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Impact of Genetic Polymorphisms on Human Immune Cell Gene Expression

Open access

Schmiedel, Benjamin J., Singh, Divya, Madrigal, Ariel, Valdovino-Gonzalez, Alan G., White, Brandie M., Zapardiel-Gonzalo, Jose, Ha, Brendan, Altay, Gokmen, Greenbaum, Jason A., McVicker, Graham, Seumois, Grégory, Rao, Anjana, Kronenberg, Mitchell, Peters, Bjoern, Vijayanand, Pandurangan·2018·Cell

DOI 10.1016/j.cell.2018.10.022PMC PMC6289654

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Abstract

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).

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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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Crossref · 10.1016/j.cell.2018.10.022

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Claims the paper makes

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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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
Load-bearingStrength:plain· not yet calibratedCausal:correlational· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes

Show all 65 claims

Probably not a substantial claim. 17
It may state two separate claims as one. 18

A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
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
Load-bearingStrength:hedged· not yet calibratedCausal:conditional· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
It may state two separate claims as one.
- 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.
In the paper
“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
Strength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it.
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
Load-bearingStrength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
Probably not a substantial claim. It may state two separate claims as one.
In the paper
“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
Load-bearingStrength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
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
Strength:plain· not yet calibratedn = 1,544
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.

30 of 30

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”

3 of 3

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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: Schmiedel, Benjamin J.
Singh, Divya
Madrigal, Ariel
White, Brandie M.
Greenbaum, Jason A.
McVicker, Graham
Seumois, Grégory
Rao, Anjana
Kronenberg, Mitchell
Peters, Bjoern
Vijayanand, Pandurangan
Venue: Cell
Publisher: Elsevier BV
Published: 2018-11
Volume: 175
Issue: 6
Pages: 1701-1715.e16
ISSN: 0092-8674 (ISSN-L)
DOI: 10.1016/j.cell.2018.10.022
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: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (vor, 393 d)
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

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Impact of Genetic Polymorphisms on Human Immune Cell Gene Expression

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