Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature. 2006;444(7122):1027–31.

Article
Google Scholar

John GK, Mullin GE. The gut microbiome and obesity. Curr Oncol Rep. 2016;18(7):1–7.

Article
CAS
Google Scholar

Maruvada P, Leone V, Kaplan LM, Chang EB. The human microbiome and obesity: moving beyond associations. Cell Host Microbe. 2017;22(5):589–99.

Article
CAS
Google Scholar

Hartstra AV, Bouter KE, Bäckhed F, Nieuwdorp M. Insights into the role of the microbiome in obesity and type 2 diabetes. Diabetes Care. 2015;38(1):159–65.

Article
CAS
Google Scholar

Komaroff AL. The microbiome and risk for obesity and diabetes. JAMA. 2017;317(4):355–6.

Article
Google Scholar

Vallianou NG, Stratigou T, Tsagarakis S. Microbiome and diabetes: Where are we now? Diabetes Res Clin Pract. 2018;146:111–8.

Article
CAS
Google Scholar

Saxena D, Li Y, Yang L, Pei Z, Poles M, Abrams WR, Malamud D. Human microbiome and HIV/AIDS. Curr HIV/AIDS Rep. 2012;9(1):44–51.

Article
Google Scholar

Bandera A, De Benedetto I, Bozzi G, Gori A. Altered gut microbiome composition in HIV infection: causes, effects and potential intervention. Curr Opin HIV AIDS. 2018;13(1):73–80.

Article
CAS
Google Scholar

Desai SN, Landay AL. HIV and aging: role of the microbiome. Curr Opin HIV AIDS. 2018;13(1):22–7.

Article
CAS
Google Scholar

Mallick H, Rahnavard A, McIver LJ, Ma S, Zhang Y, Nguyen LH, Tickle TL, Weingart G, Ren B, Schwager EH, et al. Multivariable association discovery in population-scale meta-omics studies. PLoS Comput Biol. 2021;17(11):1009442.

Article
Google Scholar

Zhou H, He K, Chen J, Zhang X. Linda: linear models for differential abundance analysis of microbiome compositional data. Genome Biol. 2022;23(1):1–23.

Article
Google Scholar

Kim KJ, Park J, Park S-C, Won S. Phylogenetic tree-based microbiome association test. Bioinformatics. 2020;36(4):1000–6.

CAS
Google Scholar

Huang C, Callahan BJ, Wu MC, Holloway ST, Brochu H, Lu W, Peng X, Tzeng J-Y. Phylogeny-guided microbiome otu-specific association test (post). 2021.

Hu T, Gallins P, Zhou Y-H. A zero-inflated beta-binomial model for microbiome data analysis. Stat. 2018;7(1):185.

Article
Google Scholar

Ai D, Pan H, Li X, Gao Y, Liu G, Xia LC. Identifying gut microbiota associated with colorectal cancer using a zero-inflated lognormal model. Front Microbiol. 2019;10:826.

Article
Google Scholar

Ling W, Zhao N, Plantinga AM, Launer LJ, Fodor AA, Meyer KA, Wu MC. Powerful and robust non-parametric association testing for microbiome data via a zero-inflated quantile approach (zinq). Microbiome. 2021;9(1):1–19.

Article
Google Scholar

Zhao N, Chen J, Carroll IM, Ringel-Kulka T, Epstein MP, Zhou H, Zhou JJ, Ringel Y, Li H, Wu MC. Testing in microbiome-profiling studies with Mirkat, the microbiome regression-based kernel association test. Am J Human Genet. 2015;96(5):797–807.

Article
CAS
Google Scholar

Zhan X, Tong X, Zhao N, Maity A, Wu MC, Chen J. A small-sample multivariate kernel machine test for microbiome association studies. Genet Epidemiol. 2017;41(3):210–20.

Article
Google Scholar

Zhan X, Xue L, Zheng H, Plantinga A, Wu MC, Schaid DJ, Zhao N, Chen J. A small-sample kernel association test for correlated data with application to microbiome association studies. Genet Epidemiol. 2018;42(8):772–82.

Article
Google Scholar

Koh H, Li Y, Zhan X, Chen J, Zhao N. A distance-based kernel association test based on the generalized linear mixed model for correlated microbiome studies. Front Genet. 2019;10:458.

Article
Google Scholar

Chen J, Bittinger K, Charlson ES, Hoffmann C, Lewis J, Wu GD, Collman RG, Bushman FD, Li H. Associating microbiome composition with environmental covariates using generalized unifrac distances. Bioinformatics. 2012;28(16):2106–13.

Article
CAS
Google Scholar

Zhang Y, Han SW, Cox LM, Li H. A multivariate distance-based analytic framework for microbial interdependence association test in longitudinal study. Genet Epidemiol. 2017;41(8):769–78.

Article
Google Scholar

Zhang J, Wei Z, Chen J. A distance-based approach for testing the mediation effect of the human microbiome. Bioinformatics. 2018;34(11):1875–83.

Article
CAS
Google Scholar

Pan AY. Statistical analysis of microbiome data: the challenge of sparsity. Curr Opin Endoc Metab Res. 2021;19:35–40.

Article
CAS
Google Scholar

Chen L, Reeve J, Zhang L, Huang S, Wang X, Chen J. Gmpr: a robust normalization method for zero-inflated count data with application to microbiome sequencing data. PeerJ. 2018;6:4600.

