812-3814.
  4. Kumar P, Henikoff S, Ng PC. Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat Protoc 2009; 4: 1073-1081.
  5. Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P et al. A method and server for predicting damaging missense mutations. Nat Methods 2010; 7: 248-249.
  6. O'Donovan MC, Craddock N, Norton N, Williams H, Peirce T, Moskvina V, et al. (2008): Identification of loci associated with schizophrenia by genome-wide association and follow-up. Nat Genet. 40:1053-1055.
  7. Purcell SM, Wray NR, Stone JL, Visscher PM, O'Donovan MC, Sullivan PF, et al. (2009): Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature. 460:748-752.
  8. Ripke S, O'Dushlaine C, Chambert K, Moran JL, Kahler AK, Akterin S, et al. (2013): Genome-wide association analysis identifies 13 new risk loci for schizophrenia. Nat Genet. 45:1150-1159.
  9. Ikeda M, Aleksic B, Kinoshita Y, Okochi T, Kawashima K, Kushima I, et al. (2011): Genome-wide association study of schizophrenia in a Japanese population. Biol Psychiatry. 69:472-478.
  10. Hamshere ML, Walters JT, Smith R, Richards AL, Green E, Grozeva D, et al. (2013): Genome-wide significant associations in schizophrenia to ITIH3/4, CACNA1C and SDCCAG8, and extensive replication of associations reported by the Schizophrenia PGC. Mol Psychiatry. 18:708-712.
  11. Rietschel M, Mattheisen M, Degenhardt F, Muhleisen TW, Kirsch P, Esslinger C, et al. (2012): Association between genetic variation in a region on chromosome 11 and schizophrenia in large samples from Europe. Mol Psychiatry. 17:906-917.
  12. Irish Schizophrenia Genomics Consortium & Wellcome Trust Case Control Consortium (2012): Genome-wide association study implicates HLA-C*01:02 as a risk factor at the major histocompatibility complex locus in schizophrenia. Biol Psychiatry. 72:620-628.
  13. Shi J, Levinson DF, Duan J, Sanders AR, Zheng Y, Pe'er I, et al. (2009): Common variants on chromosome 6p22.1 are associated with schizophrenia. Nature. 460:753-757.
  14. Shi Y, Li Z, Xu Q, Wang T, Li T, Shen J, et al. (2011): Common variants on 8p12 and 1q24.2 confer risk of schizophrenia. Nat Genet. 43:1224-1227.
  15. Stefansson H, Ophoff RA, Steinberg S, Andreassen OA, Cichon S, Rujescu D, et al. (2009): Common variants conferring risk of schizophrenia. Nature. 460:744-747.
  16. Li J, Zhou G, Ji W, Feng G, Zhao Q, Liu J, et al. (2011): Common variants in the BCL9 gene conferring risk of schizophrenia. Arch Gen Psychiatry. 68:232-240.
  17. Steinberg S, de Jong S, Andreassen OA, Werge T, Borglum AD, Mors O, et al. (2011): Common variants at VRK2 and TCF4 conferring risk of schizophrenia. Hum Mol Genet. 20:4076-4081.
  18. Wong EH, So HC, Li M, Wang Q, Butler AW, Paul B, et al. (2014): Common variants on Xq28 conferring risk of schizophrenia in Han Chinese. Schizophr Bull. 40:777-786.
  19. Yue WH, Wang HF, Sun LD, Tang FL, Liu ZH, Zhang HX, et al. (2011): Genome-wide association study identifies a susceptibility locus for schizophrenia in Han Chinese at 11p11.2. Nat Genet. 43:1228-1231.
  20. Lencz T, Guha S, Liu C, Rosenfeld J, Mukherjee S, DeRosse P, et al. (2013): Genome-wide association study implicates NDST3 in schizophrenia and bipolar disorder. Nat Commun. 4:2739.
  21. Schizophrenia Working Group of the Psychiatric Genomics Consortium* (2014): Biological insights from 108 schizophrenia-associated genetic loci. Nature. 511:421-427.
  22. Stefansson H, Rujescu D, Cichon S, Pietilainen OP, Ingason A, Steinberg S, et al. (2008): Large recurrent microdeletions associated with schizophrenia. Nature. 455:232-236.
  23. The International Schizophrenia Consortium (2008): Rare chromosomal deletions and duplications increase risk of schizophrenia. Nature. 455:237-241.
  24. Luo X, Huang L, Han L, Luo Z, Hu F, Tieu R et al. Systematic prioritization and integrative analysis of copy number variations in schizophrenia reveal key schizophrenia susceptibility genes. Schizophr Bull 2014; 40: 1285-1299.
  25. Lewis CM, Levinson DF, Wise LH, DeLisi LE, Straub RE, Hovatta I et al. Genome scan meta-analysis of schizophrenia and bipolar disorder, part II: Schizophrenia. Am J Hum Genet 2003; 73: 34-48.
