The Experts below are selected from a list of 187623 Experts worldwide ranked by ideXlab platform
Nilanjan Chatterjee - One of the best experts on this subject based on the ideXlab platform.
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testing gene environment interaction in large scale case control association studies possible choices and comparisons
2012Co-Authors: Bhramar Mukherjee, Stephen B Gruber, Nilanjan ChatterjeeAbstract:Several methods for screening gene-environment interaction have recently been proposed that address the issue of using gene-environment independence in a data-adaptive way. In this report, the authors present a comparative simulation study of power and type I error properties of 3 classes of procedures: 1) the standard 1-step case-control method; 2) the case-only method that requires an assumption of gene-environment independence for the Underlying Population; and 3) a variety of hybrid methods, including empirical-Bayes, 2-step, and model averaging, that aim at gaining power by exploiting the assumption of gene-environment independence and yet can protect against false positives when the independence assumption is violated. These studies suggest that, although the case-only method generally has maximum power, it has the potential to create substantial false positives in large-scale studies even when a small fraction of markers are associated with the exposure under study in the Underlying Population. All the hybrid methods perform well in protecting against such false positives and yet can retain substantial power advantages over standard case-control tests. The authors conclude that, for future genome-wide scans for gene-environment interactions, major power gain is possible by using alternatives to standard case-control analysis. Whether a case-only type scan or one of the hybrid methods should be used depends on the strength and direction of gene-environment interaction and association, the level of tolerance for false positives, and the nature of replication strategies.
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semiparametric maximum likelihood estimation exploiting gene environment independence in case control studies
2005Co-Authors: Nilanjan Chatterjee, Raymond J CarrollAbstract:We consider the problem of maximum-likelihood estimation in case-control studies of gene-environment associations with disease when genetic and environmental exposures can be assumed to be independent in the Underlying Population. Traditional logistic regression analysis may not be efficient in this setting. We study the semiparametric maximum likelihood estimates of logistic regression parameters that exploit the gene-environment independence assumption and leave the distribution of the environmental exposures to be nonparametric. We use a profile-likelihood technique to derive a simple algorithm for obtaining the estimator and we study the asymptotic theory. The results are extended to situations where genetic and environmental factors are independent conditional on some other factors. Simulation studies investigate small-sample properties. The method is illustrated using data from a case-control study designed to investigate the interplay of BRCA1/2 mutations and oral contraceptive use in the aetiology of ovarian cancer.
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semiparametric maximum likelihood estimation exploiting gene environment independence in case control studies
2005Co-Authors: Nilanjan Chatterjee, Raymond J CarrollAbstract:We consider the problem of maximum-likelihood estimation in case-control studies of gene-environment associations with disease when genetic and environmental exposures can be assumed to be independent in the Underlying Population. Traditional logistic regression analysis may not be efficient in this setting. We study the semiparametric maximum likelihood estimates of logistic regression parameters that exploit the gene-environment independence assumption and leave the distribution of the environmental exposures to be nonparametric. We use a profile-likelihood technique to derive a simple algorithm for obtaining the estimator and we study the asymptotic theory. The results are extended to situations where genetic and environmental factors are independent conditional on some other factors. Simulation studies investigate small-sample properties. The method is illustrated using data from a case-control study designed to investigate the interplay of BRCA1s2 mutations and oral contraceptive use in the aetiology of ovarian cancer. Copyright 2005, Oxford University Press.
