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SNP500Cancer: a public resource for sequence validation, assay development, and frequency analysis for genetic variation in candidate genes
1Intramural Research Support Program, SAIC-Frederick, NCI-FCRDC Frederick, MD, USA 2Division of Cancer Epidemiology and Genetics, National Cancer Institute Bethesda, MD, USA 3Department of Bioinformatics, George Mason University Manassas, VA, USA 4Office of Cancer Genomics, National Cancer Institute Bethesda, MD, USA 5Section on Genomic Variation, Pediatric Oncology Branch, National Cancer Institute, National Institutes of Health Gaithersburg, MD, USA
*To whom correspondence should be addressed. Tel: +1 301 496 6019; Fax: +1 301 402 3134; Email: packerb{at}mail.nih.gov
Received September 14, 2005. Revised October 28, 2005. Accepted October 28, 2005.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONDNA SAMPLESSELECTION OF GENES AND...SEQUENCING PROTOCOLGENOTYPING PROTOCOLS AND...ANALYSIS OF ALLELE FREQUENCIESdbSNP SUBMISSIONDATABASE AND WEB SERVER...USING THE SNP500CANCER WEBSITEFUTURE DIRECTIONSREFERENCES |
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The SNP500Cancer database provides sequence and genotype assay information for candidate SNPs useful in mapping complex diseases, such as cancer. The database is an integral component of the NCI Cancer Genome Anatomy Project (http://cgap.nci.nih.gov). SNP500Cancer reports sequence analysis of anonymized control DNA samples (n = 102 Coriell samples representing four self-described ethnic groups: African/African-American, Caucasian, Hispanic and Pacific Rim). The website is searchable by gene, chromosome, gene ontology pathway, dbSNP ID and SNP500Cancer SNP ID. As of October 2005, the database contains >13 400 SNPs, 9124 of which have been sequenced in the SNP500Cancer population. For each analysed SNP, gene location and >200 bp of surrounding annotated sequence (including nearby SNPs) are provided, with frequency information in total and per subpopulation as well as calculation of Hardy–Weinberg equilibrium for each subpopulation. The website provides the conditions for validated sequencing and genotyping assays, as well as genotype results for the 102 samples, in both viewable and downloadable formats. A subset of sequence validated SNPs with minor allele frequency >5% are entered into a high-throughput pipeline for genotyping analysis to determine concordance for the same 102 samples. In addition, the results of genotype analysis for select validated SNP assays (defined as 100% concordance between sequence analysis and genotype results) are posted for an additional 280 samples drawn from the Human Diversity Panel (HDP). SNP500Cancer provides an invaluable resource for investigators to select SNPs for analysis, design genotyping assays using validated sequence data, choose selected assays already validated on one or more genotyping platforms, and select reference standards for genotyping assays. The SNP500Cancer database is freely accessible via the web page at http://snp500cancer.nci.nih.gov.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONDNA SAMPLESSELECTION OF GENES AND...SEQUENCING PROTOCOLGENOTYPING PROTOCOLS AND...ANALYSIS OF ALLELE FREQUENCIESdbSNP SUBMISSIONDATABASE AND WEB SERVER...USING THE SNP500CANCER WEBSITEFUTURE DIRECTIONSREFERENCES |
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SNP500Cancer is a component of the Cancer Genome Anatomy Project (CGAP) of the National Cancer Institute (NCI) and is specifically designed to generate resources for the identification and characterization of genetic variation in genes important in cancer (1). The database reports the validation of SNPs by sequence analysis and optimizes genotyping assays for SNPs of interest to molecular epidemiology studies in cancer.
CGAP is dedicated to the development of technology, including both assays and utilization of technical platforms, and to determining the gene expression profiles of normal, precancer and cancer cells (2). Accordingly, data pertaining to genes and their variation are available on the public website http://cgap.nci.nih.gov. SNP500Cancer represents one of several initiatives designed to characterize sequence variation and is a resource for studying common germ-line genetic variation in the etiology of different cancers as well as related phenotypes. A validated SNP in the database has 100% concordance between sequence analysis and genotyping results on one or more platforms. Assays are developed and optimized in the Core Genotyping Facility (CGF) of the NCI for associations studies conducted in the Division of Cancer Epidemiology and Genetics (DCEG) and the Center for Cancer Research within the Intramural Program of the NCI. The primary focus of the DCEG's intramural studies is to conduct population-based research on environmental and genetic determinants of cancer.
