Development of Microbiome Biomarkers for IgA: a Joint Modeling Approach

Abstract

Our aim in this study is to develop predictive microbiome biomarkers for intestinal IgA levels. In this article, a operational taxonomic units(OTU)-specific (family-specific) and time-specific joint model is presented as a tool to model the association between OTU (or family) and biological response (measured by IgA level) taking into account the treatment group (Control or PAT) of the subjects. The model allows detecting OTUs (families) that are associated with the IgA; for some OTUs (families), the association is driven by the treatment while for others the association reflects the correlation between the OTUs (families) and IgA.The results of the analysis reveal that: (1) the observed diversity of S24-7 family can be used as a biomarker to classify samples according to treatment group for days 6 and 12; (2) the treatment effect induces the corrlelation between the S24-7 diversity and the IgA level at day 20; (3) The OTUs that are identified to be significantly differentially abundant (FDR level of 0.05) between the two treatment groups for days 12 and 20 are all part of the S24-7 family, although most of the differentially abundant ones at day 1 are from the Lactobacillaceae family; (4) only the Lachnospiraceae family diversity at day 6, and 20 can be used as predictive biomarker for the IgA level at day 20; (5) New.ReferenceOTU513, correlated with the IgA level at day 20, since day 12, belongs to the Lachnospiraceae family and all other OTUs among the top 10 significantly associated OTUs at day 20 are from the S24-7 family; (6) the observed alpha diversity at day 6 is significantly differentially abundant and can be used as predictive biomarker for IgA level at day 20.

Modeling the positive testing rate of COVID-19 in South Africa using a semi-parametric smoother for binomial data

“…The results for these countries are presented in the Supplementary material for the paper. In addition, an online R dashboard (32) was developed to estimate and visualize the positive testing rate and the rate of change using a publicly available dataset (33) using the methodology discussed in this paper.…”