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Find video protocols related to scientific articles indexed in Pubmed.
Cigarette Smoking Prior to First Cancer and Risk of Second Smoking-Associated Cancers Among Survivors of Bladder, Kidney, Head and Neck, and Stage I Lung Cancers.
J. Clin. Oncol.
PUBLISHED: 11-12-2014
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Data on smoking and second cancer risk among cancer survivors are limited. We assessed associations between smoking before first cancer diagnosis and risk of second primary smoking-associated cancers among survivors of lung (stage I), bladder, kidney, and head/neck cancers.
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DNA methylation levels at chromosome 8q24 in peripheral blood are associated with 8q24 cancer susceptibility loci.
Cancer Prev Res (Phila)
PUBLISHED: 10-16-2014
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Chromosome 8q24 has emerged as an important region for genetic susceptibility to various cancers, but little is known about the contribution of DNA methylation at 8q24. To evaluate variability in DNA methylation levels at 8q24 and the relationship with cancer susceptibility single nucleotide polymorphisms (SNPs) in this region, we quantified DNA methylation levels in peripheral blood at 145 CpG sites nearby 8q24 cancer susceptibility SNPs or MYC using pyrosequencing among 80 Caucasian men in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. For the 60 CpG sites meeting quality control, which also demonstrated temporal stability over a 5-year period, we calculated pairwise Spearman correlations for DNA methylation levels at each CpG site with 42 8q24 cancer susceptibility SNPs. To account for multiple testing, we adjusted p-values into q-values reflecting the False Discovery Rate (FDR). In contrast to the MYC CpG sites, most sites nearby the SNPs demonstrated good reproducibility, high methylation levels, and moderate-high between-individual variation. We observed 10 statistically significant (FDR<0.05) CpG site-SNP correlations. These included correlations between an intergenic CpG site at Chr8:128393157 and the prostate cancer SNP rs16902094 (rho=-0.54; p-value=9.7x10-7; q-value=0.002), a PRNCR1 CpG site at Chr8:128167809 and the prostate cancer SNP rs1456315 (rho=0.52; p-value=1.4x10-6; q-value=0.002), and 2 POU5F1B CpG sites and several prostate/colorectal cancer SNPs (for Chr8:128498051 and rs6983267, rho=0.46; p-value=2.0x10-5; q-value=0.01). This is the first report of correlations between blood DNA methylation levels and cancer susceptibility SNPs at 8q24, suggesting that DNA methylation at this important susceptibility locus may contribute to cancer risk.
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Body Mass Index and Risk of Second Obesity-Associated Cancers After Colorectal Cancer: A Pooled Analysis of Prospective Cohort Studies.
J. Clin. Oncol.
PUBLISHED: 10-01-2014
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To determine whether prediagnostic body mass index (BMI) is associated with risk of second obesity-associated cancers in colorectal cancer (CRC) survivors, and whether CRC survivors have increased susceptibility to obesity-associated cancer compared with cancer-free individuals.
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1-stearoylglycerol is associated with risk of prostate cancer: results from serum metabolomic profiling.
Metabolomics
PUBLISHED: 09-26-2014
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Although prostate cancer is the most commonly diagnosed cancer among men in developed populations, recent recommendations against routine prostate-specific antigen screening have cast doubt on its utility for early detection. We compared the metabolomic profiles of prospectively collected fasting serum from 74 prostate cancer cases and 74 controls selected from the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study cohort of male smokers. Circulating 1-stearoylglycerol (1-SG, or 1-monostearin) was statistically significantly inversely associated with risk of prostate cancer after Bonferroni correction for multiple comparisons (i.e., 420 identified metabolites) (OR=0.34, 95% CI=0.20 - 0.58, p=6.3 × 10(-5)). The magnitude of this association did not differ by disease aggressiveness and was observed for cases diagnosed up to 23 years after blood collection. Similar but somewhat weaker prostate cancer risk signals were also evident for glycerol and alpha-ketoglutarate. In this population, men with higher serum 1-SG were less likely to develop prostate cancer, supporting a role for dysregulation of lipid metabolism in this malignancy. Additional studies are needed to retest the association and to examine 1-SG for its potential as a prostate cancer early detection marker.
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Human metabolic correlates of body mass index.
Metabolomics
PUBLISHED: 09-26-2014
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A high body mass index (BMI) is a major risk factor for several chronic diseases, but the biology underlying these associations is not well-understood. Dyslipidemia, inflammation, and elevated levels of growth factors and sex steroid hormones explain some of the increased disease risk, but other metabolic factors not yet identified may also play a role.
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Risk of subsequent malignant neoplasms in long-term hereditary retinoblastoma survivors after chemotherapy and radiotherapy.
J. Clin. Oncol.
PUBLISHED: 09-02-2014
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Hereditary retinoblastoma (Rb) survivors have increased risk of subsequent malignant neoplasms (SMNs). Previous studies reported elevated radiotherapy (RT) -related SMN risks, but less is known about chemotherapy-related risks.
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Etiologic heterogeneity among non-Hodgkin lymphoma subtypes: the InterLymph Non-Hodgkin Lymphoma Subtypes Project.
J. Natl. Cancer Inst. Monographs
PUBLISHED: 09-01-2014
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Non-Hodgkin lymphoma (NHL) comprises biologically and clinically heterogeneous subtypes. Previously, study size has limited the ability to compare and contrast the risk factor profiles among these heterogeneous subtypes.
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Medical history, lifestyle, family history, and occupational risk factors for adult acute lymphocytic leukemia: the InterLymph Non-Hodgkin Lymphoma Subtypes Project.
J. Natl. Cancer Inst. Monographs
PUBLISHED: 09-01-2014
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Acute lymphoblastic leukemia/lymphoma (ALL) in adults is a rare malignancy with a poor clinical outcome, and few reported etiologic risk factors.
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Medical history, lifestyle, and occupational risk factors for hairy cell leukemia: the InterLymph Non-Hodgkin Lymphoma Subtypes Project.
J. Natl. Cancer Inst. Monographs
PUBLISHED: 09-01-2014
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Little is known about the etiology of hairy cell leukemia (HCL), a rare B-cell lymphoproliferative disorder with marked male predominance. Our aim was to identify key risk factors for HCL.
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Medical history, lifestyle, family history, and occupational risk factors for sporadic Burkitt lymphoma/leukemia: the Interlymph Non-Hodgkin Lymphoma Subtypes Project.
J. Natl. Cancer Inst. Monographs
PUBLISHED: 09-01-2014
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The etiologic role of medical history, lifestyle, family history, and occupational risk factors in sporadic Burkitt lymphoma (BL) is unknown, but epidemiologic and clinical evidence suggests that risk factors may vary by age.
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Medical history, lifestyle, family history, and occupational risk factors for lymphoplasmacytic lymphoma/Waldenström's macroglobulinemia: the InterLymph Non-Hodgkin Lymphoma Subtypes Project.
J. Natl. Cancer Inst. Monographs
PUBLISHED: 09-01-2014
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Lymphoplasmacytic lymphoma/Waldenström's macroglobulinemia (LPL/WM), a rare non-Hodgkin lymphoma subtype, shows strong familial aggregation and a positive association with chronic immune stimulation, but evidence regarding other risk factors is very limited.
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Medical history, lifestyle, family history, and occupational risk factors for peripheral T-cell lymphomas: the InterLymph Non-Hodgkin Lymphoma Subtypes Project.
J. Natl. Cancer Inst. Monographs
PUBLISHED: 09-01-2014
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Accounting for 10%-15% of all non-Hodgkin lymphomas in Western populations, peripheral T-cell lymphomas (PTCL) are the most common T-cell lymphoma but little is known about their etiology. Our aim was to identify etiologic risk factors for PTCL overall, and for specific PTCL subtypes, by analyzing data from 15 epidemiologic studies participating in the InterLymph Consortium.
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Medical history, lifestyle, family history, and occupational risk factors for marginal zone lymphoma: the InterLymph Non-Hodgkin Lymphoma Subtypes Project.
