Positions

Assistant Professor
Pediatrics-Nutrition
Baylor College of Medicine
Houston, TX, US
Assistant Professor
Molecular and Human Genetics
Baylor College of Medicine
Assistant Professor
USDA/ARS Children's Nutrition Research Center
Baylor College of Medicine

Education

BS from Nanjing University
MS from University Of Minnesota
PhD from University of Idaho
Post-Doctoral Fellowship at University Of Chicago

Professional Interests

  • Statistical genetics/genomics
  • Statistical and computational methods for next-gen sequencing
  • Bayesian statistics
  • Markov chain Monte Carlo

Professional Statement

Dr. Guan is a statistical geneticist specialized in developing statistical and computational methods to analyze genetic and genomic data. He studied probability and stochastic processes, as well as classical population genetics, with Professors Steve Krone and Paul Joyce. For his PhD work, Dr. Guan invented the small-world Markov chain Monte Carlo algorithm and proved theorems to demonstrate its polynomial convergence. He then studied Bayesian statistics with Professor Matthew Stephens. During the extended postdoc training, Dr. Guan developed a software package BIMBAM for Bayesian imputation based association mapping, and a software package piMASS for multi-SNP association mapping via Bayesian model averaging and subset selection.

Currently, Dr. Guan and his group have three main research interests. The first is to model structured haplotype variation. The model will quantify genetic relatedness between two individuals at an arbitrary marker. One successful application is the local ancestry inference, for which Dr. Guan distributed the software package, ELAI. The other successful application is to detect haplotype-phenotype association, for which Dr. Guan distributed hapQTL.

The second objective is to design computational tools for DNA sequencing analysis. These include de novo assembly, variants calling, and quantifying sequence diversity. The third objective is Bayesian statistics. These include elucidating connections between Bayes factors and p-values, developing efficient computational methods for Bayesian model selection, and applying Bayesian graphic model to analyze transcriptome data.

Selected Publications