A. Mark J. Hewison - One of the best experts on this subject based on the ideXlab platform.
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no difference between the sexes in fine scale spatial genetic structure of roe deer
2010Co-Authors: Nadege Bonnot, Jean-michel Gaillard, Maxime Galan, Daniel Delorme, Aurelie Coulon, Jeanfrancois Cosson, Francois Klein, A. Mark J. HewisonAbstract:BACKGROUND: Data on spatial genetic patterns may provide information about the ecological and behavioural mechanisms Underlying Population structure. Indeed, social organization and dispersal patterns of species may be reflected by the pattern of genetic structure within a Population. METHODOLOGY/PRINCIPAL FINDINGS: We investigated the fine-scale spatial genetic structure of a roe deer (Capreolus capreolus) Population in Trois-Fontaines (France) using 12 microsatellite loci. The roe deer is weakly polygynous and highly sedentary, and can form matrilineal clans. We show that relatedness among individuals was negatively correlated with geographic distance, indicating that spatially proximate individuals are also genetically close. More unusually for a large mammalian herbivore, the link between relatedness and distance did not differ between the sexes, which is consistent with the lack of sex-biased dispersal and the weakly polygynous mating system of roe deer. CONCLUSIONS/SIGNIFICANCE: Our results contrast with previous reports on highly polygynous species with male-biased dispersal, such as red deer, where local genetic structure was detected in females only. This divergence between species highlights the importance of socio-spatial organization in determining local genetic structure of vertebrate Populations.
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No difference between the sexes in fine-scale spatial genetic structure of roe deer
2010Co-Authors: Jean-michel Gaillard, Maxime Galan, Daniel Delorme, A. Mark J. HewisonAbstract:Background: Data on spatial genetic patterns may provide information about the ecological and behavioural mechanisms Underlying Population structure. Indeed, social organization and dispersal patterns of species may be reflected by the pattern of genetic structure within a Population. Methodology/Principal Findings: We investigated the fine-scale spatial genetic structure of a roe deer (Capreolus capreolus) Population in Trois-Fontaines (France) using 12 microsatellite loci. The roe deer is weakly polygynous and highly sedentary, and can form matrilineal clans. We show that relatedness among individuals was negatively correlated with geographic distance, indicating that spatially proximate individuals are also genetically close. More unusually for a large mammalian herbivore, the link between relatedness and distance did not differ between the sexes, which is consistent with the lack of sex-biased dispersal and the weakly polygynous mating system of roe deer. Conclusions/Significance: Our results contrast with previous reports on highly polygynous species with male-biased dispersal, such as red deer, where local genetic structure was detected in females only. This divergence between specie
Stephen G Walker - One of the best experts on this subject based on the ideXlab platform.
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a bayesian nonparametric meta analysis model
2015Co-Authors: George Karabatsos, Elizabeth Talbott, Stephen G WalkerAbstract:In a meta-analysis, it is important to specify a model that adequately describes the effect-size distribution of the Underlying Population of studies. The conventional normal fixed-effect and normal random-effects models assume a normal effect-size Population distribution, conditionally on parameters and covariates. For estimating the mean overall effect size, such models may be adequate, but for prediction, they surely are not if the effect-size distribution exhibits non-normal behavior. To address this issue, we propose a Bayesian nonparametric meta-analysis model, which can describe a wider range of effect-size distributions, including unimodal symmetric distributions, as well as skewed and more multimodal distributions. We demonstrate our model through the analysis of real meta-analytic data arising from behavioral-genetic research. We compare the predictive performance of the Bayesian nonparametric model against various conventional and more modern normal fixed-effects and random-effects models. Copyright © 2014 John Wiley & Sons, Ltd.
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a bayesian nonparametric meta analysis model
2013Co-Authors: George Karabatsos, Elizabeth Talbott, Stephen G WalkerAbstract:In a meta-analysis, it is important to specify a model that adequately describes the effect-size distribution of the Underlying Population of studies. The conventional normal fixed-effect and normal random-effects models assume a normal effect-size Population distribution, conditionally on parameters and covariates. For estimating the mean overall effect size, such models may be adequate, but for prediction they surely are not if the effect size distribution exhibits non-normal behavior. To address this issue, we propose a Bayesian nonparametric meta-analysis model, which can describe a wider range of effect-size distributions, including unimodal symmetric distributions, as well as skewed and more multimodal distributions. We demonstrate our model through the analysis of real meta-analytic data arising from behavioral-genetic research. We compare the predictive performance of the Bayesian nonparametric model against various conventional and more modern normal fixed-effects and random-effects models.
Martijn Egas - One of the best experts on this subject based on the ideXlab platform.
