83 research outputs found

    Natural and Orthogonal Interaction framework for modeling gene-environment interactions with application to lung cancer

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    Objectives: We aimed at extending the Natural and Orthogonal Interaction (NOIA) framework, developed for modeling gene-gene interactions in the analysis of quantitative traits, to allow for reduced genetic models, dichotomous traits, and gene-environment interactions. We evaluate the performance of the NOIA statistical models using simulated data and lung cancer data. Methods: The NOIA statistical models are developed for additive, dominant, and recessive genetic models as well as for a binary environmental exposure. Using the Kronecker product rule, a NOIA statistical model is built to model gene-environment interactions. By treating the genotypic values as the logarithm of odds, the NOIA statistical models are extended to the analysis of case-control data. Results: Our simulations showed that power for testing associations while allowing for interaction using the NOIA statistical model is much higher than using functional models for most of the scenarios we simulated. When applied to lung cancer data, much smaller p values were obtained using the NOIA statistical model for either the main effects or the SNP-smoking interactions for some of the SNPs tested. Conclusion: The NOIA statistical models are usually more powerful than the functional models in detecting main effects and interaction effects for both quantitative traits and binary traits. Copyright (C) 2012 S. Karger AG, Base

    A multi-stage genome-wide association study of bladder cancer identifies multiple susceptibility loci.

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    We conducted a multi-stage, genome-wide association study of bladder cancer with a primary scan of 591,637 SNPs in 3,532 affected individuals (cases) and 5,120 controls of European descent from five studies followed by a replication strategy, which included 8,382 cases and 48,275 controls from 16 studies. In a combined analysis, we identified three new regions associated with bladder cancer on chromosomes 22q13.1, 19q12 and 2q37.1: rs1014971, (P = 8 × 10⁻¹²) maps to a non-genic region of chromosome 22q13.1, rs8102137 (P = 2 × 10⁻¹¹) on 19q12 maps to CCNE1 and rs11892031 (P = 1 × 10⁻⁷) maps to the UGT1A cluster on 2q37.1. We confirmed four previously identified genome-wide associations on chromosomes 3q28, 4p16.3, 8q24.21 and 8q24.3, validated previous candidate associations for the GSTM1 deletion (P = 4 × 10⁻¹¹) and a tag SNP for NAT2 acetylation status (P = 4 × 10⁻¹¹), and found interactions with smoking in both regions. Our findings on common variants associated with bladder cancer risk should provide new insights into the mechanisms of carcinogenesis

    A multi-stage genome-wide association study of bladder cancer identifies multiple susceptibility loci.

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    We conducted a multi-stage, genome-wide association study of bladder cancer with a primary scan of 591,637 SNPs in 3,532 affected individuals (cases) and 5,120 controls of European descent from five studies followed by a replication strategy, which included 8,382 cases and 48,275 controls from 16 studies. In a combined analysis, we identified three new regions associated with bladder cancer on chromosomes 22q13.1, 19q12 and 2q37.1: rs1014971, (P = 8 × 10⁻¹²) maps to a non-genic region of chromosome 22q13.1, rs8102137 (P = 2 × 10⁻¹¹) on 19q12 maps to CCNE1 and rs11892031 (P = 1 × 10⁻⁷) maps to the UGT1A cluster on 2q37.1. We confirmed four previously identified genome-wide associations on chromosomes 3q28, 4p16.3, 8q24.21 and 8q24.3, validated previous candidate associations for the GSTM1 deletion (P = 4 × 10⁻¹¹) and a tag SNP for NAT2 acetylation status (P = 4 × 10⁻¹¹), and found interactions with smoking in both regions. Our findings on common variants associated with bladder cancer risk should provide new insights into the mechanisms of carcinogenesis

    Genetic correction of PSA values using sequence variants associated with PSA levels

