Guo Lab of Genetic Epidemiology, Cancer Research, Computational Genomics & Bioinformatics

Our lab is affiliated with Vanderbilt University Medical Center, Departments of Medicine and Bioinformatical Informatics. We have a broad interest in the research of cancer etiology, prevention, precision medicine and drug repurposing through developing bioinformatics, statistical and machine learning approaches and intergraring multi-omics (including single cell data) and Electronic Health Records (EHR).  Dr Guo has been the PI for transcriptome-wide association studies (TWAS) and contact PI for proteome-wide association studies (PWAS) in colorectal cancer (CRC), funded by NIH/NCI (R37CA227130 with >10 years of support, MERIT; R37CA227130-05S1;  R01CA269589) . Since 2022, he has been teaching two PhD courses at VUMC: "Foundation of Bioinformatics" and "Genetic Epidemiology".

 i) We are applying and developing bioinformatics tools and pipelines to process large multi-omics data, including whole genome sequencing (WGS), whole exome sequencing (WES), RNA-seq, array-based genotype, epigenetics, proteomics data, and metabolomics data. We have established computing platforms via our local university computing resource (Advanced Computing Center for Research & Education, ACCRE) and Amazon Web Services (AWS) to handle population-based sequencing and high-dimensional omicis data for identifying risk genetic variants (i.e. coding and structure variants) and somatic mutations in human cancers.  

ii) We are highly interested in developing computational epigenetics (i.e. scRNA-seq, ChIP-seq and scATAC-seq), machine learning and statistical approaches to improve discovery of risk genes and advance the understanding of risk TF-gene-variant interactions underlying disease mechanisms. 

iii) We are building statistical models to explore the inter-relationship of somatic alterations in tumor tissues (i.e. tumor mutational budern/mutational signatures/tumor-infiltrating lymphocytes (TILs)) with genetic susceptibility and carcinogens environmental exposures in human cancers.

 iv) We are conducting in-depth analyses of omics data and developing natural language processing algorithms using resoouces from approximately 3.5 million patients’ EHRs at VUMC , to provide potential avenues for the prevention and therapeutic intervention of human cancers . 

v) In order to understand the underlying molecular mechanisms of carcinogenesis, we are conducting cell and molecular biology studies, including in vitro and in vivo functional assays (i.e. CRISPR/Cas9) and ChIP-seq to investigate transcription factor regulation mechanisms and biological functions of genes in cell proliferation, invasion, and clonogenesis, as well as generating preclinical data for promising drugs identified from popuplaton-bsased data. 

Selected publications (# corresponding author):
He J, Perera D, Wen W, Ping J, Li Q, Lyu L, Chen Z, Shu X, Long J, Cai Q, Shu XO, Yin Z, Zheng W, Long Q#, Guo X#. Enhancing disease risk gene discovery by integrating transcription factor-linked trans-variants into transcriptome-wide association analyses. Nucleic Acids Research. 2025 Jan 7;53(1):gkae1035. doi: 10.1093/nar/gkae1035. PMID: 39535029.

Guo X#, Ping J, Yang Y, Su X, Shu XO, Wen W, Chen Z, Zhang Y, Tao R, Jia G, He J, Cai Q, Zhang Q, Giles GG, Pearlman R, Rennert G, Vodicka P, Phipps A, Gruber SB, Casey G, Peters U, Long J, Lin W#, Zheng W#. Large-Scale Alternative Polyadenylation-Wide Association Studies to Identify Putative Cancer Susceptibility Genes. Cancer Research 2024 Aug 15;84(16):2707-2719. doi: 10.1158/0008-5472.CAN-24-0521. PMID: 38759092.

Chen Z, Song W, Shu XO, Wen W, Devall M, Dampier C, Moratalla-Navarro F, Cai Q, Long J, Van Kaer L, Wu L, Huyghe JR, Thomas M, Hsu L, Woods MO, Albanes D, Buchanan DD, Gsur A, Hoffmeister M, Vodicka P, Wolk A, Marchand LL, Wu AH, Phipps AI, Moreno V, Ulrike P, Zheng W, Casey G, Guo X#. Novel insights into genetic susceptibility for colorectal cancer from transcriptome-wide association and functional investigation. J Natl Cancer Inst. 2023 Aug 26:djad178. doi: 10.1093/jnci/djad178. MID: 37632791

Holowatyj AN#, Wen W, Gibbs T, Seagle HM, Keller SR, Velez Edwards DR, Washington MK, Eng C, Perea J, Zheng W, Guo X#. Racial/ethnic and sex differences in somatic cancer gene mutations among patients with early-onset colorectal cancer. Cancer Discovery. 2022 Dec 15:CD-22-0764. doi: 10.1158/2159-8290.CD-22-0764. PMID: 36520636 (cover feature).

He J, Wen W, Beeghly A, Chen Z, Cao C, Shu XO, Zheng W, Long Q#, Guo X#. Integrating transcription factor occupancy with transcriptome-wide association analysis identifies susceptibility genes in human cancers. Nature Communcations. 2022 Nov 19;13(1):7118. doi: 10.1038/s41467-022-34888-0. PMID: 36402776

Wen W#, Chen Z, Bao J, Long Q, Shu XO, Zheng W, Guo X#. Genetic variations of DNA bindings of FOXA1 and co-factors in breast cancer susceptibility. Nature communcations 10.1038/s41467-021-25670-9. 

Holowatyj AN#, Eng C, Wen W, Idrees K, Guo X#. Spectrum of Somatic Cancer Gene Variations Among Adults With Appendiceal Cancer by Age at Disease Onset. JAMA Netw Open. 2020 Dec 1;3(12):e2028644. doi: 10.1001/jamanetworkopen.2020.28644. PMID: 33295976.

Guo X#,*, Lin W,*, Wen W, Huyghe J, Bien S, Cai Q, Harrison T, Chen Z, Qu C, Bao J, Long J, ... Casey G, Hsu L, Jenkins MA, Gruber SB, Peters U, Zheng W. Identifying Novel Susceptibility Genes for Colorectal Cancer Risk From a Transcriptome-Wide Association Study of 125,478 Subjects. Gastroenterology. 2021 Mar;160(4):1164-1178.e6. PMID: 33058866. 

Chen Z, Wen W, Beeghly-Fadiel A, Shu XO, Díez-Obrero V, Long J, Bao J, Wang J, Liu Q, Cai Q, Moreno V, Zheng W, Guo X#. Identifying Putative Susceptibility Genes and Evaluating Their Associations with Somatic Mutations in Human Cancers. Am J Hum Genet. 2019 Jul 26. pii: S0002-9297(19)30269-1. PMID: 31402092.

Guo X#,* , Lin W*, Bao J, Cai Q, Pan X, Bai M, Yuan Y, Shi J, Sun Y, Han MR, Wang J, Liu Q, Wen W, Li B, Long J, Chen J, Zheng W. A Comprehensive cis-eQTL Analysis Revealed Target Genes in Breast Cancer Susceptibility Loci Identified in Genome-wide Association Studies. Am J Hum Genet. 2018 May 3;102(5):890-903. PMID: 29727689. PMCID: PMC5986971. 

We are looking for enthusiastic postdoctoral associates to join our group. Candidates should have PhD in bioinformatics, computational biology, biostatistics, epidemiology, genetics/genomics or other related fields. Please contact the PI, Xingyi Guo (xingyi.guo@vumc.org), for details.