Dissecting shared genetic architecture between obesity and multiple sclerosis

Abstract

Background: Observational studies have associated obesity with an increased risk of multiple sclerosis (MS). However, the role of genetic factors in their comorbidity remains largely unknown. Our study aimed to investigate the shared genetic architecture underlying obesity and MS.

Methods: By leveraging data from genome-wide association studies, we investigated the genetic correlation of body mass index (BMI) and MS by linkage disequilibrium score regression and genetic covariance analyser. The casualty was identified by bidirectional Mendelian randomisation. Linkage disequilibrium score regression in specifically expressed genes and multimarker analysis of GenoMic annotation was utilised to explore single-nucleotide polymorphism (SNP) enrichment at the tissue and cell-type levels. Shared risk SNPs were derived using cross-trait meta-analyses and Heritability Estimation from Summary Statistics. We explored the potential functional genes using summary-data-based Mendelian randomization (SMR). The expression profiles of the risk gene in tissues were further examined.

Findings: We found a significantly positive genetic correlation between BMI and MS, and the causal association of BMI with MS was supported (β = 0.22, P = 8.03E-05). Cross-trait analysis yielded 39 shared risk SNPs, and the risk gene GGNBP2 was consistently identified in SMR. We observed tissue-specific level SNP heritability enrichment for BMI mainly in brain tissues for MS in immune-related tissues, and cell-type-specific level SNP heritability enrichment in 12 different immune cell types in brain, spleen, lung, and whole blood. The expressions of GGNBP2 were significantly altered in the tissues of patients with obesity or MS compared to those of control subjects.

Interpretation: Our study indicates the genetic correlation and shared risk genes between obesity and MS. These findings provide insights into the potential mechanisms behind their comorbidity and the future development of therapeutics.

Funding: This work was funded by the National Natural Science Foundation of China (82171698, 82170561, 81300279, and 81741067), the Program for High-level Foreign Expert Introduction of China (G2022030047L), the Natural Science Foundation for Distinguished Young Scholars of Guangdong Province (2021B1515020003), Natural Science Foundation of Guangdong Province (2022A1515012081), the Foreign Distinguished Teacher Program of Guangdong Science and Technology Department (KD0120220129), the Climbing Programme of Introduced Talents and High-level Hospital Construction Project of Guangdong Provincial People's Hospital (DFJH201803, KJ012019099, KJ012021143, and KY012021183), and in part by VA Clinical Merit and ASGE clinical research funds (FWL).

Keywords: Body mass index; Multiple sclerosis; Obesity; Shared genetic architecture.

Fig. 1

Overview of statistical analyses performed in the study. CPASSOC: Cross Phenotype Association; GNOVA: Genetic covariance analyser; GSMR: Generalised summary-data-based Mendelian randomisation; GWAS: Genome-wide Association Study; LD: linkage disequilibrium; MAGMA: Multi-marker Analysis of GenoMic Annotation; MTAG: Multi-Trait Analysis of GWAS; MR: Mendelian Randomisation; ρ-HESS: Heritability Estimation from Summary Statistics; scRNA-seq: single-cell RNA sequencing; SMR: Summary-databased Mendelian randomisation.

Fig. 2

Local genetic correlations (r_g) between BMI and MS. (a) Average local r_g estimates for two traits in regions harbouring disease-specific risk variants, regions harbouring shared risk variants (“intersection”), and all other regions (“neither”). Local genetic correlations with estimates less than −1 or greater than 1 were forced to −1 or 1, respectively. Error bars represent the 95% confidence intervals (CIs), which were calculated using a jackknife method. (b) Density distribution of local r_g estimates for two traits in disease-specific regions (red, green), intersection regions (blue) and other (purple) regions. BMI, body mass index; MS, multiple sclerosis.

Fig. 3

Summary of bi-directional MR analyses between BMI and MS. BMI, body mass index; GSMR: Generalised summary-data-based Mendelian randomisation; IVW: inverse variance weighting; MS, multiple sclerosis. Error bars represent the 95% confidence intervals for the associated MR estimates.

Fig. 4

Tissue type-specific enrichment of SNP heritability for BMI and MS estimated using LDSC-SEG. (a) The heritability enrichment of tissues for BMI; (b) The heritability enrichment of tissues for MS. The x axis displays negative log₁₀ P-values of coefficient Z-scores for each individual test. BMI, body mass index; MS, multiple sclerosis; SNP, single nucleotide polymorphism; LDSC-SEG: linkage disequilibrium score regression applied to specifically expressed genes.