Publication Date

10-1-2023

Journal

United European Gastroenterology Journal

DOI

10.1002/ueg2.12453

PMID

37688361

PMCID

37688361

PubMedCentral® Posted Date

9-8-2023

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes

Keywords

Humans, Genome-Wide Association Study, Gastroparesis, Genetic Predisposition to Disease, Abdominal Pain, abdominal pain, delayed gastric emptying, diabetes, enteric nervous system, gastroparesis, genetics, immune dysregulation, inflammation, motor function, PXDNL

Abstract

BACKGROUND: Gastroparesis (GP) is characterized by delayed gastric emptying in the absence of mechanical obstruction.

OBJECTIVE: Genetic predisposition may play a role; however, investigation at the genome-wide level has not been performed.

METHODS: We carried out a genome-wide association study (GWAS) meta-analysis on (i) 478 GP patients from the National Institute of Diabetes and Digestive and Kidney Diseases Gastroparesis Clinical Research Consortium (GpCRC) compared to 9931 population-based controls from the University of Michigan Health and Retirement Study; and (ii) 402 GP cases compared to 48,340 non-gastroparesis controls from the Michigan Genomics Initiative. Associations for 5,811,784 high-quality SNPs were tested on a total of 880 GP patients and 58,271 controls, using logistic mixed models adjusted for age, sex, and principal components. Gene mapping was obtained based on genomic position and expression quantitative trait loci, and a gene-set network enrichment analysis was performed. Genetic associations with clinical data were tested in GpCRC patients. Protein expression of selected candidate genes was determined in full thickness gastric biopsies from GpCRC patients and controls.

RESULTS: While no SNP associations were detected at strict significance (p ≤ 5 × 10

CONCLUSION: We report preliminary GWAS findings for GP, which highlight candidate genes and pathways related to immune and sensory-motor dysregulation. Larger studies are needed to validate and expand these findings in independent datasets.

UEG2-11-784-g002.jpg (92 kB)
Graphical Abstract

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.