  26. Ng MY, Levinson DF, Faraone SV, Suarez BK, DeLisi LE, Arinami T et al. Meta-analysis of 32 genome-wide linkage studies of schizophrenia. Mol Psychiatry 2009; 14: 774-785.
  27. Allen NC, Bagade S, McQueen MB, Ioannidis JP, Kavvoura FK, Khoury MJ et al. Systematic meta-analyses and field synopsis of genetic association studies in schizophrenia: the SzGene database. Nat Genet 2008; 40: 827-834.
  28. Ayalew M, Le-Niculescu H, Levey DF, Jain N, Changala B, Patel SD et al. Convergent functional genomics of schizophrenia: from comprehensive understanding to genetic risk prediction. Mol Psychiatry 2012; 17: 887-905.
  29. Luo X, Huang L, Han L, Luo Z, Hu F, Tieu R et al. Systematic prioritization and integrative analysis of copy number variations in schizophrenia reveal key schizophrenia susceptibility genes. Schizophr Bull 2014; 40: 1285-1299.
  30. He X, Fuller CK, Song Y, Meng Q, Zhang B, Yang X et al. Sherlock: detecting gene-disease associations by matching patterns of expression QTL and GWAS. Am J Hum Genet 2013; 92: 667-680.
  31. Lamparter D, Marbach D, Rueedi R, Kutalik Z, Bergmann S. Fast and Rigorous Computation of Gene and Pathway Scores from SNP-Based Summary Statistics. PLoS Comput Biol 2016; 12: e1004714.
  32. Lanz TA, Joshi JJ, Reinhart V, Johnson K, Grantham LE, 2nd, Volfson D. STEP levels are unchanged in pre-frontal cortex and associative striatum in post-mortem human brain samples from subjects with schizophrenia, bipolar disorder and major depressive disorder. PLoS One 2015; 10: e0121744.
  33. Iwamoto K, Bundo M, Kato T. Altered expression of mitochondria-related genes in postmortem brains of patients with bipolar disorder or schizophrenia, as revealed by large-scale DNA microarray analysis. Hum Mol Genet 2005; 14: 241-253.
  34. Narayan S, Tang B, Head SR, Gilmartin TJ, Sutcliffe JG, Dean B et al. Molecular profiles of schizophrenia in the CNS at different stages of illness. Brain Res 2008; 1239: 235-248.
  35. Chen C, Cheng L, Grennan K, Pibiri F, Zhang C, Badner JA et al. Two gene co-expression modules differentiate psychotics and controls. Mol Psychiatry 2013; 18: 1308-1314.
  36. de Baumont A, Maschietto M, Lima L, Carraro DM, Olivieri EH, Fiorini A et al. Innate immune response is differentially dysregulated between bipolar disease and schizophrenia. Schizophr Res 2015; 161: 215-221.
  37. Fillman SG, Cloonan N, Catts VS, Miller LC, Wong J, McCrossin T et al. Increased inflammatory markers identified in the dorsolateral prefrontal cortex of individuals with schizophrenia. Mol Psychiatry 2013; 18: 206-214.
  38. Willsey AJ, Sanders SJ, Li M, Dong S, Tebbenkamp AT, Muhle RA et al. Coexpression networks implicate human midfetal deep cortical projection neurons in the pathogenesis of autism. Cell 2013; 155: 997-1007.
  39. Gulsuner S, Walsh T, Watts AC, Lee MK, Thornton AM, Casadei S et al. Spatial and temporal mapping of de novo mutations in schizophrenia to a fetal prefrontal cortical network. Cell 2013; 154: 518-529.
  40. Myers AJ, Gibbs JR, Webster JA, Rohrer K, Zhao A, Marlowe L et al. A survey of genetic human cortical gene expression. Nat Genet 2007; 39: 1494-1499.
  41. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. American journal of human genetics 2007; 81: 559-575.
  42. Lage K, Karlberg EO, Storling ZM, Olason PI, Pedersen AG, Rigina O et al. A human phenome-interactome network of protein complexes implicated in genetic disorders. Nature biotechnology 2007; 25: 309-316.
  43. Lage K, Hansen NT, Karlberg EO, Eklund AC, Roque FS, Donahoe PK et al. A large-scale analysis of tissue-specific pathology and gene expression of human disease genes and complexes. Proceedings of the National Academy of Sciences of the United States of America 2008; 105: 20870-20875.
  44. Colantuoni C, Lipska BK, Ye T, Hyde TM, Tao R, Leek JT et al. Temporal dynamics and genetic control of transcription in the human prefrontal cortex. Nature 2011; 478: 519-523.
  45. Tang J, Fan Y, Li H et al. Whole-genome sequencing of monozygotic twins discordant for schizophrenia indicates multiple genetic risk factors for schizophrenia. Journal of Genetics and Genomics, accepted manuscript.