J. Natl. Cancer Inst. Monographs
PUBLISHED: 09-01-2014
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Marginal zone lymphoma (MZL), comprised of nodal, extranodal, and splenic subtypes, accounts for 5%-10% of non-Hodgkin lymphoma cases. A detailed evaluation of the independent effects of risk factors for MZL and its subtypes has not been conducted.
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Medical history, lifestyle, family history, and occupational risk factors for chronic lymphocytic leukemia/small lymphocytic lymphoma: the InterLymph Non-Hodgkin Lymphoma Subtypes Project.
J. Natl. Cancer Inst. Monographs
PUBLISHED: 09-01-2014
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Chronic lymphocytic leukemia (CLL) and small lymphocytic lymphoma (SLL) are two subtypes of non-Hodgkin lymphoma. A number of studies have evaluated associations between risk factors and CLL/SLL risk. However, these associations remain inconsistent or lacked confirmation. This may be due, in part, to the inadequate sample size of CLL/SLL cases.
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Medical history, lifestyle, family history, and occupational risk factors for diffuse large B-cell lymphoma: the InterLymph Non-Hodgkin Lymphoma Subtypes Project.
J. Natl. Cancer Inst. Monographs
PUBLISHED: 09-01-2014
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Although risk factors for diffuse large B-cell lymphoma (DLBCL) have been suggested, their independent effects, modification by sex, and association with anatomical sites are largely unknown.
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Rationale and Design of the International Lymphoma Epidemiology Consortium (InterLymph) Non-Hodgkin Lymphoma Subtypes Project.
J. Natl. Cancer Inst. Monographs
PUBLISHED: 09-01-2014
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Non-Hodgkin lymphoma (NHL), the most common hematologic malignancy, consists of numerous subtypes. The etiology of NHL is incompletely understood, and increasing evidence suggests that risk factors may vary by NHL subtype. However, small numbers of cases have made investigation of subtype-specific risks challenging. The International Lymphoma Epidemiology Consortium therefore undertook the NHL Subtypes Project, an international collaborative effort to investigate the etiologies of NHL subtypes. This article describes in detail the project rationale and design.
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Fecal metabolomics: assay performance and association with colorectal cancer.
Carcinogenesis
PUBLISHED: 07-18-2014
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Metabolomic analysis of feces may provide insights on colorectal cancer (CRC) if assay performance is satisfactory. In lyophilized feces from 48 CRC cases, 102 matched controls, and 48 masked quality control specimens, 1043 small molecules were detected with a commercial platform. Assay reproducibility was good for 527 metabolites [technical intraclass correlation coefficient (ICC) >0.7 in quality control specimens], but reproducibility in 6-month paired specimens was lower for the majority of metabolites (within-subject ICC ?0.5). In the CRC cases and controls, significant differences (false discovery rate ?0.10) were found for 41 of 1043 fecal metabolites. Direct cancer association was found with three fecal heme-related molecules [covariate-adjusted 90th versus 10th percentile odds ratio (OR) = 17-345], 18 peptides/amino acids (OR = 3-14), palmitoyl-sphingomyelin (OR = 14), mandelate (OR = 3) and p-hydroxy-benzaldehyde (OR = 4). Conversely, cancer association was inverse with acetaminophen metabolites (OR <0.1), tocopherols (OR = 0.3), sitostanol (OR = 0.2), 3-dehydrocarnitine (OR = 0.4), pterin (OR = 0.3), conjugated-linoleate-18-2N7 (OR = 0.2), N-2-furoyl-glycine (OR = 0.3) and p-aminobenzoate (PABA, OR = 0.2). Correlations suggested an independent role for palmitoyl-sphingomyelin and a central role for PABA (which was stable over 6 months, within-subject ICC 0.67) modulated by p-hydroxy-benzaldehyde. Power calculations based on ICCs indicate that only 45% of metabolites with a true relative risk 5.0 would be found in prospectively collected, prediagnostic specimens from 500 cases and 500 controls. Thus, because fecal metabolites vary over time, very large studies will be needed to reliably detect associations of many metabolites that potentially contribute to CRC.
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Imputation and subset-based association analysis across different cancer types identifies multiple independent risk loci in the TERT-CLPTM1L region on chromosome 5p15.33.
Zhaoming Wang, Bin Zhu, Mingfeng Zhang, Hemang Parikh, Jinping Jia, Charles C Chung, Joshua N Sampson, Jason W Hoskins, Amy Hutchinson, Laurie Burdette, Abdisamad Ibrahim, Christopher Hautman, Preethi S Raj, Christian C Abnet, Andrew A Adjei, Anders Ahlbom, Demetrius Albanes, Naomi E Allen, Christine B Ambrosone, Melinda Aldrich, Pilar Amiano, Christopher Amos, Ulrika Andersson, Gerald Andriole, Irene L Andrulis, Cecilia Arici, Alan A Arslan, Melissa A Austin, Dalsu Baris, Donald A Barkauskas, Bryan A Bassig, Laura E Beane Freeman, Christine D Berg, Sonja I Berndt, Pier Alberto Bertazzi, Richard B Biritwum, Amanda Black, William Blot, Heiner Boeing, Paolo Boffetta, Kelly Bolton, Marie-Christine Boutron-Ruault, Paige M Bracci, Paul Brennan, Louise A Brinton, Michelle Brotzman, H Bas Bueno-de-Mesquita, Julie E Buring, Mary Ann Butler, Qiuyin Cai, Géraldine Cancel-Tassin, Federico Canzian, Guangwen Cao, Neil E Caporaso, Alfredo Carrato, Tania Carreon, Angela Carta, Gee-Chen Chang, I-Shou Chang, Jenny Chang-Claude, Xu Che, Chien-Jen Chen, Chih-Yi Chen, Chung-Hsing Chen, Constance Chen, Kuan-Yu Chen, Yuh-Min Chen, Anand P Chokkalingam, Lisa W Chu, Francoise Clavel-Chapelon, Graham A Colditz, Joanne S Colt, David Conti, Michael B Cook, Victoria K Cortessis, E David Crawford, Olivier Cussenot, Faith G Davis, Immaculata De Vivo, Xiang Deng, Ti Ding, Colin P Dinney, Anna Luisa Di Stefano, W Ryan Diver, Eric J Duell, Joanne W Elena, Jin-Hu Fan, Heather Spencer