A test for gene –environment interaction in the presence of measurement error in the environmental variable
Genetic Epidemiology,Volume 42, Issue 3, Page 250-264, April 2018. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - February 8, 2018 Category: Epidemiology Source Type: research
An analytic approach for interpretable predictive models in high ‐dimensional data in the presence of interactions with exposures
Genetic Epidemiology,Volume 42, Issue 3, Page 233-249, April 2018. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - February 8, 2018 Category: Epidemiology Source Type: research
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Genetic Epidemiology,Volume 42, Issue 3, Page 250-264, April 2018. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - February 8, 2018 Category: Epidemiology Source Type: research
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Genetic Epidemiology,Volume 42, Issue 3, Page 233-249, April 2018. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - February 8, 2018 Category: Epidemiology Source Type: research
A test for gene –environment interaction in the presence of measurement error in the environmental variable
Abstract
The identification of gene–environment interactions in relation to risk of human diseases has been challenging. One difficulty has been that measurement error in the exposure can lead to massive reductions in the power of the test, as well as in bias toward the null in the interaction effect estimates. Leveraging previous work on linear discriminant analysis, we develop a new test of interaction between genetic variants and a continuous exposure that mitigates these detrimental impacts of exposure measurement error in ExG testing by reversing the role of exposure and the diseases status in the fitted model, thus...
Source: Genetic Epidemiology - February 8, 2018 Category: Epidemiology Authors: Hugues Aschard, Donna Spiegelman, Vincent Laville, Pete Kraft, Molin Wang Tags: RESEARCH ARTICLE Source Type: research
Whole genome association study of brain ‐wide imaging phenotypes: A study of the ping cohort
Genetic Epidemiology,Volume 42, Issue 3, Page 265-275, April 2018. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - February 7, 2018 Category: Epidemiology Source Type: research
Integrating eQTL data with GWAS summary statistics in pathway ‐based analysis with application to schizophrenia
Genetic Epidemiology,Volume 42, Issue 3, Page 303-316, April 2018. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - February 7, 2018 Category: Epidemiology Source Type: research
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Genetic Epidemiology,Volume 42, Issue 3, Page 265-275, April 2018. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - February 7, 2018 Category: Epidemiology Source Type: research
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Genetic Epidemiology,Volume 42, Issue 3, Page 303-316, April 2018. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - February 7, 2018 Category: Epidemiology Source Type: research
Whole genome association study of brain ‐wide imaging phenotypes: A study of the ping cohort
Abstract
Neuropsychological disorders have a biological basis rooted in brain function, and neuroimaging data are expected to better illuminate the complex genetic basis of neuropsychological disorders. Because they are biological measures, neuroimaging data avoid biases arising from clinical diagnostic criteria that are subject to human understanding and interpretation. A challenge with analyzing neuroimaging data is their high dimensionality and complex spatial relationships. To tackle this challenge, we introduced a novel distance covariance tests that can assess the association between genetic markers and multivariate ...
Source: Genetic Epidemiology - February 7, 2018 Category: Epidemiology Authors: Canhong Wen, Chintan M. Mehta, Haizhu Tan, Heping Zhang Tags: RESEARCH ARTICLE Source Type: research
A meta ‐analysis approach with filtering for identifying gene‐level gene–environment interactions
Abstract
There is a growing recognition that gene–environment interaction (G × E) plays a pivotal role in the development and progression of complex diseases. Despite a wealth of genetic data on various complex diseases/traits generated from association and sequencing studies, detecting G × E via genome‐wide analysis remains challenging due to power issues. In genome‐wide G × E studies, a common strategy to improve power is to first conduct a filtering test and retain only the genetic variants that pass the filtering step for subsequent G × E analyses. Two‐stage, multistage, and unified tests have been ...
Source: Genetic Epidemiology - February 1, 2018 Category: Epidemiology Authors: Jiebiao Wang, Qianying Liu, Brandon L. Pierce, Dezheng Huo, Olufunmilayo I. Olopade, Habibul Ahsan, Lin S. Chen Tags: RESEARCH ARTICLE Source Type: research
An analytic approach for interpretable predictive models in high ‐dimensional data in the presence of interactions with exposures
ABSTRACT
Predicting a phenotype and understanding which variables improve that prediction are two very challenging and overlapping problems in the analysis of high‐dimensional (HD) data such as those arising from genomic and brain imaging studies. It is often believed that the number of truly important predictors is small relative to the total number of variables, making computational approaches to variable selection and dimension reduction extremely important. To reduce dimensionality, commonly used two‐step methods first cluster the data in some way, and build models using cluster summaries to predict the phenotype. ...
Source: Genetic Epidemiology - February 1, 2018 Category: Epidemiology Authors: Sahir Rai Bhatnagar, Yi Yang, Budhachandra Khundrakpam, Alan C. Evans, Mathieu Blanchette, Luigi Bouchard, Celia M.T. Greenwood Tags: RESEARCH ARTICLE Source Type: research
Integrating eQTL data with GWAS summary statistics in pathway ‐based analysis with application to schizophrenia
ABSTRACT
Many genetic variants affect complex traits through gene expression, which can be exploited to boost statistical power and enhance interpretation in genome‐wide association studies (GWASs) as demonstrated by the transcriptome‐wide association study (TWAS) approach. Furthermore, due to polygenic inheritance, a complex trait is often affected by multiple genes with similar functions as annotated in gene pathways. Here, we extend TWAS from gene‐based analysis to pathway‐based analysis: we integrate public pathway collections, expression quantitative trait locus (eQTL) data and GWAS summary association statist...
Source: Genetic Epidemiology - February 1, 2018 Category: Epidemiology Authors: Chong Wu, Wei Pan Tags: RESEARCH ARTICLE Source Type: research
Issue Information
(Source: Genetic Epidemiology)
Source: Genetic Epidemiology - January 15, 2018 Category: Epidemiology Tags: ISSUE INFORMATION Source Type: research
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Genetic Epidemiology,Volume 42, Issue 2, Page 201-213, March 2018. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - January 10, 2018 Category: Epidemiology Source Type: research
