AUTHOR=Sharma Arunabh , Junge Olaf , Szymczak Silke , Rühlemann Malte Christoph , Enderle Janna , Schreiber Stefan , Laudes Matthias , Franke Andre , Lieb Wolfgang , Krawczak Michael , Dempfle Astrid 

TITLE=Network-based quantitative trait linkage analysis of microbiome composition in inflammatory bowel disease families

JOURNAL=Frontiers in Genetics

VOLUME=14

YEAR=2023

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2023.1048312

DOI=10.3389/fgene.2023.1048312

ISSN=1664-8021

ABSTRACT=<p><bold>Introduction:</bold> Inflammatory bowel disease (IBD) is characterized by a dysbiosis of the gut microbiome that results from the interaction of the constituting taxa with one another, and with the host. At the same time, host genetic variation is associated with both IBD risk and microbiome composition.</p><p><bold>Methods:</bold> In the present study, we defined quantitative traits (QTs) from modules identified in microbial co-occurrence networks to measure the inter-individual consistency of microbial abundance and subjected these QTs to a genome-wide quantitative trait locus (QTL) linkage analysis.</p><p><bold>Results:</bold> Four microbial network modules were consistently identified in two cohorts of healthy individuals, but three of the corresponding QTs differed significantly between IBD patients and unaffected individuals. The QTL linkage analysis was performed in a sub-sample of the Kiel IBD family cohort (IBD-KC), an ongoing study of 256 German families comprising 455 IBD patients and 575 first- and second-degree, non-affected relatives. The analysis revealed five chromosomal regions linked to one of three microbial module QTs, namely on chromosomes 3 (spanning 10.79 cM) and 11 (6.69 cM) for the first module, chr9 (0.13 cM) and chr16 (1.20 cM) for the second module, and chr13 (19.98 cM) for the third module. None of these loci have been implicated in a microbial phenotype before.</p><p><bold>Discussion:</bold> Our study illustrates the benefit of combining network and family-based linkage analysis to identify novel genetic drivers of microbiome composition in a specific disease context.</p>