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DNA SAMPLES |
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TOPABSTRACTINTRODUCTIONDNA SAMPLESSELECTION OF GENES AND...SEQUENCING PROTOCOLGENOTYPING PROTOCOLS AND...ANALYSIS OF ALLELE FREQUENCIESdbSNP SUBMISSIONDATABASE AND WEB SERVER...USING THE SNP500CANCER WEBSITEFUTURE DIRECTIONSREFERENCES |
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Sequence and genotype analysis are conducted in a set of 102 unique anonymized individuals of diverse geographic origin with self-described ethnic group affiliation information, chosen to represent four major ethnic groups in the USA; it includes 24 African/African-American, 31 Caucasian, 23 Hispanic and 24 Pacific Rim individuals. The 102 anonymized samples can be obtained from the Coriell Cell Repositories (Coriell Institute for Medical Research, Camden, NJ). The sets of individuals are not a random sampling of one or more human populations and thus the predictive value of the sequence and genotype data provided can vary for different population samples.
A subset of SNPs has been genotyped in 280 anonymized individuals drawn from the Human Diversity Panel (HDP) (3), using DNA that has been amplified by whole genome amplification (4). The ethnic distribution of the 280 individuals approximates the 102 of the SNP500Cancer set, namely, the four self-described ethnic groups listed here. It is notable that there are 49 Native Americans, 25 of Mayan background and 24 of Piman background.
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SELECTION OF GENES AND SNPS |
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TOPABSTRACTINTRODUCTIONDNA SAMPLESSELECTION OF GENES AND...SEQUENCING PROTOCOLGENOTYPING PROTOCOLS AND...ANALYSIS OF ALLELE FREQUENCIESdbSNP SUBMISSIONDATABASE AND WEB SERVER...USING THE SNP500CANCER WEBSITEFUTURE DIRECTIONSREFERENCES |
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This database is biased towards SNPs that lie within or are situated close to ‘candidate’ genes. The selection of genes and SNPs for analysis has been drawn from the following sources: (i) genes that fit a plausible model for cancer studies (e.g. by pathway), (ii) review of the published literature on SNPs and cancer, (iii) SNPs reported in public databases with some associated non-in silico determined frequency and (iv) SNPs verified during re-sequence analysis of candidate genes (5). In addition, the database contains SNPs analysed in the Breast and Prostate Cohort Consortium (http://epi.grants.cancer.gov/BPC3/ and http://cgf.nci.nih.gov/cohort.cfm), which targets the genes of the sex steroid metabolism and insulin growth factor pathways (n = 55 in total).
As of October 2005, the database contains SNPs in 812 known genes. The average number of SNPs per gene is 14.1 and the median is 8 SNPs per gene; the range is between 1 and 237 SNPs per gene.
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SEQUENCING PROTOCOL |
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TOPABSTRACTINTRODUCTIONDNA SAMPLESSELECTION OF GENES AND...SEQUENCING PROTOCOLGENOTYPING PROTOCOLS AND...ANALYSIS OF ALLELE FREQUENCIESdbSNP SUBMISSIONDATABASE AND WEB SERVER...USING THE SNP500CANCER WEBSITEFUTURE DIRECTIONSREFERENCES |
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A PCR amplicon of
600 bp is generated for each SNP, which is localized to the center, creating flanking regions of 300 bp in each direction. Additional putative SNPs (determined from dbSNP) are annotated on the sequence of the amplicon. Oligonucleotide primers are designed for bi-directional sequence analysis using Primer3 software (6) and ProbeITy primer design software (Celadon Laboratories, Hyattsville MD). Each oligonucleotide primer is extended with a universal sequencing primer, M13 forward (TGTAAAACGACGGCCAGT) or M13 reverse (CAGGAAACAGCTATGACC). The sequencing assay protocols and conditions are displayed on the SNP500Cancer website. Sequence tracings are analysed in Seqscape v2.5 (Applied Biosystems, Foster City, CA). A sequencing call is deemed acceptable if the Seqscape quality score is >20 for at least 98 of the 102 individuals. Genotype calls are determined for each of the 102 individuals. The genotype and allele frequencies are maintained in an Oracle database and displayed on the SNP500Cancer website. |
GENOTYPING PROTOCOLS AND VALIDATION |
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TOPABSTRACTINTRODUCTIONDNA SAMPLESSELECTION OF GENES AND...SEQUENCING PROTOCOLGENOTYPING PROTOCOLS AND...ANALYSIS OF ALLELE FREQUENCIESdbSNP SUBMISSIONDATABASE AND WEB SERVER...USING THE SNP500CANCER WEBSITEFUTURE DIRECTIONSREFERENCES |
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For SNPs that are determined to have >5% minor allele frequency (MAF) in at least one of the SNP500Cancer subpopulations,
200 bp of DNA sequence surrounding each SNP is submitted for design on one of the CGF's genotyping platforms: (i) Applied Biosystems' TaqManTM ‘Assay by Design’ service and (ii) EPOCH Biosciences' MGB EclipseTM probes. The genotyping assay procedures and conditions are displayed on the SNP500Cancer website. Genotyping assays are validated if there is complete concordance between the genotype results and the primary sequence analysis for the 102 samples. In addition, the 280 HDP samples are genotyped for SNPs with >5% MAF. The frequencies found in the four HDP subpopulations are displayed on the SNP500Cancer website. |
ANALYSIS OF ALLELE FREQUENCIES |
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TOPABSTRACTINTRODUCTIONDNA SAMPLESSELECTION OF GENES AND...SEQUENCING PROTOCOLGENOTYPING PROTOCOLS AND...ANALYSIS OF ALLELE FREQUENCIESdbSNP SUBMISSIONDATABASE AND WEB SERVER...USING THE SNP500CANCER WEBSITEFUTURE DIRECTIONSREFERENCES |
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For each validated SNP, allele and genotype frequencies are displayed for the 102 individuals overall (Figure 1) and for each SNP500Cancer subpopulation. The result of a test for Hardy–Weinberg equilibrium,
2 with one degree of freedom for two alleles (7) is posted for each subpopulation.
