|Grant Number:||1R01CA158473-01A1 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. PUBLIC HEALTH RELEVANCE: Genetic factors play a major role in the etiology of breast cancers, yet only a small number of cases are explained by genetic factors identified thus far. Using novel research designs and sequencing technologies, this proposed study will systematically search the human genomes for genes that contribute to breast cancer susceptibility. Results from this study will significantly improve the understanding of breast cancer biology and genetics, which will be valuable in designing new therapies and cost-efficient prevention strategies for this common malignancy.
Illumina human exome genotyping array clustering and quality control.
Authors: Guo Y, He J, Zhao S, Wu H, Zhong X, Sheng Q, Samuels DC, Shyr Y, Long J
Source: Nat Protoc, 2014 Nov;9(11), p. 2643-62.
EPub date: 2014 Oct 16.
Genome measures used for quality control are dependent on gene function and ancestry.
Authors: Wang J, Raskin L, Samuels DC, Shyr Y, Guo Y
Source: Bioinformatics, 2015 Feb 1;31(3), p. 318-23.
EPub date: 2014 Oct 8.
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 Res Int, 2014;2014, p. 319534.
EPub date: 2014 Aug 4.
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 XO, Cai Q, Zheng Y, Li C, Li B, Gao YT, Zheng W
Source: Cancer Epidemiol Biomarkers Prev, 2014 Apr;23(4), p. 622-8.
EPub date: 2014 Jan 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: Int J Comput Biol Drug Des, 2013;6(4), p. 279-93.
EPub date: 2013 Sep 30.