Skip to Main Content

An official website of the United States government

Understanding genetic basis of cancer survival time using PLCO GWAS

Principal Investigator
Name
Kai Yu
Degrees
Ph.D.
Institution
NCI
Position Title
Senior Investigator
Email
About this CDAS Project
Study
PLCO (Learn more about this study)
Project ID
PLCO-845
Initial CDAS Request Approval
Oct 15, 2021
Title
Understanding genetic basis of cancer survival time using PLCO GWAS
Summary
The Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial is a randomized, controlled trial to study impacts of various screening exams on reducing mortality from prostate, lung, colorectal and ovarian cancer. PLCO consisted of about 155,000 participants enrolled between November 1993 and July 2001. Recently, DCEG has completed genotyping of all PLCO participants. Together with its detailed demographic and clinical information, and over 10 years of follow-up, the PLCO cohort can be a unique resource to conduct genetic studies of many cancer-related traits. We plan to focus on cancer survival time and study its genetic basis by conducting a host of genetic analyses, including pathway analysis, Mendelian randomization analysis, transcriptome-wide association analyses, and polygenic risk score based analyses. In addition to using the state of art statistical and computational procedures, we will develop new statistical approaches to address some of the scientific questions.

We will focus on the following four types of cancers: prostate, colorectal, breast, and lung.
Aims

1. Single SNP survival analysis: We will conduct single SNP association analysis by studying one SNP a time to obtain its marginal association with cancer survival time. This analysis will also generate SNP-level summary statistics required for pathway and MR analyses
2. Pathway analysis: We will conduct pathway analysis on the set of over 1000 pathways defined in the Molecular Signatures Database
3. TWAS analysis: We will use the gene expression prediction models established in the PredictDB database. For each gene, we will study the association between gene expression predicted by its genotypes and the survival time
4. MR analysis: We will investigate the causal effect of some risk factors on the cancer survival time

Collaborators

Thomas Rehling, M.S., Postbaccalaureate fellow, BB
Parichoy Pal Choudhury, Ph.D., Research fellow, TDRP
Sheng Fu, Ph.D., Postdoctoral fellow, BB
Grace Hong, Ph.D. Senior Investigator, BB
Mitchell Machiela, Sc.D., Earl Stadtman Investigator, ITEB
Kai Yu, Ph.D. Senior Investigator, BB