Article
Google Scholar

Weiss S, Xu ZZ, Peddada S, Amir A, Bittinger K, Gonzalez A, Lozupone C, Zaneveld JR, Vázquez-Baeza Y, Birmingham A, et al. Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome. 2017;5(1):1–18.

Article
Google Scholar

Lin H, Peddada SD. Analysis of microbial compositions: a review of normalization and differential abundance analysis. NPJ Biofilms Microbiomes. 2020;6(1):1–13.

Article
Google Scholar

Liu Y, Xie J. Cauchy combination test: a powerful test with analytic p-value calculation under arbitrary dependency structures. J Am Stat Assoc. 2020;115(529):393–402.

Article
CAS
Google Scholar

Flynn S, Reen FJ, Caparrós-Martín JA, Woods DF, Peplies J, Ranganathan SC, Stick SM, O’Gara F. Bile acid signal molecules associate temporally with respiratory inflammation and microbiome signatures in clinically stable cystic fibrosis patients. Microorganisms. 2020;8(11):1741.

Article
CAS
Google Scholar

McMurdie PJ, Holmes S. Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Comput Biol. 2014;10(4):1003531.

Article
Google Scholar

Dillies M-A, Rau A, Aubert J, Hennequet-Antier C, Jeanmougin M, Servant N, Keime C, Marot G, Castel D, Estelle J, et al. A comprehensive evaluation of normalization methods for illumina high-throughput rna sequencing data analysis. Brief Bioinform. 2013;14(6):671–83.

Article
CAS
Google Scholar

Fang Y, Tseng GC, Chang C. Heavy-tailed distribution for combining dependent \(p\) values with asymptotic robustness. arXiv:2103.12967 (2021).

Sun S, Lulla A, Sioda M, Winglee K, Wu MC, Jacobs DR Jr, Shikany JM, Lloyd-Jones DM, Launer LJ, Fodor AA, et al. Gut microbiota composition and blood pressure: the cardia study. Hypertension. 2019;73(5):998–1006.

Article
CAS
Google Scholar

Friedman GD, Cutter GR, Donahue RP, Hughes GH, Hulley SB, Jacobs DR Jr, Liu K, Savage PJ. Cardia: study design, recruitment, and some characteristics of the examined subjects. J Clin Epidemiol. 1988;41(11):1105–16.

Article
CAS
Google Scholar

Song X, Li G, Zhou Z, Wang X, Ionita-Laza I, Wei Y. Qrank: a novel quantile regression tool for eqtl discovery. Bioinformatics. 2017;33(14):2123–30.

Article
CAS
Google Scholar

Liu H, Ling W, Hua X, Moon J-Y, Williams-Nguyen JS, Zhan X, Plantinga AM, Zhao N, Zhang A, Knight R, et al. Kernel-based genetic association analysis for microbiome phenotypes identifies host genetic drivers of beta-diversity. bioRxiv (2021)

Shlyakhter I, Sabeti PC, Schaffner SF. Cosi2: an efficient simulator of exact and approximate coalescent with selection. Bioinformatics. 2014;30(23):3427–9.

Article
CAS
Google Scholar

Charlson ES, Chen J, Custers-Allen R, Bittinger K, Li H, Sinha R, Hwang J, Bushman FD, Collman RG. Disordered microbial communities in the upper respiratory tract of cigarette smokers. PLoS ONE. 2010;5(12):15216.

Article
Google Scholar

Zhan X, Zhao N, Plantinga A, Thornton TA, Conneely KN, Epstein MP, Wu MC. Powerful genetic association analysis for common or rare variants with high-dimensional structured traits. Genetics. 2017;206(4):1779–90.

Article
Google Scholar

Zhan X, Plantinga A, Zhao N, Wu MC. A fast small-sample kernel independence test for microbiome community-level association analysis. Biometrics. 2017;73(4):1453–63.

Article
Google Scholar

Hensley-McBain T, Wu MC, Manuzak JA, Cheu RK, Gustin A, Driscoll CB, Zevin AS, Miller CJ, Coronado E, Smith E, et al. Increased mucosal neutrophil survival is associated with altered microbiota in hiv infection. PLoS Pathog. 2019;15(4):1007672.

Article
Google Scholar

Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, Huttenhower C. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12(6):1–18.

Article
Google Scholar

Paulson JN, Stine OC, Bravo HC, Pop M. Differential abundance analysis for microbial marker-gene surveys. Nat Methods. 2013;10(12):1200–2.

Article
CAS
Google Scholar

Trapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li S, Morse M, Lennon NJ, Livak KJ, Mikkelsen TS, Rinn JL. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol. 2014;32(4):381–6.

Article
CAS
Google Scholar

Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for rna-seq data with deseq2. Genome Biol. 2014;15(12):1–21.

Article
Google Scholar

Hawinkel S, Mattiello F, Bijnens L, Thas O. A broken promise: microbiome differential abundance methods do not control the false discovery rate. Brief Bioinform. 2019;20(1):210–21.

Article
Google Scholar

Ferreira JA, Fuentes S. Some comments on certain statistical aspects of the study of the microbiome. Brief Bioinform. 2020;21(4):1487–94.

Article
Google Scholar

Egozcue JJ, Pawlowsky-Glahn V, Mateu-Figueras G, Barcelo-Vidal C. Isometric logratio transformations for compositional data analysis. Math Geol. 2003;35(3):279–300.

Article
Google Scholar