Feigelson, Maria Feychting, Jonine D Figueroa, Adrienne M Flanagan, Joseph F Fraumeni, Neal D Freedman, Brooke L Fridley, Charles S Fuchs, Manuela Gago-Dominguez, Steven Gallinger, Yu-Tang Gao, Susan M Gapstur, Montserrat Garcia-Closas, Reina Garcia-Closas, Julie M Gastier-Foster, J Michael Gaziano, Daniela S Gerhard, Carol A Giffen, Graham G Giles, Elizabeth M Gillanders, Edward L Giovannucci, Michael Goggins, Nalan Gokgoz, Alisa M Goldstein, Carlos González, Richard Gorlick, Mark H Greene, Myron Gross, H Barton Grossman, Robert Grubb, Jian Gu, Peng Guan, Christopher A Haiman, Göran Hallmans, Susan E Hankinson, Curtis C Harris, Patricia Hartge, Claudia Hattinger, Richard B Hayes, Qincheng He, Lee Helman, Brian E Henderson, Roger Henriksson, Judith Hoffman-Bolton, Chancellor Hohensee, Elizabeth A Holly, Yun-Chul Hong, Robert N Hoover, H Dean Hosgood, Chin-Fu Hsiao, Ann W Hsing, Chao Agnes Hsiung, Nan Hu, Wei Hu, Zhibin Hu, Ming-Shyan Huang, David J Hunter, Peter D Inskip, Hidemi Ito, Eric J Jacobs, Kevin B Jacobs, Mazda Jenab, Bu-Tian Ji, Christoffer Johansen, Mattias Johansson, Alison Johnson, Rudolf Kaaks, Ashish M Kamat, Aruna Kamineni, Margaret Karagas, Chand Khanna, Kay-Tee Khaw, Christopher Kim, In-Sam Kim, Jin Hee Kim, Yeul Hong Kim, Young-Chul Kim, Young Tae Kim, Chang Hyun Kang, Yoo Jin Jung, Cari M Kitahara, Alison P Klein, Robert Klein, Manolis Kogevinas, Woon-Puay Koh, Takashi Kohno, Laurence N Kolonel, Charles Kooperberg, Christian P Kratz, Vittorio Krogh, Hideo Kunitoh, Robert C Kurtz, Nilgun Kurucu, Qing Lan, Mark Lathrop, Ching C Lau, Fernando Lecanda, Kyoung-Mu Lee, Maxwell P Lee, Loic Le Marchand, Seth P Lerner, Donghui Li, Linda M Liao, Wei-Yen Lim, Dongxin Lin, Jie Lin, Sara Lindstrom, Martha S Linet, Jolanta Lissowska, Jianjun Liu, Börje Ljungberg, Josep Lloreta, Daru Lu, Jing Ma, Nuria Malats, Satu Mannisto, Neyssa Marina, Giuseppe Mastrangelo, Keitaro Matsuo, Katherine A McGlynn, Roberta Mckean-Cowdin, Lorna H McNeill, Robert R McWilliams, Beatrice S Melin, Paul S Meltzer, James E Mensah, Xiaoping Miao, Dominique S Michaud, Alison M Mondul, Lee E Moore, Kenneth Muir, Shelley Niwa, Sara H Olson, Nick Orr, Salvatore Panico, Jae Yong Park, Alpa V Patel, Ana Patiño-García, Sofia Pavanello, Petra H M Peeters, Beata Peplonska, Ulrike Peters, Gloria M Petersen, Piero Picci, Malcolm C Pike, Stefano Porru, Jennifer Prescott, Xia Pu, Mark P Purdue, You-Lin Qiao, Preetha Rajaraman, Elio Riboli, Harvey A Risch, Rebecca J Rodabough, Nathaniel Rothman, Avima M Ruder, Jeong-Seon Ryu, Marc Sanson, Alan Schned, Fredrick R Schumacher, Ann G Schwartz, Kendra L Schwartz, Molly Schwenn, Katia Scotlandi, Adeline Seow, Consol Serra, Massimo Serra, Howard D Sesso, Gianluca Severi, Hongbing Shen, Min Shen, Sanjay Shete, Kouya Shiraishi, Xiao-Ou Shu, Afshan Siddiq, Luis Sierrasesúmaga, Sabina Sierri, Alan Dart Loon Sihoe, Debra T Silverman, Matthias Simon, Melissa C Southey, Logan Spector, Margaret Spitz, Meir Stampfer, Pär Stattin, Mariana C Stern, Victoria L Stevens, Rachael Z Stolzenberg-Solomon, Daniel O Stram, Sara S Strom, Wu-Chou Su, Malin Sund, Sook Whan Sung, Anthony Swerdlow, Wen Tan, Hideo Tanaka, Wei Tang, Ze-Zhang Tang, Adonina Tardón, Evelyn Tay, Philip R Taylor, Yao Tettey, David M Thomas, Roberto Tirabosco, Anne Tjonneland, Geoffrey S Tobias, Jorge R Toro, Ruth C Travis, Dimitrios Trichopoulos, Rebecca Troisi, Ann Truelove, Ying-Huang Tsai, Margaret A Tucker, Rosario Tumino, David Van Den Berg, Stephen K Van Den Eeden, Roel Vermeulen, Paolo Vineis, Kala Visvanathan, Ulla Vogel, Chaoyu Wang, Chengfeng Wang, Junwen Wang, Sophia S Wang, Elisabete Weiderpass, Stephanie J Weinstein, Nicolas Wentzensen, William Wheeler, Emily White, John K Wiencke, Alicja Wolk, Brian M Wolpin, Maria Pik Wong, Margaret Wrensch, Chen Wu, Tangchun Wu, Xifeng Wu, Yi-Long Wu, Jay S Wunder, Yong-Bing Xiang, Jun Xu, Hannah P Yang, Pan-Chyr Yang, Yasushi Yatabe, Yuanqing Ye, Edward D Yeboah, Zhihua Yin, Chen Ying, Chong-Jen Yu, Kai Yu, Jian-Min Yuan, Krista A Zanetti, Anne Zeleniuch-Jacquotte, Wei Zheng, Baosen Zhou, Lisa Mirabello, Sharon A Savage, Peter Kraft, Stephen J Chanock, Meredith Yeager, Maria Terese Landi, Jianxin Shi, Nilanjan Chatterjee, Laufey T Amundadottir.
Hum. Mol. Genet.
PUBLISHED: 07-15-2014
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Genome-wide association studies (GWAS) have mapped risk alleles for at least 10 distinct cancers to a small region of 63 000 bp on chromosome 5p15.33. This region harbors the TERT and CLPTM1L genes; the former encodes the catalytic subunit of telomerase reverse transcriptase and the latter may play a role in apoptosis. To investigate further the genetic architecture of common susceptibility alleles in this region, we conducted an agnostic subset-based meta-analysis (association analysis based on subsets) across six distinct cancers in 34 248 cases and 45 036 controls. Based on sequential conditional analysis, we identified as many as six independent risk loci marked by common single-nucleotide polymorphisms: five in the TERT gene (Region 1: rs7726159, P = 2.10 × 10(-39); Region 3: rs2853677, P = 3.30 × 10(-36) and PConditional = 2.36 × 10(-8); Region 4: rs2736098, P = 3.87 × 10(-12) and PConditional = 5.19 × 10(-6), Region 5: rs13172201, P = 0.041 and PConditional = 2.04 × 10(-6); and Region 6: rs10069690, P = 7.49 × 10(-15) and PConditional = 5.35 × 10(-7)) and one in the neighboring CLPTM1L gene (Region 2: rs451360; P = 1.90 × 10(-18) and PConditional = 7.06 × 10(-16)). Between three and five cancers mapped to each independent locus with both risk-enhancing and protective effects. Allele-specific effects on DNA methylation were seen for a subset of risk loci, indicating that methylation and subsequent effects on gene expression may contribute to the biology of risk variants on 5p15.33. Our results provide strong support for extensive pleiotropy across this region of 5p15.33, to an extent not previously observed in other cancer susceptibility loci.
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Genome-wide association study identifies multiple susceptibility loci for diffuse large B cell lymphoma.