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The observed allele frequencies in the SNP500Cancer population of n = 102 can provide a useful estimate overall, as well as for the four subgroups. In an analysis of 1164 SNPs genotyped in the additional 280 individuals compared with the data for the 102 which were both genotyped and sequenced, the coefficient of correlation (r2) for each group was as follows: Caucasian 0.948, African-American 0.922, Pacific Rim 0.919 and Hispanic 0.742. The latter is notable because of the differences in the degree of admixture between the Hispanic subgroup of the 102 and the two Native American populations, of Maya and Pima heritage. Analysis of the SNP500Cancer dataset can be useful in estimating gene diversity. For instance, consistently reduced genetic diversity has been observed for SNP loci causing amino acid changes, especially radical shifts in protein structure (8); a similar trend was observed for 5'-untranslated regions (5'-UTRs). Moreover, the reduction of genetic diversity of nonsynonymous SNPs and those within the 5'-UTR are evidence that purifying selection could have acted at these sites, leading to reduced interpopulation divergence (9).
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dbSNP SUBMISSION |
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TOPABSTRACTINTRODUCTIONDNA SAMPLESSELECTION OF GENES AND...SEQUENCING PROTOCOLGENOTYPING PROTOCOLS AND...ANALYSIS OF ALLELE FREQUENCIESdbSNP SUBMISSIONDATABASE AND WEB SERVER...USING THE SNP500CANCER WEBSITEFUTURE DIRECTIONSREFERENCES |
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All analysed SNPs from the SNP500Cancer project are submitted to dbSNP (10)—http://www.ncbi.nlm.nih.gov/SNP. This information includes flanking sequence, observed variation, assay primers, probes, and conditions, and frequency of the sequence variation among the SNP500Cancer total population and subpopulations. Figure 2 depicts the distribution of SNPs already in dbSNP and those newly observed in close proximity to target SNPs. It is also notable that
10% of targeted SNPs are monoallelic in the 102 samples when compared with data in dbSNP build 124.
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DATABASE AND WEB SERVER SPECIFICATIONS |
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TOPABSTRACTINTRODUCTIONDNA SAMPLESSELECTION OF GENES AND...SEQUENCING PROTOCOLGENOTYPING PROTOCOLS AND...ANALYSIS OF ALLELE FREQUENCIESdbSNP SUBMISSIONDATABASE AND WEB SERVER...USING THE SNP500CANCER WEBSITEFUTURE DIRECTIONSREFERENCES |
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The SNP500Cancer database is implemented using Oracle 9i (Oracle Corporation, Redwood Shores, CA). The web interface is written in ColdFusion (Macromedia, San Francisco, CA). The web server is a Compaq (Cupertino, CA) DL585 running Linux version 2.6.5. The database server is a Sun Microsystems (Santa Clara, CA) Sunfire 4800 running Oracle version 9.2.0.6 [EC] .0. Both servers are supported by NCI Computer Services (Rockville, MD).
SNP500Cancer data are available through the caGRID, part of the Cancer Biomedical Informatics Grid (caBIG), a common interface providing data sharing among cancer researchers worldwide (http://cabig.nci.nih.gov) (11).
