Analytic perspective

Sample size requirements to detect the effect of a group of genetic variants in case-control studies

Ramal Moonesinghe¹ , Quanhe Yang² and Muin J Khoury²

¹  Office of Minority Health and Health Disparities, Centers for Disease Control and Prevention, Atlanta, Georgia, USA

²  National Office of Public Health Genomics, Coordinating Center for Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia, USA

author email corresponding author email

Emerging Themes in Epidemiology 2008, 5:24doi:10.1186/1742-7622-5-24

Published:	3 December 2008

Abstract

Background

Because common diseases are caused by complex interactions among many genetic variants along with environmental risk factors, very large sample sizes are usually needed to detect such effects in case-control studies. Nevertheless, many genetic variants act in well defined biologic systems or metabolic pathways. Therefore, a reasonable first step may be to detect the effect of a group of genetic variants before assessing specific variants.

Methods

We present a simple method for determining approximate sample sizes required to detect the average joint effect of a group of genetic variants in a case-control study for multiplicative models.

Results

For a range of reasonable numbers of genetic variants, the sample size requirements for the test statistic proposed here are generally not larger than those needed for assessing marginal effects of individual variants and actually decline with increasing number of genetic variants in many situations considered in the group.

Conclusion

When a significant effect of the group of genetic variants is detected, subsequent multiple tests could be conducted to detect which individual genetic variants and their combinations are associated with disease risk. When testing for an effect size in a group of genetic variants, one can use our global test described in this paper, because the sample size required to detect an effect size in the group is comparatively small. Our method could be viewed as a screening tool for assessing groups of genetic variants involved in pathogenesis and etiology of common complex human diseases.