Protein Folding Genes Linked to Depression in Multi-Omics Genetic Study

Depression is one of the most common and disabling psychiatric conditions in the world, yet the biological machinery that underlies its susceptibility remains only partly mapped. Now, a study published in Annals of General Psychiatry has turned a spotlight on an unexpected corner of cellular biology: the systems that keep proteins correctly folded. Using a multi-omics genetic strategy that integrates layers of DNA methylation, gene expression and circulating protein abundance, researchers led by Juan Wang, Shen He, Junfang Cui and Huafang Li identified two candidate genes, PDIA3 and FNIP2, as genetically supported players in the risk architecture of major depressive disorder. The work is explicitly hypothesis-generating, but it offers one of the most systematic looks to date at how proteostasis, the cell’s protein quality-control network, may be wired into depression vulnerability through inherited variation.

The study’s central engine was summary-data-based Mendelian randomization, or SMR, a statistical technique that exploits naturally occurring genetic variation to probe whether molecular traits influence disease risk. Because genetic variants are randomly allocated at conception, much like coins flipped by nature, they are largely immune to the confounding and reverse causation that plague conventional observational studies. In an SMR analysis, variants that alter, say, the methylation level of a DNA site can be used as instruments to ask whether that methylation change has a downstream causal effect on disease. The method requires that the same variant be associated with both the molecular trait and the outcome, and the strength of the inferred relationship depends on the biology linking the two.

What made this study ambitious was the stacking of three molecular layers on top of a genome-wide association study (GWAS) of major depressive disorder. The researchers merged the MDD GWAS summary statistics with blood-derived cis-methylation quantitative trait loci (mQTLs), cis-expression quantitative trait loci (eQTLs), and cis-protein quantitative trait loci (pQTLs) datasets. Each layer captures a different rung on the ladder from DNA to function: methylation marks influence how genes are regulated, eQTLs reveal how those regulatory differences change gene expression, and pQTLs show how genetic variation alters the abundance of proteins circulating in the blood. By screening candidate signals at every rung, the team could trace a provisional causal chain from epigenetic regulation through gene activity to protein level and finally to disease risk.

The initial screening in the discovery cohort yielded a substantial haul. Eighty-six methylation sites, fifteen genes, and three proteins passed the prespecified SMR thresholds, suggesting that protein folding-related biology was repeatedly intersecting with depression-associated genetic signals. But genomic screening is notoriously prone to false positives, so the investigators applied a false discovery rate (FDR) correction to rein in spurious findings. After this stricter accounting, eleven methylation sites mapping to five genes remained statistically significant at the methylation layer. The expression and protein-level signals, by contrast, did not survive multiple-testing correction and were classified as nominal or suggestive. This hierarchy of evidence is itself informative: it points to methylation, the layer closest to gene regulation, as the tier where protein folding-related genetics and depression risk most clearly converge.

Colocalization analysis provided a second, independent filter. When two traits appear to share a genetic association, there is always a possibility that the signal is actually produced by two different variants sitting near each other on the same chromosome, a phenomenon rooted in linkage disequilibrium. Colocalization methods test whether the two association signals are driven by the same causal variant. In this study, colocalization supported shared genetic signals for ten methylation sites, four expression-associated genes, and two proteins. Of those, four methylation signals, two expression signals, and one protein signal showed strong colocalization evidence, with posterior probabilities exceeding 0.8, a benchmark widely regarded as compelling. Signals that survive both SMR and colocalization are far less likely to be artifacts of chromosomal proximity.

Cross-omic integration then allowed the researchers to stitch the layers together. The analyses provided genetically supported evidence for potential regulatory relationships between FNIP2 methylation-related signals and the gene’s expression, and between PDIA3 expression and its protein abundance. In plain terms, the data suggest a plausible chain of causation in which inherited variation alters chemical tags on the FNIP2 gene, which in turn shifts how actively the gene is expressed; separately, variants affecting PDIA3 expression appear to propagate upward to change the amount of PDIA3 protein detectable in blood. Both molecular traits also showed exploratory associations with MDD risk across their corresponding omic layers, hinting at complete, if provisional, chains from variant to molecule to disorder.

The two prioritized genes are biologically intriguing in their own right. PDIA3 encodes a protein disulfide isomerase resident in the endoplasmic reticulum, where it catalyzes the reshuffling of disulfide bonds that allow newly made proteins to assume their correct three-dimensional shapes. FNIP2 interacts with folliculin and participates in AMP-activated protein kinase (AMPK) signaling, a cellular energy-sensing pathway with documented ties to stress responses. Neither gene is a household name in depression research, which is precisely why a systematic, hypothesis-free approach was needed to surface them. Their emergence from an unbiased screen suggests that depression genetics may be whispering about cellular stress biology that conventional candidate-gene studies have overlooked.