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USING THE SNP500CANCER WEBSITE |
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TOPABSTRACTINTRODUCTIONDNA SAMPLESSELECTION OF GENES AND...SEQUENCING PROTOCOLGENOTYPING PROTOCOLS AND...ANALYSIS OF ALLELE FREQUENCIESdbSNP SUBMISSIONDATABASE AND WEB SERVER...USING THE SNP500CANCER WEBSITEFUTURE DIRECTIONSREFERENCES |
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Searching for genes
The SNP500Cancer website provides capabilities for searching for genes in several ways: (i) gene name or alias (including wild card searches), (ii) chromosome location and (iii) Gene Ontology (GO) pathway (12)—numeric or text. The gene is displayed with a list of SNPs that have been validated, and those that were not found to occur in the SNP500Cancer population.
Searching for SNPs
SNPs can be searched for using the dbSNP ID (rs cluster number, e.g. rs799917), or the internal SNP500Cancer polymorphism ID (gene symbol followed by a sequence number, e.g. BRCA1-02). The SNP is displayed in the center of surrounding sequence, and other SNPs in the sequence are annotated with IUPAC codes (Figure 1). Clicking on the SNP variation links to Genewindow (13), a publicly available genome browser developed at the CGF.
Viewing genotypic and allelic frequencies
For the SNP of interest, genotypic and allelic frequencies for the entire SNP500Cancer population of 102 individuals are displayed. The ‘view subpopulation frequencies’ link displays a page with genotypic and allelic frequencies for each subpopulation—African/African-American, Caucasian, Hispanic and Pacific Rim. Each subpopulation link leads to a list of individual genotypes for the samples within that subpopulation.
Viewing haplotype and htSNP data
For the gene of interest, haplotypes and haplotype-tagging SNPs (htSNP) data can be displayed for the entire control population or by subpopulation. Two different programs are available for determination of htSNPs: TagSNPs (14) and Tagger (http://www.broad.mit.edu/mpg/tagger/). The haplotype block structure can be visualized using Haploview (15).
Displaying assay conditions
For the SNP of interest, links are displayed for all validated assays (sequencing, MGB EclipseTM, TaqManTM). The link displays a page with detailed information on primers, probes, temperature and procedural steps.
Downloading information via FTP
FTP files are posted for SNPs by gene (including identifiers, genomic location and amino acid change), assays (identifier, primers, probes and conditions), frequencies and genotypes for each SNP with variation, as well as genotypes for all SNPs with >5% allelic frequency. These files can be found at ftp://ftp-snp500cancer.nci.nih.gov.
Connecting to other information sources
Each gene and SNP page on the SNP500Cancer website includes links to external resources; for genes: Entrez (16) and GO database; for SNPs: dbSNP, NCBI MapViewer (17) and Ensembl (18).
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FUTURE DIRECTIONS |
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TOPABSTRACTINTRODUCTIONDNA SAMPLESSELECTION OF GENES AND...SEQUENCING PROTOCOLGENOTYPING PROTOCOLS AND...ANALYSIS OF ALLELE FREQUENCIESdbSNP SUBMISSIONDATABASE AND WEB SERVER...USING THE SNP500CANCER WEBSITEFUTURE DIRECTIONSREFERENCES |
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SNP500Cancer is a genetic resource designed to provide data on SNPs with neighboring sequence, for design and optimization of genotyping assays to be used in molecular epidemiology studies of cancer. A major goal of SNP500Cancer is to increase the breadth of genes and specifically develop sets of genes within a common pathway (e.g. DNA repair or telomere stability) (19). To this end, the project will utilize public resources to identify and validate SNPs across each gene at a greater density (e.g. one SNP every 1–3 kb) (20). The additional SNPs needed to densely cover the genes of interest will be selected based on their location, as well as suitability for inclusion as an htSNP in one or more population. The project will also add SNPs of great importance to cancer based on high quality citations in the published literature (21). There will be a special emphasis on SNPs drawn from coding and regulatory regions (including microRNAs) as well as those SNPs that have been shown to be associated with cancer susceptibility or outcome, particularly in pharmacogenomics.
SNPs with validated sequencing performed in other public projects will be imported, and assays optimized based on the concordance between sequence and genotype analysis. For instance, current plans are underway to optimize and validate assays for SNPs identified in the NIEHS Environmental Genome Project (NIEHS SNPs. NIEHS Environmental Genome Project, University of Washington, Seattle, WA, http://egp.gs.washington.edu/).
Though the primary goal of the SNP500Cancer database has been to focus on SNPs in and around known genes, future plans include the results of sequence analysis around small RNA sequences and their possible targets in cancer. Lastly, SNP500Cancer will make available results of dense, whole genome SNP scans using the 102 samples.
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ACKNOWLEDGEMENTS |
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This research was supported (in part) by the Intramural Research Program of the National Cancer Institute, NIH, and Office of Cancer Genomics, NCI, NIH. Funding to pay the Open Access publication charges for this article was provided by the National Cancer Institute.
