Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

MicrobiomeGWAS: A Tool for Identifying Host Genetic Variants Associated with Microbiome Composition.

Genes - 9 Jul 2022

Hua Xing, Song Lei, Yu Guoqin, Vogtmann Emily, Goedert James J, Abnet Christian C, Landi Maria Teresa, Shi Jianxin

Abstract excerpt

The microbiome is the collection of all microbial genes and can be investigated by sequencing highly variable regions of 16S ribosomal RNA (rRNA) genes. Evidence suggests that environmental factors and host genetics may interact to impact human microbiome composition. Identifying host genetic variants associated with human microbiome composition not only provides clues for characterizing microbiome variation but...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Genome-Wide Association Study
Share to Lung

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Microbiota

Share to Polymorphism, Single Nucleotide

Share to RNA, Ribosomal, 16S

Source

Open in PubMed Open full text

Identifiers

PMID: 35886007
DOI: 10.3390/genes13071224
PMCID: PMC9317577

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 6 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

MicrobiomeGWAS: A Tool for Identifying Host Genetic Variants Associated with Microbiome Composition.PMID 35886007Read ↗

MicrobiomeGWAS: a tool for identifying host genetic variants associated with microbiome compositionSemantic proximity Read ↗

Identifying Host Genetic Variants Associated with Microbiome Composition by Testing Multiple Beta Diversity Matrices.Semantic proximity Read ↗

Associating microbiome composition with environmental covariates using generalized UniFrac distancesSemantic proximity Read ↗

Testing in Microbiome-Profiling Studies with MiRKAT, the Microbiome Regression-Based Kernel Association Test.Semantic proximity Read ↗

The predictive power of the microbiome exceeds that of genome-wide association studies in the discrimination of complex human diseaseSemantic proximity Read ↗

Host genetic variation impacts microbiome composition across human body sites.Semantic proximity Read ↗

Bacterial SNPs in the human gut microbiome associate with host BMI.Semantic proximity Read ↗

Kernel-based genetic association analysis for microbiome phenotypes identifies host genetic drivers of beta-diversity.Semantic proximity Read ↗

Application of the distance-based F test in an mGWAS investigating β diversity of intestinal microbiota identifies variants in SLC9A8 (NHE8) and 3 other loci.Semantic proximity Read ↗

A comprehensive assessment of demographic, environmental, and host genetic associations with gut microbiome diversity in healthy individualsSemantic proximity Read ↗

Incorporating genome-based phylogeny and functional similarity into diversity assessments helps to resolve a global collection of human gut metagenomesSemantic proximity Read ↗

Kernel-based genetic association analysis for microbiome phenotypes identifies host genetic drivers of beta-diversitySemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.