Proceedings Article•
Variants in the CDKN2B and RTEL1 regions are associated with high-grade glioma susceptibility
08 Jul 2009-
TL;DR: Wrensch et al. as mentioned in this paper found that variants in the CDKN2B and RTEL1 regions are associated with high-grade glioma susceptibility and showed that the direction of association was the same in discovery and replication phases.
Abstract: LETTERS Variants in the CDKN2B and RTEL1 regions are associated with high-grade glioma susceptibility Margaret Wrensch 1,2,12 , Robert B Jenkins 3,12 , Jeffrey S Chang 4,12 , Ru-Fang Yeh 4,12 , Yuanyuan Xiao 4 , Paul A Decker 5 , Karla V Ballman 5 , Mitchel Berger 1 , Jan C Buckner 6 , Susan Chang 1 , Caterina Giannini 3 , Chandralekha Halder 3 , Thomas M Kollmeyer 3 , Matthew L Kosel 5 , Daniel H LaChance 7 , Lucie McCoy 1 , Brian P O’Neill 7 , Joe Patoka 1 , Alexander R Pico 8 , Michael Prados 1 , Charles Quesenberry 9 , Terri Rice 1 , Amanda L Rynearson 3 , Ivan Smirnov 1 , Tarik Tihan 10 , Joe Wiemels 2,4 , Ping Yang 11,13 & John K Wiencke 1,2,13 The causes of glioblastoma and other gliomas remain obscure 1,2 To discover new candidate genes influencing glioma susceptibility, we conducted a principal component– adjusted 3 genome-wide association study (GWAS) of 275,895 autosomal variants among 692 adult high-grade glioma cases (622 from the San Francisco Adult Glioma Study (AGS) and 70 from the Cancer Genome Atlas (TCGA)) 4 and 3,992 controls (602 from AGS and 3,390 from Illumina iControlDB (iControls)) For replication, we analyzed the 13 SNPs with P o 10 A6 using independent data from 176 high-grade glioma cases and 174 controls from the Mayo Clinic On 9p21, rs1412829 near CDKN2B had discovery P ¼ 34 Â 10 A8 , replication P ¼ 00038 and combined P ¼ 185 Â 10 A10 On 20q133, rs6010620 intronic to RTEL1 had discovery P ¼ 15 Â 10 A7 , replication P ¼ 000035 and combined P ¼ 340 Â 10 A9 For both SNPs, the direction of association was the same in discovery and replication phases Subject characteristics, including participation rates for the discovery GWAS and replication phases, are listed in Supplementary Table 1a,b The distribution of P values from the principal component–adjusted logistic regression additive model across the genome for high-grade glioma cases versus controls (Fig 1) suggests potentially meaningful associations for several SNPs on chromosomes 1, 5, 9, 11 and 20 Supplementary Table 2 summarizes results for the 13 SNPs with P o 10 A6 for association with high-grade glioma in discovery data along with results from replication data; SNPs with Hardy-Weinberg P o 10 A5 in controls or with 45% missing data in any case or control group were excluded Three of these 13 SNPs (rs1412829 on 9p21, and rs6010620 and rs4809324 intronic to RTEL1 on 20q133) had significant association with high-grade glioma risk in the discovery phase (principal component analysis P o 18 Â 10 A7 ), were inde- pendent risk predictors in a multivariable analysis of 13 top hits, and were replicated in the Mayo Clinic dataset (Table 1) As shown in Table 1 and Supplementary Table 2, the minor allele frequencies for the three SNPs consistently differed in the same direction between high-grade glioma cases and controls regardless of data source (AGS, TCGA, iControls or Mayo Clinic) Supplementary Table 3 shows results from the multivariable model of discovery data that included all 13 SNPs (four from the 9p21 region, three in RTEL1, plus six others in other locations) Eight SNPs, including one in the 9p21 region and two intronic to RTEL1, remained independently associated with high- grade glioma risk after adjustment for other SNPs This was expected given the strong linkage disequilibrium (LD) evident for the four 9p21 SNPs and two of the three RTEL1 SNPs (Supplementary Table 4) In discovery data, only the interaction between chromosome 9p21 SNP rs1412829 and TERT SNP rs2736100 on chromosome 5 was statistically significant with Wald test P ¼ 0019 (see Supplementary P value Chromosome © 2009 Nature America, Inc All rights reserved Figure 1 Distribution of P values from principal component–adjusted logistic regression additive model across the genome for high-grade glioma cases versus controls The 13 SNPs with P o 10 A6 are shown in red 1 Department of Neurological Surgery, University of California, San Francisco, San Francisco, California, USA 2 Institute of Human Genetics, University of California, San Francisco, San Francisco, California, USA 3 Department of Experimental Pathology, Mayo Clinic, Rochester, Minnesota, USA 4 Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California, USA 5 Division of Biostatistics, 6 Department of Oncology and 7 Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA 8 Gladstone Institute of Cardiovascular Disease, University of California, San Francisco, San Francisco, California, USA 9 Division of Research, Kaiser Permanente, Oakland, California, USA 10 Department of Pathology, University of California, San Francisco, San Francisco, California, USA 11 Division of Epidemiology, Mayo Clinic, Rochester, Minnesota, USA 12 These authors contributed equally to this work 13 These authors jointly directed the work Correspondence should be addressed to MW (margaretwrensch@ucsfedu) Received 13 March; accepted 1 June; published online 5 July 2009; doi:101038/ng408 NATURE GENETICS ADVANCE ONLINE PUBLICATION
Citations
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TL;DR: A “state of the science” review of current research into causes and risk factors for gliomas in adults is provided.
