The role of blood-based biomarkers in advancing personalized therapy of schizophrenia

References

• Van Os, JEA. Schizophrenia. The Lancet. 2009;374(9690):635–645.
   PubMed Web of Science ®Google Scholar
• Su CT, Yang AL, Lin CY. Comparing two schizophrenia-specific quality of life instruments in institutionalized people with schizophrenia. Psychiatry Res. 2017 Aug 23. pii: S0165-1781(17)30674-1.
   Google Scholar
• Su C-T, Yang A-L, Lin C-Y. The construct of the schizophrenia quality of life scale revision 4 for the population of Taiwan. Occup Ther Int. 2017;2017:1–11.
   Web of Science ®Google Scholar
• Chang CC, Wu T-H, Chen C-Y, et al. Comparing self-stigma between people with different mental disorders in Taiwan. J Nerv Ment Dis. 2016;204(7):547–553.
   PubMed Web of Science ®Google Scholar
• Li Y, Wu FX, Ngom A. A review on machine learning principles for multi-view biological data integration. Brief Bioinform. 2016.
   Google Scholar
• Iniesta R, Stahl D, McGuffin P. Machine learning, statistical learning and the future of biological research in psychiatry. Psychol Med. 2016;46(12):2455–2465.
   PubMed Web of Science ®Google Scholar
• Veronese E, Castellani U, Peruzzo D, et al. Machine learning approaches: from theory to application in schizophrenia. Comput Math Methods Med. 2013;2013:867924.
   PubMedGoogle Scholar
• Lin E, Lane HY. Machine learning and systems genomics approaches for multi-omics data. Biomark Res. 2017;5(1):2.
   PubMedGoogle Scholar
• Schneider CE, White T, Hass J, et al. Smoking status as a potential confounder in the study of brain structure in schizophrenia. J Psychiatr Res. 2014;50:84–91.
   PubMed Web of Science ®Google Scholar
• Gage SH, Munafò MR. Smoking as a causal risk factor for schizophrenia. The Lancet Psychiatry. 2015;2(9):778–779.
   PubMed Web of Science ®Google Scholar
• Hall WADL. Cannabis use and the risk of developing a psychotic disorder. World Psychiatry. 2008;7(2):68–71.
   PubMed Web of Science ®Google Scholar
• Hannon E, Dempster E, Viana J, et al. An integrated genetic-epigenetic analysis of schizophrenia: evidence for co-localization of genetic associations and differential DNA methylation. Genome Biol. 2016;17(1):176.
   PubMedGoogle Scholar
• De Leon J, Diaz FJ. A meta-analysis of worldwide studies demonstrates an association between schizophrenia and tobacco smoking behaviors. Schizophr Res. 2005;76(2):135–157.
   PubMed Web of Science ®Google Scholar
• De Leon J, Becona E, Gurpegui M, et al. The association between high nicotine dependence and severe mental illness may be consistent across countries. J Clin Psychiatry. 2002;63(9):812–816.
   PubMed Web of Science ®Google Scholar
• Richfield, O., et al. Learning schizophrenia imaging genetics data via multiple kernel canonical correlation analysis. arXiv Preprint arXiv. 2016;1609:04699.
   Google Scholar
• Singh, A., et al. DIABLO-an integrative, multi-omics, multivariate method for multi-group classification. bioRxiv. 2016;067611.
   Google Scholar
• Chaudhary, K., et al. Deep learning based multi-omics integration robustly predicts survival in liver cancer. bioRxiv. 2017;114892.
   Google Scholar
• Schizophrenia Working Group of the Psychiatric Genomics, C. Biological insights from 108 schizophrenia-associated genetic loci. Nature. 2014;511(7510):421–427.
   PubMed Web of Science ®Google Scholar
• Brown AS, Derkits EJ. Prenatal infection and schizophrenia: a review of epidemiologic and translational studies. Am J Psychiatry. 2009;167(3):261–280.
   Web of Science ®Google Scholar
• Brown AS. Prenatal infection as a risk factor for schizophrenia. Schizophr Bull. 2006;32(2):200–202.
   PubMed Web of Science ®Google Scholar
• Bitanihirwe BK, Woo TU. Oxidative stress in schizophrenia: an integrated approach. Neurosci Biobehav Rev. 2011;35(3):878–893.
   PubMed Web of Science ®Google Scholar
• Swofford CD, Kasckow JW, Scheller-Gilkey G, et al. Substance use: a powerful predictor of relapse in schizophrenia. Schizophr Res. 1996;20(1):145–151.
