The Experts below are selected from a list of 6660 Experts worldwide ranked by ideXlab platform
Gang Zheng - One of the best experts on this subject based on the ideXlab platform.
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impact of population substructure on trend tests for genetic case control association studies
Biometrics, 2010Co-Authors: Gang Zheng, Zhaohai Li, Mitchell H Gail, Joseph L GastwirthAbstract:Summary Hidden population substructure in case–control data has the potential to distort the performance of Cochran–Armitage trend tests (CATTs) for genetic associations. Three possible scenarios that may arise are investigated here: (i) heterogeneity of genotype frequencies across unidentified subpopulations (PSI), (ii) heterogeneity of genotype frequencies and disease risk across unidentified subpopulations (PSII), and (iii) cryptic correlations within unidentified subpopulations. A unified approach is presented for deriving the bias and variance distortion under the three scenarios for any CATT in a general family. Using these analytical formulas, we evaluate the excess type I errors of the CATTs numerically in the presence of population substructure. Our results provide insight into the properties of some proposed corrections for bias and variance distortion and show why they may not fully correct for the effects of population substructure.
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genetic model selection in two phase analysis for case control association studies
Biostatistics, 2008Co-Authors: Gang ZhengAbstract:The Cochran-Armitage trend test (CATT) is well suited for testing association between a marker and a disease in case-control studies. When the underlying genetic model for the disease is known, the CATT optimal for the genetic model is used. For complex diseases, however, the genetic models of the true disease loci are unknown. In this situation, robust tests are preferable. We propose a two-phase analysis with model selection for the case-control design. In the first phase, we use the difference of Hardy-Weinberg disequilibrium coefficients between the cases and the controls for model selection. Then, an optimal CATT corresponding to the selected model is used for testing association. The correlation of the statistics used for selection and the test for association is derived to adjust the two-phase analysis with control of the Type-I error rate. The simulation studies show that this new approach has greater efficiency robustness than the existing methods.
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on estimation of the variance in cochran Armitage trend tests for genetic association using case control studies
Statistics in Medicine, 2006Co-Authors: Gang Zheng, Joseph L GastwirthAbstract:The Cochran–Armitage trend test has been used in case–control studies for testing genetic association. As the variance of the test statistic is a function of unknown parameters, e.g. disease prevalence and allele frequency, it must be estimated. The usual estimator combining data for cases and controls assumes they follow the same distribution under the null hypothesis. Under the alternative hypothesis, however, the cases and controls follow different distributions. Thus, the power of the trend tests may be affected by the variance estimator used. In particular, the usual method combining both cases and controls is not an asymptotically unbiased estimator of the null variance when the alternative is true. Two different estimates of the null variance are available which are consistent under both the null and alternative hypotheses. In this paper, we examine sample size and small sample power performance of trend tests, which are optimal for three common genetic models as well as a robust trend test based on the three estimates of the variance and provide guidelines for choosing an appropriate test. Copyright © 2006 John Wiley & Sons, Ltd.
Mark P Little - One of the best experts on this subject based on the ideXlab platform.
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REVIEW Open Access Cancer models, genomic instability and somatic cellular Darwinian evolution
2013Co-Authors: Mark P LittleAbstract:1,2 Abstract: The biology of cancer is critically reviewed and evidence adduced that its development can be modelled as a somatic cellular Darwinian evolutionary process. The evidence for involvement of genomic instability (GI) is also reviewed. A variety of quasi-mechanistic models of carcinogenesis are reviewed, all based on this somatic Darwinian evolutionary hypothesis; in particular, the multi-stage model of Armitage and Doll (Br. J. Cancer 1954:8;1-12), the two-mutation model of Moolgavkar, Venzon, and Knudson (MVK) (Math. Biosci. 1979:47;55-77), the generalized MVK model of Little (Biometrics 1995:51;1278-1291) and various generalizations of these incorporating effects of GI (Little and Wright Math. Biosci. 2003:183;111-134; Little et al. J. Theoret. Biol. 2008:254;229-238)
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cancer models genomic instability and somatic cellular darwinian evolution
Biology Direct, 2010Co-Authors: Mark P LittleAbstract:The biology of cancer is critically reviewed and evidence adduced that its development can be modelled as a somatic cellular Darwinian evolutionary process. The evidence for involvement of genomic instability (GI) is also reviewed. A variety of quasi-mechanistic models of carcinogenesis are reviewed, all based on this somatic Darwinian evolutionary hypothesis; in particular, the multi-stage model of Armitage and Doll (Br. J. Cancer 1954:8;1-12), the two-mutation model of Moolgavkar, Venzon, and Knudson (MVK) (Math. Biosci. 1979:47;55-77), the generalized MVK model of Little (Biometrics 1995:51;1278-1291) and various generalizations of these incorporating effects of GI (Little and Wright Math. Biosci. 2003:183;111-134; Little et al. J. Theoret. Biol. 2008:254;229-238).
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parameter identifiability and redundancy theoretical considerations
arXiv: Statistics Theory, 2008Co-Authors: Mark P Little, Wolfgang F HeidenreichAbstract:In this paper we outline general considerations on parameter identifiability, and introduce the notion of weak local identifiability and gradient weak local identifiability. These are based on local properties of the likelihood, in particular the rank of the Hessian matrix. We relate these to the notions of parameter identifiability and redundancy previously introduced by Rothenberg (Econometrica 39 (1971) 577-591) and Catchpole and Morgan (Biometrika 84 (1997) 187-196). Within the exponential family parameter irredundancy, local identifiability, gradient weak local identifiability and weak local identifiability are shown to be equivalent. We consider applications to a recently developed class of cancer models of Little and Wright (Math Biosciences 183 (2003) 111-134) and Little et al. (J Theoret Biol 254 (2008) 229-238) that generalize a large number of other recently used quasi-biological cancer models, in particular those of Armitage and Doll (Br J Cancer 8 (1954) 1-12) and the two-mutation model (Moolgavkar and Venzon Math Biosciences 47 (1979) 55-77).