James R Cerhan, Sonja I Berndt, Joseph Vijai, Hervé Ghesquières, James McKay, Sophia S Wang, Zhaoming Wang, Meredith Yeager, Lucia Conde, Paul I W de Bakker, Alexandra Nieters, David Cox, Laurie Burdett, Alain Monnereau, Christopher R Flowers, Anneclaire J De Roos, Angela R Brooks-Wilson, Qing Lan, Gianluca Severi, Mads Melbye, Jian Gu, Rebecca D Jackson, Eleanor Kane, Lauren R Teras, Mark P Purdue, Claire M Vajdic, John J Spinelli, Graham G Giles, Demetrius Albanes, Rachel S Kelly, Mariagrazia Zucca, Kimberly A Bertrand, Anne Zeleniuch-Jacquotte, Charles Lawrence, Amy Hutchinson, Degui Zhi, Thomas M Habermann, Brian K Link, Anne J Novak, Ahmet Dogan, Yan W Asmann, Mark Liebow, Carrie A Thompson, Stephen M Ansell, Thomas E Witzig, George J Weiner, Amelie S Veron, Diana Zelenika, Hervé Tilly, Corinne Haioun, Thierry Jo Molina, Henrik Hjalgrim, Bengt Glimelius, Hans-Olov Adami, Paige M Bracci, Jacques Riby, Martyn T Smith, Elizabeth A Holly, Wendy Cozen, Patricia Hartge, Lindsay M Morton, Richard K Severson, Lesley F Tinker, Kari E North, Nikolaus Becker, Yolanda Benavente, Paolo Boffetta, Paul Brennan, Lenka Foretova, Marc Maynadié, Anthony Staines, Tracy Lightfoot, Simon Crouch, Alex Smith, Eve Roman, W Ryan Diver, Kenneth Offit, Andrew Zelenetz, Robert J Klein, Danylo J Villano, Tongzhang Zheng, Yawei Zhang, Theodore R Holford, Anne Kricker, Jenny Turner, Melissa C Southey, Jacqueline Clavel, Jarmo Virtamo, Stephanie Weinstein, Elio Riboli, Paolo Vineis, Rudolph Kaaks, Dimitrios Trichopoulos, Roel C H Vermeulen, Heiner Boeing, Anne Tjonneland, Emanuele Angelucci, Simonetta Di Lollo, Marco Rais, Brenda M Birmann, Francine Laden, Edward Giovannucci, Peter Kraft, Jinyan Huang, Baoshan Ma, Yuanqing Ye, Brian C H Chiu, Joshua Sampson, Liming Liang, Ju-Hyun Park, Charles C Chung, Dennis D Weisenburger, Nilanjan Chatterjee, Joseph F Fraumeni, Susan L Slager, Xifeng Wu, Silvia de Sanjosé, Karin E Smedby, Gilles Salles, Christine F Skibola, Nathaniel Rothman, Stephen J Chanock.
Nat. Genet.
PUBLISHED: 06-26-2014
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Diffuse large B cell lymphoma (DLBCL) is the most common lymphoma subtype and is clinically aggressive. To identify genetic susceptibility loci for DLBCL, we conducted a meta-analysis of 3 new genome-wide association studies (GWAS) and 1 previous scan, totaling 3,857 cases and 7,666 controls of European ancestry, with additional genotyping of 9 promising SNPs in 1,359 cases and 4,557 controls. In our multi-stage analysis, five independent SNPs in four loci achieved genome-wide significance marked by rs116446171 at 6p25.3 (EXOC2; P = 2.33 × 10(-21)), rs2523607 at 6p21.33 (HLA-B; P = 2.40 × 10(-10)), rs79480871 at 2p23.3 (NCOA1; P = 4.23 × 10(-8)) and two independent SNPs, rs13255292 and rs4733601, at 8q24.21 (PVT1; P = 9.98 × 10(-13) and 3.63 × 10(-11), respectively). These data provide substantial new evidence for genetic susceptibility to this B cell malignancy and point to pathways involved in immune recognition and immune function in the pathogenesis of DLBCL.
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Pooling prospective studies to investigate the etiology of second cancers.
Cancer Epidemiol. Biomarkers Prev.
PUBLISHED: 05-15-2014
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With over 13 million cancer survivors in the United States today, second cancers are of rapidly growing importance. However, data on nontreatment risk factors for second cancers are sparse. We explored the feasibility of pooling data from cohort studies of cancer incidence to investigate second cancer etiology.
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Metabolomics in nutritional epidemiology: identifying metabolites associated with diet and quantifying their potential to uncover diet-disease relations in populations.
Am. J. Clin. Nutr.
PUBLISHED: 04-16-2014
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Metabolomics is an emerging field with the potential to advance nutritional epidemiology; however, it has not yet been applied to large cohort studies.OBJECTIVES: Our first aim was to identify metabolites that are biomarkers of usual dietary intake. Second, among serum metabolites correlated with diet, we evaluated metabolite reproducibility and required sample sizes to determine the potential for metabolomics in epidemiologic studies.DESIGN: Baseline serum from 502 participants in the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial was analyzed by using ultra-high-performance liquid-phase chromatography with tandem mass spectrometry and gas chromatography-mass spectrometry. Usual intakes of 36 dietary groups were estimated by using a food-frequency questionnaire. Dietary biomarkers were identified by using partial Pearson's correlations with Bonferroni correction for multiple comparisons. Intraclass correlation coefficients (ICCs) between samples collected 1 y apart in a subset of 30 individuals were calculated to evaluate intraindividual metabolite variability.RESULTS: We detected 412 known metabolites. Citrus, green vegetables, red meat, shellfish, fish, peanuts, rice, butter, coffee, beer, liquor, total alcohol, and multivitamins were each correlated with at least one metabolite (P < 1.093 × 10(-6); r = -0.312 to 0.398); in total, 39 dietary biomarkers were identified. Some correlations (citrus intake with stachydrine) replicated previous studies; others, such as peanuts and tryptophan betaine, were novel findings. Other strong associations included coffee (with trigonelline-N-methylnicotinate and quinate) and alcohol (with ethyl glucuronide). Intraindividual variability in metabolite levels (1-y ICCs) ranged from 0.27 to 0.89. Large, but attainable, sample sizes are required to detect associations between metabolites and disease in epidemiologic studies, further emphasizing the usefulness of metabolomics in nutritional epidemiology.Conclusions: We identified dietary biomarkers by using metabolomics in an epidemiologic data set. Given the strength of the associations observed, we expect that some of these metabolites will be validated in future studies and later used as biomarkers in large cohorts to study diet-disease associations. The PLCO trial was registered at clinicaltrials.gov as NCT00002540.
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A prospective study of serum metabolites and colorectal cancer risk.
Cancer
PUBLISHED: 04-03-2014
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Colorectal cancer is highly prevalent, and the vast majority of cases are thought to be sporadic, although few risk factors have been identified. Using metabolomics technology, our aim was to identify biomarkers prospectively associated with colorectal cancer.
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Metabolites of tobacco smoking and colorectal cancer risk.
Carcinogenesis
PUBLISHED: 03-19-2014
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Colorectal cancer is not strictly considered a tobacco-related malignancy, but modest associations have emerged from large meta-analyses. Most studies, however, use self-reported data, which are subject to misclassification. Biomarkers of tobacco exposure may reduce misclassification and provide insight into metabolic variability that potentially influences carcinogenesis. Our aim was to identify metabolites that represent smoking habits and individual variation in tobacco metabolism, and investigate their association with colorectal cancer. In a nested case-control study of 255 colorectal cancers and 254 matched controls identified in the Prostate, Lung, Colorectal and Ovarian cancer screening trial, baseline serum was used to identify metabolites by ultra-high-performance liquid-phase chromatography and mass spectrometry, as well as gas chromatography with tandem mass spectrometry. Odds ratios (OR) and 95% confidence intervals (CI) were estimated by logistic regression. Self-reported current smoking was associated with serum cotinine, O-cresol sulfate and hydroxycotinine. Self-reported current smoking of any tobacco (OR = 1.90, 95% CI: 1.02-3.54) and current cigarette smoking (OR = 1.51, 95% CI: 0.75-3.04) were associated with elevated colorectal cancer risks, although the latter was not statistically significant. Individuals with detectable levels of hydroxycotinine had an increased colorectal cancer risk compared with those with undetectable levels (OR = 2.68, 95% CI: 1.33-5.40). Although those with detectable levels of cotinine had a suggestive elevated risk of this malignancy (OR = 1.81, 95% CI: 0.98-3.33), those with detectable levels of O-cresol sulfate did not (OR = 1.16, 95% CI: 0.57-2.37). Biomarkers capturing smoking behavior and metabolic variation exhibit stronger associations with colorectal cancer than self-report, providing additional evidence for a role for tobacco in this malignancy.
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Rare missense variants in POT1 predispose to familial cutaneous malignant melanoma.
Nat. Genet.