   PubMed Web of Science ®Google Scholar
• Cantor-Graae E, Selten JP. Schizophrenia and migration: a meta-analysis and review. Am J Psychiatry. 2005;162(1):12–24.
   PubMed Web of Science ®Google Scholar
• Hoffmann A, Ziller M, Spengler D. The future is the past: methylation QTLs in Schizophrenia. Genes (Basel). 2016;7(12):104.
   PubMed Web of Science ®Google Scholar
• Hannon E, Spiers H, Viana J, et al. Methylation QTLs in the developing brain and their enrichment in schizophrenia risk loci. Nat Neurosci. 2016;19(1):48–54.
   PubMed Web of Science ®Google Scholar
• Auta J, Smith RC, Dong E, et al. DNA-methylation gene network dysregulation in peripheral blood lymphocytes of schizophrenia patients. Schizophr Res. 2013;150(1):312–318.
   PubMed Web of Science ®Google Scholar
• Teroganova N, Girshkin L, Suter CM, et al. DNA methylation in peripheral tissue of schizophrenia and bipolar disorder: a systematic review. BMC Genet. 2016;17:27.
   PubMed Web of Science ®Google Scholar
• Aberg KA, McClay JL, Nerella S, et al. Methylome-wide association study of schizophrenia: identifying blood biomarker signatures of environmental insults. JAMA Psychiatry. 2014;71(3):255–264.
   PubMed Web of Science ®Google Scholar
• Beveridge NJ, Cairns MJ. MicroRNA dysregulation in schizophrenia. Neurobiol Dis. 2012;46(2):263–271.
   PubMed Web of Science ®Google Scholar
• Wang J, Wang Y, Yang J, et al. microRNAs as novel biomarkers of schizophrenia (Review). Exp Ther Med. 2014;8(6):1671–1676.
   PubMed Web of Science ®Google Scholar
• Camkurt MA, Karababa F, Erdal ME, et al. Investigation of dysregulation of several MicroRNAs in peripheral blood of schizophrenia patients. Clin Psychopharmacol Neurosci. 2016;14(3):256–260.
   PubMed Web of Science ®Google Scholar
• Lai CY, Scarr E, Udawela M, et al. Biomarkers in schizophrenia: a focus on blood based diagnostics and theranostics. World J Psychiatry. 2016;6(1):102–117.
   PubMed Web of Science ®Google Scholar
• Xu Y, Li F, Zhang B, et al. MicroRNAs and target site screening reveals a pre-microRNA-30e variant associated with schizophrenia. Schizophr Res. 2010;119(1–3):219–227.
   PubMed Web of Science ®Google Scholar
• Hansen T, Olsen L, Lindow M, et al. Brain expressed microRNAs implicated in schizophrenia etiology. PLoS One. 2007;2(9):e873.
   PubMed Web of Science ®Google Scholar
• Williamson VS, Mamdani M, McMichael GO, et al. Expression quantitative trait loci (eQTLs) in microRNA genes are enriched for schizophrenia and bipolar disorder association signals. Psychol Med. 2015;45(12):2557–2569.
   PubMed Web of Science ®Google Scholar
• Vawter MP, Mamdani F, Macciardi F. An integrative functional genomics approach for discovering biomarkers in schizophrenia. Brief Funct Genomics. 2011;10(6):387–399.
   PubMed Web of Science ®Google Scholar
• De Jong S, Van Eijk KR, Zeegers DWLH, et al. Expression QTL analysis of top loci from GWAS meta-analysis highlights additional schizophrenia candidate genes. Eur J Hum Genet. 2012;20(9):1004–1008.
   PubMed Web of Science ®Google Scholar
• Xu Y, Yue W, Shugart YY, et al. Potential involvement of the interleukin-18 pathway in schizophrenia. J Psychiatr Res. 2016;74:10–16.
   PubMed Web of Science ®Google Scholar
• Yoshida M, Shiroiwa K, Mouri K, et al. Haplotypes in the expression quantitative trait locus of interleukin-1beta gene are associated with schizophrenia. Schizophr Res. 2012;140(1–3):185–191.
   PubMed Web of Science ®Google Scholar
• Pergola G, Wingård L, Nilsson H, et al. DRD2 co-expression network and a related polygenic index predict imaging, behavioral and clinical phenotypes linked to schizophrenia. Transl Psychiatry. 2017;7(1):e1006.
   PubMed Web of Science ®Google Scholar
• De Jong S, Boks MPM, Fuller TF, et al. A gene co-expression network in whole blood of schizophrenia patients is independent of antipsychotic-use and enriched for brain-expressed genes. PLoS One. 2012;7(6):e39498.
