Author
Bruce H. Alexander
Other affiliations: Monsanto, United States Department of Health and Human Services, Business International Corporation ...read more
Bio: Bruce H. Alexander is an academic researcher from University of Minnesota. The author has contributed to research in topics: Poison control & Population. The author has an hindex of 49, co-authored 211 publications receiving 11344 citations. Previous affiliations of Bruce H. Alexander include Monsanto & United States Department of Health and Human Services.
Topics: Poison control, Population, Injury prevention, Cancer, Breast cancer
Papers published on a yearly basis
Papers
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University of Cambridge1, National Institutes of Health2, University of Southern California3, International Agency for Research on Cancer4, Academia Sinica5, Princess Anne Hospital6, St Mary's Hospital7, University of London8, The Breast Cancer Research Foundation9, Wellcome Trust Sanger Institute10, QIMR Berghofer Medical Research Institute11, Peter MacCallum Cancer Centre12, University of Copenhagen13, Curie Institute14, Nofer Institute of Occupational Medicine15, University of Helsinki16, Seoul National University17, University of Ulsan18, Harvard University19, Karolinska Institutet20, Agency for Science, Technology and Research21, Hannover Medical School22, Leiden University23, Erasmus University Rotterdam24, University of Minnesota25, University of Sheffield26, Mayo Clinic27, VU University Amsterdam28, Carlos III Health Institute29, University of Melbourne30, Cancer Council New South Wales31, University of Otago32, Cancer Council Victoria33, University of Tübingen34, Bosch35, German Cancer Research Center36, University of Eastern Finland37
TL;DR: To identify further susceptibility alleles, a two-stage genome-wide association study in 4,398 breast cancer cases and 4,316 controls was conducted, followed by a third stage in which 30 single nucleotide polymorphisms were tested for confirmation.
Abstract: Breast cancer exhibits familial aggregation, consistent with variation in genetic susceptibility to the disease. Known susceptibility genes account for less than 25% of the familial risk of breast cancer, and the residual genetic variance is likely to be due to variants conferring more moderate risks. To identify further susceptibility alleles, we conducted a two-stage genome-wide association study in 4,398 breast cancer cases and 4,316 controls, followed by a third stage in which 30 single nucleotide polymorphisms (SNPs) were tested for confirmation in 21,860 cases and 22,578 controls from 22 studies. We used 227,876 SNPs that were estimated to correlate with 77% of known common SNPs in Europeans at r2.0.5. SNPs in five novel independent loci exhibited strong and consistent evidence of association with breast cancer (P,1027). Four of these contain plausible causative genes (FGFR2, TNRC9, MAP3K1 and LSP1). At the second stage, 1,792 SNPs were significant at the P,0.05 level compared with an estimated 1,343 that would be expected by chance, indicating that many additional common susceptibility alleles may be identifiable by this approach.
2,288 citations
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TL;DR: Using a population-based hospital discharge registry with E codes, it is found that fall-related trauma accounted for 5.3% of all hospitalizations of older adults in Washington State, with hospital charges totaling $53,346,191, and resulted in discharge to nursing care more often than other such hospitalizations.
Abstract: Using a population-based hospital discharge registry with E codes, we examine the 1989 hospitalizations of older adults in Washington State for fall-related injuries. Fall-related trauma accounted for 5.3% of all hospitalizations of older adults, with hospital charges totaling $53,346,191, and resulted in discharge to nursing care more often than other such hospitalizations. An annual hospitalization rate of 13.5 per 1000 persons and an annual cost of $92 per person is reported. The importance of preventing fall-related injuries in older adults is discussed.
615 citations
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University of Sheffield1, University of Cambridge2, National Institutes of Health3, Curie Institute4, Nofer Institute of Occupational Medicine5, University of Melbourne6, University of Otago7, Cancer Council Victoria8, University of London9, University of Copenhagen10, German Cancer Research Center11, University of Tübingen12, Bosch13, University of Ulm14, Hannover Medical School15, University of Helsinki16, International Agency for Research on Cancer17, QIMR Berghofer Medical Research Institute18, University of Eastern Finland19, Mayo Clinic20, Netherlands Cancer Institute21, Seoul National University22, University of Ulsan23, Karolinska Institutet24, Agency for Science, Technology and Research25, Carlos III Health Institute26, University of Minnesota27
TL;DR: It is demonstrated that common breast cancer susceptibility alleles with small effects on risk can be identified, given sufficiently powerful studies, as well as the need for further studies to confirm putative genetic associations with breast cancer.
