IDDF2024-ABS-0089 Investigating the shared genetic architecture between body mass index and non-alcoholic fatty liver disease

<h3>Background</h3> Non-alcoholic fatty liver disease (NAFLD) poses a growing public health concern, often intertwined with elevated body mass index (BMI). While observational studies underscore their association, the role of genetic factors in their comorbidity remains unknown. Our study aimed to elucidate the shared genetic architecture between NAFLD and BMI to shed light on their comorbidity. <h3>Methods</h3> We obtained summary statistics from the largest genome-wide association study (GWAS) of BMI and NAFLD. Our analytical framework included linkage disequilibrium score regression (LDSC) for estimating genetic correlation and Mendelian randomization to test causality. Local genetic correlation was assessed through HESS, pinpointing specific genomic regions involved. To identify the tissues most strongly associated with the shared genes, we conducted GTEx tissue enrichment analysis using S-LDSC. As a sensitivity analysis for S-LDSC, we conducted tissue-specific enrichment and gene-set enrichment analyses using MAGMA analysis. Finally, cross-trait meta-analysis was conducted to identify shared significant SNPs, followed by colocalization analysis to determine shared genetic loci, and summary-data-based Mendelian randomization (SMR) to pinpoint potential functional genes. <h3>Results</h3> The analysis revealed a significant genetic correlation between BMI and NAFLD (P=2.85E-07, Rg=0.64), supporting a genetic predisposition linking these conditions. Both SLDSC and MAGMA tissue enrichment analyses highlighted the cerebellar hemispheres and cerebellum as important sites for the expression of shared genes affecting both diseases (IDDF2024-ABS-0089 Figure 1. MR analysisgenetic correlation analysis provides insights into the genetic basis and common factors between BMI and NAFLD a tissue-specific gene expression analysis for genes IMPLIC (A,B)). Local genetic correlation identified 19 regions with significant shared genetic architecture (IDDF2024-ABS-0089 Figure 1. MR analysisgenetic correlation analysis provides insights into the genetic basis and common factors between BMI and NAFLD a tissue-specific gene expression analysis for genes IMPLIC (C)). Mendelian randomization confirmed a causal relationship, with increased BMI significantly contributing to NAFLD risk (β = 0.46, P = 1.2E-15, IDDF2024-ABS-0089 Figure 1. MR analysisgenetic correlation analysis provides insights into the genetic basis and common factors between BMI and NAFLD a tissue-specific gene expression analysis for genes IMPLIC (D)). The cross-trait analysis identified 25 significant SNPs common to both traits, with 12 loci showing high evidence of colocalization. Notably, genes such as BPTF and TM6SF2 were implicated, suggesting their potential role in the pathogenesis of both BMI and NAFLD (IDDF2024-ABS-0089 Figure 1. MR analysisgenetic correlation analysis provides insights into the genetic basis and common factors between BMI and NAFLD a tissue-specific gene expression analysis for genes IMPLIC (E)). <h3>Conclusions</h3> Our findings underscore a substantial shared genetic basis between BMI and NAFLD, emphasizing the pivotal involvement of the cerebellar hemisphere and cerebellum in their co-occurrence. Further investigation into genes, SNPs, and tissues may yield profound implications for the treatment of both BMI and NAFLD.

Authors

Institutions

Publication Details

Published
2024-08-01
DOI
https://doi.org/10.1136/gutjnl-2024-iddf.16
Primary Topic
Liver Disease Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

IDDF2024-ABS-0089 Investigating the shared genetic architecture between body mass index and non-alcoholic fatty liver disease

Jialin Meng, Feixiang Yang, T.L. Liu, Xiangyu Zhang et al.
Liver Disease Diagnosis and Treatment
article

IDDF2024-ABS-0089 Investigating the shared genetic architecture between body mass index and non-alcoholic fatty liver disease

Jialin Meng, Feixiang Yang, T.L. Liu, Xiangyu Zhang, Yinan Du, Kun Wang
article en

Abstract

<h3>Background</h3> Non-alcoholic fatty liver disease (NAFLD) poses a growing public health concern, often intertwined with elevated body mass index (BMI). While observational studies underscore their association, the role of genetic factors in their comorbidity remains unknown. Our study aimed to elucidate the shared genetic architecture between NAFLD and BMI to shed light on their comorbidity. <h3>Methods</h3> We obtained summary statistics from the largest genome-wide association study (GWAS) of BMI and NAFLD. Our analytical framework included linkage disequilibrium score regression (LDSC) for estimating genetic correlation and Mendelian randomization to test causality. Local genetic correlation was assessed through HESS, pinpointing specific genomic regions involved. To identify the tissues most strongly associated with the shared genes, we conducted GTEx tissue enrichment analysis using S-LDSC. As a sensitivity analysis for S-LDSC, we conducted tissue-specific enrichment and gene-set enrichment analyses using MAGMA analysis. Finally, cross-trait meta-analysis was conducted to identify shared significant SNPs, followed by colocalization analysis to determine shared genetic loci, and summary-data-based Mendelian randomization (SMR) to pinpoint potential functional genes. <h3>Results</h3> The analysis revealed a significant genetic correlation between BMI and NAFLD (P=2.85E-07, Rg=0.64), supporting a genetic predisposition linking these conditions. Both SLDSC and MAGMA tissue enrichment analyses highlighted the cerebellar hemispheres and cerebellum as important sites for the expression of shared genes affecting both diseases (IDDF2024-ABS-0089 Figure 1. MR analysisgenetic correlation analysis provides insights into the genetic basis and common factors between BMI and NAFLD a tissue-specific gene expression analysis for genes IMPLIC (A,B)). Local genetic correlation identified 19 regions with significant shared genetic architecture (IDDF2024-ABS-0089 Figure 1. MR analysisgenetic correlation analysis provides insights into the genetic basis and common factors between BMI and NAFLD a tissue-specific gene expression analysis for genes IMPLIC (C)). Mendelian randomization confirmed a causal relationship, with increased BMI significantly contributing to NAFLD risk (β = 0.46, P = 1.2E-15, IDDF2024-ABS-0089 Figure 1. MR analysisgenetic correlation analysis provides insights into the genetic basis and common factors between BMI and NAFLD a tissue-specific gene expression analysis for genes IMPLIC (D)). The cross-trait analysis identified 25 significant SNPs common to both traits, with 12 loci showing high evidence of colocalization. Notably, genes such as BPTF and TM6SF2 were implicated, suggesting their potential role in the pathogenesis of both BMI and NAFLD (IDDF2024-ABS-0089 Figure 1. MR analysisgenetic correlation analysis provides insights into the genetic basis and common factors between BMI and NAFLD a tissue-specific gene expression analysis for genes IMPLIC (E)). <h3>Conclusions</h3> Our findings underscore a substantial shared genetic basis between BMI and NAFLD, emphasizing the pivotal involvement of the cerebellar hemisphere and cerebellum in their co-occurrence. Further investigation into genes, SNPs, and tissues may yield profound implications for the treatment of both BMI and NAFLD.

Anhui Medical University (CN), First Affiliated Hospital of Anhui Medical University (CN)
Good health and well-being, Zero hunger
Openalex Percentile: Top 80%
Liver Disease Diagnosis and Treatment
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.