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Impact of Genetic Polymorphisms on Human Immune Cell Gene Expression
Open accessAbstract
SUMMARY While many genetic variants have been associated with risk for human diseases, how these variants affect gene expression in various cell types remains largely unknown. To address this gap, the DICE (Database of Immune Cell Expression, Expression quantitative trait loci (eQTLs) and Epigenomics) project was established. Considering all human immune cell types and conditions studied, we identified cis-eQTLs for a total of 12,254 unique genes, which represent 61% of all protein-coding genes expressed in these cell types. Strikingly, a large fraction (41%) of these genes showed a strong cis-association with genotype only in a single cell type. We also found that biological sex is associated with major differences in immune cell gene expression in a highly cell-specific manner. These datasets will help reveal the effects of disease risk-associated genetic polymorphisms on specific immune cell types, providing mechanistic insights into how they might influence pathogenesis (http://dice-database.org).
Overview
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TL;DR
The DICE project mapped transcriptomes and cis-eQTLs across 13 human immune cell types and activation conditions from 91 healthy subjects, identifying 12,254 eGenes with highly cell-specific genetic effects. It also found extensive sex-biased immune gene expression and linked GWAS disease variants to specific immune cell types.
Key findings
- cis-eQTLs were identified for 12,254 unique genes, representing 61% of expressed protein-coding genes and 49% of lncRNAs; 41% showed a strong cis-association with genotype in only a single cell type. More than 2,000 eGenes were found per common cell type, and CD4+ memory/TREG subsets yielded over 7,800 eGenes, of which >2,500 were unique to those subsets.
- About 19% of eGenes per immune cell type overlapped GWAS SNPs (2,297 unique GWAS eGenes; 599–987 eGenes/cell type), and for about 36% of these eGenes associations were observed in only one or two cell types. Examples included IRAK3 in naïve B cells (rs1732886, adj. P = 6.82 × 10−17) and NAB1 in NK cells (rs13028201, adj. P = 1.41 × 10−14).
- Sex-biased expression was found for 1,875 unique transcripts, including 1,553 protein-coding transcripts and 196 lncRNAs; 93% were autosomal and >72% showed sex-biased expression in only a single immune cell type. FAM13A was female-biased in naïve CD4+ T cells, with homozygous CC versus TT females showing 3.5-fold greater FAM13A transcripts compared to <1.5-fold in males.
- Cell-specific transcriptional signatures included 4,602 highly cell-specific transcripts, and differential expression among CD4+ T cell subsets revealed over 700 genes enriched in one specific subset.
Methods
- The study analyzed 13 immune cell types plus activated naïve CD4+ and CD8+ T cells from 106 leukapheresis samples provided by 91 healthy subjects (54 males, 37 females; median age 27 years, range 18–61).
- RNA-seq was performed on FACS-sorted cells, with genotyping and imputation using the 1,000 Genomes Project phase 3 reference; cis-eQTLs were tested within ±1 Mb of the transcription start site using Matrix eQTL with thresholds FDR < 0.05, raw P < 0.0001, and TPM > 1.0.
- Differential expression used DESeq2; GWAS overlap used PheGenI data with 14,321 lead GWAS SNPs and 846 traits; functional validation used siRNA knockdowns in primary immune cells.
Limitations
- The paper states that next phases of DICE will add additional rare immune cell types and cell-specific epigenomic profiles, and that several lncRNA and disease-gene links require further study and functional confirmation.
- The cohort was limited to 91 healthy volunteers from the San Diego area with a median age of 27 years, so generalizability to older, diseased, or more ancestrally diverse populations is not established by this dataset.
- The eQTL analysis did not cover the pseudoautosomal regions of chr X or chr Y (stated in the paper), and it focused on cis windows of ±1 Mb around the TSS rather than trans effects.
