Immune disease variants modulate gene expression in regulatory CD4+ T cells and inform drug targets

Bossini‐Castillo, Lara; Glinos, Dafni A.; Kunowska, Natalia; Golda, Gosia; Lamikanra, Abigail; Spitzer, Michaela; Soskic, Blagoje; Cano-Gamez, Eddie; Dj, Smyth; Cattermole, Claire; Alasoo, Kaur; Mann, Alice L.; Kundu, Kousik; Soranzo, Nicole; Dunham, Ian; Roberts, David; Trynka, Gosia

doi:10.1101/654632

Cited by 12 publications

(10 citation statements)

References 91 publications

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“…Attempts to colocalise disease and eQTL signals have ranged from underwhelming [52] to positive. [53] One key difference between outcomes is the disease-specific relevance of the cell types considered, which is consistent with variable chromatin state enrichment in different Average posterior probabilities for each hypothesis under different analysis strategies when trait 1 has two causal variants, A and B, and trait 2 has just one. The left column shows the identity of causal variants for each trait and their relative effect sizes under four different models.…”

Section: Plos Geneticsmentioning

confidence: 87%

Eliciting priors and relaxing the single causal variant assumption in colocalisation analyses

2020

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Horizontal integration of summary statistics from different GWAS traits can be used to evaluate evidence for their shared genetic causality. One popular method to do this is a Bayesian method, coloc, which is attractive in requiring only GWAS summary statistics and no linkage disequilibrium estimates and is now being used routinely to perform thousands of comparisons between traits. Here we show that while most users do not adjust default software values, misspecification of prior parameters can substantially alter posterior inference. We suggest data driven methods to derive sensible prior values, and demonstrate how sensitivity analysis can be used to assess robustness of posterior inference. The flexibility of coloc comes at the expense of an unrealistic assumption of a single causal variant per trait. This assumption can be relaxed by stepwise conditioning, but this requires external software and an LD matrix aligned to study alleles. We have now implemented conditioning within coloc, and propose a new alternative method, masking, that does not require LD and approximates conditioning when causal variants are independent. Importantly, masking can be used in combination with conditioning where allelically aligned LD estimates are available for only a single trait. We have implemented these developments in a new version of coloc which we hope will enable more informed choice of priors and overcome the restriction of the single causal variant assumptions in coloc analysis.

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Section: Plos Geneticsmentioning

confidence: 87%

Eliciting priors and relaxing the single causal variant assumption in colocalisation analyses

2020

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“…The authors concluded that GWAS variants could act via more complicated mechanisms and regulate other molecular traits rather than gene expression. A study by Bossini-Castillo et al (2019) mapped QTLs for gene expression and chromatin traits (histone modifications and chromatin accessibility) in regulatory CD4+ T cells, a rare cell type that plays a central role in regulating the immune response. The authors integrated chromatin and gene expression QTLs with GWAS loci for 14 immune-mediated diseases and identified 253 colocalizations, the majority of which implicated histone acetylation (H3K27ac) QTLs (acQTL).…”

Section: Application Of Colocalization To Complex Diseasesmentioning

confidence: 99%

From GWAS to Function: Using Functional Genomics to Identify the Mechanisms Underlying Complex Diseases

2020

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Genome-wide association studies (GWAS) have successfully mapped thousands of loci associated with complex traits. These associations could reveal the molecular mechanisms altered in common complex diseases and result in the identification of novel drug targets. However, GWAS have also left a number of outstanding questions. In particular, the majority of disease-associated loci lie in non-coding regions of the genome and, even though they are thought to play a role in gene expression regulation, it is unclear which genes they regulate and in which cell types or physiological contexts this regulation occurs. This has hindered the translation of GWAS findings into clinical interventions. In this review we summarize how these challenges have been addressed over the last decade, with a particular focus on the integration of GWAS results with functional genomics datasets. Firstly, we investigate how the tissues and cell types involved in diseases can be identified using methods that test for enrichment of GWAS variants in genomic annotations. Secondly, we explore how to find the genes regulated by GWAS loci using methods that test for colocalization of GWAS signals with molecular phenotypes such as quantitative trait loci (QTLs). Finally, we highlight potential future research avenues such as integrating GWAS results with single-cell sequencing readouts, designing functionally informed polygenic risk scores (PRS), and validating disease associated genes using genetic engineering. These tools will be crucial to identify new drug targets for common complex diseases.

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“…Quality control was performed by removing samples with <95% called genotypes, as well as keeping only variants with MAF > 5%, SNP call rate > 95%, and in Hardy-Weinberg equilibrium (HWE; p-value < 0.001). Imputation of untyped variants was performed as described in Bossini-Castillo et al 49 . Briefly we used BEAGLE 4.1 50 with a reference panel consisting of the 1000 Genomes Phase 3 51 and the UK10K samples 52 .…”

Section: Genotyping Data Analysis and Imputationmentioning

confidence: 99%

Immune disease risk variants regulate gene expression dynamics during CD4+ T cell activation

Soskic

Cano-Gamez

Smyth

et al. 2021

Preprint

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During activation, T cells undergo extensive changes in gene expression which shape the properties of cells to exert their effector function. Therefore, understanding the genetic regulation of gene expression during T cell activation provides essential insights into how genetic variants influence the response to infections and immune diseases. We generated a single-cell map of expression quantitative trait loci (eQTL) across a T cell activation time-course. We profiled 655,349 CD4+ naive and memory T cells, capturing transcriptional states of unstimulated cells and three time points of cell activation in 119 healthy individuals. We identified 38 cell clusters, including stable clusters such as central and effector memory T cells and transient clusters that were only present at individual time points of activation, such as interferon-responding cells. We mapped eQTLs using a T cell activation trajectory and identified 6,407 eQTL genes, of which a third (2,265 genes) were dynamically regulated during T cell activation. We integrated this information with GWAS variants for immune-mediated diseases and observed 127 colocalizations, with significant enrichment in dynamic eQTLs. Immune disease loci colocalized with genes that are involved in the regulation of T cell activation, and genes with similar functions tended to be perturbed in the same direction by disease risk alleles. Our results emphasize the importance of mapping context-specific gene expression regulation, provide insights into the mechanisms of genetic susceptibility of immune diseases, and help prioritize new therapeutic targets.

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Immune disease variants modulate gene expression in regulatory CD4+ T cells and inform drug targets

Cited by 12 publications

References 91 publications

Eliciting priors and relaxing the single causal variant assumption in colocalisation analyses

Eliciting priors and relaxing the single causal variant assumption in colocalisation analyses

From GWAS to Function: Using Functional Genomics to Identify the Mechanisms Underlying Complex Diseases

Immune disease risk variants regulate gene expression dynamics during CD4+ T cell activation

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