Estimation of gene-environment interaction by pooling biospecimens

Abstract

Case-control studies are prone to low power for testing gene-environment interactions (GXE) given the need for a sufficient number of individuals on each strata of disease, gene, and environment. We propose a new study design to increase power by strategically pooling biospecimens. Pooling biospecimens allows us to increase the number of subjects significantly, thereby providing substantial increase in power. We focus on a special, although realistic case, where disease and environmental statuses are binary, and gene status is ordinal with each individual having 0, 1, or 2 minor alleles. Through pooling, we obtain an allele frequency for each level of disease and environmental status. Using the allele frequencies, we develop a new methodology for estimating and testing GXE that is comparable to the situation when we have complete data on gene status for each individual. We also explore the measurement process and its effect on the GXE estimator. Using an illustration, we show the effectiveness of pooling with an epidemiologic study, which tests an interaction for fiber and paraoxonase on anovulation. Through simulation, we show that taking 12 pooled measurements from 1000 individuals achieves more power than individually genotyping 500 individuals. Our findings suggest that strategic pooling should be considered when an investigator designs a pilot study to test for a GXE.

Figure 1

Illustration of obtaining minor allele frequencies using PCR.

Figure 2

Power functions of five different scenarios. All solid lines are for pooled estimators with 1000 individuals. The solid line with the least power uses 2 repeated measurements of pools on each level of disease and environments. The solid line above that uses 3 repeated measurements, and the solid line with the most power uses 1000 repeated measurements. The dotted line with the least power observes 500 individual measurements, and the dotted line with the most power uses 1000 individual measurements

Figure 3

Power functions of four different scenarios. All solid lines are for pooled estimators, with 1000 individuals and dotted lines are for the traditional estimators with 500 individual measurements. The solid and dotted lines with the most power uses _c D_1, and the solid and dotted lines with the least power uses _c D 1.