Conflict of interest statement. None declared.
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N. D. Freedman, J. Ahn, L. Hou, J. Lissowska, W. Zatonski, M. Yeager, S. J. Chanock, W. H. Chow, and C. C. Abnet Polymorphisms in estrogen- and androgen-metabolizing genes and the risk of gastric cancer Carcinogenesis, January 1, 2009; 30(1): 71 - 77. [Abstract] [Full Text] [PDF]
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J. S. Chang, M. R. Wrensch, H. M. Hansen, J. D. Sison, M. C. Aldrich, C. P. Quesenberry Jr, M. F. Seldin, K. T. Kelsey, and J. K. Wiencke Base excision repair genes and risk of lung cancer among San Francisco Bay Area Latinos and African-Americans Carcinogenesis, January 1, 2009; 30(1): 78 - 87. [Abstract] [Full Text] [PDF]
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 M.-h. Chang, M. L. Lindegren, M. A. Butler, S. J. Chanock, N. F. Dowling, M. Gallagher, R. Moonesinghe, C. A. Moore, R. M. Ned, M. R. Reichler, et al. Prevalence in the United States of Selected Candidate Gene Variants: Third National Health and Nutrition Examination Survey, 1991-1994 Am. J. Epidemiol., January 1, 2009; 169(1): 54 - 66. [Abstract] [Full Text] [PDF]
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 L. M. Morton, S. S. Wang, W. Cozen, M. S. Linet, N. Chatterjee, S. Davis, R. K. Severson, J. S. Colt, M. A. Vasef, N. Rothman, et al. Etiologic heterogeneity among non-Hodgkin lymphoma subtypes Blood, December 15, 2008; 112(13): 5150 - 5160. [Abstract] [Full Text] [PDF]
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D. C. Johnson, S. Corthals, C. Ramos, A. Hoering, K. Cocks, N. J. Dickens, J. Haessler, H. Goldschmidt, J. A. Child, S. E. Bell, et al. Genetic associations with thalidomide mediated venous thrombotic events in myeloma identified using targeted genotyping Blood, December 15, 2008; 112(13): 4924 - 4934. [Abstract] [Full Text] [PDF]
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M. M. Gaudet, S. Chanock, A. Dunning, K. Driver, L. A. Brinton, J. Lissowska, B. Peplonska, P. Pharoah, and M. Garcia-Closas HSD17B1 Genetic Variants and Hormone Receptor-Defined Breast Cancer Cancer Epidemiol. Biomarkers Prev., October 1, 2008; 17(10): 2766 - 2772. [Abstract] [Full Text] [PDF]
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J. D. Figueroa, N. Malats, M. Garcia-Closas, F. X. Real, D. Silverman, M. Kogevinas, S. Chanock, R. Welch, M. Dosemeci, Q. Lan, et al. Bladder cancer risk and genetic variation in AKR1C3 and other metabolizing genes Carcinogenesis, October 1, 2008; 29(10): 1955 - 1962. [Abstract] [Full Text] [PDF]
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 R. Mahajan, E. M. El-Omar, J. Lissowska, P. Grillo, C. S. Rabkin, A. Baccarelli, M. Yeager, L. H. Sobin, W. Zatonski, S. J. Channock, et al. Genetic Variants in T Helper Cell Type 1, 2 and 3 Pathways and Gastric Cancer Risk in a Polish Population Jpn. J. Clin. Oncol., September 1, 2008; 38(9): 626 - 633. [Abstract] [Full Text] [PDF]
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M. Zhang, W.-Y. Huang, G. Andreotti, Y.-T. Gao, A. Rashid, J. Chen, L. C. Sakoda, M.-C. Shen, B.-S. Wang, S. Chanock, et al. Variants of DNA Repair Genes and the Risk of Biliary Tract Cancers and Stones: A Population-Based Study in China Cancer Epidemiol. Biomarkers Prev., August 1, 2008; 17(8): 2123 - 2127. [Abstract] [Full Text] [PDF]
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A. W. Hsing, L. C. Sakoda, A. Rashid, G. Andreotti, J. Chen, B.-S. Wang, M.-C. Shen, B. E. Chen, P. S. Rosenberg, M. Zhang, et al. Variants in Inflammation Genes and the Risk of Biliary Tract Cancers and Stones: A Population-Based Study in China Cancer Res., August 1, 2008; 68(15): 6442 - 6452. [Abstract] [Full Text] [PDF]
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S. M. Beckett, S. J. Laughton, L. D. Pozza, G. B. McCowage, G. Marshall, R. J. Cohn, E. Milne, and L. J. Ashton Buccal Swabs and Treated Cards: Methodological Considerations for Molecular Epidemiologic Studies Examining Pediatric Populations Am. J. Epidemiol., May 15, 2008; 167(10): 1260 - 1267. [Abstract] [Full Text] [PDF]
