Publications

* indicates names are Dr. Tang's advisees

A comprehensive analysis comparing linear and generalized linear models in detecting adaptive SNPs.
*Luo L, Tang ZZ, Schoville S, Zhu J (2020).
Molecular Ecology Resources, https://doi.org/10.1111/1755-0998.13298

PSCAN: Spatial scan tests guided by protein structures improve complex disease gene discovery and signal variant detection.
Tang ZZ, Sliwoski GR, Chen G, Jin B, Bush WS, Li B, Capra JA (2020).
Genome Biology, 21(217), https://doi.org/10.1186/s13059-020-02121-0.

Multi-trait analysis of rare-variant association summary statistics using MTAR.
*Luo L, Shen J, Zhang H, Chhibber A, Mehrotra DV, Tang ZZ (2020).
Nature Communications, 11(1), 1-11.

Soy food intake associates with changes in the metabolome and reduced blood pressure in a gut microbiota dependent manner.
Shah RD, Tang ZZ, Chen G, Huang S, Ferguson JF (2020).
Nutrition, Metabolism and Cardiovascular Diseases, 10.1016/j.numecd.2020.05.001.

Expression quantitative trait locus fine mapping of the 17q12–21 asthma locus in African American children: a genetic association and gene expression study.
Ober C, ..., ECHO-CREW investigators (2020).
The Lancet Respiratory Medicine, 8(5), 482-492.

Robust and powerful differential composition tests for clustered microbiome data.
Tang ZZ and Chen G(2019).
Statistics in Biosciences, 1-17.

High dietary salt-induced dendritic cell activation underlies microbial dysbiosis-associated hypertension.
Ferguson JF, Aden LA, Barbaro NR, Van Beusecum JP, Xiao L, Simons AJ, Warden C, Pasic L, Himmel LE, Washington MK, Revetta FL, Zhao S, Kumaresan S, Scholz MB, Tang ZZ, Chen G, Reilly MP, Kirabo A (2019).
JCI Insight, doi: 10.1172/jci.insight.126241.

Multi-omic analysis of the microbiome and metabolome in healthy subjects reveals microbiome-dependent relationships between diet and metabolites.
Tang ZZ, Chen G, *Hong Q, Huang S, Smith HM, Shah RD, Scholz MB, Ferguson JF (2019).
Frontiers in Genetics, 10:454.

Close social relationships correlate with human gut microbiome composition.
Dill-Mcfarland K, Tang ZZ, ..., Rey F, and Herd P (2019).
Scientific Reports, 9(1):703.

Cost-effectiveness analysis of magnetic resonance imaging-conditional pacemaker implantation: insights from a multicenter study and implications in the current era.
Mar PL, Chen G, Gandhi G, Tang ZZ, ..., Granato JE, and Gopinathannair R (2018).
Heart Rhythm, doi: 10.1016/j.hrthm.2018.05.024.

Zero-inflated generalized Dirichlet multinomial regression model for microbiome compositional data analysis.
Tang ZZ and Chen G (2018).
Biostatistics, kxy025.

PreMeta: a tool to facilitate meta-analysis of rare-variant associations.
Tang ZZ, Bunn P, Tao R, Liu Z, Lin DY (2017).
BMC Genomics, DOI: 10.1186/s12864-017-3573-1.

A general framework for association analysis of microbial communities on a taxonomic tree.
Tang ZZ, Chen G, Alekseyenko AV, Li H (2017).
Bioinformatics, 33, 1278-1285.

PERMANOVA-S: Association test for microbial community composition that accommodates confounders and multiple distances.
Tang ZZ, Chen G, Alekseyenko AV (2016).
Bioinformatics, 32, 2618-2625.

Meta-analysis for discovering rare-variant associations: statistical methods and software programs.
Tang ZZ, Lin DY (2015).
American Journal of Human Genetics, 97, 35-53.

Inactivating mutations in NPC1L1 and protection from coronary heart disease.
Stitziel NO, Won HH, Morrison AC, Peloso GM, Do R, ..., Tang ZZ, ..., Kathiresan S (2014).
New England Journal of Medicine, 371(22):2072-2082.

Meta-analysis of sequencing studies with heterogeneous genetic associations.
Tang ZZ, Lin DY (2014).
Genetic Epidemiology, 38(5):389-401.

Rare loss-of-function mutations in the APOC3 gene, plasma triglycerides, and risk for coronary heart disease.
Crosby J, Peloso GM, Auer PL, Crosslin D, Stitziel NO, Lange LA, Lu K, Tang ZZ, ..., Reiner AP, Boerwinkle E, Kathiresan S (2014).
New England Journal of Medicine, 235(2):e30-e31.

Whole-exome sequencing identifies rare and low-frequency coding variants associated with LDL cholesterol.
Lange LA, Hu Y, Zhang H, Xue C, Schmidt EM, Tang ZZ, ..., Willer CJ (2014).
American Journal of Human Genetics, 94, 233-245.

MASS: meta-analysis of score statistics for sequencing studies.
Tang ZZ, Lin DY (2013).
Bioinformatics, 29, 1803-1805.

Quantitative trait analysis in sequencing studies under trait-dependent sampling
Lin DY, Zeng D, Tang ZZ (2013).
Proceedings of the National Academy of Sciences of the United States of America, 110, 12247-12252

An abundance of rare functional variants in 202 drug target genes sequenced in 14,002 people.
Nelson MR, Wegmann D, Ehm MG, Kessner D, Jean PS, Verzilli C, Shen J, Tang Z, ..., Novembre J, Mooser V (2012).
Science, 337, 100-104.

Carotid arterial wall characteristics are associated with incident ischemic stroke but not coronary heart disease in the Atherosclerosis Risk in Communities (ARIC) study.
Yang EY, Chambless L, Sharrett AR, Virani SS, Liu X, Tang Z, Boerwinkle E, Ballantyne CM, Nambi V (2012).
Stroke, 43, 103-108.

A general framework for detecting disease associations with rare variants in sequencing studies.
Lin DY, Tang ZZ (2011).
American Journal of Human Genetics, 89, 354-367.

Clinical implications of JUPITER (Justification for the Use of statins in Prevention: an Intervention Trial Evaluating Rosuvastatin) in a U.S. population insights from the ARIC (Atherosclerosis Risk in Communities) study.
Yang EY, Nambi V, Tang Z, Virani SS, Boerwinkle E, Hoogeveen RC, Astor BC, Mosley TH, Coresh J, Chambless L, Ballantyne CM (2009).
Journal of the American College of Cardiology, 54, 2388-2395.

Integrated study of copy number states and genotype calls using high-density SNP arrays.
Sun W, Wright FA, Tang Z, Nordgard SH, Van Loo P, Yu T, Kristensen VN, Perou CM (2009).
Nucleic Acids Research, 37, 5365-5377