ArticleTranslational psychiatry2021
A machine learning case-control classifier for schizophrenia based on DNA methylation in blood.
Article in Translational psychiatry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.
- Disease classification for whole-blood DNA methylation: Meta-analysis, missing values imputation, and XAI.GigaScience · 2022Pooled it
- DNA Methylation as a Mediator of Cardiovascular Disease Risk in Relation to PTSD Severity: Identification of Potential Epigenetic Biomarkers.medRxiv : the preprint server for health sciences · 2026Article
- DNA methylation and machine learning: challenges and perspective toward enhanced clinical diagnostics.Clinical epigenetics · 2025Review
- A comprehensive guide to genomeZhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2025Review
- Temporally discordant chromatin accessibility and DNA demethylation define short- and long-term enhancer regulation during cell fate specification.Cell reports · 2025Article
- Human genomic regions of systemic interindividual epigenetic variation are implicated in neurodevelopmental and metabolic disorders.medRxiv : the preprint server for health sciences · 2025Article
- Exploring the Intersection of Schizophrenia, Machine Learning, and Genomics: Scoping Review.JMIR bioinformatics and biotechnology · 2024Article
- A novel blood-based epigenetic biosignature in first-episode schizophrenia patients through automated machine learning.Translational psychiatry · 2024Article
- Identification of important gene signatures in schizophrenia through feature fusion and genetic algorithm.Mammalian genome : official journal of the International Mammalian Genome Society · 2024Article
- DNA methylation of imprint control regions associated with Alzheimer's disease in non-Hispanic Blacks and non-Hispanic Whites.Clinical epigenetics · 2024Article
- DiseaseNet: a transfer learning approach to noncommunicable disease classification.BMC bioinformatics · 2024Article
- The Potential for Artificial Intelligence Applied to Epigenetics.Mayo Clinic proceedings. Digital health · 2023Article
- Machine learning techniques in diagnostics and prediction of the clinical features of schizophrenia: a narrative review.Consortium psychiatricum · 2023Review
- Systemic interindividual epigenetic variation in humans is associated with transposable elements and under strong genetic control.Genome biology · 2023Article
- Maladaptive cognitive regulation moderates the mediating role of emotion dysregulation on the association between psychosocial factors and non-suicidal self-injury in depression.Frontiers in psychiatry · 2023Article
- Association of DNA methylation with energy and fear-related behaviors in canines.Frontiers in psychology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 5 institutions in 2 countries.
Funding
Abstract
Epigenetic dysregulation is thought to contribute to the etiology of schizophrenia (SZ), but the cell type-specificity of DNA methylation makes population-based epigenetic studies of SZ challenging. To train an SZ case-control classifier based on DNA methylation in blood, therefore, we focused on human genomic regions of systemic interindividual epigenetic variation (CoRSIVs), a subset of which are represented on the Illumina Human Methylation 450K (HM450) array. HM450 DNA methylation data on whole blood of 414 SZ cases and 433 non-psychiatric controls were used as training data for a classification algorithm with built-in feature selection, sparse partial least squares discriminate analysis (SPLS-DA); application of SPLS-DA to HM450 data has not been previously reported. Using the first two SPLS-DA dimensions we calculated a "risk distance" to identify individuals with the highest probability of SZ. The model was then evaluated on an independent HM450 data set on 353 SZ cases and 322 non-psychiatric controls. Our CoRSIV-based model classified 303 individuals as cases with a positive predictive value (PPV) of 80%, far surpassing the performance of a model based on polygenic risk score (PRS). Importantly, risk distance (based on CoRSIV methylation) was not associated with medication use, arguing against reverse causality. Risk distance and PRS were positively correlated (Pearson r = 0.28, P = 1.28 × 10
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.