Literature DB >> 8122051

Non-hierarchical logistic models and case-only designs for assessing susceptibility in population-based case-control studies.

W W Piegorsch1, C R Weinberg, J A Taylor.   

Abstract

This article describes how genetic components of disease susceptibility can be evaluated in case-control studies, where cases and controls are sampled independently from the population at large. Subjects are assumed unrelated, in contrast to studies of familial aggregation and linkage. The logistic model can be used to test collapsibility over phenotypes or genotypes, and to estimate interactions between environmental and genetic factors. Such interactions provide an example of a context where non-hierarchical models make sense biologically. Also, if the exposure and genetic categories occur independently and the disease is rare, then analyses based only on cases are valid, and offer better precision for estimating gene-environment interactions than those based on the full data.

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Year:  1994        PMID: 8122051     DOI: 10.1002/sim.4780130206

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  187 in total

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3.  Gene-environment interactions in genome-wide association studies: a comparative study of tests applied to empirical studies of type 2 diabetes.

Authors:  Marilyn C Cornelis; Eric J Tchetgen Tchetgen; Liming Liang; Lu Qi; Nilanjan Chatterjee; Frank B Hu; Peter Kraft
Journal:  Am J Epidemiol       Date:  2011-12-22       Impact factor: 4.897

4.  Testing gene-environment interaction in large-scale case-control association studies: possible choices and comparisons.

Authors:  Bhramar Mukherjee; Jaeil Ahn; Stephen B Gruber; Nilanjan Chatterjee
Journal:  Am J Epidemiol       Date:  2011-12-22       Impact factor: 4.897

5.  A sibling-augmented case-only approach for assessing multiplicative gene-environment interactions.

Authors:  Clarice R Weinberg; Min Shi; David M Umbach
Journal:  Am J Epidemiol       Date:  2011-10-20       Impact factor: 4.897

6.  Pseudo semiparametric maximum likelihood estimation exploiting gene environment independence for population-based case-control studies with complex samples.

Authors:  Yan Li; Barry I Graubard
Journal:  Biostatistics       Date:  2012-04-20       Impact factor: 5.899

7.  Simultaneously testing for marginal genetic association and gene-environment interaction.

Authors:  James Y Dai; Benjamin A Logsdon; Ying Huang; Li Hsu; Alexander P Reiner; Ross L Prentice; Charles Kooperberg
Journal:  Am J Epidemiol       Date:  2012-07-06       Impact factor: 4.897

8.  Testing haplotype-environment interactions using case-parent triads.

Authors:  Min Shi; David M Umbach; Clarice R Weinberg
Journal:  Hum Hered       Date:  2010-04-23       Impact factor: 0.444

9.  Efficient genome-wide association testing of gene-environment interaction in case-parent trios.

Authors:  W James Gauderman; Duncan C Thomas; Cassandra E Murcray; David Conti; Dalin Li; Juan Pablo Lewinger
Journal:  Am J Epidemiol       Date:  2010-06-11       Impact factor: 4.897

10.  BAYESIAN SEMIPARAMETRIC ANALYSIS FOR TWO-PHASE STUDIES OF GENE-ENVIRONMENT INTERACTION.

Authors:  Jaeil Ahn; Bhramar Mukherjee; Stephen B Gruber; Malay Ghosh
Journal:  Ann Appl Stat       Date:  2013-03       Impact factor: 2.083

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