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    To access publisher full text version of this article. Please click on the hyperlink in Additional Links fieldMeasuring serum levels of the prostate-specific antigen (PSA) is the most common screening method for prostate cancer. However, PSA levels are affected by a number of factors apart from neoplasia. Notably, around 40% of the variability of PSA levels in the general population is accounted for by inherited factors, suggesting that it may be possible to improve both sensitivity and specificity by adjusting test results for genetic effects. To search for sequence variants that associate with PSA levels, we performed a genome-wide association study and follow-up analysis using PSA information from 15,757 Icelandic and 454 British men not diagnosed with prostate cancer. Overall, we detected a genome-wide significant association between PSA levels and single-nucleotide polymorphisms (SNPs) at six loci: 5p15.33 (rs2736098), 10q11 (rs10993994), 10q26 (rs10788160), 12q24 (rs11067228), 17q12 (rs4430796), and 19q13.33 [rs17632542 (KLK3: I179T)], each with P(combined) <3 × 10(-10). Among 3834 men who underwent a biopsy of the prostate, the 10q26, 12q24, and 19q13.33 alleles that associate with high PSA levels are associated with higher probability of a negative biopsy (odds ratio between 1.15 and 1.27). Assessment of association between the six loci and prostate cancer risk in 5325 cases and 41,417 controls from Iceland, the Netherlands, Spain, Romania, and the United States showed that the SNPs at 10q26 and 12q24 were exclusively associated with PSA levels, whereas the other four loci also were associated with prostate cancer risk. We propose that a personalized PSA cutoff value, based on genotype, should be used when deciding to perform a prostate biopsy.info:eu-repo/grantAgreement/EC/FP7/202059/ 218071 Urological Research Foundation P50 CA90386-05S2 Robert H. Lurie Comprehensive Cancer Center p30 CA60553 Health Technology Assessment Programme 96/20/06 96/20/99 Department of Health, England Cancer Research UK C522/A8649 Medical Research Council of England G0500966 ID 75466 National Cancer Research Institute (NCRI), UK Southwest National Health Service Research and Development NCRI National Institute for Health Resear

    Replication of Lung Cancer Susceptibility Loci at Chromosomes 15q25, 5p15, and 6p21: A Pooled Analysis From the International Lung Cancer Consortium

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    Background Genome-wide association studies have identified three chromosomal regions at 15q25, 5p15, and 6p21 as being associated with the risk of lung cancer. To confirm these associations in independent studies and investigate heterogeneity of these associations within specific subgroups, we conducted a coordinated genotyping study within the International Lung Cancer Consortium based on independent studies that were not included in previous genome-wide association studies. Methods Genotype data for single-nucleotide polymorphisms at chromosomes 15q25 (rs16969968, rs8034191), 5p15 (rs2736100, rs402710), and 6p21 (rs2256543, rs4324798) from 21 case-control studies for 11 645 lung cancer case patients and 14 954 control subjects, of whom 85% were white and 15% were Asian, were pooled. Associations between the variants and the risk of lung cancer were estimated by logistic regression models. All statistical tests were two-sided. Results Associations between 15q25 and the risk of lung cancer were replicated in white ever-smokers (rs16969968: odds ratio [OR] = 1.26, 95% confidence interval [CI] = 1.21 to 1.32, Ptrend = 2 × 10−26), and this association was stronger for those diagnosed at younger ages. There was no association in never-smokers or in Asians between either of the 15q25 variants and the risk of lung cancer. For the chromosome 5p15 region, we confirmed statistically significant associations in whites for both rs2736100 (OR = 1.15, 95% CI = 1.10 to 1.20, Ptrend = 1 × 10−10) and rs402710 (OR = 1.14, 95% CI = 1.09 to 1.19, Ptrend = 5 × 10−8) and identified similar associations in Asians (rs2736100: OR = 1.23, 95% CI = 1.12 to 1.35, Ptrend = 2 × 10−5; rs402710: OR = 1.15, 95% CI = 1.04 to 1.27, Ptrend = .007). The associations between the 5p15 variants and lung cancer differed by histology; odds ratios for rs2736100 were highest in adenocarcinoma and for rs402710 were highest in adenocarcinoma and squamous cell carcinomas. This pattern was observed in both ethnic groups. Neither of the two variants on chromosome 6p21 was associated with the risk of lung cancer. Conclusions In this international genetic association study of lung cancer, previous associations found in white populations were replicated and new associations were identified in Asian populations. Future genetic studies of lung cancer should include detailed stratification by histolog

    Author Correction:GWAS of thyroid stimulating hormone highlights the pleiotropic effects and inverse association with thyroid cancer

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    The original version of this article contained an error in the results, in the second paragraph of the subsection entitled “Fine-mapping for potentially causal variants among TSH loci”, in which effect sizes for two variants were incorrectly reported

    Lung Cancer Risk in Never-Smokers of European Descent is Associated With Genetic Variation in the 5(p)15.33 TERT-CLPTM1Ll Region

