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Additional file 1 Figure on histograms of some simulated abundances vs. true abundances.

Additional file 1. A PDF file that includes additional methods and results (Section S1-S3), supplemental figures (Fig. S1-S7) and short tables (Table S1-S2).

Additional file 7 Section S1. In this section, we conducted association analysis at the ASV level (i.e., 0% dissimilarity) using the same analysis strategies as described in the main texts for the bacterial vaginosis (BV) and the preterm birth (PTB) association studies.

Additional file 1: Supplementary material, including a detailed example of the TCRhom calculation, the proof of independence of score statistics and more simulation studies.

Additional file 6 Table S2. OTUs significantly associated with preterm birth at FDR level of 0.05. TF: TreeFDR; SO: Single-OTU test implemented by POST with c=0; DE: DESeq2; WR-P: Wilcoxon rank-sum test using proportional data; WR-R: Wilcoxon rank-sum test using CLR transformed data.

Additional file 8 Table S3. The AUC using different pseudo-counts in Simulation A. The AUCs based on pseudo-count 0.5 are very close to the AUCs based on pseudo-count 1 across different scenarios and effect sizes.

Successful prediction of clinical outcomes facilitates tailored diagnosis and treatment. The microbiome has been shown to be an important biomarker to predict host clinical outcomes. Further, the incorporation of microbial phylogeny, the evolutionary relationship among microbes, has been demonstrated to improve prediction accuracy. We propose a phylogeny-driven deep neural network (PhyNN) and develop an ensemble method, DeepEn-Phy, for host clinical outcome prediction. The method is designed to optimally extract features from phylogeny, thereby take full advantage of the information in phylogeny while harnessing the core principles of phylogeny (in contrast to taxonomy). We apply DeepEn-Phy to a real large microbiome data set to predict both categorical and continuous clinical outcomes. DeepEn-Phy demonstrates superior prediction performance to existing machine learning and deep learning approaches. Overall, DeepEn-Phy provides a new strategy for designing deep neural network architectures within the context of phylogeny-constrained microbiome data.

The Hilbert-Schmidt Independence Criterion (HSIC) is a powerful kernel-based statistic for assessing the generalized dependence between two multivariate variables. However, independence testing based on the HSIC is not directly possible for cluster-correlated data. Such a correlation pattern among the observations arises in many practical situations, e.g., family-based and longitudinal data, and requires proper accommodation. Therefore, we propose a novel HSIC-based independence test to evaluate the dependence between two multivariate variables based on cluster-correlated data. Using the previously proposed empirical HSIC as our test statistic, we derive its asymptotic distribution under the null hypothesis of independence between the two variables but in the presence of sample correlation. Based on both simulation studies and real data analysis, we show that, with clustered data, our approach effectively controls type I error and has a higher statistical power than competing methods.

Additional file 1 PDF file includes supplemental tables (Tables S1–S18).

The number of pollinosis patients in Japan has significantly increased over the past 20 years. The majority of genome-wide association studies (GWAS) on pollinosis have been conducted in subjects of European descent, with few studies in Japanese populations. The aim of our GWAS was to identify genetic loci associated with self-reported pollinosis in a Japanese population and to understand its molecular background using a combination of single nucleotide polymorphisms (SNPs) and gene- and pathway-based analyses. A total of 731 and 560 individuals who were recruited as participants of the Japan Multi- Institutional Collaborative Cohort Study participated in the discovery and replication phases, respectively. The phenotype of pollinosis was based on the information from a self-administered questionnaire. In the single-SNP analysis, four SNPs (rs11975199, rs11979076, rs11979422, and rs12669708) reached suggestive significance level (P < 1 × 10^−4) and had effects in the same direction in both phases of the study. The pathway-based analysis identified two suggestive pathways (nucleotide-binding oligomerization domain -like receptor and tumor necrosis factor signaling pathways). Both rs1143633 and rs3917368 in the interleukin-1B gene showed associations in the retrace (from pathway to gene and SNP) analysis. We performed single-SNP, gene, and pathway analysis and shed light on the molecular mechanisms underlying pollinosis in a Japanese population.