Abstract: Gliomas are the most common primary intracranial tumor, representing 81% of malignant brain tumors Although relatively rare, they cause significant mortality and morbidity Glioblastoma, the most common glioma histology (∼45% of all gliomas), has a 5-year relative survival of ∼5% A small portion of these tumors are caused by Mendelian disorders, including neurofibromatosis, tuberous sclerosis, and Li-Fraumeni syndrome Genomic analyses of glioma have also produced new evidence about risk and prognosis Recently discovered biomarkers that indicate improved survival include O⁶-methylguanine-DNA methyltransferase methylation, isocitrate dehydrogenase mutation, and a glioma cytosine-phosphate-guanine island methylator phenotype Genome-wide association studies have identified heritable risk alleles within 7 genes that are associated with increased risk of glioma Many risk factors have been examined as potential contributors to glioma risk Most significantly, these include an increase in risk by exposure to ionizing radiation and a decrease in risk by history of allergies or atopic disease(s) The potential influence of occupational exposures and cellular phones has also been examined, with inconclusive results We provide a “state of the science” review of current research into causes and risk factors for gliomas in adults
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TL;DR: The current epidemiology of GBM is reported with new data from the Central Brain Tumor Registry of the United States 2006 to 2010 as well as demonstrate and discuss trends in incidence and survival.
Abstract: Glioblastoma multiforme (GBM) is the most common and aggressive primary central nervous system malignancy with a median survival of 15 months. The average incidence rate of GBM is 3.19/100,000 population, and the median age of diagnosis is 64 years. Incidence is higher in men and individuals of white race and non-Hispanic ethnicity. Many genetic and environmental factors have been studied in GBM, but the majority are sporadic, and no risk factor accounting for a large proportion of GBMs has been identified. However, several favorable clinical prognostic factors are identified, including younger age at diagnosis, cerebellar location, high performance status, and maximal tumor resection. GBMs comprise of primary and secondary subtypes, which evolve through different genetic pathways, affect patients at different ages, and have differences in outcomes. We report the current epidemiology of GBM with new data from the Central Brain Tumor Registry of the United States 2006 to 2010 as well as demonstrate and discuss trends in incidence and survival. We also provide a concise review on molecular markers in GBM that have helped distinguish biologically similar subtypes of GBM and have prognostic and predictive value.
876 citations
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TL;DR: The results identify novel circular RNA products emanating from the ANRIL locus and suggest causal variants at 9p21.3 regulate INK4/ARF expression and ASVD risk by modulating ANRil expression and/or structure.
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789 citations
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TL;DR: The discovery of two separate molecular subtypes within the glioma classification that appear to correlate with biological etiology, prognosis, and response to therapy suggests that molecular genetic tests are and will be useful, beyond classical histology, for the clinical classification of gliomas.
735 citations
References
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TL;DR: Haploview is a software package that provides computation of linkage disequilibrium statistics and population haplotype patterns from primary genotype data in a visually appealing and interactive interface.
Abstract: Summary: Research over the last few years has revealed significant haplotype structure in the human genome. The characterization of these patterns, particularly in the context of medical genetic association studies, is becoming a routine research activity. Haploview is a software package that provides computation of linkage disequilibrium statistics and population haplotype patterns from primary genotype data in a visually appealing and interactive interface.
Availability: http://www.broad.mit.edu/mpg/haploview/
Contact: jcbarret@broad.mit.edu
13,862 citations
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TL;DR: This work describes a method that enables explicit detection and correction of population stratification on a genome-wide scale and uses principal components analysis to explicitly model ancestry differences between cases and controls.
Abstract: Population stratification—allele frequency differences between cases and controls due to systematic ancestry differences—can cause spurious associations in disease studies. We describe a method that enables explicit detection and correction of population stratification on a genome-wide scale. Our method uses principal components analysis to explicitly model ancestry differences between cases and controls. The resulting correction is specific to a candidate marker’s variation in frequency across ancestral populations, minimizing spurious associations while maximizing power to detect true associations. Our simple, efficient approach can easily be applied to disease studies with hundreds of thousands of markers. Population stratification—allele frequency differences between cases and controls due to systematic ancestry differences—can cause spurious associations in disease studies 1‐8 . Because the effects of stratification vary in proportion to the number of samples 9 , stratification will be an increasing problem in the large-scale association studies of the future, which will analyze thousands of samples in an effort to detect common genetic variants of weak effect. The two prevailing methods for dealing with stratification are genomic control and structured association 9‐14 . Although genomic control and structured association have proven useful in a variety of contexts, they have limitations. Genomic control corrects for stratification by adjusting association statistics at each marker by a uniform overall inflation factor. However, some markers differ in their allele frequencies across ancestral populations more than others. Thus, the uniform adjustment applied by genomic control may be insufficient at markers having unusually strong differentiation across ancestral populations and may be superfluous at markers devoid of such differentiation, leading to a loss in power. Structured association uses a program such as STRUCTURE 15 to assign the samples to discrete subpopulation clusters and then aggregates evidence of association within each cluster. If fractional membership in more than one cluster is allowed, the method cannot currently be applied to genome-wide association studies because of its intensive computational cost on large data sets. Furthermore, assignments of individuals to clusters are highly sensitive to the number of clusters, which is not well defined 14,16 .