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S.-C. Chang, A. Rashid, Y.-T. Gao, G. Andreotti, M.-C. Shen, B.-S. Wang, T.-Q. Han, B.-H. Zhang, L. C. Sakoda, M. F. Leitzmann, et al. Polymorphism of genes related to insulin sensitivity and the risk of biliary tract cancer and biliary stone: a population-based case-control study in Shanghai, China Carcinogenesis, May 1, 2008; 29(5): 944 - 948. [Abstract] [Full Text] [PDF]
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J. Brooks, R. E. Shore, A. Zeleniuch-Jacquotte, D. Currie, Y. Afanasyeva, K. L. Koenig, A. A. Arslan, P. Toniolo, and I. Wirgin Polymorphisms in RAD51, XRCC2, and XRCC3 Are Not Related to Breast Cancer Risk Cancer Epidemiol. Biomarkers Prev., April 1, 2008; 17(4): 1016 - 1019. [Full Text] [PDF]
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V. M. Chia, L. C. Sakoda, B. I. Graubard, M. V. Rubertone, S. J. Chanock, R. L. Erickson, and K. A. McGlynn Risk of Testicular Germ Cell Tumors and Polymorphisms in the Insulin-Like Growth Factor Genes Cancer Epidemiol. Biomarkers Prev., March 1, 2008; 17(3): 721 - 726. [Abstract] [Full Text] [PDF]
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G. Andreotti, J. Chen, Y.-T. Gao, A. Rashid, B. E. Chen, P. Rosenberg, L. C. Sakoda, J. Deng, M.-C. Shen, B.-S. Wang, et al. Polymorphisms of Genes in the Lipid Metabolism Pathway and Risk of Biliary Tract Cancers and Stones: A Population-Based Case-Control Study in Shanghai, China Cancer Epidemiol. Biomarkers Prev., March 1, 2008; 17(3): 525 - 534. [Abstract] [Full Text] [PDF]
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A. Hazra, S. Chanock, E. Giovannucci, D. G. Cox, T. Niu, C. Fuchs, W. C. Willett, and D. J. Hunter Large-Scale Evaluation of Genetic Variants in Candidate Genes for Colorectal Cancer Risk in the Nurses' Health Study and the Health Professionals' Follow-up Study Cancer Epidemiol. Biomarkers Prev., February 1, 2008; 17(2): 311 - 319. [Abstract] [Full Text] [PDF]
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 P. Rajaraman, A. Hutchinson, N. Rothman, P. M. Black, H. A. Fine, J. S. Loeffler, R. G. Selker, W. R. Shapiro, M. S. Linet, and P. D. Inskip Oxidative response gene polymorphisms and risk of adult brain tumors Neuro-oncol, January 1, 2008; 10(5): 709 - 715. [Abstract] [Full Text] [PDF]
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V. W. Setiawan, F. R. Schumacher, C. A. Haiman, D. O. Stram, D. Albanes, D. Altshuler, G. Berglund, J. Buring, E. E. Calle, F. Clavel-Chapelon, et al. CYP17 Genetic Variation and Risk of Breast and Prostate Cancer from the National Cancer Institute Breast and Prostate Cancer Cohort Consortium (BPC3) Cancer Epidemiol. Biomarkers Prev., November 1, 2007; 16(11): 2237 - 2246. [Abstract] [Full Text] [PDF]
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M. Garcia-Closas, M. A. Troester, Y. Qi, A. Langerod, M. Yeager, J. Lissowska, L. Brinton, R. Welch, B. Peplonska, D. S. Gerhard, et al. Common Genetic Variation in GATA-Binding Protein 3 and Differential Susceptibility to Breast Cancer by Estrogen Receptor {alpha} Tumor Status Cancer Epidemiol. Biomarkers Prev., November 1, 2007; 16(11): 2269 - 2275. [Abstract] [Full Text] [PDF]
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 C. D. Collard, S. K. Shernan, A. A. Fox, T. Bernig, S. J. Chanock, W. K. Vaughn, K. Takahashi, A. B. Ezekowitz, P. Jarolim, and S. C. Body The MBL2 'LYQA Secretor' Haplotype Is an Independent Predictor of Postoperative Myocardial Infarction in Whites Undergoing Coronary Artery Bypass Graft Surgery Circulation, September 11, 2007; 116(11_suppl): I-106 - I-112. [Abstract] [Full Text] [PDF]
