Genetic associations between gut microbiota and allergic rhinitis: an LDSC and MR analysis

Abstract

Background: Several studies have suggested a potential link between allergic rhinitis (AR) and gut microbiota. In response, we conducted a meta-analysis of Linkage Disequilibrium Score Regression (LDSC) and Mendelian randomization (MR) to detect their genetic associations.

Methods: Summary statistics for 211 gut microbiota taxa were gathered from the MiBioGen study, while data for AR were sourced from the Pan-UKB, the FinnGen, and the Genetic Epidemiology Research on Aging (GERA). The genetic correlation between gut microbiota and AR was assessed using LDSC. The principal estimate of causality was determined using the Inverse-Variance Weighted (IVW) method. To assess the robustness of these findings, sensitivity analyses were conducted employing methods such as the weighted median, MR-Egger, and MR-PRESSO. The summary effect estimates of LDSC, forward MR and reverse MR were combined using meta-analysis for AR from different data resources.

Results: Our study indicated a significant genetic correlation between genus Sellimonas (Rg = -0.64, p = 3.64 × 10^-5, Adjust_P = 3.64 × 10^-5) and AR, and a suggestive genetic correlation between seven bacterial taxa and AR. Moreover, the forward MR analysis identified genus Gordonibacter, genus Coprococcus2, genus LachnospiraceaeUCG010, genus Methanobrevibacter, and family Victivallaceae as being suggestively associated with an increased risk of AR. The reverse MR analysis indicated that AR was suggestively linked to an increased risk for genus Coprococcus2 and genus RuminococcaceaeUCG011.

Conclusion: Our findings indicate a causal relationship between specific gut microbiomes and AR. This enhances our understanding of the gut microbiota's contribution to the pathophysiology of AR and lays the groundwork for innovative approaches and theoretical models for future prevention and treatment strategies in this patient population.

Keywords: Mendelian randomization; allergic rhinitis; gut microbiota; linkage disequilibrium score regression; meta-analysis.

Figure 1

Three assumptions of MR analysis and overview of the study design. MR, Mendelian randomization; GERA, genetic epidemiology research on aging; LDSC, linkage disequilibrium score regression; MR-PRESSO, MR pleiotropy residual sum and outlier; SNPs, single nucleotide polymorphisms.

Figure 2

Circular heat map of meta-analysis of genetic correlation between gut microbiota and allergic rhinitis. Rg, estimate of genetic correlation; Rg_P, p-value for Rg.

Figure 3

Forest plot of suggestive associations in forward MR analysis. IVs, instrumental variables; CI, confident interval; Adjust_P, p-value after Benjamini–Hochberg correction; P_heterogeneity, p-value for heterogeneity in meta-analysis; P_intercept, p-value for MR-Egger intercept test; P_global, p-value for Global test.

Figure 4

Circular heat map of meta-analysis of forward MR analysis between gut microbiota and allergic rhinitis. IVW, inverse-variance weighted; ME, MR-Egger; WM, weighted median; MP, MR-PRESSO. The color variations represented the size of the p-value. The scatter plots reflect OR, with OR > 1 labeled red and OR < 1 labeled green.

Figure 5

Forest plot of suggestive associations in reverse MR analysis. IVs, instrumental variables; CI, confident interval; Adjust_P, p-value after Benjamini–Hochberg correction; P_heterogeneity, p-value for heterogeneity in meta-analysis; P_intercept, p-value for MR-Egger intercept test; P_global, p-value for Global test.

Figure 6

Circular heat map of meta-analysis of reverse MR analysis between gut microbiota and allergic rhinitis. IVW, inverse-variance weighted; ME, MR-Egger; WM, weighted median; MP, MR-PRESSO. The color variations represented the size of the p-value. The scatter plots reflect OR, with OR > 1 labeled red and OR < 1 labeled green.