Functional enrichment analyses reinforced that interpretation. Gene Ontology and KEGG pathway analyses of the candidate genes implicated proteostasis-related modules, including endoplasmic reticulum stress, the unfolded protein response, chaperone-mediated folding, protein processing, quality control, and antigen presentation. The unfolded protein response is the cell’s emergency program when misfolded proteins accumulate in the endoplasmic reticulum, and chronic activation of this stress pathway has been observed in animal models of depression, including those using chronic unpredictable mild stress. The enrichment of antigen presentation and major histocompatibility complex-related terms also dovetails with a growing literature connecting immune dysregulation to mood disorders, suggesting that protein folding quality control and inflammation may be intertwined strands of the same biological rope.

The team also sought external support through additional analyses, though these remained exploratory. Replication attempts in the FinnGen depression dataset, cross-disorder checks against bipolar disorder, and surveys of brain-region expression data drawn from the Gene Expression Omnibus provided directionally consistent but not definitive signals. The authors are appropriately measured in their conclusions: the findings are genetically supported and hypothesis-generating, and they explicitly call for independent replication and functional validation before PDIA3 or FNIP2 can be considered established depression genes. Genetic instruments indicate association with disease risk through molecular traits; they do not, by themselves, prove how the genes act in neurons or glia.

Even with those caveats, the study’s design offers a template for the next generation of psychiatric genetics. Rather than asking which single variant raises disease risk, multi-omics Mendelian randomization asks which molecular mechanisms inheritable variation plausibly perturbs, and then interrogates those mechanisms layer by layer. The prioritization of protein folding and proteostasis pathways in major depressive disorder reframes depression not merely as a disorder of neurotransmitters but as a condition in which cellular stress, protein quality control and immune signaling may help set the threshold at which adversity tips into illness. If PDIA3 and FNIP2 hold up under replication and laboratory scrutiny, they could point toward biomarkers measurable in blood and, ultimately, toward therapeutic strategies that shore up the cell’s faltering protein-folding machinery in the most vulnerable patients.

One methodological detail worth noting is how the authors guarded against a known weakness of SMR: horizontal pleiotropy, in which an instrument variant influences the disease through a pathway unrelated to the molecular trait under study. The HEIDI test addresses this by examining whether the association between the instrument and the outcome is consistent across many variants scattered across the locus. A genuine causal effect should show a uniform signal, whereas linkage-driven artifacts tend to concentrate among variants closest to the probe. By requiring HEIDI testing alongside colocalization, the analysis applied two complementary safeguards against the same class of false positive, which strengthens confidence in the methylation-layer findings that survived both filters.

The choice of blood as the tissue source for all three molecular layers also deserves consideration. Blood is far easier to sample than brain tissue, which is why most large-scale QTL reference panels are built from it, and peripheral methylation and protein signals can serve as accessible biomarkers. Yet depression is a disorder of the brain, and regulatory biology in blood does not always mirror that in neural tissue. The exploratory surveys of brain-region expression data, including the anterior cingulate cortex, represent an early attempt to bridge this gap, and the authors themselves flag these analyses as preliminary rather than confirmatory.

The reporting of the study followed STROBE-MR guidelines, a checklist designed to improve transparency in Mendelian randomization research by requiring explicit documentation of instrument selection, sensitivity analyses, and potential pleiotropy. Such standardized reporting matters because genetic instruments can fail in subtle ways, and readers need enough detail to judge whether assumptions hold. The open-access publication also means that the supplementary tables, which contain the full sets of screened methylation sites, genes, and proteins, are available to any laboratory wishing to reanalyze the signals or design follow-up experiments targeting PDIA3 or FNIP2 in cellular models of stress.

Subject of Research: Genetically supported associations between protein folding-related genes and major depressive disorder identified through multi-omics Mendelian randomization.

Article Title: Unraveling the role of protein folding-related genes in depression through multi-omics mendelian randomization

Article References: Wang, J., He, S., Cui, J., & Li, H. (2026). Unraveling the role of protein folding-related genes in depression through multi-omics mendelian randomization. Annals of General Psychiatry. https://doi.org/10.1186/s12991-026-00697-8

Image Credits: AI Generated

DOI: 10.1186/s12991-026-00697-8

Keywords: depression, major depressive disorder, protein folding, Mendelian randomization, multi-omics, PDIA3, FNIP2, DNA methylation, colocalization, unfolded protein response, genetics, psychiatry