Faculty and Staff


  • 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.

    SOFTWARE

    snpGeneSets

    The snpGeneSets is developed as an R package, and aims to facilitate genetic map annotations and pathway enrichment tests for post-GWAS analysis.

    Reference manual: snpGeneSets.pdf
    Package source: snpGeneSets_1.0.tar.gz
    Windows binaries: snpGeneSets_1.0.zip

    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.

    Download here

    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.