medRxivGenetic & Genomic MedicinePreprint
Investigating shared genetic architecture between obesity and multiple sclerosis
Ruijie ZengiD, Rui JiangiD, Wentao HuangiD, Jiaxuan WangiD, Lijun ZhangiD, Yuying MaiD, Yanjun WuiD, Meijun Meng, Felix W. LeungiD, Qizhou LianiD, Weihong ShaiD, Hao Hui CheniD
Abstract
Background and aimsObservational 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. MethodsBased 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 ({rho}-HESS). We further explore the potential functional genes for BMI and MS using summary-data-based Mendelian randomization (SMR). ResultWe 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. ConclusionOur 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.
- organism: human
- assay: GWAS summary statistics
- research focus: GWAS / association mapping
- approach: statistical model
- approach: Bayesian inference
- Citations
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