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“which as a class were expressed at lower levels compared to protein-coding transcripts”
§ Impact of genetic polymorphisms on human immune cell gene expression > RESULTS > Transcriptome of common human immune cell types
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In the paper
“Considering all human immune cell types and conditions studied, we identified cis-eQTLs for a total of 12,254 unique genes”
§ Impact of genetic polymorphisms on human immune cell gene expression > Abstract
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“RNA-Seq analysis showed that similar numbers of transcripts (ranging from 14,500 to 17,000, ~39% of all annotated transcripts) were detected (>1.0 transcripts per million, TPM) across all cell types and activation conditions”
§ Impact of genetic polymorphisms on human immune cell gene expression > RESULTS > Transcriptome of common human immune cell types
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In the paper
“We also found that biological sex is associated with major differences in immune cell gene expression in a highly cell-specific manner.”
§ Impact of genetic polymorphisms on human immune cell gene expression > Abstract
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“representing 61% of all expressed protein-coding genes and 49% of all lncRNAs, respectively”
§ Impact of genetic polymorphisms on human immune cell gene expression > RESULTS > Expression QTLs are highly cell-specific
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“Strikingly, a large fraction (41%) of these genes showed a strong cis-association with genotype only in a single cell type.”
- Also stated in: § Abstract · plain
“a large fraction (41%) of these genes showed a strong cis-association with genotype only in a single cell type”
§ Impact of genetic polymorphisms on human immune cell gene expression > Abstract
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“which represent 61% of all protein-coding genes expressed in these cell types”
§ Impact of genetic polymorphisms on human immune cell gene expression > Abstract
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“Interestingly, several lncRNAs showed high abundance in specific immune cell types.”
§ Impact of genetic polymorphisms on human immune cell gene expression > RESULTS > Cell-specific transcriptional signatures
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“We also identified 2 genes in the Fc receptor like (FCRL) gene locus that showed strong cis-eQTLs primarily in TREG subsets”
§ Impact of genetic polymorphisms on human immune cell gene expression > RESULTS > CD4+ T cell subset-specific eQTLs
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In the paper
“NCF4, which encodes neutrophilic cytosolic factor 4, a component of the NADPH oxidase complex, showed prominent cis-QTLs specifically in CD4+ T subsets”
§ Impact of genetic polymorphisms on human immune cell gene expression > RESULTS > CD4+ T cell subset-specific eQTLs
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In the paper
“About 18% of these transcripts were long-noncoding RNAs (lncRNAs)”
§ Impact of genetic polymorphisms on human immune cell gene expression > RESULTS > Transcriptome of common human immune cell types
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“Though a majority of transcripts were detected in multiple cell types (Fig. S1H,I), their relative abundance varied substantially, resulting in distinct clustering patterns of different cell types”
§ Impact of genetic polymorphisms on human immune cell gene expression > RESULTS > Transcriptome of common human immune cell types
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In the paper
“The PBC-risk variant rs74804869 influenced SYNGR1 expression in NK and also CD8+ T cells”
§ Impact of genetic polymorphisms on human immune cell gene expression > RESULTS > Cell types most susceptible to GWAS SNPs
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“Our eQTL analysis of CD4+ memory and TREG subsets yielded over 7,800 eGenes, of which >2,500 were unique to these subsets”
§ Impact of genetic polymorphisms on human immune cell gene expression > RESULTS > CD4+ T cell subset-specific eQTLs
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“the CD-risk variant (rs9567293C/C) was associated with reduced expression of LACC1 in CD4+ memory T cell subsets, as well as in resting and activated naïve T cells but not in monocytes”
§ Impact of genetic polymorphisms on human immune cell gene expression > RESULTS > Cell types most susceptible to GWAS SNPs
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“we found that the expression of NAB1 displayed prominent genotype-dependent association (rs13028201, adj. P = 1.41 × 10−14) only in NK cells”
§ Impact of genetic polymorphisms on human immune cell gene expression > RESULTS > Cell types most susceptible to GWAS SNPs
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“CCNG2, encoding for a cell cycle protein, cyclin G2, showed a peak eQTL (rs4859751) specifically in TH1/17 and TH17 cells although it was also highly expressed in other TH subsets and immune cell types”
§ Impact of genetic polymorphisms on human immune cell gene expression > RESULTS > CD4+ T cell subset-specific eQTLs
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“an RA-risk variant (rs3093026) and celiac disease-risk variant (rs7374671) that were associated with expression of chemokine receptor genes CCR6 and CCR2, respectively, in TH1/17 and TH17 subsets”
§ Impact of genetic polymorphisms on human immune cell gene expression > RESULTS > Cell types most susceptible to GWAS SNPs
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“1,875 unique transcripts, including 1,553 protein-coding transcripts and 196 lncRNAs, were differentially expressed between sexes”
§ Impact of genetic polymorphisms on human immune cell gene expression > RESULTS > Sex has major effects on immune cell gene expression
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In the paper
“We also found a cis-eQTL (rs1246642) whose impact on expression was stronger in female subjects (Fig. 6F,G and Fig. S6B,C); homozygous females carrying the CC versus TT genotype had 3.5-fold greater levels of FAM13A transcripts compared to a <1.5-fold increase in males.”
