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Grant Details

Grant Number: 5R01CA134674-04 Interpret this number
Primary Investigator: Camp, Nicola
Organization: University Of Utah
Project Title: Genetic Epidemiology of Chronic Lymphocytic Leukemia
Fiscal Year: 2013
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DESCRIPTION (provided by applicant): Chronic Lymphocytic Leukemia (CLL) is a B-cell lymphoproliferative disorder primarily involving the bone marrow, blood and lymph nodes. CLL is the most common type of leukemia in adults and although median survival can be quite long, between 8-12 years, most eventually succumb to their disease. The evidence for a genetic component to CLL is compelling but remains unknown, and is likely complex. However, opportunities to identify underlying variants are apparent -both by varied and unique study and analysis designs and via collaborative efforts. The research plan we propose is multifaceted, highly collaborative and includes several innovative techniques. We will pursue two study designs, each powerful to identify susceptibility genes with different underlying genetic models: high-risk pedigree-based shared genomic segment analysis and case- control association analyses. Genome-wide shared genomic segment analysis is a new method that requires extremely extended, high-risk pedigrees which are available only to researchers with genealogic resources, such as Utah. Our strategy for association will be both genome wide and candidate region. Ascertainment will involve two sites (Utah and Sheffield, UK) and will include both a discordant family-based element (Utah) and a population-based sample (UK). This approach exploits both the increased power of familial cases with the perspective of population-based samples. We are able to pursue these together due to software that we have developed. In addition to conventional analyses, we will develop new methods for the high-risk pedigree and case-control settings: homozygosity mapping in the high-risk pedigrees and case-control SGS and homozygosity mapping. Both conventional and novel methods will be performed as part of broader collaborative efforts. The resource that we will build is timely. CLL genetic research is still in its infancy. The concurrent development of these designs defines an extensive strategy for identifying regions of the genome harboring CLL susceptibility genes and will afford us the opportunity to play a significant role in shaping the direction of CLL genetic research. Particularly, Utah pedigrees, through their structure and high-risk nature, add a previously unrealized aspect to the global picture. If one design or collaborative effort can identify even a single susceptibility gene for CLL, we will have made an important and critical discovery in the etiology of CLL. Such a discovery would not only help our understanding of the etiology of CLL, but also may provide information about other lymphoproliferative disorders and may translate to other cancers.

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Genome-wide Association Analysis Implicates Dysregulation Of Immunity Genes In Chronic Lymphocytic Leukaemia
Authors: Law P.J. , Berndt S.I. , Speedy H.E. , Camp N.J. , Sava G.P. , Skibola C.F. , Holroyd A. , Joseph V. , Sunter N.J. , Nieters A. , et al. .
Source: Nature Communications, 2017-02-06 00:00:00.0; 8, p. 14175.
PMID: 28165464
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Authors: Rand K.A. , Song C. , Dean E. , Serie D.J. , Curtin K. , Sheng X. , Hu D. , Huff C.A. , Bernal-Mizrachi L. , Tomasson M.H. , et al. .
Source: Cancer Epidemiology, Biomarkers & Prevention : A Publication Of The American Association For Cancer Research, Cosponsored By The American Society Of Preventive Oncology, 2016 Dec; 25(12), p. 1609-1618.
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Genetically predicted longer telomere length is associated with increased risk of B-cell lymphoma subtypes.
Authors: Machiela M.J. , Lan Q. , Slager S.L. , Vermeulen R.C. , Teras L.R. , Camp N.J. , Cerhan J.R. , Spinelli J.J. , Wang S.S. , Nieters A. , et al. .
Source: Human Molecular Genetics, 2016-04-15 00:00:00.0; 25(8), p. 1663-76.
EPub date: 2016-04-15 00:00:00.0.
PMID: 27008888
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Meta-analysis of genome-wide association studies discovers multiple loci for chronic lymphocytic leukemia.
Authors: Berndt S.I. , Camp N.J. , Skibola C.F. , Vijai J. , Wang Z. , Gu J. , Nieters A. , Kelly R.S. , Smedby K.E. , Monnereau A. , et al. .
Source: Nature Communications, 2016-03-09 00:00:00.0; 7, p. 10933.
EPub date: 2016-03-09 00:00:00.0.
PMID: 26956414
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Genome-wide Association Study Identifies Variants At 16p13 Associated With Survival In Multiple Myeloma Patients
Authors: Ziv E. , Dean E. , Hu D. , Martino A. , Serie D. , Curtin K. , Campa D. , Aftab B. , Bracci P. , Buda G. , et al. .
Source: Nature Communications, 2015; 6, p. 7539.
PMID: 26198393
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Genome-wide association study identifies multiple risk loci for chronic lymphocytic leukemia.
Authors: Berndt S.I. , Skibola C.F. , Joseph V. , Camp N.J. , Nieters A. , Wang Z. , Cozen W. , Monnereau A. , Wang S.S. , Kelly R.S. , et al. .
Source: Nature Genetics, 2013 Aug; 45(8), p. 868-76.
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Common Variants Within 6p21.31 Locus Are Associated With Chronic Lymphocytic Leukaemia And, Potentially, Other Non-hodgkin Lymphoma Subtypes
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Source: American Journal Of Medical Genetics. Part A, 2012 Dec; 158A(12), p. 3137-47.
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Pairwise shared genomic segment analysis in three Utah high-risk breast cancer pedigrees.
Authors: Cai Z. , Thomas A. , Teerlink C. , Farnham J.M. , Cannon-Albright L.A. , Camp N.J. .
Source: Bmc Genomics, 2012-11-28 00:00:00.0; 13, p. 676.
EPub date: 2012-11-28 00:00:00.0.
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Shared Genomic Segment Analysis: The Power To Find Rare Disease Variants
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Source: Annals Of Human Genetics, 2012 Nov; 76(6), p. 500-9.
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Fine-mapping CASP8 risk variants in breast cancer.
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Identification Of Regions Of Positive Selection Using Shared Genomic Segment Analysis
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Genetic Distance And Markers Used In Linkage Mapping
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Fine-scale Structure Of The Genome And Markers Used In Association Mapping
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Source: Methods In Molecular Biology (clifton, N.j.), 2011; 713, p. 71-88.
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Pairwise Shared Genomic Segment Analysis In High-risk Pedigrees: Application To Genetic Analysis Workshop 17 Exome-sequencing Snp Data
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Source: Bmc Proceedings, 2011; 5 Suppl 9, p. S9.
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Haplotype Association Analyses In Resources Of Mixed Structure Using Monte Carlo Testing
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Source: Bmc Bioinformatics, 2010; 11, p. 592.
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