A nonparametric alternative to the Cochran-Armitage trend test in genetic case-control association studies: The Jonckheere-Terpstra trend test.

Identifications of novel genetic signals conferring susceptibility to human complex diseases is pivotal to the disease diagnosis, prevention, and treatment. Genetic association study is a powerful tool to discover candidate genetic signals that contribute to diseases, through statistical tests for correlation between the disease status and genetic variations in study samples. In such studies with a case-control design, a standard practice is to perform the Cochran-Armitage (CA) trend test under an additive genetic model, which suffers from power loss when the model assumption is wrong.

A Varying Coefficient Model to Jointly Test Genetic and Gene-Environment Interaction Effects.

Most human traits are influenced by the interplay between genetic and environmental factors. Many statistical methods have been proposed to screen for gene-environment interaction (GxE) in the post genome-wide association study era. However, most of the existing methods assume a linear interaction between genetic and environmental factors toward phenotypic variations, which diminishes statistical power in the case of nonlinear GxE.

Decomposing Pearson's χ test: A linear regression and its departure from linearity.

In case-control genetic association studies, a standard practice is to perform the Cochran-Armitage (CA) trend test under the assumption of the additive model because of its robustness. We could even identify situations in which it outperformed the analysis model consistent with the underlying inheritance mode. In this article, we analytically reveal the statistical basis that leads to the phenomenon.

Differentiating the Cochran-Armitage Trend Test and Pearson's χ Test: Location and Dispersion.

In genetic case-control association studies, a standard practice is to perform the Cochran-Armitage (CA) trend test with 1 degree-of-freedom (d.f.) under the assumption of an additive model.

Genome-wide association analysis of radiation resistance in Drosophila melanogaster.

Background: Ionizing radiation is genotoxic to cells. Healthy tissue toxicity in patients and radiation resistance in tumors present common clinical challenges in delivering effective radiation therapies. Radiation response is a complex, polygenic trait with unknown genetic determinants.

Enhancing the power to detect low-frequency variants in genome-wide screens.

In genetic association studies a conventional test statistic is proportional to the correlation coefficient between the trait and the variant, with the result that it lacks power to detect association for low-frequency variants. Considering the link between the conventional association test statistics and the linkage disequilibrium measure r(2), we propose a test statistic analogous to the standardized linkage disequilibrium D' to increase the power of detecting association for low-frequency variants. By both simulation and real data analysis we show that the proposed D' test is more powerful than the conventional methods for detecting association for low-frequency variants in a genome-wide setting.

Association and familial segregation of CTG18.1 trinucleotide repeat expansion of TCF4 gene in Fuchs' endothelial corneal dystrophy.

Purpose: We tested the association between two intronic polymorphisms (CTG18.1 and rs613872) in TCF4 and Fuchs' endothelial corneal dystrophy (FECD), and analyzed their segregation patterns in families.

Methods: We recruited 120 unrelated Caucasian subjects with FECD and 100 controls.

A comparison of the likelihood ratio test and the variance-stabilising transformation-based tests for detecting association of rare variants.

In a recent paper in this journal, the use of variance-stabilising transformation techniques was proposed to overcome the problem of inadequacy in normality approximation when testing association for a low-frequency variant in a case-control study. It was shown that tests based on the variance-stabilising transformations are more powerful than Fisher's exact test while controlling for type I error rate. Earlier in the journal, another study had shown that the likelihood ratio test (LRT) is superior to Fisher's exact test, Wald's test, and Pearson's χ(2) test in testing association for low-frequency variants.

Genetic variant I148M in PNPLA3 is associated with the ultrasonography-determined steatosis degree in a Chinese population.

Background: Nonalcoholic fatty liver disease (NAFLD) is an escalating medical problem worldwide. A nonsynonymous single nucleotide polymorphism rs738409 (I148M) in patatin-like phospholipase domain-containing protein 3 (PNPLA3) predisposes susceptibility to NAFLD; however, its association with steatosis grade is inconsistent in the literature. In particular, there was no significant association found between I148M and steatosis grade in two East Asian-based studies.