Evidence map›Paper›PMID 35886007›Full record

ArticleGenes2022

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

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

Abstract read
In one paragraph

Article in Genes, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

24 citing papers in PubMed.

  1. Article
  2. Review
  3. International journal of molecular sciences · 2024
    Review
  4. Article
  5. Review
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Review
  17. Review
  18. Article
  19. Review
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Xing HuaDivision of Cancer Epidemiology and Genetics, National Cancer Institute, National Institute of Health, Rockville, MD 20850, USA.
Lei SongDivision of Cancer Epidemiology and Genetics, National Cancer Institute, National Institute of Health, Rockville, MD 20850, USA.
Guoqin YuMolecular Genetics and Genomics Branch, Center for Scientific Review, National Institute of Health, Bethesda, MD 20817, USA.
Emily VogtmannDivision of Cancer Epidemiology and Genetics, National Cancer Institute, National Institute of Health, Rockville, MD 20850, USA.
James J GoedertDivision of Cancer Epidemiology and Genetics, National Cancer Institute, National Institute of Health, Rockville, MD 20850, USA.
Christian C AbnetDivision of Cancer Epidemiology and Genetics, National Cancer Institute, National Institute of Health, Rockville, MD 20850, USA.
Maria Teresa LandiDivision of Cancer Epidemiology and Genetics, National Cancer Institute, National Institute of Health, Rockville, MD 20850, USA.
Jianxin ShiDivision of Cancer Epidemiology and Genetics, National Cancer Institute, National Institute of Health, Rockville, MD 20850, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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 also helps to elucidate biological mechanisms of genetic associations, prioritize genetic variants, and improve genetic risk prediction. Since a microbiota functions as a community, it is best characterized by β diversity; that is, a pairwise distance matrix. We develop a statistical framework and a computationally efficient software package, microbiomeGWAS, for identifying host genetic variants associated with microbiome β diversity with or without interacting with an environmental factor. We show that the score statistics have positive skewness and kurtosis due to the dependent nature of the pairwise data, which makes

Indexed as

Genome-Wide Association StudyMicrobiotaHumansLungPolymorphism, Single NucleotideRNA, Ribosomal, 16SRNA, Ribosomal, 16Sgene–environment interactiongenome-wide association studyhost geneticsmicrobiomeskewness and kurtosistail probabilities

Identifiers

PMID35886007
PMCPMC9317577

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.