Phylogeny-guided microbiome OTU-specific association test (POST)

Microbiome

Xinxia Peng¹  Hayden Brochu¹  Jung-Ying Tzeng¹  Benjamin J. Callahan¹  Caizhi Huang¹  Shannon T. Holloway²  Wenbin Lu²  Michael C. Wu³ 

[1] Bioinformatics Research Center, North Carolina State University;Department of Statistics, North Carolina State University;Division of Public Health Sciences, Fred Hutchinson Cancer Research Center;

关键词: Association test;    Phylogenetic tree;    Kernel machine regression;   

DOI  :  10.1186/s40168-022-01266-3

来源: DOAJ

【 摘 要 】

Abstract Background The relationship between host conditions and microbiome profiles, typically characterized by operational taxonomic units (OTUs), contains important information about the microbial role in human health. Traditional association testing frameworks are challenged by the high dimensionality and sparsity of typical microbiome profiles. Phylogenetic information is often incorporated to address these challenges with the assumption that evolutionarily similar taxa tend to behave similarly. However, this assumption may not always be valid due to the complex effects of microbes, and phylogenetic information should be incorporated in a data-supervised fashion. Results In this work, we propose a local collapsing test called phylogeny-guided microbiome OTU-specific association test (POST). In POST, whether or not to borrow information and how much information to borrow from the neighboring OTUs in the phylogenetic tree are supervised by phylogenetic distance and the outcome-OTU association. POST is constructed under the kernel machine framework to accommodate complex OTU effects and extends kernel machine microbiome tests from community level to OTU level. Using simulation studies, we show that when the phylogenetic tree is informative, POST has better performance than existing OTU-level association tests. When the phylogenetic tree is not informative, POST achieves similar performance as existing methods. Finally, in real data applications on bacterial vaginosis and on preterm birth, we find that POST can identify similar or more outcome-associated OTUs that are of biological relevance compared to existing methods. Conclusions Using POST, we show that adaptively leveraging the phylogenetic information can enhance the selection performance of associated microbiome features by improving the overall true-positive and false-positive detection. We developed a user friendly R package POSTm which is freely available on CRAN ( https://CRAN.R-project.org/package=POSTm ). Video Abstract.

【 授权许可】

Unknown