§ Impact of genetic polymorphisms on human immune cell gene expression > RESULTS > Sex has major effects on immune cell gene expression
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“TREG cells were most susceptible to the effects of several autoimmune disease-risk variants. Notable eGenes associated with these variants include IKZF4, PDE4A and PTGIR”
§ Impact of genetic polymorphisms on human immune cell gene expression > RESULTS > Cell types most susceptible to GWAS SNPs
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In the paper
“Together, these results suggested that PBC-risk variants that modulate NAB1 and SYNGR1 expression are likely to impact NK cell function and influence PBC disease susceptibility.”
§ Impact of genetic polymorphisms on human immune cell gene expression > RESULTS > Cell types most susceptible to GWAS SNPs
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- Rests on an assumption: Overlap between GWAS SNPs and eQTLs indicates that the disease risk variant modulates expression of the linked gene in the susceptible cell type.
- Rests on an assumption: Knockdown of a gene in immune cells reflects the functional consequence of genetically reduced expression of that gene.
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In the paper
“The identity of all individual RNA-Seq samples was confirmed by cross-comparison with genotyping data for a given subject by genome-wide analysis of SNPs overlapping exons.”
§ Impact of genetic polymorphisms on human immune cell gene expression > METHOD DETAILS > RNA-Seq analysis
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“Further, samples from each specific cell types were processed for RNASeq in one batch to avoid any experimental batch effects.”
§ Impact of genetic polymorphisms on human immune cell gene expression > METHOD DETAILS > RNA-Seq analysis
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“Sex-biased transcripts were identified by performing pairwise comparison (female (n = 37) versus male (n = 54) subjects) using DESeq2 and considering ancestry and age as covariates; we considered a transcript expressed differentially when the DESeq2 analysis resulted in a Benjamini-Hochberg adj. P value of < 0.05.”
§ Impact of genetic polymorphisms on human immune cell gene expression > METHOD DETAILS > RNA-Seq analysis
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“The first two principal components of the genotyping data were used as covariates to minimize possible differences introduced by the different ancestry in the samples.”
§ Impact of genetic polymorphisms on human immune cell gene expression > METHOD DETAILS > Genotyping, imputation and determination of Eqtl
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“The Institutional Review Board (IRB) of the La Jolla Institute for Allergy and Immunology (LJI; IRB protocol no. SGE-121–0714) approved the study.”
§ Impact of genetic polymorphisms on human immune cell gene expression > STAR METHODS > EXPERIMENTAL MODEL AND SUBJECT DETAILS
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“To diminish the effects of possible outliers, the regularized log transformation of the DESeq2 package (Love et al., 2014) was applied and the homoscedastic data were then quantile-normalized.”
- Also stated in: § Genotyping, imputation and determination of Eqtl · plain
“The regularized log transformation of the DESeq2 package (Love et al., 2014) was applied and the homoscedastic data were then quantile-normalized to diminish the effects of possible outliers.”