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    Introduction: Inherited susceptibility to lung cancer risk in never-smokers is poorly understood. The major reason for this gap in knowledge is that this disease is relatively uncommon (except in Asians), making it difficult to assemble an adequate study sample. In this study we conducted a genome-wide association study on the largest, to date, set of European-descent never-smokers with lung cancer. Methods: We conducted a two-phase (discovery and replication) genome-wide association study in never-smokers of European descent. We further augmented the sample by performing a meta-analysis with never-smokers from the recent OncoArray study, which resulted in a total of 3636 cases and 6295 controls. We also compare our findings with those in smokers with lung cancer. Results: We detected three genome-wide statistically significant single nucleotide polymorphisms rs31490 (odds ratio [OR]: 0.769, 95% confidence interval [CI]: 0.722-0.820; p value 5.31 x 10(-16)), rs380286 (OR: 0.770, 95% CI: 0.723-0.820; p value 4.32 x 10(-16)), and rs4975616 OR: 0.778, 95% CI: 0.730-0.829; p value 1.04 x 10(-14)). All three mapped to Chromosome 5 CLPTM1L-TERT region, previously shown to be associated with lung cancer risk in smokers and in never-smoker Asian women, and risk of other cancers including breast, ovarian, colorectal, and prostate. Conclusions: We found that genetic susceptibility to lung cancer in never-smokers is associated to genetic variants with pan-cancer risk effects. The comparison with smokers shows that top variants previously shown to be associated with lung cancer risk only confer risk in the presence of tobacco exposure, underscoring the importance of gene-environment interactions in the etiology of this disease. (C) 2019 International Association for the Study of Lung Cancer. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

    Updated measurement of decay-time-dependent CP asymmetries in D-0 -> K+ K- and D-0 -> pi(+)pi(-) decays

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    A search for decay-time-dependent charge-parity (CP) asymmetry in D0 \u2192 K+ K 12 and D0 \u2192 \u3c0+ \u3c0 12 decays is performed at the LHCb experiment using proton-proton collision data recorded at a center-of-mass energy of 13 TeV, and corresponding to an integrated luminosity of 5.4 fb^ 121. The D0 mesons are required to originate from semileptonic decays of b hadrons, such that the charge of the muon identifies the flavor of the neutral D meson at production. The asymmetries in the effective decay widths of D0 and anti-D0 mesons are determined to be A_\u393(K+ K 12) = ( 124.3 \ub1 3.6 \ub1 0.5) 7 10^ 124 and A_\u393(\u3c0+ \u3c0 12) = (2.2 \ub1 7.0 \ub1 0.8) 7 10^ 124 , where the uncertainties are statistical and systematic, respectively. The results are consistent with CP symmetry and, when combined with previous LHCb results, yield A_\u393(K+ K 12) = ( 124.4 \ub1 2.3 \ub1 0.6) 7 10^ 124 and A_\u393(\u3c0+ \u3c0 12) = (2.5 \ub1 4.3 \ub1 0.7) 7 10^ 124

    Determination of quantum numbers for several excited charmed mesons observed in B- -> D*(+)pi(-) pi(-) decays

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    A four-body amplitude analysis of the B − → D * + π − π − decay is performed, where fractions and relative phases of the various resonances contributing to the decay are measured. Several quasi-model-independent analyses are performed aimed at searching for the presence of new states and establishing the quantum numbers of previously observed charmed meson resonances. In particular the resonance parameters and quantum numbers are determined for the D 1 ( 2420 ) , D 1 ( 2430 ) , D 0 ( 2550 ) , D ∗ 1 ( 2600 ) , D 2 ( 2740 ) and D ∗ 3 ( 2750 ) states. The mixing between the D 1 ( 2420 ) and D 1 ( 2430 ) resonances is studied and the mixing parameters are measured. The dataset corresponds to an integrated luminosity of 4.7     fb − 1 , collected in proton-proton collisions at center-of-mass energies of 7, 8 and 13 TeV with the LHCb detector

    Updated measurement of decay-time-dependent CP asymmetries in D-0 -> K+ K- and D-0 -> pi(+)pi(-) decays

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    A search for decay-time-dependent charge-parity (CP) asymmetry in D-0 -> K+ K- and D-0 -> pi(+)pi(-) eff decays is performed at the LHCb experiment using proton-proton collision data recorded at a center-of-mass energy of 13 TeV, and corresponding to an integrated luminosity of 5.4 fb(-1). The D-0 mesons are required to originate from semileptonic decays of b hadrons, such that the charge of the muon identifies the flavor of the neutral D meson at production. The asymmetries in the effective decay widths of D-0 and (D) over bar (0) mesons are determined to be A(Gamma)(K+ K-) = (-4.3 +/- 3.6 +/- 0.5) x 10(-4) and A(Gamma) (K+ K- ) = (2.2 +/- 7.0 +/- 0.8) x 10(-4), where the uncertainties are statistical and systematic, respectively. The results are consistent with CP symmetry and, when combined with previous LHCb results, yield A(Gamma) (K+ K-) = (-4.4 +/- 2.3 +/- 0.6) x 10(-4) and A(Gamma) (pi(+)pi(-))= (2.5 +/- 4.3 +/- 0.7) x 10(-4)
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