A Novel Machine-learning Model to Classify Schizophrenia Using Methylation Data Based on Gene Expression

Karthikeyan A. Vijayakumar; Gwang-Won Cho

doi:10.2174/0115748936293407240222113019

ISSN: 1574-8936
E-ISSN: 2212-392X

A Novel Machine-learning Model to Classify Schizophrenia Using Methylation Data Based on Gene Expression
Authors: Karthikeyan A. Vijayakumar^1,2 and Gwang-Won Cho^1,2,3
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Affiliations: ¹ Department of Biological Science, College of Natural Science, Chosun University, Gwangju, 501-759, Korea ; ² BK21 FOUR Education Research Group for Age-Associated Disorder Control Technology, Department of Integrative Biological Science, Chosun University, Gwangju, 501-759, Korea ; ³ The Basic Science Institute of Chosun University, Chosun University, Gwangju, 61452, Korea
Source: Current Bioinformatics, Volume 20, Issue 1, Jan 2025, p. 31 - 45
DOI: https://doi.org/10.2174/0115748936293407240222113019
- Received: 17 Nov 2023
- Accepted: 15 Feb 2024
- Available online: 07 Mar 2024

Abstract

Introduction

The recent advancement in artificial intelligence has compelled medical research to adapt the technologies. The abundance of molecular data and AI technology has helped in explaining various diseases, even cancers. Schizophrenia is a complex neuropsychological disease whose etiology is unknown. Several gene-wide association studies attempted to narrow down the cause of the disease but did not successfully point out the mechanism behind the disease. There are studies regarding the epigenetic changes in the schizophrenia disease condition, and a classification machine-learning model has been trained using the blood methylation data.

Methods

In this study, we have demonstrated a novel approach to elucidating the molecular cause of the disease. We used a two-step machine-learning approach to determine the causal molecular markers. By doing so, we developed classification models using both gene expression microarray and methylation microarray data.

Results

Our models, because of our novel approach, achieved good classification accuracy with the available data size. We analyzed the important features, and they add up as evidence for the glutamate hypothesis of schizophrenia.

Conclusion

In this way, we have demonstrated explaining a disease through machine learning models.