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P. Rajaraman, S. S. Wang, N. Rothman, M. M. Brown, P. M. Black, H. A. Fine, J. S. Loeffler, R. G. Selker, W. R. Shapiro, S. J. Chanock, et al. Polymorphisms in Apoptosis and Cell Cycle Control Genes and Risk of Brain Tumors in Adults Cancer Epidemiol. Biomarkers Prev., August 1, 2007; 16(8): 1655 - 1661. [Abstract] [Full Text] [PDF]
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S. A. Savage, K. Woodson, E. Walk, W. Modi, J. Liao, C. Douglass, R. N. Hoover, S. J. Chanock, and The National Osteosarcoma Etiology Study Group Analysis of Genes Critical for Growth Regulation Identifies Insulin-like Growth Factor 2 Receptor Variations with Possible Functional Significance as Risk Factors for Osteosarcoma Cancer Epidemiol. Biomarkers Prev., August 1, 2007; 16(8): 1667 - 1674. [Abstract] [Full Text] [PDF]
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D. Kang, K.-M. Lee, S. K. Park, S. I. Berndt, U. Peters, D. Reding, N. Chatterjee, R. Welch, S. Chanock, W.-Y. Huang, et al. Functional Variant of Manganese Superoxide Dismutase (SOD2 V16A) Polymorphism Is Associated with Prostate Cancer Risk in the Prostate, Lung, Colorectal, and Ovarian Cancer Study Cancer Epidemiol. Biomarkers Prev., August 1, 2007; 16(8): 1581 - 1586. [Abstract] [Full Text] [PDF]
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B. L. Sprague, A. Trentham-Dietz, M. Garcia-Closas, P. A. Newcomb, L. Titus-Ernstoff, J. M. Hampton, S. J. Chanock, J. L. Haines, and K. M. Egan Genetic variation in TP53 and risk of breast cancer in a population-based case control study Carcinogenesis, August 1, 2007; 28(8): 1680 - 1686. [Abstract] [Full Text] [PDF]
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S. Michiels, P. Danoy, P. Dessen, A. Bera, T. Boulet, C. Bouchardy, M. Lathrop, A. Sarasin, and S. Benhamou Polymorphism discovery in 62 DNA repair genes and haplotype associations with risks for lung and head and neck cancers Carcinogenesis, August 1, 2007; 28(8): 1731 - 1739. [Abstract] [Full Text] [PDF]
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L. M. Morton, L. Bernstein, S. S. Wang, D. W. Hein, N. Rothman, J. S. Colt, S. Davis, J. R. Cerhan, R. K. Severson, R. Welch, et al. Hair dye use, genetic variation in N-acetyltransferase 1 (NAT1) and 2 (NAT2), and risk of non-Hodgkin lymphoma Carcinogenesis, August 1, 2007; 28(8): 1759 - 1764. [Abstract] [Full Text] [PDF]
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J. D. Figueroa, N. Malats, N. Rothman, F. X. Real, D. Silverman, M. Kogevinas, S. Chanock, M. Yeager, R. Welch, M. Dosemeci, et al. Evaluation of genetic variation in the double-strand break repair pathway and bladder cancer risk Carcinogenesis, August 1, 2007; 28(8): 1788 - 1793. [Abstract] [Full Text] [PDF]
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 Z. Yu, Z. Li, N. Jolicoeur, L. Zhang, Y. Fortin, E. Wang, M. Wu, and S.-H. Shen Aberrant allele frequencies of the SNPs located in microRNA target sites are potentially associated with human cancers Nucleic Acids Res., July 26, 2007; 35(13): 4535 - 4541. [Abstract] [Full Text] [PDF]
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K.-M. Lee, M. Shen, R. S. Chapman, M. Yeager, R. Welch, X. He, T. Zheng, H. D. Hosgood, D. Yang, S. I. Berndt, et al. Polymorphisms in immunoregulatory genes, smoky coal exposure and lung cancer risk in Xuan Wei, China Carcinogenesis, July 1, 2007; 28(7): 1437 - 1441. [Abstract] [Full Text] [PDF]
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E. E. Solomou, F. Gibellini, B. Stewart, D. Malide, M. Berg, V. Visconte, S. Green, R. Childs, S. J. Chanock, and N. S. Young Perforin gene mutations in patients with acquired aplastic anemia Blood, June 15, 2007; 109(12): 5234 - 5237. [Abstract] [Full Text] [PDF]
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D. Kang, K.-M. Lee, S. K. Park, S. I. Berndt, D. Reding, N. Chatterjee, R. Welch, S. Chanock, W.-Y. Huang, and R. B. Hayes Lack of Association of Transforming Growth Factor-{beta}1 Polymorphisms and Haplotypes with Prostate Cancer Risk in the Prostate, Lung, Colorectal, and Ovarian Trial Cancer Epidemiol. Biomarkers Prev., June 1, 2007; 16(6): 1303 - 1305. [Full Text] [PDF]