Joseph L Gastwirth - One of the best experts on this subject based on the ideXlab platform.
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impact of population substructure on trend tests for genetic case control association studies
Biometrics, 2010Co-Authors: Gang Zheng, Zhaohai Li, Mitchell H Gail, Joseph L GastwirthAbstract:Summary Hidden population substructure in case–control data has the potential to distort the performance of Cochran–Armitage trend tests (CATTs) for genetic associations. Three possible scenarios that may arise are investigated here: (i) heterogeneity of genotype frequencies across unidentified subpopulations (PSI), (ii) heterogeneity of genotype frequencies and disease risk across unidentified subpopulations (PSII), and (iii) cryptic correlations within unidentified subpopulations. A unified approach is presented for deriving the bias and variance distortion under the three scenarios for any CATT in a general family. Using these analytical formulas, we evaluate the excess type I errors of the CATTs numerically in the presence of population substructure. Our results provide insight into the properties of some proposed corrections for bias and variance distortion and show why they may not fully correct for the effects of population substructure.
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on estimation of the variance in cochran Armitage trend tests for genetic association using case control studies
Statistics in Medicine, 2006Co-Authors: Gang Zheng, Joseph L GastwirthAbstract:The Cochran–Armitage trend test has been used in case–control studies for testing genetic association. As the variance of the test statistic is a function of unknown parameters, e.g. disease prevalence and allele frequency, it must be estimated. The usual estimator combining data for cases and controls assumes they follow the same distribution under the null hypothesis. Under the alternative hypothesis, however, the cases and controls follow different distributions. Thus, the power of the trend tests may be affected by the variance estimator used. In particular, the usual method combining both cases and controls is not an asymptotically unbiased estimator of the null variance when the alternative is true. Two different estimates of the null variance are available which are consistent under both the null and alternative hypotheses. In this paper, we examine sample size and small sample power performance of trend tests, which are optimal for three common genetic models as well as a robust trend test based on the three estimates of the variance and provide guidelines for choosing an appropriate test. Copyright © 2006 John Wiley & Sons, Ltd.
Katrin Finsterwalder - One of the best experts on this subject based on the ideXlab platform.
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mutations in grin2a cause idiopathic focal epilepsy with rolandic spikes
Nature Genetics, 2013Co-Authors: Johannes R Lemke, Dennis Lal, Eva M Reinthaler, Isabelle Steiner, Michael Nothnagel, Michael Alber, Kirsten Geider, Bodo Laube, Michael Schwake, Katrin FinsterwalderAbstract:Idiopathic focal epilepsy (IFE) with rolandic spikes is the most common childhood epilepsy, comprising a phenotypic spectrum from rolandic epilepsy (also benign epilepsy with centrotemporal spikes, BECTS) to atypical benign partial epilepsy (ABPE), Landau-Kleffner syndrome (LKS) and epileptic encephalopathy with continuous spike and waves during slow-wave sleep (CSWS). The genetic basis is largely unknown. We detected new heterozygous mutations in GRIN2A in 27 of 359 affected individuals from 2 independent cohorts with IFE (7.5%; P = 4.83 × 10(-18), Fisher's exact test). Mutations occurred significantly more frequently in the more severe phenotypes, with mutation detection rates ranging from 12/245 (4.9%) in individuals with BECTS to 9/51 (17.6%) in individuals with CSWS (P = 0.009, Cochran-Armitage test for trend). In addition, exon-disrupting microdeletions were found in 3 of 286 individuals (1.0%; P = 0.004, Fisher's exact test). These results establish alterations of the gene encoding the NMDA receptor NR2A subunit as a major genetic risk factor for IFE.
Zuoheng Wang - One of the best experts on this subject based on the ideXlab platform.
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a comparison of association methods correcting for population stratification in case control studies
Annals of Human Genetics, 2011Co-Authors: Chengqing Wu, Andrew T Dewan, Zuoheng WangAbstract:Population stratification is an important issue in case–control studies of disease-marker association. Failure to properly account for population structure can lead to spurious association or reduced power. In this article, we compare the performance of six methods correcting for population stratification in case–control association studies. These methods include genomic control (GC), EIGENSTRAT, principal component-based logistic regression (PCA-L), LAPSTRUCT, ROADTRIPS, and EMMAX. We also include the uncorrected Armitage test for comparison. In the simulation studies, we consider a wide range of population structure models for unrelated samples, including admixture. Our simulation results suggest that PCA-L and LAPSTRUCT perform well over all the scenarios studied, whereas GC, ROADTRIPS, and EMMAX fail to correct for population structure at single nucleotide polymorphisms (SNPs) that show strong differentiation across ancestral populations. The Armitage test does not adjust for confounding due to stratification thus has inflated type I error. Among all correction methods, EMMAX has the greatest power, based on the population structure settings considered for samples with unrelated individuals. The three methods, EIGENSTRAT, PCA-L, and LAPSTRUCT, are comparable, and outperform both GC and ROADTRIPS in almost all situations.