§ Impact of genetic polymorphisms on human immune cell gene expression > METHOD DETAILS > RNA-Seq analysis
What the sources say
- Not checked yetcites Love et al., 2014 source
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- Also stated in: § Genotyping, imputation and determination of Eqtl · plain
In the paper
“The data quality was assessed using the snpQC package (Gondro et al., 2014) for R v3.2.3 (http://www.R-project.org) and low-quality SNPs were removed for downstream analysis: SNPs failing in more than 5% of the samples and SNPs with Illumina’s GC scores less than 0.2 in more than 10% of the samples were filtered out.”
§ Impact of genetic polymorphisms on human immune cell gene expression > METHOD DETAILS > Genotyping, imputation and determination of Eqtl
What the sources say
- Not checked yetcites Gondro et al., 2014 source
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“To define the collection of SNPs from GWAS studies, we downloaded the dataset of the Phenotype-Genotype Integrator (PheGenI) (Ramos et al., 2014), which merges NHGRI GWAS Catalog data with several databases of the National Center for Biotechnology Information (NCBI), including Gene, dbGaP, OMIM, eQTL and dbSNP (September 2017; https://www.ncbi.nlm.nih.gov/gap/phegeni).”
- Also stated in: § GWAS SNP database construction and analysis · plain
“the resulting catalog of SNP-trait associations included 14,321 significant associations (lead GWAS SNPs; defined as P < 5 × 10−8) and 846 distinct phenotypes/traits.”
§ Impact of genetic polymorphisms on human immune cell gene expression > METHOD DETAILS > GWAS SNP database construction and analysis
What the sources say
- Not checked yetcites Ramos et al., 2014 source
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- Also stated in: § GWAS SNP database construction and analysis · plain
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In the paper
“Transcripts with mean TPM < 1.0 across all samples of a given cell type were excluded from the analysis.”
§ Impact of genetic polymorphisms on human immune cell gene expression > METHOD DETAILS > RNA-Seq analysis
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In the paper
“Thus, the products of autoimmune risk-eGenes PDE4A and PTGIR are likely to modulate cAMP-mediated immunoregulation and influence disease pathogenesis.”
§ Impact of genetic polymorphisms on human immune cell gene expression > RESULTS > Cell types most susceptible to GWAS SNPs
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In the paper
“Notably, the eQTL analysis did not cover the pseudoautosomal regions of chr X or chr Y, thereby slightly lowering the annotated transcript count from 57,820 to 57,215 (605 missing genes).”
§ Impact of genetic polymorphisms on human immune cell gene expression > METHOD DETAILS > Genotyping, imputation and determination of Eqtl
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“SNPs missing for more than 5% of the samples or with a minimum allele frequency (MAF) lower than 5% were removed from the analysis.”
§ Impact of genetic polymorphisms on human immune cell gene expression > METHOD DETAILS > Genotyping, imputation and determination of Eqtl
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In the paper
“Briefly, transcripts with low expression values – the bottom one-third of all transcripts for at least 80% of the samples – were filtered out.”
§ Impact of genetic polymorphisms on human immune cell gene expression > METHOD DETAILS > Genotyping, imputation and determination of Eqtl
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“The cohort consisted of 54 males and 37 female subjects with a median age of 27 years (range 18 to 61 years).”
§ Impact of genetic polymorphisms on human immune cell gene expression > STAR METHODS > EXPERIMENTAL MODEL AND SUBJECT DETAILS
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In the paper
“Samples failing any quality control step (RNA quality and quantity, PCR amplification, library fragment size) were eliminated from further downstream steps.”
- Also stated in: § QUANTIFICATION AND STATISTICAL ANALYSIS · plain
“Any samples failing established quality controls (QC) were eliminated from downstream analysis; assay details and QCs on RNA-Seq samples included are provided in Table S1B (1,544 samples in total).”