   PubMed Web of Science ®Google Scholar
• Shi J, Levinson DF, Duan J, et al. Common variants on chromosome 6p22.1 are associated with schizophrenia. Nature. 2009;460(7256):753–757.
   PubMed Web of Science ®Google Scholar
• Hess JL, Tylee DS, Barve R, et al. Transcriptome-wide mega-analyses reveal joint dysregulation of immunologic genes and transcription regulators in brain and blood in schizophrenia. Schizophr Res. 2016;176(2–3):114–124.
   PubMed Web of Science ®Google Scholar
• Sullivan PF, Fan C, Perou CM. Evaluating the comparability of gene expression in blood and brain. Am J Med Genet B Neuropsychiatr Genet. 2006;141B(3):261–268.
   PubMed Web of Science ®Google Scholar
• Xu Y, Yue W, Yao Shugart Y, et al. Exploring transcription factors-microRNAs co-regulation networks in schizophrenia. Schizophr Bull. 2016;42(4):1037–1045.
   PubMed Web of Science ®Google Scholar
• Ganapathiraju MK, Thahir M, Handen A, et al. Schizophrenia interactome with 504 novel protein-protein interactions. NPJ Schizophr. 2016;2:16012.
   PubMed Web of Science ®Google Scholar
• Yu H, Bi W, Liu C, et al. Protein-interaction-network-based analysis for genome-wide association analysis of schizophrenia in Han Chinese population. J Psychiatr Res. 2014;50:73–78.
   PubMed Web of Science ®Google Scholar
• Schwarz E, Izmailov R, Liò P, et al. Protein interaction networks link schizophrenia risk loci to synaptic function. Schizophrenia Bulletin. 2016;42(6):1334–1342.
   PubMed Web of Science ®Google Scholar
• Arion D, Corradi JP, Tang S, et al. Distinctive transcriptome alterations of prefrontal pyramidal neurons in schizophrenia and schizoaffective disorder. Mol Psychiatry. 2015;20(11):1397–1405.
   PubMed Web of Science ®Google Scholar
• Kukreti R, Tripathi S, Bhatnagar P, et al. Association of DRD2 gene variant with schizophrenia. Neurosci Lett. 2006;392(1–2):68–71.
   PubMed Web of Science ®Google Scholar
• Liu L, Fan D, Ding N, et al. The relationship between DRD2 gene polymorphisms (C957T and C939T) and schizophrenia: a meta-analysis. Neurosci Lett. 2014;583:43–48.
   PubMed Web of Science ®Google Scholar
• Liu L, Yuan G, Cheng Z, et al. Identification of the mRNA expression status of the dopamine D2 receptor and dopamine transporter in peripheral blood lymphocytes of schizophrenia patients. PLoS One. 2013;8(9):e75259.
   PubMed Web of Science ®Google Scholar
• Ritchie MD, Holzinger ER, Li R, et al. Methods of integrating data to uncover genotype-phenotype interactions. Nat Rev Genet. 2015;16(2):85–97.
   PubMed Web of Science ®Google Scholar
• Holzinger ER, Ritchie MD. Integrating heterogeneous high-throughput data for meta-dimensional pharmacogenomics and disease-related studies. Pharmacogenomics. 2012;13(2):213–222.
   PubMed Web of Science ®Google Scholar
• Zhu Z, Zhang F, Hu H, et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet. 2016;48(5):481–487.
   PubMed Web of Science ®Google Scholar
• Schadt EE, Lamb J, Yang X, et al. An integrative genomics approach to infer causal associations between gene expression and disease. Nat Genet. 2005;37(7):710–717.
   PubMed Web of Science ®Google Scholar
• Huang S, Chaudhary K, Garmire LX. More is better: recent progress in multi-omics data integration methods. Front Genet. 2017;8:84.
   PubMed Web of Science ®Google Scholar
• Bersanelli M, Mosca E, Remondini D, et al. Methods for the integration of multi-omics data: mathematical aspects. BMC Bioinformatics. 2016;17(Suppl 2):15.
   PubMedGoogle Scholar
• Thompson, P.M., et al. The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging Behav. 2014;8(2):153–182.
   PubMed Web of Science ®Google Scholar
• Jaffe AE, Kleinman JE. Genetic and epigenetic analysis of schizophrenia in blood-a no-brainer? Genome Med. 2016;8(1):96.
   PubMedGoogle Scholar
• Frangou S, Schwarz E, Meyer-Lindenberg A. Identifying multimodal signatures associated with symptom clusters: the example of the IMAGEMEND project. World Psychiatry. 2016;15(2):178–179.
   PubMed Web of Science ®Google Scholar