Cell type-specific and disease-associated eQTL in the human lung
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ABSTRACT:
PROVIDER: PRJNA1062913 | ENA |
REPOSITORIES: ENA
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Project description:Understanding how the genetic control of gene expression varies between cell types and contexts is key for our understanding of complex traits including disease. To this end, we leveraged single cell RNA sequencing (scRNA-seq) to characterize the genetic architecture of gene regulation in an organ with one of the most cellularly diverse organs, the human lung. We profile these effects across two conditions, tissue samples from healthy controls and patients with pulmonary fibrosis (PF), a chronic, progressive condition characterized by the scarring of lung tissue. In total, we have generated expression profiles of 475,047 cells from primary human lung tissue from 116 individuals, including 67 with PF and 49 unaffected donors. Employing a pseudo-bulk approach, we have mapped expression quantitative trait loci (eQTL) across 38 cell types, identifying shared and cell type-specific effects. Further, we identify disease-interaction eQTL demonstrating this class of associations are more likely to be cell-type specific and linked to key drivers of dysregulation in PF. Finally, we connect PF risk variants implicated by genome-wide association studies to their regulatory targets in disease-relevant cell types. This study represents the first use of scRNA-seq to identify cell type level eQTL in the human lung, and one of only a small number of studies to carry out these characterizations in solid tissues. These results provide valuable insights into lung biology and disease risk.
2023-03-20 | GSE227136 | GEO
Project description:Cell type-specific and disease-associated eQTL in the human lung
| PRJNA945854 | ENA
Project description:Cell type-specific and disease-associated eQTL in the human lung
| PRJNA1062914 | ENA
Project description:Background: Cases where genotype-phenotype relationships depend on environmental factors have been quantified for many complex diseases. Such genotype-environment interactions (GEI or GxE) may also affect expression Quantitative Trait Loci (eQTL) present in tissues critical for the manifestation of disease. To assess this hypothesis, we performed an analysis of eQTL-GEI resulting from an individual's smoking environment in the lung small airway epithelium (SAE). While the SAE is challenging to sample, this is the cell population that shows the first signs of smoking related stress and gene expression in the SAE appears to play a role in mediating smoking effects on lung disease. Results: We used expression microarrays to assay the SAE transcriptome for a small sample of African-American individuals and we analyzed SNPs genotyped genome-wide to identify GEI affecting eQTL. While a genome-wide trans- analysis identified few instances of GEI after a multiple test correction, an analysis of cis-genotypes identified a small but significant number of GEI affecting lung SAE gene expression. We determined that significant cases of eQTL-GEI were not driven by outliers and we were also able to find corroborative evidence for a few of these eQTL-GEI in a small, independent sample of individuals of European ancestry. Conclusion: Given that the power of GEI tests is low compared to tests of genotype association and that the total sample size of our study was small, including only 61 African American individuals in our focal population, the identification of significant GEI in our study implies that there may be considerable genotype-specific effects on eQTL due to smoking environment. We discuss individual cases of GEI of interest for lung disease, such as SDC1 and ZAK, as well as the broader implications of our results for the analysis of eQTL and for genome-wide association analysis of complex diseases.
2024-06-05 | GSE23087 | GEO
Project description:Single-cell eQTL mapping identifies cell type specific genetic control of autoimmune disease
| PRJNA807386 | ENA
Project description:The human immune system displays remarkable variation between individuals, leading to differences in susceptibility to autoimmune disease. We present single cell RNA sequence data from 1,267,768 peripheral blood mononuclear cells from 982 healthy human subjects. For 14 cell types, we identified 26,597 independent cis-expression quantitative trait loci (eQTLs) and 990 trans-eQTL, with the majority showing cell type specific effects on gene expression. We subsequently show how eQTLs have dynamic allelic effects in B cells transitioning from naïve to memory states and demonstrate how commonly segregating alleles lead to inter-individual variation in immune function. Finally, utilizing a Mendelian randomization approach, we identify the causal route by which 305 risk loci contribute to autoimmune disease at the cellular level. This work brings together genetic epidemiology with scRNA-seq to uncover drivers of inter-individual variation in the immune system.
2022-04-08 | GSE196829 | GEO
Project description:The human immune system displays remarkable variation between individuals, leading to differences in susceptibility to autoimmune disease. We present single cell RNA sequence data from 1,267,768 peripheral blood mononuclear cells from 982 healthy human subjects. For 14 cell types, we identified 26,597 independent cis-expression quantitative trait loci (eQTLs) and 990 trans-eQTL, with the majority showing cell type specific effects on gene expression. We subsequently show how eQTLs have dynamic allelic effects in B cells transitioning from naïve to memory states and demonstrate how commonly segregating alleles lead to inter-individual variation in immune function. Finally, utilizing a Mendelian randomization approach, we identify the causal route by which 305 risk loci contribute to autoimmune disease at the cellular level. This work brings together genetic epidemiology with scRNA-seq to uncover drivers of inter-individual variation in the immune system.
2022-04-08 | GSE196735 | GEO
Project description:Single-cell eQTL mapping identifies cell type specific genetic control of autoimmune disease [scRNAseq_data]
| PRJNA807049 | ENA