§ Impact of genetic polymorphisms on human immune cell gene expression > METHOD DETAILS > RNA sequencing
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- Also stated in: § QUANTIFICATION AND STATISTICAL ANALYSIS · plain
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“Sequencing library preparation steps were performed in a 96-well format, utilizing the automated platform Biomek FXP (Beckman Coulter), to reduce assay-to-assay variability.”
§ Impact of genetic polymorphisms on human immune cell gene expression > METHOD DETAILS > RNA sequencing
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In the paper
“A total of 91 healthy volunteers were recruited in the San Diego area, who provided leukapheresis samples at the San Diego Blood Bank (SDBB) after written informed consent.”
§ Impact of genetic polymorphisms on human immune cell gene expression > STAR METHODS > EXPERIMENTAL MODEL AND SUBJECT DETAILS
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In the paper
“All study subjects self-reported ethnicity and race details, and were tested negative for hepatitis B, hepatitis C and human immunodeficiency virus (HIV).”
§ Impact of genetic polymorphisms on human immune cell gene expression > STAR METHODS > EXPERIMENTAL MODEL AND SUBJECT DETAILS
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The paper relies on this without support.
Overlap between GWAS SNPs and eQTLs indicates that the disease risk variant modulates expression of the linked gene in the susceptible cell type.
“To shed light on the disease relevance of the eQTLs identified in human immune cells, we next focused on the subset of eGenes linked to eQTLs that directly overlapped haploblocks of significant GWAS SNPs”
The paper relies on this without support.
Knockdown of a gene in immune cells reflects the functional consequence of genetically reduced expression of that gene.
“As a first step in that process, we knocked down these genes in human NK cells and tested their impact on NK cell activity”
The paper relies on this without support.
The 13 immune cell types and activation conditions studied are representative enough of human immune cells to generalize eQTL and sex-biased expression findings.
“The cell types presented here comprise over 60% of all circulating mononuclear cells”
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In the citing paper
“Within these correlated gene sets were genes known to be enriched or functionally important in the memory T cell subset driven by the TF in question, such as IL4R for GATA3 and CTLA4 for both Helios and FOXP324.”
From NEAT-seq: simultaneous profiling of intra-nuclear proteins, chromatin accessibility and gene expression in single cells (2022) · cites this paper as [24]
Citations
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Sex differences in immune responses
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Integrative analysis of 111 reference human epigenomes
Roadmap Epigenomics Consortium, Kundaje, A., Meuleman, W., Ernst, J., Bilenky, M., Yen, A., Heravi-Moussavi, A., Kheradpour, P., Zhang, Z., Wang, J., et al. (2015) · Nature · doi:10.1038/nature14248 · Cited by 6,368 · cited 1×
“…large-scale epigenomic initiatives analyzing multiple human cell types have attributed cis-regulatory function to nearly 20% of the non-coding DNA sequences, and showed that disease risk variants are enriched in cell-specific cis-regulatory DNA sequences (Roadmap Epigenomics et al., 2015).”
