How longitudinal data can contribute to our understanding of host genetic effects on the gut microbiome. (31st December 2023)

• Record Type: Journal Article
• Authors: Grieneisen, Laura; Blekhman, Ran; Archie, Elizabeth
• Is Part Of: Gut microbes. Volume 15:Isuse 1(2023)
• Publication Date: 2023-12-31
• Subjects: Gut microbiome -- host genetics -- time series -- host-microbiome interactions -- heritability -- plasticity; Gastrointestinal system -- Microbiology -- Periodicals; Microbiology -- Periodicals; Intestine, Small -- Periodicals; 616.3
• DOI: 10.1080/19490976.2023.2178797
• Languages: English
• ISSNs: 1949-0984

Abstract

ABSTRACT: A key component of microbiome research is understanding the role of host genetic influence on gut microbial composition. However, it can be difficult to link host genetics with gut microbial composition because host genetic similarity and environmental similarity are often correlated. Longitudinal microbiome data can supplement our understanding of the relative role of genetic processes in the microbiome. These data can reveal environmentally contingent host genetic effects, both in terms of controlling for environmental differences and in comparing how genetic effects differ by environment. Here, we explore four research areas where longitudinal data could lend new insights into host genetic effects on the microbiome: microbial heritability, microbial plasticity, microbial stability, and host and microbiome population genetics. We conclude with a discussion of methodological considerations for future studies. Plain Language Summary: For humans and animals, host genes play a role in shaping the gut microbiome. However, measuring these effects is difficult because host genetic and environmental similarities are often correlated. For instance, relatives often live together and share similar diets and lifestyles—forces that can also change gut microbial communities. Watching the microbiome over time, through longitudinal sampling, can help solve this problem by breaking gene-environment correlations. Here, we review this idea and several other research areas where longitudinal data will be helpful for understanding host genetic effects on the microbiome. We believe this approach will shed new light on the evolution of host–microbe relationships and can inform new microbiome therapies.