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Emory Rollins School of Public Health
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Yijuan  Hu

Professor

Faculty, Biostatistics and Bioinformatics

Professor of Biostatistics

My research focuses on the development of statistical methods and software programs for analyzing highthroughput microbiome data and genetic data in human epidemiological and clinical studies.

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Contact Information

1518 Clifton Rd. NE, 3rd floor, room 342 ,

Atlanta , GA 30322

mailstop: 1518-002-3AA

Phone: (404) 712-4466

Fax: (404)727-1370

Email: yijuan.hu@emory.edu

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Areas of Interest

  • Bioinformatics
  • Genetic Association Studies
  • Microbiome Research

Education

  • PhD 2011, The University of North Carolina at Chapel Hill
  • BS 2005, Peking University, China

Courses Taught

  • BIOS 516: Intr.Lrge Scale Biomed Data An
  • BIOS 709: Generalized Linear Models

Publications

  • , , A New Approach to Testing Mediation of the Microbiome at Both the Community and Individual Taxon Levels, Bioinformatics, 38, 3173-3180
  • , , A rarefaction-without-resampling extension of PERMANOVA for testing presence-absence associations in the microbiome., Bioinformatics, , doi.org/10.1093/bioinformatics/btac399
  • , , Extension of PERMANOVA to Testing the Mediation Effect of the Microbiome, Genes, 13, 940
  • , , Integrative analysis of relative abundance data and presence-absence data of the microbiome using the LDM, Bioinformatics, 38, 2915-2917
  • , , LOCOM: A logistic regression model for testing differential abundance in compositional microbiome data with false discovery rate control, Proceedings of the National Academy of Sciences, 119, e2122788119
  • , , A Rarefaction-Based Extension of the LDM for Testing Presence-Absence Associations in the Microbiome, Bioinformatics, , https://doi.org/10.1093/bioinformatics/btab012
  • , , Analyzing matched sets of microbiome data using the LDM and PERMANOVA, Microbiome, 9, https://doi.org/10.1186/s40168-021-01034-9
  • , , A Bottom-up Approach to Testing Hypotheses That Have a Branching Tree Dependence Structure, with Error Rate Control, Journal of American Statistical Association (T&M), , DOI: 10.1080/01621459.2020.1799811
  • , , Testing hypotheses about microbiome using the linear decomposition model (LDM), Bioinformatics, 36, 4106-4115
  • , , PhredEM: a Phred-score-informed genotype- calling approach for next-generation sequencing studies, Genetic Epidemiology, 41, 375-387
  • , , Robust inference of population structure from next-generation sequencing data with systematic differences in sequencing, Bioinformatics, 34, 1157-1163
  • , , The impact of selection bias on estimation of subsequent event risk, Circulation: Cardiovascular Genetics, 10, e001616
  • , , Testing rare-variant association without calling genotypes allows for systematic differences in sequencing between cases and controls, PLoS Genetics, , https://doi.org/10.1371/journal.pgen.1006040
  • , , A Likelihood-Based Framework for Association Analysis of Allele-Specic Copy Numbers, Journal of American Statistical Association, T&M, 109, 1533-1545
  • , , Integrative Analysis of Sequencing and Array Genotype Data for Discovering Disease Associations with Rare Mutations, Proceedings of National Academy of Sciences (PNAS), 112, 1019-1024
  • , , Proper use of allele-specific expression improves statistical power for cis-eQTL mapping with RNA-seq data, Journal of American Statistical Association (A&C), 110, 962-974
  • , , Meta-Analysis of Gene-Level Associations With Rare Variants Based on Single-Variant Statistics, American Journal of Human Genetics, 93, 236-248
  • , , eQTL mapping using RNA-seq data, Statistics in Biosciences, , doi:10.1007/s12561-012-9068-3
  • , , A General Framework for Studying Genetic Effects and Gene-Environment Interactions with Missing Data, Biostatistics, 11, 583-598
  • , , Analysis of Untyped SNPs: Maximum Likelihood and Imputation Methods, Genetic Epidemiology, 34, 803-815
  • , , Simple and Efficient Analysis of Disease Association with Missing Genotype Data, American Journal of Human Genetics, 82, 444-452