PD-1 and Its Ligands in Tolerance and Immunity
Keir, M. E., Butte, M. J., Freeman, G. J., & Sharpe, A. H. (2008) · Annual Review of Immunology · doi:10.1146/annurev.immunol.26.021607.090331 · Cited by 5,378
The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans
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Epigenetics and its role in development and regulation of allergy — a systematic review
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Genetic determinants of monocyte splicing are enriched for disease susceptibility loci
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CHASERR-CHD2 dynamics in T cell quiescence and its modulation by cyclosporine
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Single-nucleus RNA sequencing illuminates a functional analog of lymphoid organ in crustacean
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Design and interpretation of eQTL-GWAS colocalisation studies: Lessons from a large-scale evaluation
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scTWAS: a powerful statistical framework for single-cell transcriptome-wide association studies
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Bujnis, M. N., Sterenborg, R. B. T. M., Li, Y., Åsvold, B. O., Brčić, L., Perica, V. B., Babbar, A., Denny, J. C., Fritsche, L. G., Kanai, M., et al. (2026) · Nature Genetics · doi:10.1038/s41588-026-02704-w · Cited by 2
Population-scale immune multiome atlas reveals regulatory disease mechanisms
Kanai, M., Delorey, T. M., Jarno, H., Rodosthenous, R. S., Juvila, J., Murphy, S., Teixeira-Soldano, I., Hwang, H. S., Karjalainen, J. M., Halonen, J., et al. (2026) · Nature · doi:10.1038/s41586-026-11078-2 · Cited by 16
Discovery of Novel Host Molecular Factors Underlying HBV/HCV Infection
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Association of eNOS gene 4a/4b VNTR and T786C polymorphism with Crimean-Congo hemorrhagic fever
Coşkun, U. S. Ş., Yığıt, S., Özmen, Z. C., Deveci, K., Tekcan, A., Barut, H. Ş., & Dağcıoğlu, Y. (2022) · Nucleosides Nucleotides & Nucleic Acids · doi:10.1080/15257770.2022.2162542 · Cited by 2
Schroth, D., Garg, R., Bocova, X., Hansmann, J., Haass, M., Yan, A. T.-K., Fernando, C., Chacko, B. R., Oikonomou, A., White, J. A., et al. (2023) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2023.1204232 · Cited by 2
Machine learning reveals genetic modifiers of the immune microenvironment of cancer
Riley-Gillis, B. M., Tsaih, S., King, E. A., Wollenhaupt, S., Reeb, J., Peck, A. R., Wackman, K. K., Lemke, A., Rui, H., Dezső, Z., et al. (2023) · iScience · doi:10.1016/j.isci.2023.107576 · Cited by 2
Dressman, D., Tasaki, S., Yu, L., Schneider, J. A., Bennett, D. A. A., Elyaman, W., & Vardarajan, B. N. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1337831 · Cited by 3
Post-COVID-19: Hematological Factors Changes in Patients at Three-time Intervals
Sharif-zak, M., Abbasi-Jorjandi, M., Asadikaram, G., Ghoreshi, Z., Abolhassani, M., & Rosen, C. J. (2024) · Current Medicinal Chemistry · doi:10.2174/0109298673299246240625084103 · Cited by 2
A scalable approach to topic modelling in single-cell data by approximate pseudobulk projection
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Comparative analysis of LAG3 antibodies shows differential binding patterns by flow cytometry
Graydon, C. G., Balasko, A. L., Kowatsch, M. M., & Fowke, K. R. (2024) · Journal of Immunological Methods · doi:10.1016/j.jim.2024.113757 · Cited by 2
Rodrigues, A. B. M., Passetti, F., & Guimarães, A. C. R. (2025) · Neuroinformatics · doi:10.1007/s12021-024-09713-2 · Cited by 2
Transcription-based identification of uncharacterized genes in the human immune response
Vorsteveld, E. E., Kersten, S., Kaffa, C., Simons, A., Hoen, P. A. C. ’., Netea, M. G., & Hoischen, A. (2026) · European Journal of Human Genetics · doi:10.1038/s41431-026-02117-7 · Cited by 2
Silencer variants are key drivers of gene up-regulation in Alzheimer’s disease
Huang, D., & Ovcharenko, I. (2026) · Science Advances · doi:10.1126/sciadv.adz3323 · Cited by 3
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Novel Genome-Wide Interactions Mediated via BOLL and EDNRA Polymorphisms in Intracranial Aneurysm
Hong, E. P., Youn, D. H., Kim, B. J., Lee, J. J., Nam, S., Yoo, H., Kim, H. C., Rhim, J. K., Park, J. J., & Jeon, J. P. (2022) · Journal of Korean Neurosurgical Society · doi:10.3340/jkns.2022.0026 · Cited by 2
Epigenetic fine-mapping: identification of causal mechanisms for autoimmunity
Lincoln, M. R., Axisa, P., & Hafler, D. A. (2020) · Current Opinion in Immunology · doi:10.1016/j.coi.2020.09.002 · Cited by 2
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Allele Specific Expression in Human – Genomic Makeup and Phenotypic ImplicationsPreprint
Wainer-Katsir, K., & Michal Linial, B. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/757997 · Cited by 2
