Chong Wu

Assistant Professor Department of Statistics Florida State University Research interest: Statistical genetics/genomics, machine learning, and causal inference Curriculum Vitae (Oct 2021) GitHub Google Scholar

Short Bio

I am an Assistant Professor in the Department of Statistics at Florida State University. Prior to FSU, I was a biostatistics Ph.D. student at the University of Minnesota, co-advised by Profs. Weihua Guan and Wei Pan. I received my bachelor's degree in Applied Math from Huazhong University of Science and Technology in 2013.

My research interests are in statistical genetics and genomics, statistical learning, and machine learning. I have obtained diverse training and experiences in developing statistical methods and algorithms for analyzing multiple types of genetic and genomic data, including genome-wide association study (GWAS) data, DNA methylation data, and human microbiome data. My work includes gene- and pathway-based association testing, integrative -omics analysis, new algorithms in clustering, Mendelian randomization, and polygenic risk scores. To practice and facilitate reproducible research, I have developed and currently maintain several software (most in R packages) and their online manuals. My recent work involves integrative analysis of GWAS with multiple sources of omics data.

News

• 2021-09: Our SUMMIT manuscript won a poster talk (top 10% among all posters) at ASHG 2021. Congraulations, Zichen!

• 2021-09: Our polygenic risk score in prostate cancer paper has been published in Cancer Communications.

• 2021-05: Our COVID-19 integrative analysis paper has been accepted by Genetics in Medicine.

• 2021-01: Our R03 proposal (Novel Statistical Methods for Multi-omics Data Integration in Alzheimer's Disease) has been funded by NIA.

Selected Publications [by topic]

* Corresponding author

1. He, Y., Xu, G., Wu, C., and Pan, W. (2021) Asymptotically Independent U-Statistics in High-Dimensional Testing. Annals of Statistics, 49(1), 154-181.

2. Wu, C.*, Bradley J., Li, Y., Wu, L., and Deng, HW. (2021) A gene-level methylome-wide association analysis identifies novel Alzheimer’s disease genes. Bioinformatics.

3. Wu, C.*, Xu, G., Shen, X., and Pan, W.* (2020) A regularization-based adaptive test for high-dimensional generalized linear models. Journal of Machine Learning Research, 21(128), 1-67.

4. Wu, C. (2020) Multi-trait genome-wide analyses of the brain imaging phenotypes in UK Biobank. Genetics, 215(4), 947-958.
   (Highlight in Genetics August issue; presented as a platform presentation at ASHG 2019 Annual Meeting)

5. Wu, C.* and Pan, W.* (2019) Integration of methylation QTL and enhancer-target gene maps with schizophrenia GWAS summary results identifies novel genes. Bioinformatics, 35(19), 3576-3583.

6. Wu, C., Chen, J., Kim, J., and Pan, W. (2016) An adaptive association test for microbiome data. Genome Medicine, 8(1), 1-12.

7. Wu, C.#, Kwon, S.#, Shen, X., and Pan, W. (2016). A new algorithm and theory for penalized regression-based clustering. Journal of Machine Learning Research, 17(1), 6479-6503. #, co-first author

Selected Software

We put all recent developed software into a lab github page.

• prclust: Penalized Regression-Based Clustering Methods;

• MiSPU: Microbiome Based Sum of Powered Score (MiSPU) Tests;

• GLMaSPU: Adaptive Tests on High Dimensional Parameters in Generalized Linear Models;

• IWAS: Imaging-Wide Association Study;

• TWAS: Integrating eQTL and GWAS data;

• aSPUpath2: Integrating eQTL data with GWAS summary statistics in pathway-based analysis.

Grants

• R03 AG070669, NIA
  Contact PI, (Wu and Bradley), 01-01-21 – 12-31-22
  Novel Statistical Methods for Multi-omics Data Integration in Alzheimer's Disease

• The Committee on Faculty Research Support, FSU
  PI, 05-07-20 – 06-30-20
  Novel Machine Learning Methods for Alzheimer’s Disease

• First Year Assistant Professor Grant, FSU
  PI, 05-08-19 - 08-06-19
  Novel Statistical Methods for Transcriptome-wide Association Studies

Teaching

The opportunity for teaching is one of the key reasons I love academia. For me, teaching can be described as a rewarding and fulfilling experience. I am the instructor for the following courses.

• 2020 Fall: STA5934 Statistical Genetics. Syllabus

• 2018 Fall to 2020 Spring, 2021 Spring: STAT 4321/4323 Introduction of Math Stat

• Codes for Statistical Learning and Data Mining

Selected Presentations

I enjoy sharing ideas and presenting my works. I attend JSM, ENAR, and ASHG regularly.

• Multi-trait genome-wide analyses of the brain imaging phenotypes in UK Biobank

  • Platform presentation at ASHG 2019

• Integration of methylation QTL and enhancer-target gene maps with schizophrenia GWAS summary results identifies novel genes

  • Oral presentation at IGES 2018

• An adaptive association test for microbiome data

  • Presented at 2016 JSM, ENAR

• An adaptive test on high dimensional parameters in GLMs

  • Presented at 2017 JSM, ENAR

Awards

• Dean's Faculty Travel Award, Florida State University, 2020

• ENAR Distinguished Student Paper Award, 2019

• James R. Boen Student Achievement Award, Division of Biostatistics, University of Minnesota, 2018

• Pre-Doctoral Trainee Award, Association of Chinese Geneticists in America (ACGA), 2017

• Elected to Delta Omega (Public Health Honorary Society), 2017

• Elected to Simga Xi (The Scientific Research Society), 2017

• JSM Distinguished Student Paper Award on Statistics in Genomics and Genetics Section, 2016

• Doctoral Dissertation Fellowship, University of Minnesota, 2016

• Travel Award, Computational Neuroscience Summer School, Statistical and Applied Mathematical Sciences Institute, 2015

• Dean's Ph.D. Scholarship, University of Minnesota, 2013

• Honorable Mention in Mathematical Contest in Modeling, Consortium for Mathematics and Its Application, 2012

• National Scholarship, Ministry of Education, China, 2011

Contact

Chong Wu
314 OSB, 117 N. Woodward Ave.
P.O. Box 3064330, Tallahassee, FL, 32306-4330
E-mail: cwu3@fsu.edu