PUBLISHED: 03-07-2014
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Although CDKN2A is the most frequent high-risk melanoma susceptibility gene, the underlying genetic factors for most melanoma-prone families remain unknown. Using whole-exome sequencing, we identified a rare variant that arose as a founder mutation in the telomere shelterin gene POT1 (chromosome 7, g.124493086C>T; p.Ser270Asn) in five unrelated melanoma-prone families from Romagna, Italy. Carriers of this variant had increased telomere lengths and numbers of fragile telomeres, suggesting that this variant perturbs telomere maintenance. Two additional rare POT1 variants were identified in all cases sequenced in two separate Italian families, one variant per family, yielding a frequency for POT1 variants comparable to that for CDKN2A mutations in this population. These variants were not found in public databases or in 2,038 genotyped Italian controls. We also identified two rare recurrent POT1 variants in US and French familial melanoma cases. Our findings suggest that POT1 is a major susceptibility gene for familial melanoma in several populations.
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Discovery and validation of methylation markers for endometrial cancer.
Int. J. Cancer
PUBLISHED: 01-29-2014
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The prognosis of endometrial cancer is strongly associated with stage at diagnosis, suggesting that early detection may reduce mortality. Women who are diagnosed with endometrial carcinoma often have a lengthy history of vaginal bleeding, which offers an opportunity for early diagnosis and curative treatment. We performed DNA methylation profiling on population-based endometrial cancers to identify early detection biomarkers and replicated top candidates in two independent studies. We compared DNA methylation values of 1,500 probes representing 807 genes in 148 population-based endometrial carcinoma samples and 23 benign endometrial tissues. Markers were replicated in another set of 69 carcinomas and 40 benign tissues profiled on the same platform. Further replication was conducted in The Cancer Genome Atlas and in prospectively collected endometrial brushings from women with and without endometrial carcinomas. We identified 114 CpG sites showing methylation differences with p values of ? 10(-7) between endometrial carcinoma and normal endometrium. Eight genes (ADCYAP1, ASCL2, HS3ST2, HTR1B, MME, NPY and SOX1) were selected for further replication. Age-adjusted odds ratios for endometrial cancer ranged from 3.44 (95%-CI: 1.33-8.91) for ASCL2 to 18.61 (95%-CI: 5.50-62.97) for HTR1B. An area under the curve (AUC) of 0.93 was achieved for discriminating carcinoma from benign endometrium. Replication in The Cancer Genome Atlas and in endometrial brushings from an independent study confirmed the candidate markers. This study demonstrates that methylation markers may be used to evaluate women with abnormal vaginal bleeding to distinguish women with endometrial carcinoma from the majority of women without malignancy.
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Sources of variability in metabolite measurements from urinary samples.
PLoS ONE
PUBLISHED: 01-01-2014
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The application of metabolomics in epidemiological studies would potentially allow researchers to identify biomarkers associated with exposures and diseases. However, within-individual variability of metabolite levels caused by temporal variation of metabolites, together with technical variability introduced by laboratory procedures, may reduce the study power to detect such associations. We assessed the sources of variability of metabolites from urine samples and the implications for designing epidemiologic studies.
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Testing multiple biological mediators simultaneously.
Bioinformatics
PUBLISHED: 11-06-2013
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?Modern biomedical and epidemiological studies often measure hundreds or thousands of biomarkers, such as gene expression or metabolite levels. Although there is an extensive statistical literature on adjusting for multiple comparisons when testing whether these biomarkers are directly associated with a disease, testing whether they are biological mediators between a known risk factor and a disease requires a more complex null hypothesis, thus offering additional methodological challenges.
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Validation of a previous-day recall measure of active and sedentary behaviors.
Med Sci Sports Exerc
PUBLISHED: 07-19-2013
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A previous-day recall (PDR) may be a less error-prone alternative to traditional questionnaire-based estimates of physical activity and sedentary behavior (e.g., past year), but the validity of the method is not established. We evaluated the validity of an interviewer administered PDR in adolescents (12-17 yr) and adults (18-71 yr).
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Metabolomic profile of response to supplementation with ?-carotene in the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study.
Am. J. Clin. Nutr.
PUBLISHED: 06-26-2013
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Two chemoprevention trials found that supplementation with ?-carotene increased the risk of lung cancer and overall mortality. The biologic basis of these findings remains poorly understood.
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Dietary fat intake and risk of pancreatic cancer in the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial.
Ann Epidemiol
PUBLISHED: 05-16-2013
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Epidemiologic and experimental studies suggest that dietary fat intake may affect risk of pancreatic cancer, but published results are inconsistent.
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Genome-wide association study identifies multiple risk loci for chronic lymphocytic leukemia.
Sonja I Berndt, Christine F Skibola, Vijai Joseph, Nicola J Camp, Alexandra Nieters, Zhaoming Wang, Wendy Cozen, Alain Monnereau, Sophia S Wang, Rachel S Kelly, Qing Lan, Lauren R Teras, Nilanjan Chatterjee, Charles C Chung, Meredith Yeager, Angela R Brooks-Wilson, Patricia Hartge, Mark P Purdue, Brenda M Birmann, Bruce K Armstrong, Pierluigi Cocco, Yawei Zhang, Gianluca Severi, Anne Zeleniuch-Jacquotte, Charles Lawrence, Laurie Burdette, Jeffrey Yuenger, Amy Hutchinson, Kevin B Jacobs, Timothy G Call, Tait D Shanafelt, Anne J Novak, Neil E Kay, Mark Liebow, Alice H Wang, Karin E Smedby, Hans-Olov Adami, Mads Melbye, Bengt Glimelius, Ellen T Chang, Martha Glenn, Karen Curtin, Lisa A Cannon-Albright, Brandt Jones, W Ryan Diver, Brian K Link, George J Weiner, Lucia Conde, Paige M Bracci, Jacques Riby, Elizabeth A Holly, Martyn T Smith, Rebecca D Jackson, Lesley F Tinker, Yolanda Benavente, Nikolaus Becker, Paolo Boffetta, Paul Brennan, Lenka Foretova, Marc Maynadié, James McKay, Anthony Staines, Kari G Rabe, Sara J Achenbach, Celine M Vachon, Lynn R Goldin, Sara S Strom, Mark C Lanasa, Logan G Spector, Jose F Leis, Julie M Cunningham, J Brice Weinberg, Vicki A Morrison, Neil E Caporaso, Aaron D Norman, Martha S Linet, Anneclaire J De Roos, Lindsay M Morton, Richard K Severson, Elio Riboli, Paolo Vineis, Rudolph Kaaks, Dimitrios Trichopoulos, Giovanna Masala, Elisabete Weiderpass, Maria-Dolores Chirlaque, Roel C H Vermeulen, Ruth C Travis, Graham G Giles, Demetrius Albanes, Jarmo Virtamo, Stephanie Weinstein, Jacqueline Clavel, Tongzhang Zheng, Theodore R Holford, Kenneth Offit, Andrew Zelenetz, Robert J Klein, John J Spinelli, Kimberly A Bertrand, Francine Laden, Edward Giovannucci, Peter Kraft, Anne Kricker, Jenny Turner, Claire M Vajdic, Maria Grazia Ennas, Giovanni M Ferri, Lucia Miligi, Liming Liang, Joshua Sampson, Simon Crouch, Ju-Hyun Park, Kari E North, Angela Cox, John A Snowden, Josh Wright, Angel Carracedo, Carlos Lopez-Otin, Sílvia Beà, Itziar Salaverria, David Martín-Garcia, Elias Campo, Joseph F Fraumeni, Silvia de Sanjosé, Henrik Hjalgrim, James R Cerhan, Stephen J Chanock, Nathaniel Rothman, Susan L Slager.
Nat. Genet.