9,387 citations
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Roger E. McLendon1, Allan H. Friedman1, Darrell D. Bigner1, Erwin G. Van Meir2 +230 more•Institutions (23)
TL;DR: The interim integrative analysis of DNA copy number, gene expression and DNA methylation aberrations in 206 glioblastomas reveals a link between MGMT promoter methylation and a hypermutator phenotype consequent to mismatch repair deficiency in treated gliobeasts, demonstrating that it can rapidly expand knowledge of the molecular basis of cancer.
Abstract: Human cancer cells typically harbour multiple chromosomal aberrations, nucleotide substitutions and epigenetic modifications that drive malignant transformation. The Cancer Genome Atlas ( TCGA) pilot project aims to assess the value of large- scale multi- dimensional analysis of these molecular characteristics in human cancer and to provide the data rapidly to the research community. Here we report the interim integrative analysis of DNA copy number, gene expression and DNA methylation aberrations in 206 glioblastomas - the most common type of primary adult brain cancer - and nucleotide sequence aberrations in 91 of the 206 glioblastomas. This analysis provides new insights into the roles of ERBB2, NF1 and TP53, uncovers frequent mutations of the phosphatidylinositol- 3- OH kinase regulatory subunit gene PIK3R1, and provides a network view of the pathways altered in the development of glioblastoma. Furthermore, integration of mutation, DNA methylation and clinical treatment data reveals a link between MGMT promoter methylation and a hypermutator phenotype consequent to mismatch repair deficiency in treated glioblastomas, an observation with potential clinical implications. Together, these findings establish the feasibility and power of TCGA, demonstrating that it can rapidly expand knowledge of the molecular basis of cancer.
6,761 citations
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TL;DR: An approach to studying population structure (principal components analysis) is discussed that was first applied to genetic data by Cavalli-Sforza and colleagues, and results from modern statistics are used to develop formal significance tests for population differentiation.
Abstract: Current methods for inferring population structure from genetic data do not provide formal significance tests for population differentiation. We discuss an approach to studying population structure (principal components analysis) that was first applied to genetic data by Cavalli-Sforza and colleagues. We place the method on a solid statistical footing, using results from modern statistics to develop formal significance tests. We also uncover a general “phase change” phenomenon about the ability to detect structure in genetic data, which emerges from the statistical theory we use, and has an important implication for the ability to discover structure in genetic data: for a fixed but large dataset size, divergence between two populations (as measured, for example, by a statistic like FST) below a threshold is essentially undetectable, but a little above threshold, detection will be easy. This means that we can predict the dataset size needed to detect structure.
4,456 citations
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TL;DR: New methods of testing the statistical association between haplotypes and a wide variety of traits, including binary, ordinal, and quantitative traits are developed, which allow adjustment for nongenetic covariates, which may be critical when analyzing genetically complex traits.
Abstract: A key step toward the discovery of a gene related to a trait is the finding of an association between the trait and one or more haplotypes. Haplotype analyses can also provide critical information regarding the function of a gene; however, when unrelated subjects are sampled, haplotypes are often ambiguous because of unknown linkage phase of the measured sites along a chromosome. A popular method of accounting for this ambiguity in case-control studies uses a likelihood that depends on haplotype frequencies, so that the haplotype frequencies can be compared between the cases and controls; however, this traditional method is limited to a binary trait (case vs. control), and it does not provide a method of testing the statistical significance of specific haplotypes. To address these limitations, we developed new methods of testing the statistical association between haplotypes and a wide variety of traits, including binary, ordinal, and quantitative traits. Our methods allow adjustment for nongenetic covariates, which may be critical when analyzing genetically complex traits. Furthermore, our methods provide several different global tests for association, as well as haplotype-specific tests, which give a meaningful advantage in attempts to understand the roles of many different haplotypes. The statistics can be computed rapidly, making it feasible to evaluate the associations between many haplotypes and a trait. To illustrate the use of our new methods, they are applied to a study of the association of haplotypes (composed of genes from the human-leukocyte-antigen complex) with humoral immune response to measles vaccination. Limited simulations are also presented to demonstrate the validity of our methods, as well as to provide guidelines on how our methods could be used.
1,813 citations