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2025-01-19

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References

GiotakosO. Persistence of psychosis in the population: The cost and the price for humanity.Psychiatriki201929431632610.22365/jpsych.2018.294.316 30814041
[Google Scholar]
InselT.R. Rethinking schizophrenia.Nature2010468732118719310.1038/nature09552 21068826
[Google Scholar]
Moreno-KüstnerB. MartínC. PastorL. Prevalence of psychotic disorders and its association with methodological issues. A systematic review and meta-analyses.PLoS One2018134e019568710.1371/journal.pone.0195687 29649252
[Google Scholar]
HowesO.D. KapurS. The dopamine hypothesis of schizophrenia: Version III--the final common pathway.Schizophr. Bull.200935354956210.1093/schbul/sbp006 19325164
[Google Scholar]
MoghaddamB. JavittD. From revolution to evolution: The glutamate hypothesis of schizophrenia and its implication for treatment.Neuropsychopharmacology201237141510.1038/npp.2011.181 21956446
[Google Scholar]
FatemiS.H. FolsomT.D. The neurodevelopmental hypothesis of schizophrenia, revisited.Schizophr. Bull.200935352854810.1093/schbul/sbn187 19223657
[Google Scholar]
MoghaddamB. Dopamine and glutamate hypotheses of schizophrenia.Handbook of Contemporary Neuropharmacolog200710.1002/9780470101001.hcn030
[Google Scholar]
SchwartzT.L. SachdevaS. StahlS.M. Glutamate neurocircuitry: Theoretical underpinnings in schizophrenia.Front. Pharmacol.2012319510.3389/fphar.2012.00195 23189055
[Google Scholar]
MorenoJ.L. KuritaM. HollowayT. Maternal influenza viral infection causes schizophrenia-like alterations of 5-HT2A and mGlu2 receptors in the adult offspring.J. Neurosci.20113151863187210.1523/JNEUROSCI.4230‑10.2011 21289196
[Google Scholar]
OwenM.J. SawaA. MortensenP.B. Schizophrenia.Lancet201638810039869710.1016/S0140‑6736(15)01121‑6 26777917
[Google Scholar]
PetronisA. The origin of schizophrenia: Genetic thesis, epigenetic antithesis, and resolving synthesis. Biol Psychiatry 200455109657010.1016/j.biopsych.2004.02.005
[Google Scholar]
McguffinP. AshersonP. OwenM. FarmerA. The strength of the genetic effect. Is there room for an environmental influence in the aetiology of schizophrenia?Br. J. Psychiatry1994164559359910.1192/bjp.164.5.593 7921708
[Google Scholar]
SullivanP.F. KendlerK.S. NealeM.C. Schizophrenia as a complex trait: Evidence from a meta-analysis of twin studies.Arch. Gen. Psychiatry200360121187119210.1001/archpsyc.60.12.1187 14662550
[Google Scholar]
GunasekaraC.J. HannonE. MacKayH. A machine learning case–control classifier for schizophrenia based on DNA methylation in blood.Transl. Psychiatry202111141210.1038/s41398‑021‑01496‑3 34341337
[Google Scholar]
KeshavanM.S. CollinG. GuimondS. KellyS. PrasadK.M. LizanoP. Neuroimaging in schizophrenia.Neuroimaging Clin. N. Am.2020301738310.1016/j.nic.2019.09.007 31759574
[Google Scholar]
MerikangasA.K. ShellyM. KnightonA. KotlerN. TanenbaumN. AlmasyL. What genes are differentially expressed in individuals with schizophrenia? A systematic review.Mol. Psychiatry20222731373138310.1038/s41380‑021‑01420‑7 35091668
[Google Scholar]
TrubetskoyV. PardiñasA.F. QiT. Mapping genomic loci implicates genes and synaptic biology in schizophrenia.Nature2022604790650250810.1038/s41586‑022‑04434‑5 35396580
[Google Scholar]
JaffeA.E. GaoY. Deep-SoboslayA. Mapping DNA methylation across development, genotype and schizophrenia in the human frontal cortex.Nat. Neurosci.2016191404710.1038/nn.4181 26619358
[Google Scholar]
HannonE. DempsterE.L. MansellG. DNA methylation meta-analysis reveals cellular alterations in psychosis and markers of treatment-resistant schizophrenia.eLife202110e5843010.7554/eLife.58430 33646943
[Google Scholar]
AryeeM.J. JaffeA.E. Corrada-BravoH. Minfi: A flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays.Bioinformatics201430101363136910.1093/bioinformatics/btu049 24478339
[Google Scholar]
FortinJ.P. TricheT.J.Jr HansenK.D. Preprocessing, normalization and integration of the Illumina HumanMethylationEPIC array with minfi.Bioinformatics201733455856010.1093/bioinformatics/btw691 28035024
[Google Scholar]
TouleimatN. TostJ. Complete pipeline for Infinium ® human methylation 450K BeadChip data processing using subset quantile normalization for accurate DNA methylation estimation.Epigenomics20124332534110.2217/epi.12.21 22690668
[Google Scholar]
RaudvereU. KolbergL. KuzminI. g:Profiler: A web server for functional enrichment analysis and conversions of gene lists (2019 update).Nucleic Acids Res.201947W1W191-810.1093/nar/gkz369 31066453
[Google Scholar]
Newell-PriceJ. ClarkA.J.L. KingP. DNA methylation and silencing of gene expression.Trends Endocrinol. Metab.200011414214810.1016/S1043‑2760(00)00248‑4 10754536
[Google Scholar]
CurradiM. IzzoA. BadaraccoG. LandsbergerN. Molecular mechanisms of gene silencing mediated by DNA methylation.Mol. Cell. Biol.20022293157317310.1128/MCB.22.9.3157‑3173.2002 11940673
[Google Scholar]
EhrlichM. LaceyM. DNA methylation and differentiation: Silencing, upregulation and modulation of gene expression.Epigenomics20135555356810.2217/epi.13.43 24059801
[Google Scholar]
LimW.J. KimK.H. KimJ.Y. JeongS. KimN. Identification of DNA-Methylated CpG islands associated with gene silencing in the adult body tissues of the ogye chicken using RNA-Seq and reduced representation bisulfite sequencing.Front. Genet.20191034610.3389/fgene.2019.00346 31040866
[Google Scholar]
CorrellC.U. SchoolerN.R. Negative symptoms in schizophrenia: A review and clinical guide for recognition, assessment, and treatment.Neuropsychiatr. Dis. Treat.20201651953410.2147/NDT.S225643 32110026
[Google Scholar]
KeverneJ. BinderE.B. A Review of epigenetics in psychiatry: Focus on environmental risk factors.Med. Genetik2020321576410.1515/medgen‑2020‑2004
[Google Scholar]
HorvathS. RajK. DNA methylation-based biomarkers and the epigenetic clock theory of ageing.Nat. Rev. Genet.201819637138410.1038/s41576‑018‑0004‑3 29643443
[Google Scholar]
GhadirianA.M. Sensory perceptual limitation in schizophrenia.Psychother. Psychosom.1976-197727211511910.1159/000287005 1052192
[Google Scholar]
RamsayI.S. SchallmoM.P. BiagiantiB. FisherM. VinogradovS. SponheimS.R. Deficits in auditory and visual sensory discrimination reflect a genetic liability for psychosis and predict disruptions in global cognitive functioning.Front. Psychiatry20201163810.3389/fpsyt.2020.00638 32733293
[Google Scholar]
JavittD.C. FreedmanR. Sensory processing dysfunction in the personal experience and neuronal machinery of schizophrenia.Am. J. Psychiatry20151721173110.1176/appi.ajp.2014.13121691 25553496
[Google Scholar]
GandalM.J. HaneyJ.R. ParikshakN.N. Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap.Science2018359637669369710.1126/science.aad6469 29439242
[Google Scholar]
BoczekT. MackiewiczJ. SobolczykM. The role of G protein-coupled receptors (GPCRs) and calcium signaling in schizophrenia. Focus on GPCRs activated by neurotransmitters and chemokines.Cells2021105122810.3390/cells10051228 34067760
[Google Scholar]
MargueronR. ReinbergD. Chromatin structure and the inheritance of epigenetic information.Nat. Rev. Genet.201011428529610.1038/nrg2752 20300089
[Google Scholar]
FöckingM. DoyleB. MunawarN. DillonE.T. CotterD. CagneyG. Epigenetic factors in Schizophrenia: Mechanisms and experimental approaches.Complex Psychiatry20195161210.1159/000495063 31019914
[Google Scholar]

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A Novel Machine-learning Model to Classify Schizophrenia Using Methylation Data Based on Gene Expression

Curr Bioinform 20, 31 (2025); https://doi.org/10.2174/0115748936293407240222113019

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Supplements

Supplementary material is available on the publisher’s website along with the published article.

Article Type: Research Article

Keyword(s): chronic psychological disorder; DNA methylation; gene expression; machine learning; multi omics; schizophrenia

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A Novel Machine-learning Model to Classify Schizophrenia Using Methylation Data Based on Gene Expression

Abstract

Supplementary material is available on the publisher’s website along with the published article.

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