PUBLISHED: 05-02-2013
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Genome-wide association studies (GWAS) have previously identified 13 loci associated with risk of chronic lymphocytic leukemia or small lymphocytic lymphoma (CLL). To identify additional CLL susceptibility loci, we conducted the largest meta-analysis for CLL thus far, including four GWAS with a total of 3,100 individuals with CLL (cases) and 7,667 controls. In the meta-analysis, we identified ten independent associated SNPs in nine new loci at 10q23.31 (ACTA2 or FAS (ACTA2/FAS), P=1.22×10(-14)), 18q21.33 (BCL2, P=7.76×10(-11)), 11p15.5 (C11orf21, P=2.15×10(-10)), 4q25 (LEF1, P=4.24×10(-10)), 2q33.1 (CASP10 or CASP8 (CASP10/CASP8), P=2.50×10(-9)), 9p21.3 (CDKN2B-AS1, P=1.27×10(-8)), 18q21.32 (PMAIP1, P=2.51×10(-8)), 15q15.1 (BMF, P=2.71×10(-10)) and 2p22.2 (QPCT, P=1.68×10(-8)), as well as an independent signal at an established locus (2q13, ACOXL, P=2.08×10(-18)). We also found evidence for two additional promising loci below genome-wide significance at 8q22.3 (ODF1, P=5.40×10(-8)) and 5p15.33 (TERT, P=1.92×10(-7)). Although further studies are required, the proximity of several of these loci to genes involved in apoptosis suggests a plausible underlying biological mechanism.
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Controlling the local false discovery rate in the adaptive Lasso.
Biostatistics
PUBLISHED: 04-09-2013
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The Lasso shrinkage procedure achieved its popularity, in part, by its tendency to shrink estimated coefficients to zero, and its ability to serve as a variable selection procedure. Using data-adaptive weights, the adaptive Lasso modified the original procedure to increase the penalty terms for those variables estimated to be less important by ordinary least squares. Although this modified procedure attained the oracle properties, the resulting models tend to include a large number of "false positives" in practice. Here, we adapt the concept of local false discovery rates (lFDRs) so that it applies to the sequence, ?n, of smoothing parameters for the adaptive Lasso. We define the lFDR for a given ?n to be the probability that the variable added to the model by decreasing ?n to ?n-? is not associated with the outcome, where ? is a small value. We derive the relationship between the lFDR and ?n, show lFDR =1 for traditional smoothing parameters, and show how to select ?n so as to achieve a desired lFDR. We compare the smoothing parameters chosen to achieve a specified lFDR and those chosen to achieve the oracle properties, as well as their resulting estimates for model coefficients, with both simulation and an example from a genetic study of prostate specific antigen.
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Projecting the performance of risk prediction based on polygenic analyses of genome-wide association studies.
Nat. Genet.
PUBLISHED: 02-08-2013
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We report a new method to estimate the predictive performance of polygenic models for risk prediction and assess predictive performance for ten complex traits or common diseases. Using estimates of effect-size distribution and heritability derived from current studies, we project that although 45% of the variance of height has been attributed to SNPs, a model trained on one million people may only explain 33.4% of variance of the trait. Models based on current studies allow for identification of 3.0%, 1.1% and 7.0% of the populations at twofold or higher than average risk for type 2 diabetes, coronary artery disease and prostate cancer, respectively. Tripling of sample sizes could elevate these percentages to 18.8%, 6.1% and 12.2%, respectively. The utility of polygenic models for risk prediction will depend on achievable sample sizes for the training data set, the underlying genetic architecture and the inclusion of information on other risk factors, including family history.
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Metabolomics in epidemiology: sources of variability in metabolite measurements and implications.
Cancer Epidemiol. Biomarkers Prev.
PUBLISHED: 02-08-2013
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Metabolite levels within an individual vary over time. This within-individual variability, coupled with technical variability, reduces the power for epidemiologic studies to detect associations with disease. Here, the authors assess the variability of a large subset of metabolites and evaluate the implications for epidemiologic studies.
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Individual Variations in Serum Melatonin Levels through Time: Implications for Epidemiologic Studies.
PLoS ONE
PUBLISHED: 01-01-2013
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Melatonin, a marker for the circadian rhythm with serum levels peaking between 2AM and 5AM, is hypothesized to possess anti-cancer properties, making it a mechanistic candidate for the probable carcinogenic effect of circadian rhythm disruption. In order to weigh epidemiologic evidence on the association of melatonin with cancer, we must first understand the laboratory and biological sources of variability in melatonin levels measured in samples. Participants for this methodological study were men enrolled in the Prostate Lung Colorectal and Ovarian Cancer Screening Trial (PLCO). We measured serum melatonin levels over a five year period in 97 individuals to test if melatonin levels are steady over time. The Pearson correlation coefficient between two measures separated by 1 year was 0.87, while the correlation between two measures separated by 5 years was to 0.70. In an additional cross-sectional study of 292 individuals, we used Analysis of Variance to identify differences in melatonin levels between different lifestyle and environmental characteristics. Serum melatonin levels were slightly higher in samples collected from 130 individuals during the winter, (6.36±0.59 pg/ml) than in samples collected from 119 individuals during the summer (4.83±0.62 pg/ml). Serum melatonin levels were lowest in current smokers (3.02±1.25 pg/ml, p?=?0.007) compared to never (6.66±0.66 pg/ml) and former (5.59±0.50 pg/ml) smokers whereas BMI did not significantly affect serum melatonin levels in this study. In conclusion, the high 5 year correlation of melatonin levels implies that single measurements may be used to detect population level associations between melatonin and risk of cancer. Furthermore, our results reiterate the need to record season of sample collection, and individual characteristics in order to maximize study power and prevent confounding.
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Ovarian cancer risk factors by histologic subtypes in the NIH-AARP Diet and Health Study.
Int. J. Cancer
PUBLISHED: 07-25-2011
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Data suggest that risk factors for ovarian carcinoma vary by histologic type, but findings are inconsistent. We prospectively evaluated risk factors by histological subtypes of incident ovarian cancer (n = 849) in a cohort of 169,391 women in the NIH-AARP Diet and Health Study. We constructed Cox models of individual exposures by comparing case subtypes to the entire non-case group and assessed p-heterogeneity in case-case comparisons using serous as the reference category. Substantial risk differences between histologic subtypes were observed for menopausal hormone therapy (MHT) use, oral contraceptive (OC) use, parity and body mass index (p-heterogeneity = 0.01, 0.03, 0.05, 0.03, respectively). MHT users were at increased risk for all histologic subtypes except for mucinous carcinomas, where risk was reduced (relative risk (RR) = 0.37; 95% confidence interval (CI): 0.18, 0.80). OC users were only at significantly decreased risk for serous cancers (RR = 0.69; 95% CI: 0.55, 0.85). Although parity was inversely associated with risk of all subtypes, the RRs ranged from 0.28 (clear cell) to 0.83 (serous). Obesity was a significant risk factor only for endometrioid cancers (RR = 1.64; 95% CI: 1.00, 2.70). Our findings support a link between etiological factors and histological heterogeneity in ovarian carcinoma.
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Selecting SNPs to identify ancestry.
Ann. Hum. Genet.
PUBLISHED: 06-15-2011
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An individuals genotypes at a group of single-nucleotide polymorphisms (SNPs) can be used to predict that individuals ethnicity or ancestry. In medical studies, knowledge of a subjects ancestry can minimize possible confounding, and in forensic applications, such knowledge can help direct investigations. Our goal is to select a small subset of SNPs, from the millions already identified in the human genome, that can predict ancestry with a minimal error rate. The general form for this variable selection procedure is to estimate the expected error rates for sets of SNPs using a training dataset and consider those sets with the lowest error rates given their size. The quality of the estimate for the error rate determines the quality of the resulting SNPs. As the apparent error rate performs poorly when either the number of SNPs or the number of populations is large; we propose a new estimate, the Improved Bayesian Estimate. We demonstrate that selection procedures based on this estimate produce small sets of SNPs that can accurately predict ancestry. We also provide a list of the 100 optimal SNPs for identifying ancestry.
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Large-scale fine mapping of the HNF1B locus and prostate cancer risk.
Hum. Mol. Genet.