Abstract: The Breast Cancer Association Consortium (BCAC) has been established to conduct combined case-control analyses with augmented statistical power to try to confirm putative genetic associations with breast cancer. We genotyped nine SNPs for which there was some prior evidence of an association with breast cancer: CASP8 D302H (rs1045485), IGFBP3 -202 C --> A (rs2854744), SOD2 V16A (rs1799725), TGFB1 L10P (rs1982073), ATM S49C (rs1800054), ADH1B 3' UTR A --> G (rs1042026), CDKN1A S31R (rs1801270), ICAM5 V301I (rs1056538) and NUMA1 A794G (rs3750913). We included data from 9-15 studies, comprising 11,391-18,290 cases and 14,753-22,670 controls. We found evidence of an association with breast cancer for CASP8 D302H (with odds ratios (OR) of 0.89 (95% confidence interval (c.i.): 0.85-0.94) and 0.74 (95% c.i.: 0.62-0.87) for heterozygotes and rare homozygotes, respectively, compared with common homozygotes; P(trend) = 1.1 x 10(-7)) and weaker evidence for TGFB1 L10P (OR = 1.07 (95% c.i.: 1.02-1.13) and 1.16 (95% c.i.: 1.08-1.25), respectively; P(trend) = 2.8 x 10(-5)). These results demonstrate that common breast cancer susceptibility alleles with small effects on risk can be identified, given sufficiently powerful studies.
567 citations
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National Institutes of Health1, Science Applications International Corporation2, Harvard University3, Brigham and Women's Hospital4, Washington University in St. Louis5, United States Department of Health and Human Services6, University of Utah7, Marshfield Clinic8, American Cancer Society9, Fred Hutchinson Cancer Research Center10, Ohio State University11, University of California, Los Angeles12, Nofer Institute of Occupational Medicine13, University of Minnesota14, Norwegian University of Science and Technology15, University of Tromsø16
TL;DR: A three-stage genome-wide association study of breast cancer in 9,770 cases and 10,799 controls in the Cancer Genetic Markers of Susceptibility (CGEMS) initiative confirmed associations with loci on chromosomes 2q35, 5p12, 5q11.2, 8q24, 10q26 and 16q12.1.
Abstract: We conducted a three-stage genome-wide association study (GWAS) of breast cancer in 9,770 cases and 10,799 controls in the Cancer Genetic Markers of Susceptibility (CGEMS) initiative. In stage 1, we genotyped 528,173 SNPs in 1,145 cases of invasive breast cancer and 1,142 controls. In stage 2, we analyzed 24,909 top SNPs in 4,547 cases and 4,434 controls. In stage 3, we investigated 21 loci in 4,078 cases and 5,223 controls. Two new loci achieved genome-wide significance. A pericentromeric SNP on chromosome 1p11.2 (rs11249433; P = 6.74 x 10(-10) adjusted genotype test, 2 degrees of freedom) resides in a large linkage disequilibrium block neighboring NOTCH2 and FCGR1B; this signal was stronger for estrogen-receptor-positive tumors. A second SNP on chromosome 14q24.1 (rs999737; P = 1.74 x 10(-7)) localizes to RAD51L1, a gene in the homologous recombination DNA repair pathway. We also confirmed associations with loci on chromosomes 2q35, 5p12, 5q11.2, 8q24, 10q26 and 16q12.1.
555 citations
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University of Cambridge1, National Institutes of Health2, Princess Anne Hospital3, St Mary's Hospital4, Wellcome Trust Sanger Institute5, Science Applications International Corporation6, Fred Hutchinson Cancer Research Center7, Baylor College of Medicine8, University of Hawaii at Manoa9, University of Utah10, Marshfield Clinic11, American Cancer Society12, University of Copenhagen13, Hannover Medical School14, Russian Academy15, Seoul National University16, Leiden University17, Erasmus University Rotterdam18, Curie Institute19, Nofer Institute of Occupational Medicine20, University of Helsinki21, University of Melbourne22, QIMR Berghofer Medical Research Institute23, Netherlands Cancer Institute24, Carlos III Health Institute25, University of Cologne26, Heidelberg University27, German Cancer Research Center28, Technische Universität München29, Bosch30, University of Tübingen31, University of Ulm32, Karolinska Institutet33, University of Eastern Finland34, Mayo Clinic35, Cancer Council Victoria36, Harvard University37, Norwegian University of Science and Technology38, University of Minnesota39, Agency for Science, Technology and Research40, University of Sheffield41, China Medical University (Taiwan)42, Academia Sinica43, National Defense Medical Center44, University of California, Irvine45, University of Toronto46, Cancer Research UK47
TL;DR: Strong evidence is found for additional susceptibility loci on 3p and 17q and potential causative genes include SLC4A7 and NEK10 on3p and COX11 on 17q.
