• Hao Mei, PhD

     Mei, Hao.jpg

    Associate Professor
    Office: G561
    Phone: (601) 815-8130
    E-mail: hmei@umc.edu


    Dr. Hao Mei is a statistical geneticist and genetic epidemiologist with multidisciplinary training in medicine, computer science, bioinformatics and statistics, and broad expertise in key research areas of cardiovascular disease risk factors. In his previous work, he developed several methods and software packages for detecting family-based association, high-dimensional gene-gene and gene-environment interaction, genetic pleiotropic effects, and cumulative genetic effects. Dr. Mei has performed genetic epidemiological studies in autism, obesity, and hypertension.

    His attraction to genetics reflects a desire to study the developmental origins and genetic predisposition for common complex diseases and phenotypes. Dr. Mei's previous research experience in methods development and statistical genetic analysis makes him particularly well qualified to contribute to this exciting project.

    Selected publications

    • Mei H, Li L, Liu S, Jiang F, Griswold M, Mosley T: The uniform-score gene set analysis for identifying common pathways associated with different diabetes traits. BMC genomics 2015, 16(1):336.
    • Li C, Chen W, Jiang F, Simino J, Srinivasan SR, Berenson GS, Mei H: Genetic association and gene-smoking interaction study of carotid intima-media thickness at five GWAS-indicated genes: The Bogalusa Heart Study. Gene 2015.
    • Mei H, Gu D, Hixson JE, Rice TK, Chen J, Shimmin LC, Schwander K, Kelly TN, Liu DP, Chen S et al: Genome-wide Linkage and Positional Association Study of Blood Pressure Response to Dietary Sodium Intervention: The GenSalt Study. American journal of epidemiology 2012.
    • Mei H, Chen W, Mills K, He J, Srinivasan SR, Schork N, Murray S, Berenson GS: Influences of FTO gene on onset age of adult overweight. Human genetics 2012.
    • Mei H, Chen W, Jiang F, He J, Srinivasan S, Smith EN, Schork N, Murray S, Berenson GS: Longitudinal replication studies of GWAS risk SNPs influencing body mass index over the course of childhood and adulthood. PLoS One 2012, 7(2):e31470.
    • Wang J, Mei H, Chen W, Jiang Y, Sun W, Li F, Fu Q, Jiang F: Study of eight GWAS-identified common variants for association with obesity-related indices in Chinese children at puberty. International journal of obesity 2011.
    • Mei H, Rice TK, Gu D, Hixson JE, Jaquish CE, Zhao Q, Chen JC, Cao J, Li J, Kelly TNet al: Genetic correlation of blood pressure responses to dietary sodium and potassium intervention and cold pressor test in Chinese population. Journal of human hypertension 2010.
    • Mei H, Chen W, Srinivasan SR, Jiang F, Schork N, Murray S, Smith E, So JD, Berenson GS: FTO influences on longitudinal BMI over childhood and adulthood and modulation on relationship between birth weight and longitudinal BMI. Human genetics 2010, 128(6):589-596.
    • Mei H, Chen W, Dellinger A, He J, Wang M, Yau C, Srinivasan SR, Berenson GS: Principal-component-based multivariate regression for genetic association studies of metabolic syndrome components. BMC genetics 2010, 11(1):100.
    • Mei H, Gu D, Rice TK, Hixson JE, Chen J, Jaquish CE, Zhao Q, Chen CS, Chen JC, Gu CC et al: Heritability of blood pressure responses to cold pressor test in a Chinese population. American journal of hypertension 2009, 22(10):1096-1100.
    • Mei H, Cuccaro ML, Martin ER: Multifactor dimensionality reduction-phenomics: a novel method to capture genetic heterogeneity with use of phenotypic variables. American journal of human genetics 2007, 81(6):1251-1261.



    • snpGeneSets V1.12

    The package integrates local genomic annotation databases based on NCBI dbSNP 138 and 142, Entrez Gene 105 and 106 and MSigDB V4.0, and provides genome-wide annotation for SNP, Gene and gene sets. It aims to support interpretation of genome-wide study (GWS) results and performing post-analysis. The package implements three categories of functions: 1) genomic mapping annotation for SNPs and genes, and function annotation for gene sets; 2) bidirectional mapping relationship between SNPs and genes, and between genes and gene sets; and 3) flexible gene effect measure by SNP association and function-related gene set/pathway identification by enrichment analysis. The auxiliary functions are also provided to facilitate annotation and analysis.

    • MDR-Phenomics

      The software is written in C++ and it aims to detect gene-gene interaction in the pedigree and population data.
      Both source codes and compiled program can be downloaded here
      Reference: Mei H, Cuccaro ML, Martin ER. Multifactor dimensionality reduction-phenomics: a novel method to capture genetic heterogeneity with use of phenotypic variables. Am J Hum Genet. 2007 Dec; 81(6):1251-61

    • EMDR

      The software is written in C++ and it aims to detect gene-gene interaction for population-based case control study. The software implemented different cross-validation and statistics for fast computing permutation p-value of multi-loci interactions for balanced or un-balanced case-control data.

      Version 1.83
      Program (UNIX, Linux and Windows): EMDR1.83.zip
      C++ Source Code: EMDR1.83_C++.zip

      Reference:  Mei, H., Ma, D., Ashley-Koch, A., and Martin, E.R. (2005). Extension of multifactor dimensionality reduction for identifying multilocus effects in the GAW14 simulated data. BMC genetics 6 Suppl 1, S145.