¡@¡@Institute of Epidemiology and Preventive Medicine
¡@¡@College of Public Health
¡@¡@National Taiwan University
¡@¡@No.17, Xu-Zhou Road, Rm523
¡@¡@Taipei 10055, Taiwan
¡@Ph.D. (Statistics), 1994, Carnegie Mellon University
¡@M.S. (Statistics), 1989, Carnegie Mellon University
¡@B.S. (Mathematics), 1986, National Tsing Hua University
¡@Institute of Epidemiology and Preventive Medicine, Professor, 2003-present.
¡@Department of Public Health, National Taiwan University, Professor, 2006-present.
¡@College of Public Health, National Taiwan University, Associate Dean, 2011-2013.
¡@Department Head, Department of Public Health, National Taiwan University, 2011-2013.
¡@Master of Public Health (MPH) Degree Program, Director, 2011-2013.
Institute of Epidemiology and Department of Public Health, National Taiwan University,
Professor, 2003-pesent; Acting Director, Aug.-Dec. 2005;
Associate Professor, 1995-2003; Instructor, 1994-1995.
Department of Public Health, National Taiwan University,
Adjunct Professor, 2005-2006; Adjunct Associate Professor, 2002-2004.
Institute of Oral Biology, College of Medicine, National Taiwan University,
¡@¡@Adjunct associate professor, 1997-2001
Editorial and Professional Activities:
¡@¡@PLoS ONE, academic editor, 2011-present;
¡@¡@Frontiers in Statistical Genetics and Methodology, review editor, 2011-present;
¡@¡@Taiwan Journal of Public Health, associate editor, 2006-present;
¡@¡@The Open Statistics and Probability Journal, editorial board, 2009-present.
¡@¡@Bulletin (of the Institute of Mathematics, Academia Sinica);
¡@¡@Computational Statistics and Data Analysis;
¡@¡@Epidemiology and Infection;
¡@¡@Environmental and Ecological Statistics;
¡@¡@European Journal of Human Genetics;
¡@¡@Journal of Applied Statistics;
¡@¡@Journal of Biomedical Informatics;
¡@¡@Journal of Biomedical Science;
¡@¡@Journal of Biopharmaceutical Statistics;
¡@¡@Journal of Clinical Epidemiology;
¡@¡@Journal of Probability and Statistical Science;
¡@¡@Journal of the Formosan Medical Association;
¡@¡@The Open Statistics and Probability Journal;
¡@¡@Statistics in Medicine;
¡@¡@Taiwan Journal of Public Health.
Honors and Grants:
¡@Excellent Research Award (National Science Council 1996-98).
¡@Excellent Teaching Award, 2001, 2004, 2006, 2008, 2011.
¡@Grant Research Award, since 2002.
¡@National Science Council, 1995-present; Department of Health; Bureau of Health
| ||Testing familial aggregation with posterior distribution (NSC, 1999-2001)|
| ||Bayesian Inference of Correlation in Generalized Linear Mixed Model (NSC, 2001-2003)|
| ||Bayesian Inference of Variance Components Models for Genetic Data Analysis (NSC, 2003-2004)|
| ||Two-stage methods for multiple testing (NSC, 2004-2006)|
| ||Assessment of service quality in endodontic treatment (DOC, 2007)|
| ||Bayesian Approach to Multiple Testing Hypotheses in Haplotype-based Association Studies (NSC, 2006-2008)|
| ||Haplotype association studies using Bayesian analysis and machine learning approach (NSC, 2008-2011)|
| ||National survey of refraction errors for students 6-18 years lid (DOH, 2010)|
screening of genomic alterations and transcriptional modulation in
non-smoking female lung cancer in Taiwan (DOH, 2009-2010)|
Selected Education Activities:
short course on Markov Chain Monte Carlo Methods with Jack Lee at
Division of Biostatistics and Bioinformatics, National Health Research
short course on Biostatistics at Training Center, Department of Public
Health and Taipei Public Service Institute, Taipei City. |
short course on Bayesian Computation Taiwan International Graduate
Program (TIGP), Institute of Information Science, Academia Sinica
(TIGP-BP C4), 2005.|
Supervised Ph.D. Thesis:
Methods in Association Analysis: Likelihood Ratio Test with Clustered
Haplotypes and Kernel Canonical Correlation Analysis (Mei-Hsien Lee
2008), co-advise with Dr. Su-Yun Huang
Vector Machines: Classification with Coding and Regression for Gene
Selection (Pei-Chun Chen 2008), co-advise with Dr. Su-Yun Huang
- Bayesian Inference of Variance Components in Generalized Linear Mixed Models (Miao-Yu Tsai 2005)
- Multiple Hypothesis Testing in Large-scale Association Studies (Shu-Hui Wen 2004)
- Semiparametric Bayesian Analysis of Mixed Models for Clustered data (Yi-Lang Tong 2004)
marginal inference via optimal volume-corrected Laplace method and
marginal likelihood identity (Ching-Wei Chang 2002), co-advise with Dr.
