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Single-Cell eQTL Analysis

8’
Bioinformatics Review

Single-Cell eQTL Analysis – A new multi-omics approach to unravel disease mechanisms

The advent of single-cell omics has increased our understanding of cellular diversity and regulation exponentially. Single-cell RNA-seq allows the discovery of new cell types, cell states, and characterization of the cellular diversity of most tissues across a myriad of species.

The growing knowledge of the cellular transcriptome highlights the fact that the transcriptomic view of the cells is only one layer of a complicated process. It is now time to connect the dots and understand how DNA and its regulation impact transcription, and how DNA-transcriptomic associations correlate with phenotypic traits.

One approach to unravel this association is eQTL analysis. As the name suggests, eQTL (expression quantitative trait loci) analysis looks for loci that have impact on gene expression; it quantifies the relationship between a genetic variation and gene expression (Figure 1). It is a commonly used tool to identify how mutations regulate gene expression. By unraveling the connection between mRNA levels and genetic variations it is possible to resolve the connection between genome alterations (e.g., SNPs) and phenotype (Figure 1, dotted line).

With the emergence of single-cell genomics, eQTL analysis can now be used with single-cell transcriptome data – single-cell eQTL analysis – for the identification of cell-type-specific effects of genetic variance on gene expression. It has the potential to unravel the cellular and molecular mechanisms regulating different conditions and understand the effect of risk traits identified from GWAS studies at the cell type level.


Figure 1 – eQTL analysis is the connection between genetic variations and specified traits. Correlating genetics variations (e.g., SNPs) with gene expression can unravel disease mechanisms.

eQTLs (genomic loci that explain variance of gene expression) can be divided into two categories:

  • Cis-eQTLs refers to eQTLs that are close to the regulated gene (up to 1Mb) upstream or downstream.
  • Trans-eQTLs refers to eQTLs that are far from the regulated gene (>5Mb).

In the last few years there has been an increased number of publications that combined single-cell RNA sequencing with GWAS studies to identify cell-type-specific eQTLs. These publications unraveled eQTLs that could not be identified from bulk RNA sequencing data, where the cell type specific information is averaged across all cells.

Here are some exciting recent research projects that illustrate the power of single cell eQTL analysis:

Example 1 – Brain cell-type-specific eQTLs correlated with neurological disorders1

The project combined scRNA-seq eQTL analysis from 8 brain cell-types of 192 patients in the prefrontal cortex, temporal cortex, and white matter with GWAS disease-related loci. It allowed the identification of 7607 eGenes (genes with an associated SNP) and 46% of these showed cell type specific effects, with the strongest effects being found in microglia.

Using the database Coloc2, designed for the integration of GWAS results, the authors identified disease correlated genes that can potentially be regulated by cis-eQTLs.

Some examples of cell type specific loci and eQTL colocalization are:

  • Alzheimer’s disease – most GWAS loci and eQTL colocalize in microglia. Microglia are known to play a role in Alzheimer’s pathophysiology.
  • Multiple sclerosis – 45 loci were identified and 34 of which were also found in immune cells. As MS is a disorder driven by immune response, this enrichment in infiltrating immune cells is to be expected.
  • Schizophrenia had the most loci (102) and these were enriched in excitatory neurons. It matches the genetic enrichment of excitatory neurons genes previously found in schizophrenia studies.

The colocalization of GWAS-identified loci and eQTLs for the different neurological and psychiatric disorders matches what has already been described in literature. The findings reveal substantial differences in the genetic regulation of gene expression among brain cell types and unravel mechanisms underlying how disease risk genes influence brain disorders.

This work highlights the importance of using single cell/cell type specific transcriptomic data as eQTLs could not have been found in bulk data, as it was demonstrated by the authors1.


Figure 2 – Integration of single nuclei RNAseq data with GWAS from neurological diseases. (A) Overview of the study and analysis performed. (B) Integration of GWAS with single nuclei RNAseq data unraveled cell-type specific eQTL. Figure based on Bryois, J. et al. 2022.

Example 2 – Cell type specific regulation of auto-immune disease3

The human immune system has a great variability between individuals, which results in differences in the susceptibility to autoimmune diseases. This high heterogeneity is also found at the cell type level.

Considering the high variability found in the immune system, a large cohort is needed to identify eQTLs. With this purpose the OneK1K cohort -a scRNA-seq cohort from 1.27 million PBMCs across 982 donors-was generated3.

