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Powerful and robust inference of complex phenotypes' causal genes with dependent expression quantitative loci by a median-based Mendelian randomization.

ABSTRACT: Isolating the causal genes from numerous genetic association signals in genome-wide association studies (GWASs) of complex phenotypes remains an open and challenging question. In the present study, we proposed a statistical approach, the effective-median-based Mendelian randomization (MR) framework, for inferring the causal genes of complex phenotypes with the GWAS summary statistics (named EMIC). The effective-median method solved the high false-positive issue in the existing MR methods due to either correlation among instrumental variables or noises in approximated linkage disequilibrium (LD). EMIC can further perform a pleiotropy fine-mapping analysis to remove possible false-positive estimates. With the usage of multiple cis-expression quantitative trait loci (eQTLs), EMIC was also more powerful than the alternative methods for the causal gene inference in the simulated datasets. Furthermore, EMIC rediscovered many known causal genes of complex phenotypes (schizophrenia, bipolar disorder, and total cholesterol) and reported many new and promising candidate causal genes. In sum, this study provided an efficient solution to discriminate the candidate causal genes from vast amounts of GWAS signals with eQTLs. EMIC has been implemented in our integrative software platform KGGSEE.

SUBMITTER: Jiang L

PROVIDER: S-EPMC9118119 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

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Powerful and robust inference of complex phenotypes' causal genes with dependent expression quantitative loci by a median-based Mendelian randomization.

Jiang Lin L Miao Lin L Yi Guorong G Li Xiangyi X Xue Chao C Li Mulin Jun MJ Huang Hailiang H Li Miaoxin M

American journal of human genetics 20220422 5

Isolating the causal genes from numerous genetic association signals in genome-wide association studies (GWASs) of complex phenotypes remains an open and challenging question. In the present study, we proposed a statistical approach, the effective-median-based Mendelian randomization (MR) framework, for inferring the causal genes of complex phenotypes with the GWAS summary statistics (named EMIC). The effective-median method solved the high false-positive issue in the existing MR methods due to ...[more]

PMID: 35460606

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Project description:Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by persistent inflammation in synovial joints and influenced by genetic and environmental factors. The immune system, comprising various cells that either exacerbate or reduce inflammation, plays a central role in RA pathogenesis. Despite extensive research, the causal relationships between immune cell phenotypes and RA remain unclear. We employed a two-sample Mendelian randomization (MR) approach to investigate the causal associations between 731 immune cell phenotypes and RA. Genetic variants, carefully selected based on rigorous criteria, served as instrumental variables to ensure analytical validity. Data on RA were derived from the FinnGen database, whereas immune cell phenotype data were obtained from the genome-wide association studies catalog. We employed 5 MR methods, including inverse variance weighted and MR Egger, to ensure robust causal inference. We assessed for pleiotropy and heterogeneity and adjusted findings using the False Discovery Rate. After False Discovery Rate adjustment (threshold < 0.05), inverse variance weighted analysis revealed potential causal relationships between 4 immune cell types and RA: CD62L- dendritic cells %dendritic cells (P = 3.88E-05; 95% confidence interval [CI] = 1.056), CD19 on IgD+ CD38- naive cells (P = 1.75E-04; 95% CI = 0.969), CD45RA on TD CD8br (P = 5.59E-04; 95% CI = 0.919), and HLA DR on CD33- HLA DR+ (P = 8.13E-05; 95% CI = 1.422). In reverse Mendelian studies, specific immune cell phenotypes were found to be associated with RA risk and progression: the percentage of memory B cells among lymphocytes (P = 2.74E-04; 95% CI = 0.861), IgD+ CD24+ cells among lymphocytes (P = 6.93E-04; 95% CI = 0.867), CD4+ CD8dim cells among lymphocytes (P = 2.92E-04; 95% CI = 0.802), CD4+ CD8dim cells among leukocytes (P = 4.37E-04; 95% CI = 0.814), and CD24 expression on IgD+CD24+ cells (P = 1.05E-04; 95% CI = 0.857). These results identify immune cell phenotypes closely linked to RA susceptibility and progression. The findings suggest that specific immune cell phenotypes are not only influenced by RA but may also contribute to its development and progression. These results offer new insights into the immunological underpinnings of RA and highlight potential targets for therapeutic intervention. Future research should focus on validating these causal relationships in diverse populations and exploring the molecular mechanisms involved.

Dataset Information

Powerful and robust inference of complex phenotypes' causal genes with dependent expression quantitative loci by a median-based Mendelian randomization.

Publications

Powerful and robust inference of complex phenotypes' causal genes with dependent expression quantitative loci by a median-based Mendelian randomization.

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