---
title: "Single-cell scTWAS identifies immune cell–specific genes linked to gestational diabetes"
id: "plos-one-0-leveraging-expression-quantitative-trait-loci-information-in-single-cell"
canonical_url: "https://medichelpline.com/clinical-feed/plos-one-0-leveraging-expression-quantitative-trait-loci-information-in-single-cell"
content_type: "clinical_feed_article"
specialty: "Endocrinology"
source_name: "PLOS ONE (Medicine)"
source_url: "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339"
published_at: "2026-07-30T14:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Single-cell scTWAS identifies immune cell–specific genes linked to gestational diabetes
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/plos-one-0-leveraging-expression-quantitative-trait-loci-information-in-single-cell
- **Specialty:** [Endocrinology](https://medichelpline.com/clinical-feed/endocrinology.md)
- **Primary Source:** PLOS ONE (Medicine)
- **Source URL:** [Original Journal Publication](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339)
- **Published At:** 2026-07-30T14:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- Gestational diabetes mellitus (**GDM**) has rising prevalence and long-term metabolic risks for mothers and offspring. Bulk tissue analyses limit detection of cell-type-specific genetic effects. - The study integrated FinnGen GDM GWAS summary statistics (12,332 cases, 131,109 controls) with single-cell eQTL (sc-eQTL) maps from 12 immune cell types in the OneK1K cohort (~1.27 million PBMCs from 982 donors). - Quality control harmonization produced 1,009,861 shared SNPs for genetically regulated expression (GReX) modeling in the OTTERS framework. Gene cis-windows were ±1 Mb around gene bodies using GENCODE v41. - Stage I trained four GReX models per gene per cell type (lassosum, SDPR, PRS-CS, P+T) using an ancestry-matched LD panel; Stage II applied models to FinnGen GDM GWAS and combined method P values via ACAT-O. - Post-GWAS trait-level analyses included LDSC (SNP h2 = 0.0352, SE = 0.0052) and LAVA local heritability partitioning, which found 828 loci enriched for GDM heritability after Bonferroni correction, with top signals on chromosomes 11 and 2. - Bulk TWAS using GTEx whole blood identified two genes with nominal associations (SMCO4 and RP11-443B20.1) but none passing FDR < 0.05. - sc-eQTL counts showed marked cell-type heterogeneity; CD4 NC had the most significant cis-eQTLs (795,929). Cell abundance correlated with eQTL counts, indicating sample-size effects. - scTWAS (OTTERS + ACAT-O) detected 63 gene–cell associations corresponding to 14 unique genes (FDR < 0.05). Strongest signals were in **CD4+ T cells** and **monocytes**. - **ERAP1** and **ERAP2** showed associations in 10 of 12 cell types (ACAT P = 2.17 × 10−5). **RIOK2** emerged as a shared regulator. GO analysis highlighted **antigen processing and presentation via MHC class I** (FDR < 10−5). - Colocalization prioritized monocytic **LNPEP** as a primary causal candidate; associations for ERAP1, ERAP2, and RIOK2 may reflect complex LD or tissue-specific effects. - The scTWAS approach identified cell-type-specific genes not detected in bulk TWAS, supporting dysregulated antigen presentation in peripheral immune cells as relevant to GDM pathophysiology. - Data and pre-trained eQTL weight files were made publicly available (FinnGen GWAS, OneK1K sc-eQTL, and Figshare resource for weights).
## Clinical Analysis & Structured Key Points
[ Skip to main content ](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339#main-content) Advertisement * [plos.org](https://plos.org/) * [Create account](https://community.plos.org/registration/new) * [Sign in](https://journals.plos.org/user/secure/login?page=%2Fplosone%2Farticle%3Fid%3D10.1371%2Fjournal.pone.0355339) * * About * Browse * Publish * [](https://journals.plos.org/plosone/ "PLOS One") * Search [advanced search](https://journals.plos.org/plosone/search) * [Browse Topics](https://journals.plos.org/plosone/subjectAreaBrowse) Browse Subject Areas ? Click through the PLOS taxonomy to find articles in your field. For more information about PLOS Subject Areas, click [here](https://github.com/PLOS/plos-thesaurus/blob/master/README.md "Link opens in new window"). [](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339) [](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339) * 0 [Save](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0355339#savedHeader) [Total Mendeley and Citeulike bookmarks.](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0355339#savedHeader) * 0 [Citation](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0355339#citedHeader) [Paper's citation count computed by Dimensions.](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0355339#citedHeader) * 22 [View](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0355339#viewedHeader) [PLOS views and downloads.](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0355339#viewedHeader) * 0 [Share](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0355339#discussedHeader) [Sum of Facebook, Twitter, Reddit and Wikipedia activity.](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0355339#discussedHeader) Open Access Peer-reviewed Research Article # Leveraging expression quantitative trait loci information in single-cell resolution to identify cell-specific genes for gestational diabetes * Wenyan Zhu , Contributed equally to this work with: Wenyan Zhu, Xiaowei Wu Roles Investigation, Methodology, Visualization, Writing – original draft Affiliation Department of Epidemiology, Center for Global Health, School of Public Health, National Vaccine Innovation Platform, Nanjing Medical University, Nanjing, Jiangsu, China ⨯ * Xiaowei Wu , Contributed equally to this work with: Wenyan Zhu, Xiaowei Wu Roles Conceptualization, Methodology, Validation Affiliation Department of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China ⨯ * Rongkui Hu Roles Data curation, Supervision, Writing – review & editing * E-mail: xiangyu198110@163.com Affiliation Gynecology Department, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, Jiangsu, China [ ![ORCID logo](https://journals.plos.org/resource/img/orcid_16x16.png) https://orcid.org/0009-0002-4969-4966 ](https://orcid.org/0009-0002-4969-4966 "ORCID Registry") ⨯ # Leveraging expression quantitative trait loci information in single-cell resolution to identify cell-specific genes for gestational diabetes * Wenyan Zhu, * Xiaowei Wu, * Rongkui Hu ![PLOS](https://journals.plos.org/resource/img/logo-plos-full-color.svg) x * Published: July 30, 2026 * * [Article](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339) * [Authors](https://journals.plos.org/plosone/article/authors?id=10.1371/journal.pone.0355339) * [Metrics](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0355339) * [Comments](https://journals.plos.org/plosone/article/comments?id=10.1371/journal.pone.0355339) * [Media Coverage](http://plos.altmetric.com/details/doi/10.1371/journal.pone.0355339) * [Abstract](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339#abstract0) * [Introduction](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339#sec005) * [Methods](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339#sec006) * [Results](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339#sec012) * [Discussion](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339#sec016) * [Supporting information](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339#sec018) * [Acknowledgments](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339#ack) * [References](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339#references) * [Reader Comments](https://journals.plos.org/plosone/article/comments?id=10.1371/journal.pone.0355339) * [Figures](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339) ## Abstract ### Background Gestational diabetes mellitus (GDM) is a common pregnancy complication with long-term metabolic consequences for both mother and offspring. While genome-wide association studies (GWAS) have identified risk loci, the cell-type-specific genetic architecture remains poorly characterized due to the limitations of bulk-tissue analyses. ### Methods We integrated GWAS summary statistics from FinnGen (12,332 cases, 131,109 controls) with single-cell expression quantitative trait loci (sc-eQTL) data from 12 immune cell types in the OneK1K cohort. Using the OTTERS framework and ACAT-O omnibus testing, we performed single-cell transcriptome-wide association studies (scTWAS) to identify GDM-associated genes. We performed bulk-level TWAS as a sensitivity analysis. To investigate the function of significant genes, we