PUBLISHED: 05-16-2011
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Previous genome-wide association studies have identified two independent variants in HNF1B as susceptibility loci for prostate cancer risk. To fine-map common genetic variation in this region, we genotyped 79 single nucleotide polymorphisms (SNPs) in the 17q12 region harboring HNF1B in 10 272 prostate cancer cases and 9123 controls of European ancestry from 10 case-control studies as part of the Cancer Genetic Markers of Susceptibility (CGEMS) initiative. Ten SNPs were significantly related to prostate cancer risk at a genome-wide significance level of P < 5 × 10(-8) with the most significant association with rs4430796 (P = 1.62 × 10(-24)). However, risk within this first locus was not entirely explained by rs4430796. Although modestly correlated (r(2)= 0.64), rs7405696 was also associated with risk (P = 9.35 × 10(-23)) even after adjustment for rs4430769 (P = 0.007). As expected, rs11649743 was related to prostate cancer risk (P = 3.54 × 10(-8)); however, the association within this second locus was stronger for rs4794758 (P = 4.95 × 10(-10)), which explained all of the risk observed with rs11649743 when both SNPs were included in the same model (P = 0.32 for rs11649743; P = 0.002 for rs4794758). Sequential conditional analyses indicated that five SNPs (rs4430796, rs7405696, rs4794758, rs1016990 and rs3094509) together comprise the best model for risk in this region. This study demonstrates a complex relationship between variants in the HNF1B region and prostate cancer risk. Further studies are needed to investigate the biological basis of the association of variants in 17q12 with prostate cancer.
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Empirical Performance of Cross-Validation With Oracle Methods in a Genomics Context.
Am Stat
PUBLISHED: 04-28-2011
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When employing model selection methods with oracle properties such as the smoothly clipped absolute deviation (SCAD) and the Adaptive Lasso, it is typical to estimate the smoothing parameter by m-fold cross-validation, for example, m = 10. In problems where the true regression function is sparse and the signals large, such cross-validation typically works well. However, in regression modeling of genomic studies involving Single Nucleotide Polymorphisms (SNP), the true regression functions, while thought to be sparse, do not have large signals. We demonstrate empirically that in such problems, the number of selected variables using SCAD and the Adaptive Lasso, with 10-fold cross-validation, is a random variable that has considerable and surprising variation. Similar remarks apply to non-oracle methods such as the Lasso. Our study strongly questions the suitability of performing only a single run of m-fold cross-validation with any oracle method, and not just the SCAD and Adaptive Lasso.
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Fine mapping the KLK3 locus on chromosome 19q13.33 associated with prostate cancer susceptibility and PSA levels.
Hum. Genet.
PUBLISHED: 01-17-2011
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Measurements of serum prostate-specific antigen (PSA) protein levels form the basis for a widely used test to screen men for prostate cancer. Germline variants in the gene that encodes the PSA protein (KLK3) have been shown to be associated with both serum PSA levels and prostate cancer. Based on a resequencing analysis of a 56 kb region on chromosome 19q13.33, centered on the KLK3 gene, we fine mapped this locus by genotyping tag SNPs in 3,522 prostate cancer cases and 3,338 controls from five case-control studies. We did not observe a strong association with the KLK3 variant, reported in previous studies to confer risk for prostate cancer (rs2735839; P = 0.20) but did observe three highly correlated SNPs (rs17632542, rs62113212 and rs62113214) associated with prostate cancer [P = 3.41 × 10(-4), per-allele trend odds ratio (OR) = 0.77, 95% CI = 0.67-0.89]. The signal was apparent only for nonaggressive prostate cancer cases with Gleason score <7 and disease stage 8 or stage ?III (P = 0.31, per-allele trend OR = 1.12, 95% CI = 0.90-1.40). One of the three highly correlated SNPs, rs17632542, introduces a non-synonymous amino acid change in the KLK3 protein with a predicted benign or neutral functional impact. Baseline PSA levels were 43.7% higher in control subjects with no minor alleles (1.61 ng/ml, 95% CI = 1.49-1.72) than in those with one or more minor alleles at any one of the three SNPs (1.12 ng/ml, 95% CI = 0.96-1.28) (P = 9.70 × 10(-5)). Together our results suggest that germline KLK3 variants could influence the diagnosis of nonaggressive prostate cancer by influencing the likelihood of biopsy.
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Efficient study design for next generation sequencing.
Genet. Epidemiol.
PUBLISHED: 01-12-2011
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Next Generation Sequencing represents a powerful tool for detecting genetic variation associated with human disease. Because of the high cost of this technology, it is critical that we develop efficient study designs that consider the trade-off between the number of subjects (n) and the coverage depth (µ). How we divide our resources between the two can greatly impact study success, particularly in pilot studies. We propose a strategy for selecting the optimal combination of n and µ for studies aimed at detecting rare variants and for studies aimed at detecting associations between rare or uncommon variants and disease. For detecting rare variants, we find the optimal coverage depth to be between 2 and 8 reads when using the likelihood ratio test. For association studies, we find the strategy of sequencing all available subjects to be preferable. In deriving these combinations, we provide a detailed analysis describing the distribution of depth across a genome and the depth needed to identify a minor allele in an individual. The optimal coverage depth depends on the aims of the study, and the chosen depth can have a large impact on study success. Genet. Epidemiol. 2011.??© 2011 Wiley-Liss, Inc.
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A note on the effect on power of score tests via dimension reduction by penalized regression under the null.
Int J Biostat
PUBLISHED: 03-29-2010
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We consider the problem of score testing for certain low dimensional parameters of interest in a model that could include finite but high dimensional secondary covariates and associated nuisance parameters. We investigate the possibility of the potential gain in power by reducing the dimensionality of the secondary variables via oracle estimators such as the Adaptive Lasso. As an application, we use a recently developed framework for score tests of association of a disease outcome with an exposure of interest in the presence of a possible interaction of the exposure with other co-factors of the model. We derive the local power of such tests and show that if the primary and secondary predictors are independent, then having an oracle estimator does not improve the local power of the score test. Conversely, if they are dependent, there is the potential for power gain. Simulations are used to validate the theoretical results and explore the extent of correlation needed between the primary and secondary covariates to observe an improvement of the power of the test by using the oracle estimator. Our conclusions are likely to hold more generally beyond the model of interactions considered here.
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Identifying individuals in a complex mixture of DNA with unknown ancestry.
Stat Appl Genet Mol Biol
PUBLISHED: 09-09-2009
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A new test was recently developed that could use a high-density set of single nucleotide polymorphisms (SNPs) to determine whether a specific individual contributed to a mixture of DNA. The test statistic compared the genotype for the individual to the allele frequencies in the mixture and to the allele frequencies in a reference group. This test requires the ancestries of the reference group to be nearly identical to those of the contributors to the mixture. Here, we first quantify the bias, the increase in type I and type II error, when the ancestries are not well matched. Then, we show that the test can also be biased if the number of subjects in the two groups differ or if the platforms used to measure SNP intensities differ. We then introduce a new test statistic and a test that only requires the ancestries of the reference group to be similar to the individual of interest, and show that this test is not only robust to the number of subjects and platform, but also has increased power of detection. The two tests are compared on both HapMap and simulated data.
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Genotyping and inflated type I error rate in genome-wide association case/control studies.
BMC Bioinformatics
PUBLISHED: 02-23-2009
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One common goal of a case/control genome wide association study (GWAS) is to find SNPs associated with a disease. Traditionally, the first step in such studies is to assign a genotype to each SNP in each subject, based on a statistic summarizing fluorescence measurements. When the distributions of the summary statistics are not well separated by genotype, the act of genotype assignment can lead to more potential problems than acknowledged by the literature.
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Statistical tests for detecting associations with groups of genetic variants: generalization, evaluation, and implementation.
Eur. J. Hum. Genet.
