Genetic model selection in two-phase analysis for case-control association studies

Abstract

The Cochran-Armitage trend test (CATT) is well suited for testing association between a marker and a disease in case-control studies. When the underlying genetic model for the disease is known, the CATT optimal for the genetic model is used. For complex diseases, however, the genetic models of the true disease loci are unknown. In this situation, robust tests are preferable. We propose a two-phase analysis with model selection for the case-control design. In the first phase, we use the difference of Hardy-Weinberg disequilibrium coefficients between the cases and the controls for model selection. Then, an optimal CATT corresponding to the selected model is used for testing association. The correlation of the statistics used for selection and the test for association is derived to adjust the two-phase analysis with control of the Type-I error rate. The simulation studies show that this new approach has greater efficiency robustness than the existing methods.

A Validation Study of Type 2 Diabetes‐related Variants of the TCF7L2, HHEX, KCNJ11, and ADIPOQ Genes in one Endogamous Ethnic Group of North India

“…The association of each SNP was tested using a two-stage procedure (Zheng & Ng, 2008) and the MAX and MERT procedures (Freidlin et al, 2002). The power was studied by Monte-Carlo simulation using hypothetical settings.…”

Testing Association with Interactions by Partitioning Chi‐Squares

“…However, in real applications, the genetic model is unknown most of the time and needs to be estimated from the data. Analysis of the chi-square components of the interaction test hold promise in this regard (Zheng & Ng, 2007) and we will exlpore this issue elsewhere. In the partitioning example of the Methods section, if the last component of the interaction chi-square is the only highly significant one, then both loci are possibly recessive and proper collapsing of genotypes may gain power.…”

Robust tests for matched case-control genetic association studies

“…Specifically, first denote $T_0^{*}=(T_0+T_{0.5})/(\sqrt{2(1+{\hat{\rho }}_{0,0.5})}),T_{0.5}^{*}=T_{0.5}$ and $T_1^{*}=(T_1+T_{0.5})/(\sqrt{2(1+{\hat{\rho }}_{1,0.5})})$ where ${\hat{\rho }}_{x_1,x_2}$ is an estimate of the correlation between $T_{x_1}$ and $T_{x_2}$ under the null hypothesis of no association, then one can obtain the GME statistic from the GMS test by replacing T 0 , T 0.5 and T 1 in (1) by $T_0^{*}$, $T_{0.5}^{*}$ and $T_1^{*}$ respectively. Since the GMS and GME are two stage tests and the same data set is used twice, the critical values of the tests in the second stage need to be adjusted to control the overall type I error rates; see Zheng and Ng [11] and Joo et al [13]. …”

“…This underlying genetic model, however, can be ascertained using the Hardy-Weinberg Disequilibrium (HWD) coefficient which is de-noted as Δ = Pr ( DD ) - [ Pr ( DD )+ Pr ( Dd )/2] 2 . In the unmatched study, denote the HWD coefficients in the case group and the control group as Δ p = Pr ( DD|case ) - [ Pr ( DD|case )+ Pr ( Dd|case )/2] 2 and Δ q = Pr ( DD|control ) - [ Pr ( DD|control ) + Pr ( Dd|control )/2] 2 , Zheng and Ng [11] obtained that Δ p - Δ q > 0 under REC and Δ p - Δ q < 0 under DOM. Using the matched design described above, we denote Δ pl and Δ ql as the HWD coefficients in the case group and the control group of the l th sub-population respectively, l = 1,..., L .…”