performed functional enrichment. ### Results We detected 14 unique genes significantly associated with GDM across immune cell types (FDR . The data of sc-eQTL summary statistics are publicly available from . **Funding:** The author(s) received no specific funding for this work. **Competing interests:** The authors have declared that no competing interests exist. ## Introduction Gestational diabetes mellitus (GDM) is a type of diabetes that is diagnosed during the second or third trimester of pregnancy and was not clearly overt diabetes before pregnancy [[1](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339#pone.0355339.ref001)]. It develops during pregnancy in women whose pancreatic function is insufficient to overcome the insulin resistance associated with the pregnant state, resulting in hyperglycemia [[2](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339#pone.0355339.ref002)]. There has been a consistent rise in global morbidity [[3](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339#pone.0355339.ref003)], making GDM one of the most prevalent metabolic issues during pregnancy [[4](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339#pone.0355339.ref004)]. Despite being identified and treated during pregnancy, GDM affects both the mother’s and the fetus’s health for the rest of their lives. For women with a history of GDM, they are at an increasing risk of developing T2DM, cardiovascular disease (CVD), and chronic kidney disease (CKD). Their offspring are more likely to have metabolic disorders such as diabetes, hypertension, and obesity [[5](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339#pone.0355339.ref005)]. Large-scale epidemiological investigations have emphasized the long-term hazards [[6](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339#pone.0355339.ref006),[7](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339#pone.0355339.ref007)]. Genome-wide association studies (GWAS), transcriptome-wide association studies (TWAS), plasma proteomics have significantly improved our knowledge regarding the genetic, genomic and proteomic architecture of GDM [[3](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339#pone.0355339.ref003),[8](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339#pone.0355339.ref008)–[14](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339#pone.0355339.ref014)]. Elliott _et al._ discovered 13 GDM-associated loci using the largest GWAS of GDM to date, indicating both shared and different genetic architectures between GDM and type 2 diabetes [[8](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339#pone.0355339.ref008)]. Shan _et al._ nominated _NPC1_ and _KIAA1191_ as novel GDM risk genes with a series of popular analytic tool [[15](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339#pone.0355339.ref015)]. Although single-cell RNA sequencing (scRNA-seq) has revealed cell-type-specific transcriptional alterations in placental tissues from GDM pregnancies [[16](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339#pone.0355339.ref016)], these data alone cannot link genetic variation to gene regulation. Recent advances in single-cell expression quantitative trait locus (sc-eQTL) mapping now enable direct association of genotypes with gene expression at single cell resolution, including OneK1K [[17](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339#pone.0355339.ref017)] and TenK10K [[18](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339#pone.0355339.ref018)]. Powell _et al._ analyzed over one million immune cells from 1,000 individuals and demonstrated that the majority of eQTLs are active only in specific cell states or subtypes—a finding masked in bulk tissue analyses—thereby establishing a foundational framework for interpreting non-coding disease variants [[17](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339#pone.0355339.ref017)]. Importantly, sc-eQTL studies have shown that disease-associated SNPs often regulate gene expression exclusively in rare or context-dependent cell populations [[19](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339#pone.0355339.ref019),[20](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339#pone.0355339.ref020)]. For instance, Chen et al. identified thousands of cell-type-restricted sc-eQTLs in human tumors, with strong enrichment near transcription start sites and dramatic variability in detection across lineages [[19](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339#pone.0355339.ref019)]. Recently, the scTWAS Atlas was launched as a comprehensive knowledgebase for single-cell transcriptome-wide association studies, integrating precomputed scTWAS results across multiple cell types and complex traits [[21](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339#pone.0355339.ref021)]. While this resource provides valuable insights into cell-type-specific genetic architecture, it currently lacks coverage of pregnancy-related conditions such as GDM and does not include specialized immune or metabolic cell states relevant to maternal-fetal physiology. Moreover, as a static repository built upon existing sc-eQTL references, it cannot accommodate newly released GWAS summary statistics—such as the large-scale FinnGen