A new method to account for missing data in case-parent triad studies

Abstract

Background/aims: The case-parent triad design is commonly used in genetic association studies. Generally, samples are drawn from an affected offspring, manifesting a phenotype of interest, as well as from the parents. The trio genotypes may be analyzed using a variety of available methods, but we focus on log-linear models because they test for genetic association and additionally estimate the relative risks of transmission. The models need to be modified to adjust for missing genotypes. Furthermore, instability in the parameter estimates can arise when certain kinds of genotype combinations do not appear in the dataset.

Methods: In this paper, we kill two birds with one stone. We propose a new method to simultaneously account for missing genotype data and genotype combinations with zero counts. This method solves a zero-inflated Poisson (ZIP) regression likelihood. The maximum likelihood estimates yield relative risks and the information matrix gives appropriate variance estimates for inference. A likelihood ratio test determines the significance of genetic association.

Results: We compared the ZIP regression to previously proposed methods in both simulation studies and in a dataset that investigates the risk of orofacial clefts. The ZIP likelihood estimates regression coefficients with less bias than other methods when the minor allele frequency is small.

Fig. 1

Comparison of Type I error rates for the three methods discussed in Section 2. The upper panels show results for N = 300 and the lower panels for N = 500. The panels from left to right show results for the EM algorithm, the direct likelihood, and the ZIP likelihood respectively.

Fig. 2

Comparison of power levels for the three methods discussed in Section 2. The upper panels show results for N = 300 and the lower panels for N = 500. The panels from left to right show results for the EM algorithm, the direct likelihood, and the ZIP likelihood respectively.