Exploiting gene-environment independence in family-based case-control studies: increased power for detecting associations, interactions and joint effects.

Genet Epidemiol 2005 Feb;28(2):138-56

Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, DHHS, Rockville, Maryland 20852, USA.

Family-based case-control studies are popularly used to study the effect of genes and gene-environment interactions in the etiology of rare complex diseases. We consider methods for the analysis of such studies under the assumption that genetic susceptibility (G) and environmental exposures (E) are independently distributed of each other within families in the source population. Conditional logistic regression, the traditional method of analysis of the data, fails to exploit the independence assumption and hence can be inefficient. Alternatively, one can estimate the multiplicative interaction between G and E more efficiently using cases only, but the required population-based G-E independence assumption is very stringent. In this article, we propose a novel conditional likelihood framework for exploiting the within-family G-E independence assumption. This approach leads to a simple and yet highly efficient method of estimating interaction and various other risk parameters of scientific interest. Moreover, we show that the same paradigm also leads to a number of alternative and even more efficient methods for analysis of family-based case-control studies when parental genotype information is available on the case-control study participants. Based on these methods, we evaluate different family-based study designs by examining their relative efficiencies to each other and their efficiencies compared to a population-based case-control design of unrelated subjects. These comparisons reveal important design implications. Extensions of the methodologies for dealing with complex family studies are also discussed.

Download full-text PDF

Source
http://dx.doi.org/10.1002/gepi.20049DOI Listing
February 2005
2 Reads

Publication Analysis

Top Keywords

case-control studies
12
independence assumption
12
family-based case-control
12
methods analysis
8
g-e independence
8
case-control
5
studies
5
framework exploiting
4
approach leads
4
leads simple
4
assumption approach
4
within-family g-e
4
exploiting within-family
4
likelihood framework
4
simple highly
4
estimating interaction
4
risk parameters
4
parameters scientific
4
scientific interest
4
interaction risk
4

Altmetric Statistics

References

(Supplied by CrossRef)

Albert et al.
Am J Epidemiol 2001

Chatterjee et al.
Biometrika 2005

Clayton et al.
Am J Hum Genet 1999

Clayton et al.
Lancet 2001

Cordell et al.
Am J Hum Genet 2002

Cordell et al.
Genet Epidemiol 2004

Curtis et al.
Ann Hum Genet 1997

Gauderman et al.
Stat Med 2002

Gauderman et al.
J N Cancer Inst 1999

Godambe et al.
1991

Hsu et al.
Human Hered 2000

Similar Publications