GDM data used here—for customized, up-to-date scTWAS interrogation. Therefore, a dedicated analysis leveraging state-of-the-art frameworks like OTTERS remains essential to uncover GDM-associated genes at cellular resolution. However, GDM pathogenesis extends beyond systemic immunity to the placenta, where heterogeneous cell types—including diverse trophoblasts and resident immune cells—undergo significant disease-specific transcriptional reprogramming. Overall, existing studies have defined significant loci and genes by in- and cross-ancestry GWAS and post-GWAS analysis and cell-type specific genes by scRNA [[22](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339#pone.0355339.ref022),[23](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339#pone.0355339.ref023)]. Current population studies primarily rely on bulk tissue data, which overlooks cellular heterogeneity and may obscure cell-type-specific genetic effects. Single-cell studies are often limited by smaller sample sizes, despite revealing stronger, more precise signals in specific cell types [[21](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339#pone.0355339.ref021)]. These drawbacks make the findings at the cellular mechanism level with small sample size difficult to use at the population level. To address this issue, leveraging the summary statistics of sc-eQTL, we mainly defined the significant genes in single-cell resolution for GDM [[24](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339#pone.0355339.ref024)]. Subsequent enrichment analysis of these genes across different cell types reveals their involvement in distinct biological pathways, providing deeper insights into the pathophysiology of GDM. We also performed a side analysis for the traditional TWAS analysis using the eQTL of whole blood from GTEx ([Fig 1](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339#pone-0355339-g001)). [![thumbnail](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0355339.g001)](https://journals.plos.org/plosone/article/figure/image?size=medium&id=10.1371/journal.pone.0355339.g001 "Click for larger image") Download: * [PNG larger image](https://journals.plos.org/plosone/article/figure/image?download&size=large&id=10.1371/journal.pone.0355339.g001) * [TIFF original image](https://journals.plos.org/plosone/article/figure/image?download&size=original&id=10.1371/journal.pone.0355339.g001) Fig 1. Workflow of traditional TWAS analysis using GTEx whole blood eQTL data for GDM. [ https://doi.org/10.1371/journal.pone.0355339.g001](https://doi.org/10.1371/journal.pone.0355339.g001) ## Methods ### Data resource We received summary statistics for GDM from the FinnGen study, which included 12,332 cases and 131,109 female controls with Finnish ancestry [[8](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339#pone.0355339.ref008)]. Following protocols used in [[25](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339#pone.0355339.ref025)–[27](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339#pone.0355339.ref027)], we performed a strict process for SNP quality control (QC): removing variants with missing effect alleles or standard errors, and retaining only SNPs present in the HapMap3 (hm3) reference panel. We maintained 1,266,324 high-quality SNPs. This improved dataset was then utilized to conduct single-cell transcriptome-wide association study (scTWAS) analysis to detect cell-type-specific genetic correlations with GDM. Based on the OneK1K cohort, we used scRNA-seq data from about 1.27 million peripheral blood mononuclear cells (PBMCs) collected from 982 healthy donors [[17](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339#pone.0355339.ref017)]. The original dataset underwent rigorous quality control: SNPs with call rate < 95%, minor allele frequency (MAF) < 1%, or Hardy–Weinberg equilibrium _P_ < 1 × 10−6 were excluded prior to imputation [[17](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339#pone.0355339.ref017)]. To ensure compatibility with the GDM GWAS summary statistics and enable accurate functional interpretation in the current genomic reference, we converted the OneK1K cis-eQTL mappings from GRCh37 to GRCh38 using liftOver. Following coordinate conversion, we harmonized alleles and effect directions between the eQTL and GWAS datasets. This alignment yielded 1,009,861 shared SNPs, which served as the foundation for genetically regulated expression (GReX) modeling in the OTTERS framework. We analyzed eQTL data from 12 immune cell subtypes: CD4+ effector memory T cells (CD4ET), CD4+ naive and central memory T cells (CD4NC), CD4+ T cells expressing SOX4 (CD4SOX4), CD8+ naive and central memory T cells (CD8NC), CD8+ T cells with expression of S100B (CD8S100B), CD8+ effector memory T cells (CD8ET), Memory B cells (BMem), Immature and naive B cells (BIN), Plasma cells (Plasma), Classical monocytes (MonoC), Nonclassical monocytes (MonoNC), and Dendritic cells (DC). ### Genetic analysis for GDM Following [[3](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0355339#pone.0355339.ref003)], we performed the post-GWAS analysis of GDM in two levels. At the trait level, we first applied LD Score Regression (LDSC; v1.0.1) [[28](ht
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