Abstract: Genome-wide association studies (GWAS) have identified seven breast cancer susceptibility loci, but these explain only a small fraction of the familial risk of the disease. Five of these loci were identified through a two-stage GWAS involving 390 familial cases and 364 controls in the first stage, and 3,990 cases and 3,916 controls in the second stage. To identify additional loci, we tested over 800 promising associations from this GWAS in a further two stages involving 37,012 cases and 40,069 controls from 33 studies in the CGEMS collaboration and Breast Cancer Association Consortium. We found strong evidence for additional susceptibility loci on 3p (rs4973768: per-allele OR = 1.11, 95% CI = 1.08-1.13, P = 4.1 x 10(-23)) and 17q (rs6504950: per-allele OR = 0.95, 95% CI = 0.92-0.97, P = 1.4 x 10(-8)). Potential causative genes include SLC4A7 and NEK10 on 3p and COX11 on 17q.
480 citations
Cited by
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National Institutes of Health1, University of Chicago2, Duke University3, Harvard University4, University of Oxford5, GlaxoSmithKline6, Johns Hopkins University7, Yale University8, deCODE genetics9, Princeton University10, Howard Hughes Medical Institute11, Washington University in St. Louis12, University of California, Berkeley13, Stanford University14, University of Michigan15, Cornell University16, University of Washington17, University of Queensland18, Vanderbilt University19, North Carolina State University20, QIMR Berghofer Medical Research Institute21
TL;DR: This paper examined potential sources of missing heritability and proposed research strategies, including and extending beyond current genome-wide association approaches, to illuminate the genetics of complex diseases and enhance its potential to enable effective disease prevention or treatment.
Abstract: Genome-wide association studies have identified hundreds of genetic variants associated with complex human diseases and traits, and have provided valuable insights into their genetic architecture. Most variants identified so far confer relatively small increments in risk, and explain only a small proportion of familial clustering, leading many to question how the remaining, 'missing' heritability can be explained. Here we examine potential sources of missing heritability and propose research strategies, including and extending beyond current genome-wide association approaches, to illuminate the genetics of complex diseases and enhance its potential to enable effective disease prevention or treatment.
7,797 citations
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TL;DR: This systematic review and meta-analyses confirmed the findings of a previous study published in “Rhinitis and Asthma: Causes and Prevention, 2nd Ed.” (2015) as well as new findings of “Mechanisms of Respiratory Disease and Allergology,” which confirmed the role of EMTs in the development of these diseases.
Abstract: Authors Jan L. Brozek, MD, PhD – Department of Clinical Epidemiology & Biostatistics and Medicine, McMaster University, Hamilton, Canada Jean Bousquet, MD, PhD – Service des Maladies Respiratoires, Hopital Arnaud de Villeneuve, Montpellier, France, INSERM, CESP U1018, Respiratory and Environmental Epidemiology Team, France, and WHO Collaborating Center for Rhinitis and Asthma Carlos E. Baena-Cagnani, MD – Faculty of Medicine, Catholic University of Cordoba, Cordoba, Argentina Sergio Bonini, MD – Institute of Neurobiology and Molecular Medicine – CNR, Rome, Italy and Department of Medicine, Second University of Naples, Naples, Italy G. Walter Canonica, MD – Allergy & Respiratory Diseases, DIMI, Department of Internal Medicine, University of Genoa, Genoa, Italy Thomas B. Casale, MD – Division of Allergy and Immunology, Department of Medicine, Creighton University, Omaha, Nebraska, USA Roy Gerth van Wijk, MD, PhD – Section of Allergology, Department of Internal Medicine, Erasmus Medical Centre, Rotterdam, the Netherlands Ken Ohta, MD, PhD – Division of Respiratory Medicine and Allergology, Department of Medicine, Teikyo University School of Medicine, Tokyo, Japan Torsten Zuberbier, MD – Department of Dermatology and Allergy, Charite Universitatsmedizin Berlin, Berlin, Germany Holger J. Schunemann, MD, PhD, MSc – Department of Clinical Epidemiology & Biostatistics and Medicine, McMaster University, Hamilton, Canada
3,368 citations
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University of Oxford1, Wellcome Trust Centre for Human Genetics2, University of Michigan3, Fred Hutchinson Cancer Research Center4, Duke University5, University of Ottawa6, Tufts University7, Foundation for Research & Technology – Hellas8, Harvard University9, Broad Institute10, Boston Children's Hospital11
TL;DR: This Review highlights the knowledge gained, defines areas of emerging consensus, and describes the challenges that remain as researchers seek to obtain more complete descriptions of the susceptibility architecture of biomedical traits of interest and to translate the information gathered into improvements in clinical management.
