Zeny Feng

Zeny Feng
Associate Professor, Graduate Coordinator
Department of Mathematics and Statistics
Phone number: 
(519) 824-4120 ext. 53294
MACN 540
Available positions for grads/undergrads/postdoctoral fellows: 
Enquire by email

Education and Employment Background

Dr. Zeny Feng received her PhD in Statistics from the University of Waterloo in 2005. From 2004-2006, Feng worked as a Postdoctoral Associate at Yale University. She joined the Department of Mathematics and Statistics at the University of Guelph in 2006 where she is now an Associate Professor in Statistics and the Graduate Coordinator for the Department of Mathematics and Statistics.

Research Themes

Feng’s research centers around developing statistical and computational tools for analyzing diverse types of data and addressing issues arising from genetic and biological studies. She is also interested in modelling the spread of infectious disease, with emphasis on methodology development for improving the model fitting, efficiency, and addressing the issue of missing information in data. Key research themes include:

Statistical Genetics. Genome-wide association studies (GWAS) are often performed for identifying SNPs or genome regions that are responsible for or associated with the trait(s) or interest. However, extracting useful information from a massive amount of data can be very challenging. Feng aims to find genes responsible for complex traits using the genome-wide coverage of single nucleotide polymorphisms (SNPs). From thousands to millions of SNPs, her group screens out the noises and obtains a small subset that are significantly associated with the traits. She develops different methods for such analysis when the data are collected using different study designs (e.g., longitudinal, cross-sectional, single trait, multiple traits, family-based, and population-based) and from different target populations (e.g., human and animals). Specific projects include:

  • Genetic association with longitudinal phenotypes when family data is analyzed.
  • Gene-environmental interaction and time-varying gene
  • Missing data in longitudinal studies
  • Haplotype based association analysis
  • Genotype imputation from a small panel to a large panel
  • Low-density panel design of proven pig selection for breeding in Canadian swine industry
  • Ultra low-density panel design for within litter piglet selection 

Bioinformatics. Feng is interested in using microbiome sequencing data, to study human microbiome composition and their association with human physiology. Specific projects include:

  • Clustering microbiome data via Finite-mixture of Dirichlet-multinomial regression models
  • Finite-mixture of Dirichlet-multinomial (DM) regression models for longitudinal microbiome data
  • Variable selection problems in clustering microbiome data using finite mixture of DM regression models
  • Finite mixtures of DM regression models for unsupervised and semi-supervised clustering


  • CIHR grant Co-investigator, 2016-2019
  • NSERC Discovery Grant, 2013-2017
  • Research Committee member, Statistical Society of Canada, 2016-2019
  • NSERC Evaluation Group, Mathematics and Statistics, 2014-2017
  • Guelph Representatitve for the Statistical Society of Canada, 2014-2017

Media Coverage