𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Effect of including environmental data in investigations of gene-disease associations in the presence of qualitative interactions

✍ Scribed by Elizabeth Williamson; Anne-Louise Ponsonby; John Carlin; Terry Dwyer


Publisher
John Wiley and Sons
Year
2010
Tongue
English
Weight
152 KB
Volume
34
Category
Article
ISSN
0741-0395

No coin nor oath required. For personal study only.

✦ Synopsis


Abstract

Complex diseases are likely to be caused by the interplay of genetic and environmental factors. Despite this, gene‐disease associations are frequently investigated using models that focus solely on a marginal gene effect, ignoring environmental factors entirely. Failing to take into account a gene‐environment interaction can weaken the apparent gene‐disease association, leading to loss in statistical power and, potentially, inability to identify genuine risk factors. If a gene‐environment interaction exists, therefore, a joint analysis allowing the effect of the gene to differ between groups defined by the environmental exposure can have greater statistical power than a marginal gene‐disease model. However, environmental data are subject to measurement error. Substantial losses in statistical power for detecting gene‐environment interactions can arise from measurement error in the environmental exposure. It is unclear, however, what effect measurement error may have on the power of the joint analysis. We consider the potential benefits, in terms of statistical power, of collecting concurrent environmental data within large cohorts in order to enhance gene detection. We further consider whether these benefits remain in the presence of misclassification in both the gene and the environmental exposure. We find that when an effect of the gene is apparent only in the presence of the environmental exposure, the joint analysis has greater power than a marginal gene‐disease analysis. This comparative increase in power remains in the presence of likely levels of misclassification of either the gene or environmental exposure. Genet. Epidemiol. 34:552–560, 2010. © 2010 Wiley‐Liss, Inc.


📜 SIMILAR VOLUMES


Identification of gene-gene interactions
✍ Junghyun Namkung; Robert C. Elston; Jun-Mo Yang; Taesung Park 📂 Article 📅 2009 🏛 John Wiley and Sons 🌐 English ⚖ 559 KB

## Abstract Gene‐gene interaction is believed to play an important role in understanding complex traits. Multifactor dimensionality reduction (MDR) was proposed by Ritchie et al. [2001. Am J Hum Genet 69:138–147] to identify multiple loci that simultaneously affect disease susceptibility. Although

Genetic association tests in the presenc
✍ Kai Wang 📂 Article 📅 2008 🏛 John Wiley and Sons 🌐 English ⚖ 165 KB 👁 1 views

## Abstract A genetic variant is very likely to manifest its effect on disease through its main effect as well as through its interaction with other genetic variants or environmental factors. Power to detect genetic variants can be greatly improved by modeling their main effects and their interacti

Genotype-based association mapping of co
✍ Iryna Lobach; Ruzong Fan; Raymond J. Carroll 📂 Article 📅 2010 🏛 John Wiley and Sons 🌐 English ⚖ 173 KB 👁 1 views

With the advent of dense single nucleotide polymorphism genotyping, population-based association studies have become the major tools for identifying human disease genes and for fine gene mapping of complex traits. We develop a genotypebased approach for association analysis of case-control studies o

Power of multifactor dimensionality redu
✍ Marylyn D. Ritchie; Lance W. Hahn; Jason H. Moore 📂 Article 📅 2003 🏛 John Wiley and Sons 🌐 English ⚖ 130 KB 👁 1 views

## Abstract The identification and characterization of genes that influence the risk of common, complex multifactorial diseases, primarily through interactions with other genes and other environmental factors, remains a statistical and computational challenge in genetic epidemiology. This challenge