The scRNA-seq information was combined with genotype data to allow for mapping of the genetic effects on gene expression. There were 26,597 cis-eQTLs and 990 trans-eQTLs identified across 14 cell types, and most had cell type specific effects on gene expression. Colocalization of genetic risk variants and single cell cis-eQTLs identified cell type specific mechanisms for autoimmune diseases. Overlaps between GWAS risk variants and eQTLs were found for all diseases tested. Generally, loci located in the MHC affect all cell types and the remainder loci have cell type specificity.

The variability in immune regulation among individuals is further highlighted by how genetic loci affect the expression of immune regulatory genes in a cell type specific manner. The same SNP can increase expression in one cell type and decrease expression in another.

Figure 3 – Summary of project design and main findings. (A) Project overview, scRNA-seq was produced from 982 individuals and cells were divided into 14 clusters for cell-type specific eQTL analysis. Results were validated in two other cohorts with different ancestry. (B) Correlation between eQTL and GWAS risk traits allowed for the identification of eQTL with causal effect in immune diseases. (C) Trans-eQTLs found outside the MHC region had a cell type specific effect. (D) SNPs found along B cell trajectory impacted the expression of genes known to be relevant for B cell progression. Figure based on Yazar, S. et al. 2022.

Example 3 – Cis- and trans-eQTL analysis identifies genetic loci and polygenic scores that regulate blood cells gene expression4

eQTL analysis was performed in cis and trans by the eQTLGen Consortium using genes expressed in the blood of 31,684 individuals, from which scRNAseq data from 1,102 individuals is available for a total of 2.57 million cells4.

The study revealed that 88% of the blood expressed genes have an associated eQTL and that these associations could be replicated across multiple tissues. Nonetheless, cis-eQTLs explain very little of the disease heritability.

On the other hand, 32% of the genes showed a trans-eQTL. Trans-eQTLs effects are less likely to be dampened by compensatory post-transcriptional buffering or removed from the population by negative selection. Moreover, trans-eQTLs function through many mechanisms, but mainly through the regulation of transcription factors, which can regulate the expression of multiple genes. The expression in 13% of genes was associated with polygenic scores, further highlighting the importance of trans-eQTLs in the regulation of phenotypic traits.

The authors suggest that the combination of many trans-eQTLs is likely to explain most of the trait’s heritability. They also highlight the importance of single-cell data to identify the drivers of these traits “scRNA-seq datasets are less affected by cell-type composition and serve as the best current source for replicating, prioritizing and annotating trans-eQTLs”4.

Figure 4 – Study Overview. The eQTLGen Consortium allowed the identification of both cis and trans-eQTLs as well as SNPs with polygenic effect that could be correlated with disease traits. Figure based on Võsa, U et al. 2021.

These and more examples of single-cell eQTL research can be found here:

  1. Bryois, J. et al. Cell type specific cis-eQTLs in eight human brain cell types identify novel risk genes for psychiatric and neurological disorders. Nature Neuroscience, 2022. DOI: https://doi.org/10.1038/s41593-022-01128-z
  2. Giambartolomei, C. et al. Bayesian test for colocalization between pairs of genetic association studies using summary statistics. PLoS Genet., 2014. DOI: https://doi.org/10.1371/journal.pgen.1004383
  3. Yazar, S. et al. Single-cell eQTL mapping identifies cell type-specific genetic control of autoimmune disease. Science, 2022. DOI: 10.1126/science.abf3041
  4. Võsa, U et al. Large-scale cis- and trans- eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat Genetics, 2021. DOI: https://doi.org/10.1038/s41588-021-00913-z
  5. Zhang, M.J. et al. Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data. Nat Genetics, 2022. DOI: 10.1038/s41588-021-00913-z.
  6. Jerber, J. et al. Population-scale single-cell RNA-seq profiling across dopaminergic neuron differentiation. Nat Genetics, 2021. DOI: https://doi.org/10.1038/s41588-021-00801-6
  7. Qiu, C. et al.Renal compartment–specific genetic variation analyses identify new pathways in chronic kidney disease. Nat Med, 2018. DOI: https://doi.org/10.1038/s41591-018-0194-4
  8. Nathan, A. et al. Single-cell eQTL models reveal dynamic T cell state dependence of disease loci. Nature, DOI: https://doi.org/10.1038/s41586-022-04713-1

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