Abstract: The past year has witnessed substantial advances in understanding the genetic basis of many common phenotypes of biomedical importance. These advances have been the result of systematic, well-powered, genome-wide surveys exploring the relationships between common sequence variation and disease predisposition. This approach has revealed over 50 disease-susceptibility loci and has provided insights into the allelic architecture of multifactorial traits. At the same time, much has been learned about the successful prosecution of association studies on such a scale. This Review highlights the knowledge gained, defines areas of emerging consensus, and describes the challenges that remain as researchers seek to obtain more complete descriptions of the susceptibility architecture of biomedical traits of interest and to translate the information gathered into improvements in clinical management.
2,908 citations
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TL;DR: In this paper, the coding exons of the family of 518 protein kinases were sequenced in 210 cancers of diverse histological types to explore the nature of the information that will be derived from cancer genome sequencing.
Abstract: AACR Centennial Conference: Translational Cancer Medicine-- Nov 4-8, 2007; Singapore
PL02-05
All cancers are due to abnormalities in DNA. The availability of the human genome sequence has led to the proposal that resequencing of cancer genomes will reveal the full complement of somatic mutations and hence all the cancer genes. To explore the nature of the information that will be derived from cancer genome sequencing we have sequenced the coding exons of the family of 518 protein kinases, ~1.3Mb DNA per cancer sample, in 210 cancers of diverse histological types. Despite the screen being directed toward the coding regions of a gene family that has previously been strongly implicated in oncogenesis, the results indicate that the majority of somatic mutations detected are “passengers”. There is considerable variation in the number and pattern of these mutations between individual cancers, indicating substantial diversity of processes of molecular evolution between cancers. The imprints of exogenous mutagenic exposures, mutagenic treatment regimes and DNA repair defects can all be seen in the distinctive mutational signatures of individual cancers. This systematic mutation screen and others have previously yielded a number of cancer genes that are frequently mutated in one or more cancer types and which are now anticancer drug targets (for example BRAF , PIK3CA , and EGFR ). However, detailed analyses of the data from our screen additionally suggest that there exist a large number of additional “driver” mutations which are distributed across a substantial number of genes. It therefore appears that cells may be able to utilise mutations in a large repertoire of potential cancer genes to acquire the neoplastic phenotype. However, many of these genes are employed only infrequently. These findings may have implications for future anticancer drug development.
2,737 citations
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TL;DR: A variance component approach implemented in publicly available software, EMMA eXpedited (EMMAX), that reduces the computational time for analyzing large GWAS data sets from years to hours is reported.
Abstract: Although genome-wide association studies (GWASs) have identified numerous loci associated with complex traits, imprecise modeling of the genetic relatedness within study samples may cause substantial inflation of test statistics and possibly spurious associations. Variance component approaches, such as efficient mixed-model association (EMMA), can correct for a wide range of sample structures by explicitly accounting for pairwise relatedness between individuals, using high-density markers to model the phenotype distribution; but such approaches are computationally impractical. We report here a variance component approach implemented in publicly available software, EMMA eXpedited (EMMAX), that reduces the computational time for analyzing large GWAS data sets from years to hours. We apply this method to two human GWAS data sets, performing association analysis for ten quantitative traits from the Northern Finland Birth Cohort and seven common diseases from the Wellcome Trust Case Control Consortium. We find that EMMAX outperforms both principal component analysis and genomic control in correcting for sample structure.
2,316 citations