¡@Bayesian Statistics, Biostatistics, Bioinformatics, Genetic Statistics
The primary focus of this research group is to develop statistical
methodology from the Bayesian perspective, particularly for problems
encountered in the field of biology and medicine. Our recent projects
include genetic association studies with genetic markers like single
nucleotide polymorphism (SNP), haplotype and marker-set, and the
development of bioinformatics tools. This group also participates in
several inter-disciplinary research activities including Bayesian
surveillance system for influenza, quality of endodontic treatment and
association with systemic diseases, and national survey of myopia
Genetic Association Study
The genetic association study has been our primary research interest.
We develop new methodology for inference on gene clustering,
gene-disease association, and gene-gene (GG) and gene-environment (GE)
interaction. For examples, we considered a Bayesian mixture model for
genome-wide association studies (GWAS). Here the proportion of
associated markers was estimated first with the Bayesian model, and
then such markers were selected based on Bayes factors (Wei et al.
2010). The free code (Bmix) to be used in R is now online for free
download. The second example was for family-based association studies
with haplotypes. To deal with the complexity in family data structure,
haplotype phase determination, and the large number of parameters, we
adopted an evolutionary concept to cluster haplotypes, developed a test
based on clustering likelihood ratio test (Lee et al. 2011), and
constructed a coding matrix to incorporate various sources of
uncertainty (Huang et al. 2011). The corresponding codes LRT-C and
BRUCM are online now. For GG and GE interaction, we utilized a Bayesian
spatial multimarker genetic random-effects model and Markov chain Monte
Carlo method to detect GG interaction and a Bayesian generalized linear
mixed-effects model to detect contextual GE interaction existing
between the individual level of genetic risks and the group level of
area environmental factors (Wang et al. 2011).
With the advancement of biotechnology, the size of genetic data has
increased dramatically, and thus raises many challenges for
statisticians. One is the curse of dimensionality. To reduce
effectively the number of parameters involved in GWAS, we considered a
two-stage procedure to decrease the number of relevant markers first
and then determined its influence with a Bayesian testing procedure
(Wei et al. 2010). The code is free for download (Bmix). For continuous
type data like expression levels, we developed a regularized least
squares support vector regression model for gene selection (Chen et al.
2009), and a free software for analysis (RLS). Currently we are working
on marker-set clustering and association test, and hope to provide a
flexible and efficient computational tool for analysis.
With our biostatistics expertise, this research team also plays an
active role in several inter-disciplinary projects. Examples include a
probabilistic surveillance of ILI syndrome with a spatio-temporal
Bayesian hierarchical model, national survey of myopia among school
children, association between the quality of endodontic treatment and
systemic diseases, biomarker identification for female non-smoking lung
cancer patients, risks of air pollutant and temperature on coronary
heart diseases, schizophrenia genetic studies, and maximum number of
life births per donor in artificial insemination. The collaboration
research has been a pleasure experience working with experts from