Investigating shared genetic architecture between obesity and multiple sclerosis

Abstract

Background and aims Observational studies have suggested a complex relationship between obesity and multiple sclerosis (MS). However, the role of genetic factors in the comorbidity and whether obesity exist consistent shared genetic relationships with MS, remains unclear. Our study aims to investigate the extent of shared genetic architecture underlying obesity and MS. Methods Based on genome-wide association studies (GWAS) summary statistics, we investigate the genetic correlation by the linkage disequilibrium score regression (LDSC) and genetic covariance analyzer (GNOVA). The casualty was identified by using bidirectional Mendelian randomization. Linkage disequilibrium score regression in specifically expressed genes (LDSC-SEG) and multi-marker analysis of GenoMic annotation (MAGMA) were utilized to investigate single-nucleotide polymorphisms (SNP) enrichment in the tissue and cell-type levels. We then identified shared risk SNPs using cross-trait meta-analyses and Heritability Estimation from Summary Statistics (ρ-HESS). We further explore the potential functional genes for BMI and MS using summary-data-based Mendelian randomization (SMR). Result We found significantly positive genetic correlation and 18 novel shared genetic SNPs were identified in cross-trait meta-analyses. We found the causality of BMI on MS using Mendelian randomization, but slight inconsistent evidence for the causality of MS on BMI. We observed tissue-specific level SNP heritability enrichment for BMI in 9 tissues and MS in 4 tissues, and in cell-type-specific level SNP heritability enrichment 12 consistent cell types were identified for BMI and MS in brain, spleen, lung and whole blood. Conclusion Our study identifies the genetical correlation and shared risk SNPs between BMI and MS. These findings could provide new insights into the etiology of comorbidity and have implications for future therapeutic trials.

Genetic correlation and causality between smoking and 42 neuropsychiatric and gastrointestinal diseases

“…For diseases exhibiting significant genetic correlation with smoking according to LDSC analysis, we conducted multitrait analysis of GWAS (MTAG) (16,17) to identify shared pleiotropic SNPs between smoking and the respective diseases. MTAG is an advanced statistical fine-mapping method that leverages summary statistics from multiple GWAS to enhance the power of detecting SNPs associated with traits.…”