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Q. Lan, T. Zheng, S. Chanock, Y. Zhang, M. Shen, S. S. Wang, S. I. Berndt, S. H. Zahm, T. R. Holford, B. Leaderer, et al. Genetic variants in caspase genes and susceptibility to non-Hodgkin lymphoma Carcinogenesis, April 1, 2007; 28(4): 823 - 827. [Abstract] [Full Text] [PDF]
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T. Bernig, B. J. Boersma, T. M. Howe, R. Welch, S. Yadavalli, B. Staats, L. E. Mechanic, S. J. Chanock, and S. Ambs The mannose-binding lectin (MBL2) haplotype and breast cancer: an association study in African-American and Caucasian women Carcinogenesis, April 1, 2007; 28(4): 828 - 836. [Abstract] [Full Text] [PDF]
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U. Lim, S. S. Wang, P. Hartge, W. Cozen, L. E. Kelemen, S. Chanock, S. Davis, A. Blair, M. Schenk, N. Rothman, et al. Gene-nutrient interactions among determinants of folate and one-carbon metabolism on the risk of non-Hodgkin lymphoma: NCI-SEER Case-Control Study Blood, April 1, 2007; 109(7): 3050 - 3059. [Abstract] [Full Text] [PDF]
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M. P. Purdue, Q. Lan, A. Kricker, A. E. Grulich, C. M. Vajdic, J. Turner, D. Whitby, S. Chanock, N. Rothman, and B. K. Armstrong Polymorphisms in immune function genes and risk of non-Hodgkin lymphoma: findings from the New South Wales non-Hodgkin Lymphoma Study Carcinogenesis, March 1, 2007; 28(3): 704 - 712. [Abstract] [Full Text] [PDF]
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L. E. Mechanic, E. D. Bowman, J. A. Welsh, M. A. Khan, N. Hagiwara, L. Enewold, P. G. Shields, L. Burdette, S. Chanock, and C. C. Harris Common Genetic Variation in TP53 Is Associated with Lung Cancer Risk and Prognosis in African Americans and Somatic Mutations in Lung Tumors Cancer Epidemiol. Biomarkers Prev., February 1, 2007; 16(2): 214 - 222. [Abstract] [Full Text] [PDF]
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M. P. Purdue, L. C. Sakoda, B. I. Graubard, R. Welch, S. J. Chanock, I. A. Sesterhenn, M. V. Rubertone, R. L. Erickson, and K. A. McGlynn A Case-Control Investigation of Immune Function Gene Polymorphisms and Risk of Testicular Germ Cell Tumors Cancer Epidemiol. Biomarkers Prev., January 1, 2007; 16(1): 77 - 83. [Abstract] [Full Text] [PDF]
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L. Hou, E.M. El-Omar, J. Chen, P. Grillo, C.S. Rabkin, A. Baccarelli, M. Yeager, S.J. Chanock, W. Zatonski, L.H. Sobin, et al. Polymorphisms in Th1-type cell-mediated response genes and risk of gastric cancer Carcinogenesis, January 1, 2007; 28(1): 118 - 123. [Abstract] [Full Text] [PDF]
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L. Wang, H. Liu, Z. Zhang, M. R. Spitz, and Q. Wei Association of Genetic Variants of O6-Methylguanine-DNA Methyltransferase with Risk of Lung Cancer in Non-Hispanic Whites Cancer Epidemiol. Biomarkers Prev., December 1, 2006; 15(12): 2364 - 2369. [Abstract] [Full Text] [PDF]
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D. A. Hill, S. S. Wang, J. R. Cerhan, S. Davis, W. Cozen, R. K. Severson, P. Hartge, S. Wacholder, M. Yeager, S. J. Chanock, et al. Risk of non-Hodgkin lymphoma (NHL) in relation to germline variation in DNA repair and related genes Blood, November 1, 2006; 108(9): 3161 - 3167. [Abstract] [Full Text] [PDF]
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M. M. Gaudet, S. Chanock, J. Lissowska, S. I. Berndt, B. Peplonska, L. A. Brinton, R. Welch, M. Yeager, A. Bardin-Mikolajczak, and M. Garcia-Closas Comprehensive Assessment of Genetic Variation of Catechol-O-Methyltransferase and Breast Cancer Risk Cancer Res., October 1, 2006; 66(19): 9781 - 9785. [Abstract] [Full Text] [PDF]
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