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evolution of specialization and ecological character displacement of herbivores along a gradient of plant quality
2005Co-Authors: Martijn Egas, Maurice W. Sabelis, Ulf DieckmannAbstract:Abstract We study the combined evolutionary dynamics of herbivore specialization and ecological character displacement, taking into account foraging behavior of the herbivores, and a quality gradient of plant types. Herbivores can adapt by changing two adaptive traits: their level of specialization in feeding efficiency and their point of maximum feeding efficiency along the plant gradient. The number of herbivore phenotypes, their levels of specialization, and the amount of character displacement among them are the result of the evolutionary dynamics, which is driven by the Underlying Population dynamics, which in turn is driven by the Underlying foraging behavior. Our analysis demonstrates broad conditions for the diversification of a herbivore Population into many specialized phenotypes, for basically any foraging behavior focusing use on highest gains while also including errors. Our model predicts two characteristic phases in the adaptation of herbivore phenotypes: a fast character-displacement phase...
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evolution of specialization and ecological character displacement of herbivores along a gradient of plant quality
2005Co-Authors: Martijn Egas, Maurice W. Sabelis, Ulf DieckmannAbstract:We study the combined evolutionary dynamics of herbivore specialization and ecological character displacement, taking into account foraging behavior of the herbivores, and a quality gradient of plant types. Herbivores can adapt by changing two adaptive traits: their level of specialization in feeding efficiency and their point of maximum feeding efficiency along the plant gradient. The number of herbivore phenotypes, their levels of specialization, and the amount of character displacement among them are the result of the evolutionary dynamics, which is driven by the Underlying Population dynamics, which in turn is driven by the Underlying foraging behavior. Our analysis demonstrates broad conditions for the diversification of a herbivore Population into many specialized phenotypes, for basically any foraging behavior focusing use on highest gains while also including errors. Our model predicts two characteristic phases in the adaptation of herbivore phenotypes: a fast character-displacement phase and a slow coevolutionary niche-shift phase. This two-phase pattern is expected to be of wide relevance in various consumer-resource systems. Bringing together ecological character displacement and the evolution of specialization in a single model, our study suggests that the foraging behavior of herbivorous arthropods is a key factor promoting specialist radiation.
George Karabatsos - One of the best experts on this subject based on the ideXlab platform.
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a bayesian nonparametric meta analysis model
2015Co-Authors: George Karabatsos, Elizabeth Talbott, Stephen G WalkerAbstract:In a meta-analysis, it is important to specify a model that adequately describes the effect-size distribution of the Underlying Population of studies. The conventional normal fixed-effect and normal random-effects models assume a normal effect-size Population distribution, conditionally on parameters and covariates. For estimating the mean overall effect size, such models may be adequate, but for prediction, they surely are not if the effect-size distribution exhibits non-normal behavior. To address this issue, we propose a Bayesian nonparametric meta-analysis model, which can describe a wider range of effect-size distributions, including unimodal symmetric distributions, as well as skewed and more multimodal distributions. We demonstrate our model through the analysis of real meta-analytic data arising from behavioral-genetic research. We compare the predictive performance of the Bayesian nonparametric model against various conventional and more modern normal fixed-effects and random-effects models. Copyright © 2014 John Wiley & Sons, Ltd.
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a bayesian nonparametric meta analysis model
2013Co-Authors: George Karabatsos, Elizabeth Talbott, Stephen G WalkerAbstract:In a meta-analysis, it is important to specify a model that adequately describes the effect-size distribution of the Underlying Population of studies. The conventional normal fixed-effect and normal random-effects models assume a normal effect-size Population distribution, conditionally on parameters and covariates. For estimating the mean overall effect size, such models may be adequate, but for prediction they surely are not if the effect size distribution exhibits non-normal behavior. To address this issue, we propose a Bayesian nonparametric meta-analysis model, which can describe a wider range of effect-size distributions, including unimodal symmetric distributions, as well as skewed and more multimodal distributions. We demonstrate our model through the analysis of real meta-analytic data arising from behavioral-genetic research. We compare the predictive performance of the Bayesian nonparametric model against various conventional and more modern normal fixed-effects and random-effects models.