Integrative transcriptomic analysis of SLE reveals IFN-driven cross-talk between immune cellsPreprint
Panwar, B., Schmiedel, B. J., Liang, S. Y., White, B. M., Rodriguez, E., KALUNIAN, K. C., McKnight, A. J., Soloff, R. S., Seumois, G., Vijayanand, P., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.04.27.065227 · Cited by 2
Self-citationCell group analysis reveals changes in upper-layer neurons associated with schizophreniaPreprint
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Kubota, N., & Suyama, M. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.22.436360 · Cited by 2
Disentangling signatures of selection before and after European colonization in Latin AmericansPreprint
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Predicting protease networks through human geneticsPreprint
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Malaria drives unique regulatory responses across multiple immune cell subsetsPreprint
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Genome-wide CRISPR activation screen identifies JADE3 as an antiviral activator of NF-kBPreprint
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Proteome-wide base editor screens to assess phosphorylation site functionality in high-throughputPreprint
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The CD4 T cell epigenetic JUNB+ state is associated with proliferation and exhaustionPreprint
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Multiomics analysis of narcolepsy T cells: global hypomethylation linked to T cell proliferationPreprint
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Cell type-specific epigenomic variation and its association with genotype in the human breastPreprint
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Genetic variants that edit risk of autoimmune diseases
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Integrating Multimorbidity into a Whole-Body Understanding of Disease Using Spatial Genomics
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The impact of sex on the immune system explored at the single-cell level
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Decoding Human T Cell Immunity with Artificial Intelligence and Single-Cell Genomics
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Decoding immune signaling landscapes: what AlphaGenome reveals
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Sex differences in innate immune cell responses: Impact on cancer immunity and response to therapy
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Tissue-resident immune cells drive genetic risk in autoimmune and lung diseases
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Brain aging and microarray analysis
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Dissecting the Immune System through Gene Regulation
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A Cell Specific Effect of MBOAT7 MAFLD-risk Variant on Immune Cells
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Metadata
- Authors
- Schmiedel, B. J., Singh, D., Madrigal, A., Valdovino-Gonzalez, A. G., White, B. M., Zapardiel-Gonzalo, J., Ha, B., Altay, G., Greenbaum, J. A., McVicker, G., Seumois, G., Rao, A., Kronenberg, M., Peters, B., & Vijayanand, P.
- Affiliations
- La Jolla Institute for Immunology (US)
- Salk Institute for Biological Studies (US)
- Corresponding author
- Vijayanand, Pandurangan
- ORCID
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2018-11
- Volume
- 175
- Issue
- 6
- Pages
- 1701-1715.e16
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC6289654
- OpenAlex
- W2901765988
- Semantic Scholar
- 0b385de9f5adec440b108379bcbf8f8f00d13552
- Cited by
- 867
- References
- 68
- Citations by year
- 2026: 87 · 2025: 124 · 2024: 148 · 2023: 114 · 2022: 137 · 2021: 133 · 2020: 77 · 2019: 43 · 2018: 2
- FWCI
- 39.71
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- ePrints Soton (University of Southampton) (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- B.P. Koirala Institute of Health Sciences (S10RR027366, S10OD016262)
- National Institutes of Health (R24 AI108564)
- William K. Bowes, Jr. Foundation
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- genetic polymorphism, immune cell gene expression, cis-eQTL, expression quantitative trait loci, single-cell transcriptomics, sex differences in gene expression, cell type-specific gene regulation, immune cell types
- MeSH terms
- Adolescent, Adult, Female, Gene Expression Regulation, Genotype, Humans, Male, Middle Aged, Sex Characteristics, Polymorphism, Single Nucleotide, Gene Expression Profiling, Quantitative Trait Loci, Genome-Wide Association Study
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-07, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-07 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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