Dataset Information

Shared associations identify causal relationships between gene expression and immune cell phenotypes.

ABSTRACT: Genetic mapping studies have identified thousands of associations between common variants and hundreds of human traits. Translating these associations into mechanisms is complicated by two factors: they fall into gene regulatory regions; and they are rarely mapped to one causal variant. One way around these limitations is to find groups of traits that share associations, using this genetic link to infer a biological connection. Here, we assess how many trait associations in the same locus are due to the same genetic variant, and thus shared; and if these shared associations are due to causal relationships between traits. We find that only a subset of traits share associations, with many due to causal relationships rather than pleiotropy. We therefore suggest that simply observing overlapping associations at a genetic locus is insufficient to infer causality; direct evidence of shared associations is required to support mechanistic hypotheses in genetic studies of complex traits.

SUBMITTER: Gasperi C

PROVIDER: S-EPMC7933159 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Shared associations identify causal relationships between gene expression and immune cell phenotypes.

Gasperi Christiane C Chun Sung S Sunyaev Shamil R SR Cotsapas Chris C

Communications biology 20210304 1

Genetic mapping studies have identified thousands of associations between common variants and hundreds of human traits. Translating these associations into mechanisms is complicated by two factors: they fall into gene regulatory regions; and they are rarely mapped to one causal variant. One way around these limitations is to find groups of traits that share associations, using this genetic link to infer a biological connection. Here, we assess how many trait associations in the same locus are du ...[more]

PMID: 33664438

Similar Datasets

Project description:BackgroundThere may be potential associations between various pathogens, antibody immune responses, and breast cancer (BC), but the specific mechanisms and causal relationships remain unclear.MethodsFirst, multiple Mendelian randomization (MR) methods were used for univariable MR analysis to explore potential causal relationships between 34 antibody immune responses (related to 12 pathogens), 46 antibody immune responses (related to 13 pathogens), antibody responses post-COVID-19 vaccination, 731 immune cell types, and various BC subtypes (including overall BC, ER-positive, ER-negative, Luminal A, Luminal B, Luminal B HER2-negative, HER2-positive, and triple-negative BC). The primary results were then subjected to reverse MR analysis, heterogeneity testing using Cochran's Q, and horizontal pleiotropy testing. Robust findings were further used to design mediation pathways involving antibody immune responses, immune cells, and BC. After adjusting the effect estimates using multivariable MR (MVMR), a two-step mediation analysis was conducted to explore mediation pathways and mediation proportions. Finally, linkage disequilibrium score regression (LDSC) was applied to analyze the genetic correlation between phenotypes along mediation pathways, and cross-phenotype association analysis (CPASSOC) was performed to identify pleiotropic SNPs among three phenotypes along these pathways. Bayesian colocalization tests were conducted on pleiotropic SNPs using the multiple-trait-coloc (moloc).ResultsWe identified potential causal relationships between 15 antibody immune responses to 8 pathogens (Hepatitis B virus, Herpes Simplex Virus 2, Human Herpesvirus 6, Polyomavirus 2, BK polyomavirus, Cytomegalovirus, Helicobacter pylori, Chlamydia trachomatis), 250 immune cell phenotypes, and various BC subtypes. MVMR-adjusted mediation analysis revealed four potential mediation pathways. LDSC results showed no significant genetic correlation between phenotypes pairwise. CPASSOC analysis identified two potential mediation pathways with common pleiotropic SNPs (rs12121677, rs281378, rs2894250). However, none of these SNPs passed the Bayesian colocalization test by moloc. These results excluded horizontal pleiotropy, stabilizing MR analysis results.ConclusionThis study utilized MR methods to analyze potential causal relationships between various antibody immune responses, immune cell types, and BC subtypes, identifying four potential regulatory mediation pathways. The findings of this study offer potential targets and research directions for virus-related and immunotherapy-related studies, providing a certain level of theoretical support. However, limitations such as GWAS sample size constraints and unclear specific pathophysiological mechanisms need further improvement and validation in future studies.

Project description:ObjectivesPrevious research has indicated a potential association between immune factors and osteoarthritis (OA), but the causal relationship between CD25 expression on immune cells and hip OA remains enigmatic. To shed light on this relationship, this study utilized the two-sample Mendelian Randomization (MR) method.MethodsLeveraging genome-wide association studies (GWAS) data from the UK Biobank and arcOGEN, the investigation encompasses a substantial European cohort comprising 15,704 hip OA cases and 378,169 controls. Genetic insights into CD25 stem from a subgroup of 3,757 individuals with European ancestry, encompassing 77 CD25-related traits. Several MR methods were applied, and robustness was assessed through heterogeneity and sensitivity analysis.ResultsAmong the 77 traits examined, 66 shared the same single nucleotide polymorphisms (SNPs) with hip OA. Of these, 7 CD25-related traits were found to be causally associated with hip OA (adjusted P><0.05), with F-statistics ranging from 33 to 122. These traits are specifically related to CD4+CD25+ T cells, exhibiting odds ratios (OR) and 95% confidence intervals (CI) less than 1. Notably, no causal link was discerned with the CD8+CD25+ T cell subset. Within absolute count (AC) and relative count (RC) trait types, a significant causal relationship was observed solely between CD4+CD25+ T cells and hip OA, without subtype localization. A more intricate examination of CD25 expression levels within the CD4+CD25+ T cell subset revealed a correlation with the CD39+ regulatory T (Treg) subset and hip OA, particularly within the CD39+ activated Treg subset. Furthermore, a notable causal relationship emerged between CD25 expression levels in the CD45RA- not Treg subset and hip OA. However, no significant causal link was established with any subsets of B cells.ConclusionThe genetic prediction suggests that CD25, particularly within the realm of CD4+CD25+ T cells, may exert a protective influence against the development of hip OA. These findings provide a novel therapeutic approach for the prevention and treatment of hip OA.

Dataset Information

Shared associations identify causal relationships between gene expression and immune cell phenotypes.

Publications

Shared associations identify causal relationships between gene expression and immune cell phenotypes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets