The Experts below are selected from a list of 195 Experts worldwide ranked by ideXlab platform
Hugues Aschard - One of the best experts on this subject based on the ideXlab platform.
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deriving stratified effects from joint Models investigating gene environment interactions
BMC Bioinformatics, 2020Co-Authors: Vincent Laville, Timothy Majarian, Paul S De Vries, Amy R Bentley, Mary F Feitosa, Yun J Sung, Alisa K Manning, Hugues AschardAbstract:Models including an interaction term and performing a joint test of SNP and/or interaction effect are often used to discover Gene-Environment (GxE) interactions. When the environmental exposure is a binary variable, analyses from exposure-stratified Models which consist of estimating genetic effect in unexposed and exposed individuals separately can be of interest. In large-scale consortia focusing on GxE interactions in which only the joint test has been performed, it may be challenging to get summary statistics from both exposure-stratified and Marginal (i.e not accounting for interaction) Models. In this work, we developed a simple framework to estimate summary statistics in each stratum of a binary exposure and in the Marginal Model using summary statistics from the “joint” Model. We performed simulation studies to assess our estimators’ accuracy and examined potential sources of bias, such as correlation between genotype and exposure and differing phenotypic variances within exposure strata. Results from these simulations highlight the high theoretical accuracy of our estimators and yield insights into the impact of potential sources of bias. We then applied our methods to real data and demonstrate our estimators’ retained accuracy after filtering SNPs by sample size to mitigate potential bias. These analyses demonstrated the accuracy of our method in estimating both stratified and Marginal summary statistics from a joint Model of gene-environment interaction. In addition to facilitating the interpretation of GxE screenings, this work could be used to guide further functional analyses. We provide a user-friendly Python script to apply this strategy to real datasets. The Python script and documentation are available at https://gitlab.pasteur.fr/statistical-genetics/j2s.
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deriving stratified effects from joint Models investigating gene environment interactions
bioRxiv, 2019Co-Authors: Vincent Laville, Timothy Majarian, Paul S De Vries, Amy R Bentley, Mary F Feitosa, Yun J Sung, Alisa K Manning, Hugues AschardAbstract:Abstract Background Models including an interaction term and performing a joint test of SNP and/or interaction effect are often used to discover Gene-Environment (GxE) interactions. When the environmental exposure is a binary variable, analyses from exposure-stratified Models which consist in estimating genetic effect in unexposed and exposed individuals separately can be of interest. In large-scale consortia focusing on GxE interactions in which only the joint test has been performed, it may be challenging to get summary statistics from both exposure-stratified and Marginal (i.e not accounting for interaction) Models. Results In this work, we developed a simple framework to estimate summary statistics in each stratum of a binary exposure and in the Marginal Model using summary statistics from the “joint” Model. We performed simulation studies to assess our estimators’ accuracy and examined potential sources of bias, such as correlation between genotype and exposure and differing phenotypic variances within exposure strata. Results from these simulations highlight the high theoretical accuracy of our estimators and yield insights into the impact of potential sources of bias. We then applied our methods to real data and demonstrate our estimators’ retained accuracy after filtering SNPs by sample size to mitigate potential bias. Conclusions These analyses demonstrated the accuracy of our method in estimating both stratified and Marginal summary statistics from a joint Model of gene-environment interaction. In addition to facilitating the interpretation of GxE screenings, this work could be used to guide further functional analyses. We provide a user-friendly Python script to apply this strategy to real datasets. The Python script and documentation are available at https://gitlab.pasteur.fr/statistical-genetics/J2S.
Alisa K Manning - One of the best experts on this subject based on the ideXlab platform.
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deriving stratified effects from joint Models investigating gene environment interactions
BMC Bioinformatics, 2020Co-Authors: Vincent Laville, Timothy Majarian, Paul S De Vries, Amy R Bentley, Mary F Feitosa, Yun J Sung, Alisa K Manning, Hugues AschardAbstract:Models including an interaction term and performing a joint test of SNP and/or interaction effect are often used to discover Gene-Environment (GxE) interactions. When the environmental exposure is a binary variable, analyses from exposure-stratified Models which consist of estimating genetic effect in unexposed and exposed individuals separately can be of interest. In large-scale consortia focusing on GxE interactions in which only the joint test has been performed, it may be challenging to get summary statistics from both exposure-stratified and Marginal (i.e not accounting for interaction) Models. In this work, we developed a simple framework to estimate summary statistics in each stratum of a binary exposure and in the Marginal Model using summary statistics from the “joint” Model. We performed simulation studies to assess our estimators’ accuracy and examined potential sources of bias, such as correlation between genotype and exposure and differing phenotypic variances within exposure strata. Results from these simulations highlight the high theoretical accuracy of our estimators and yield insights into the impact of potential sources of bias. We then applied our methods to real data and demonstrate our estimators’ retained accuracy after filtering SNPs by sample size to mitigate potential bias. These analyses demonstrated the accuracy of our method in estimating both stratified and Marginal summary statistics from a joint Model of gene-environment interaction. In addition to facilitating the interpretation of GxE screenings, this work could be used to guide further functional analyses. We provide a user-friendly Python script to apply this strategy to real datasets. The Python script and documentation are available at https://gitlab.pasteur.fr/statistical-genetics/j2s.
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deriving stratified effects from joint Models investigating gene environment interactions
bioRxiv, 2019Co-Authors: Vincent Laville, Timothy Majarian, Paul S De Vries, Amy R Bentley, Mary F Feitosa, Yun J Sung, Alisa K Manning, Hugues AschardAbstract:Abstract Background Models including an interaction term and performing a joint test of SNP and/or interaction effect are often used to discover Gene-Environment (GxE) interactions. When the environmental exposure is a binary variable, analyses from exposure-stratified Models which consist in estimating genetic effect in unexposed and exposed individuals separately can be of interest. In large-scale consortia focusing on GxE interactions in which only the joint test has been performed, it may be challenging to get summary statistics from both exposure-stratified and Marginal (i.e not accounting for interaction) Models. Results In this work, we developed a simple framework to estimate summary statistics in each stratum of a binary exposure and in the Marginal Model using summary statistics from the “joint” Model. We performed simulation studies to assess our estimators’ accuracy and examined potential sources of bias, such as correlation between genotype and exposure and differing phenotypic variances within exposure strata. Results from these simulations highlight the high theoretical accuracy of our estimators and yield insights into the impact of potential sources of bias. We then applied our methods to real data and demonstrate our estimators’ retained accuracy after filtering SNPs by sample size to mitigate potential bias. Conclusions These analyses demonstrated the accuracy of our method in estimating both stratified and Marginal summary statistics from a joint Model of gene-environment interaction. In addition to facilitating the interpretation of GxE screenings, this work could be used to guide further functional analyses. We provide a user-friendly Python script to apply this strategy to real datasets. The Python script and documentation are available at https://gitlab.pasteur.fr/statistical-genetics/J2S.
Peyton Jacob - One of the best experts on this subject based on the ideXlab platform.
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tobacco alkaloids and tobacco specific nitrosamines in dust from homes of smokeless tobacco users active smokers and nontobacco users
Chemical Research in Toxicology, 2015Co-Authors: Todd P Whitehead, Christopher Havel, Catherine Metayer, Neal L Benowitz, Peyton JacobAbstract:Smokeless tobacco products, such as moist snuff or chewing tobacco, contain many of the same carcinogens as tobacco smoke; however, the impact on children of indirect exposure to tobacco constituents via parental smokeless tobacco use is unknown. As part of the California Childhood Leukemia Study, dust samples were collected from 6 homes occupied by smokeless tobacco users, 6 homes occupied by active smokers, and 20 tobacco-free homes. To assess children’s potential for exposure to tobacco constituents, vacuum-dust concentrations of five tobacco-specific nitrosamines, including N′-nitrosonornicotine [NNN] and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone [NNK], as well as six tobacco alkaloids, including nicotine and myosmine, were quantified by liquid chromatography-tandem mass spectrometry (LC-MS/MS). We used generalized estimating equations derived from a multivariable Marginal Model to compare levels of tobacco constituents between groups, after adjusting for a history of parental smoking, income, ho...
Vincent Laville - One of the best experts on this subject based on the ideXlab platform.
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deriving stratified effects from joint Models investigating gene environment interactions
BMC Bioinformatics, 2020Co-Authors: Vincent Laville, Timothy Majarian, Paul S De Vries, Amy R Bentley, Mary F Feitosa, Yun J Sung, Alisa K Manning, Hugues AschardAbstract:Models including an interaction term and performing a joint test of SNP and/or interaction effect are often used to discover Gene-Environment (GxE) interactions. When the environmental exposure is a binary variable, analyses from exposure-stratified Models which consist of estimating genetic effect in unexposed and exposed individuals separately can be of interest. In large-scale consortia focusing on GxE interactions in which only the joint test has been performed, it may be challenging to get summary statistics from both exposure-stratified and Marginal (i.e not accounting for interaction) Models. In this work, we developed a simple framework to estimate summary statistics in each stratum of a binary exposure and in the Marginal Model using summary statistics from the “joint” Model. We performed simulation studies to assess our estimators’ accuracy and examined potential sources of bias, such as correlation between genotype and exposure and differing phenotypic variances within exposure strata. Results from these simulations highlight the high theoretical accuracy of our estimators and yield insights into the impact of potential sources of bias. We then applied our methods to real data and demonstrate our estimators’ retained accuracy after filtering SNPs by sample size to mitigate potential bias. These analyses demonstrated the accuracy of our method in estimating both stratified and Marginal summary statistics from a joint Model of gene-environment interaction. In addition to facilitating the interpretation of GxE screenings, this work could be used to guide further functional analyses. We provide a user-friendly Python script to apply this strategy to real datasets. The Python script and documentation are available at https://gitlab.pasteur.fr/statistical-genetics/j2s.
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deriving stratified effects from joint Models investigating gene environment interactions
bioRxiv, 2019Co-Authors: Vincent Laville, Timothy Majarian, Paul S De Vries, Amy R Bentley, Mary F Feitosa, Yun J Sung, Alisa K Manning, Hugues AschardAbstract:Abstract Background Models including an interaction term and performing a joint test of SNP and/or interaction effect are often used to discover Gene-Environment (GxE) interactions. When the environmental exposure is a binary variable, analyses from exposure-stratified Models which consist in estimating genetic effect in unexposed and exposed individuals separately can be of interest. In large-scale consortia focusing on GxE interactions in which only the joint test has been performed, it may be challenging to get summary statistics from both exposure-stratified and Marginal (i.e not accounting for interaction) Models. Results In this work, we developed a simple framework to estimate summary statistics in each stratum of a binary exposure and in the Marginal Model using summary statistics from the “joint” Model. We performed simulation studies to assess our estimators’ accuracy and examined potential sources of bias, such as correlation between genotype and exposure and differing phenotypic variances within exposure strata. Results from these simulations highlight the high theoretical accuracy of our estimators and yield insights into the impact of potential sources of bias. We then applied our methods to real data and demonstrate our estimators’ retained accuracy after filtering SNPs by sample size to mitigate potential bias. Conclusions These analyses demonstrated the accuracy of our method in estimating both stratified and Marginal summary statistics from a joint Model of gene-environment interaction. In addition to facilitating the interpretation of GxE screenings, this work could be used to guide further functional analyses. We provide a user-friendly Python script to apply this strategy to real datasets. The Python script and documentation are available at https://gitlab.pasteur.fr/statistical-genetics/J2S.
Nader Tajvidi - One of the best experts on this subject based on the ideXlab platform.
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nonparametric analysis of temporal trend when fitting parametric Models to extreme value data
Statistical Science, 2000Co-Authors: Peter Hall, Nader TajvidiAbstract:A topic of major current interest in extreme-value analysis is the investigation of temporal trends. For example, the potential influ- ence of "greenhouse" effects may result in severe storms becoming grad- ually more frequent, or in maximum temperatures gradually increasing, with time. One approach to evaluating these possibilities is to fit, to data, a parametric Model for temporal parameter variation, as well as a Model describing the Marginal distribution of data at any given point in time. However, structural trend Models can be difficult to formulate in many circumstances, owing to the complex way in which different factors combine to influence data in the form of extremes. Moreover, it is not advisable to fit trend Models without empirical evidence of their suitability. In this paper, motivated by datasets on windstorm severity and maximum temperature, we suggest a nonparametric approach to estimating temporal trends when fitting parametric Models to extreme values from a weakly dependent time series. We illustrate the method through applications to time series where the Marginal distributions are approximately Pareto, generalized-Pareto, extreme-value or Gaussian. We introduce time-varying probability plots to assess goodness of fit, we discuss local-likelihood approaches to fitting the Marginal Model within a window and we propose temporal cross-validation for selecting window width. In cases where both location and scale are estimated together, the Gaussian distribution is shown to have special features that permit it to play a universal role as a "nominal" Model for the Marginal distribution.