|Grant Number:||5R01CA158473-02 Interpret this number|
|Primary Investigator:||Zheng, Wei|
|Project Title:||Genome Sequencing to Identify Novel Genetic Factors for Breast Cancer Risk|
DESCRIPTION (provided by applicant): Genetic factors play an important role in the etiology of both sporadic and familial breast cancer, a complex, multifactorial disease. Known genetic risk factors identified to date, including both rare high- penetrance genes and common low-penetrance variants, explain only about 28% of heritability for breast cancer. Recently emerged evidence strongly suggests that most of the heritable risk for breast cancer and other complex diseases may be due to a large number of low-frequency moderate-penetrance genes that are difficult to identify using conventional family-based linkage analyses and genome-wide association studies (GWAS). In this application, we propose a novel study to systematically search for the entire coding region in the human genome to identify new genetic susceptibility factors for breast cancer. This study will be built upon the resources we established in three NCI-funded large epidemiologic studies conducted among women in Shanghai, in which genomic DNA samples and comprehensive clinical and epidemiological data were collected from nearly 8,000 breast cancer cases and a large number of community controls. Specifically, we propose to sequence the whole exome for 600 genetically-enriched breast cancer cases and 600 controls (Stage 1). Using data from Stage 1 and those from the 1000 Genomes Project, we will select approximately 350 promising genes for replication through variant genotyping (Stage 2) in an independent set of cases and controls. Approximately 20 genes will be selected for Stage 3 replication from those that show promising association in Stage 2 but require additional evaluation to either confirm or reject the hypotheses. To our knowledge, this is the first large association study for breast cancer using whole exome sequencing. With strong methodology and the use of novel technology and study design, the proposed study will identify novel genes and pathways that will significantly improve our understanding of breast cancer genetics and biology. Newly identified genes, particularly those with a substantial effect size, could serve as targets for novel cancer treatment and be used for cancer screening and risk assessment.
Whole-Exome Sequencing Identifies Novel Somatic Mutations in Chinese Breast Cancer Patients.
Authors: Zhang Y. , Cai Q. , Shu X.O. , Gao Y.T. , Li C. , Zheng W. , Long J. .
Source: Journal of molecular and genetic medicine : an international journal of biomedical research, 2015 Dec; 9(4), .
EPub date: 2015-9-25.
Illumina human exome genotyping array clustering and quality control.
Authors: Guo Y. , He J. , Zhao S. , Wu H. , Zhong X. , Sheng Q. , Samuels D.C. , Shyr Y. , Long J. .
Source: Nature protocols, 2014 Nov; 9(11), p. 2643-62.
EPub date: 2014-10-16.
Genome measures used for quality control are dependent on gene function and ancestry.
Authors: Wang J. , Raskin L. , Samuels D.C. , Shyr Y. , Guo Y. .
Source: Bioinformatics (Oxford, England), 2015-02-01; 31(3), p. 318-23.
EPub date: 2014-10-08.
Improved variant calling accuracy by merging replicates in whole-exome sequencing studies.
Authors: Zhang Y. , Li B. , Li C. , Cai Q. , Zheng W. , Long J. .
Source: BioMed research international, 2014; 2014, p. 319534.
EPub date: 2014-08-04.
Rare coding variants and breast cancer risk: evaluation of susceptibility Loci identified in genome-wide association studies.
Authors: Zhang Y. , Long J. , Lu W. , Shu X.O. , Cai Q. , Zheng Y. , Li C. , Li B. , Gao Y.T. , Zheng W. .
Source: Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 2014 Apr; 23(4), p. 622-8.
EPub date: 2014-01-27.
An evaluation of allele frequency estimation accuracy using pooled sequencing data.
Authors: Guo Y. , Cai Q. , Li C. , Li J. , Courtney R. , Zheng W. , Long J. .
Source: International journal of computational biology and drug design, 2013; 6(4), p. 279-93.
EPub date: 2013-09-30.