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With recent advances in sequencing, genotyping arrays, and imputation, GWAS now aim to identify associations with rare and uncommon genetic variants. Here, we describe and evaluate a class of statistics, generalized score statistics (GSS), that can test for an association between a group of genetic variants and a phenotype. GSS are a simple weighted sum of single-variant statistics and their cross-products. We show that the majority of statistics currently used to detect associations with rare variants are equivalent to choosing a specific set of weights within this framework. We then evaluate the power of various weighting schemes as a function of variant characteristics, such as MAF, the proportion associated with the phenotype, and the direction of effect. Ultimately, we find that two classical tests are robust and powerful, but details are provided as to when other GSS may perform favorably. The software package CRaVe is available at our website (http://dceg.cancer.gov/bb/tools/crave).
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Improving self-reports of active and sedentary behaviors in large epidemiologic studies.
Exerc Sport Sci Rev
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Questionnaires that assess active and sedentary behaviors in large-scale epidemiologic studies are known to contain substantial errors. We present three options for improving measures of physical activity behaviors in large-scale epidemiologic studies, discuss the problems and prospects for each of these options, and highlight a new direction for measuring these behaviors in such studies.
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Detectable clonal mosaicism and its relationship to aging and cancer.
Kevin B Jacobs, Meredith Yeager, Weiyin Zhou, Sholom Wacholder, Zhaoming Wang, Benjamín Rodríguez-Santiago, Amy Hutchinson, Xiang Deng, Chenwei Liu, Marie-Josèphe Horner, Michael Cullen, Caroline G Epstein, Laurie Burdett, Michael C Dean, Nilanjan Chatterjee, Joshua Sampson, Charles C Chung, Joseph Kovaks, Susan M Gapstur, Victoria L Stevens, Lauren T Teras, Mia M Gaudet, Demetrius Albanes, Stephanie J Weinstein, Jarmo Virtamo, Philip R Taylor, Neal D Freedman, Christian C Abnet, Alisa M Goldstein, Nan Hu, Kai Yu, Jian-Min Yuan, Linda Liao, Ti Ding, You-Lin Qiao, Yu-Tang Gao, Woon-Puay Koh, Yong-Bing Xiang, Ze-Zhong Tang, Jin-Hu Fan, Melinda C Aldrich, Christopher Amos, William J Blot, Cathryn H Bock, Elizabeth M Gillanders, Curtis C Harris, Christopher A Haiman, Brian E Henderson, Laurence N Kolonel, Loic Le Marchand, Lorna H McNeill, Benjamin A Rybicki, Ann G Schwartz, Lisa B Signorello, Margaret R Spitz, John K Wiencke, Margaret Wrensch, Xifeng Wu, Krista A Zanetti, Regina G Ziegler, Jonine D Figueroa, Montserrat Garcia-Closas, Nuria Malats, Gaëlle Marenne, Ludmila Prokunina-Olsson, Dalsu Baris, Molly Schwenn, Alison Johnson, Maria Teresa Landi, Lynn Goldin, Dario Consonni, Pier Alberto Bertazzi, Melissa Rotunno, Preetha Rajaraman, Ulrika Andersson, Laura E Beane Freeman, Christine D Berg, Julie E Buring, Mary A Butler, Tania Carreon, Maria Feychting, Anders Ahlbom, J Michael Gaziano, Graham G Giles, Göran Hallmans, Susan E Hankinson, Patricia Hartge, Roger Henriksson, Peter D Inskip, Christoffer Johansen, Annelie Landgren, Roberta Mckean-Cowdin, Dominique S Michaud, Beatrice S Melin, Ulrike Peters, Avima M Ruder, Howard D Sesso, Gianluca Severi, Xiao-Ou Shu, Kala Visvanathan, Emily White, Alicja Wolk, Anne Zeleniuch-Jacquotte, Wei Zheng, Debra T Silverman, Manolis Kogevinas, Juan R Gonzalez, Olaya Villa, Donghui Li, Eric J Duell, Harvey A Risch, Sara H Olson, Charles Kooperberg, Brian M Wolpin, Li Jiao, Manal Hassan, William Wheeler, Alan A Arslan, H Bas Bueno-de-Mesquita, Charles S Fuchs, Steven Gallinger, Myron D Gross, Elizabeth A Holly, Alison P Klein, Andrea LaCroix, Margaret T Mandelson, Gloria Petersen, Marie-Christine Boutron-Ruault, Paige M Bracci, Federico Canzian, Kenneth Chang, Michelle Cotterchio, Edward L Giovannucci, Michael Goggins, Judith A Hoffman Bolton, Mazda Jenab, Kay-Tee Khaw, Vittorio Krogh, Robert C Kurtz, Robert R McWilliams, Julie B Mendelsohn, Kari G Rabe, Elio Riboli, Anne Tjønneland, Geoffrey S Tobias, Dimitrios Trichopoulos, Joanne W Elena, Herbert Yu, Laufey Amundadottir, Rachael Z Stolzenberg-Solomon, Peter Kraft, Fredrick Schumacher, Daniel Stram, Sharon A Savage, Lisa Mirabello, Irene L Andrulis, Jay S Wunder, Ana Patiño García, Luis Sierrasesúmaga, Donald A Barkauskas, Richard G Gorlick, Mark Purdue, Wong-Ho Chow, Lee E Moore, Kendra L Schwartz, Faith G Davis, Ann W Hsing, Sonja I Berndt, Amanda Black, Nicolas Wentzensen, Louise A Brinton, Jolanta Lissowska, Beata Peplonska, Katherine A McGlynn, Michael B Cook, Barry I Graubard, Christian P Kratz, Mark H Greene, Ralph L Erickson, David J Hunter, Gilles Thomas, Robert N Hoover, Francisco X Real, Joseph F Fraumeni, Neil E Caporaso, Margaret Tucker, Nathaniel Rothman, Luis A Pérez-Jurado, Stephen J Chanock.
Nat. Genet.
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In an analysis of 31,717 cancer cases and 26,136 cancer-free controls from 13 genome-wide association studies, we observed large chromosomal abnormalities in a subset of clones in DNA obtained from blood or buccal samples. We observed mosaic abnormalities, either aneuploidy or copy-neutral loss of heterozygosity, of >2 Mb in size in autosomes of 517 individuals (0.89%), with abnormal cell proportions of between 7% and 95%. In cancer-free individuals, frequency increased with age, from 0.23% under 50 years to 1.91% between 75 and 79 years (P = 4.8 × 10(-8)). Mosaic abnormalities were more frequent in individuals with solid tumors (0.97% versus 0.74% in cancer-free individuals; odds ratio (OR) = 1.25; P = 0.016), with stronger association with cases who had DNA collected before diagnosis or treatment (OR = 1.45; P = 0.0005). Detectable mosaicism was also more common in individuals for whom DNA was collected at least 1 year before diagnosis with leukemia compared to cancer-free individuals (OR = 35.4; P = 3.8 × 10(-11)). These findings underscore the time-dependent nature of somatic events in the etiology of cancer and potentially other late-onset diseases.
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A two-platform design for next generation genome-wide association studies.
Genet. Epidemiol.
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Genome-wide association studies (GWAS) have been successful in their search for common genetic variants associated with complex traits and diseases. With new advances in array technologies together with available genetic reference sets, the next generation of GWAS will extend the search for associations with uncommon SNPs (1% ? MAF ? 10%). Two possible approaches are genotyping all participants, a prohibitively expensive option for large GWAS, or using a combination of genotyping and imputation. Here, we consider a two platform method that genotypes all participants on a standard genotyping array, designed to identify common variants, and then supplements that data by genotyping only a small proportion of the participants on a platform that has higher coverage for uncommon SNPs. This subset of the study population is then included as part of the imputation reference set. To demonstrate the use of this two-platform design, we evaluate its potential efficiency using a newly available dataset containing 756 individuals genotyped on both the Illumina Human OmniExpress and Omni2.5 Quad. Although genotyping all individuals on the denser array would be ideal, we find that genotyping only 100 individuals on this array, in combination with imputation, leads to only a modest loss of power for detecting associations. However, the loss of power due to imputation can be more substantial if the relative risks for rare variants are significantly larger than those previously observed for common variants.
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What is Visualize?

JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.

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We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.

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In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.