Genetic epidemiology studies often adjust for numerous potential confounders, yet the influences of confounder misclassification and selection bias are rarely considered. We used simulated data to evaluate the effect of confounder misclassification and selection bias in a case-control study of incident myocardial infarction. We show that putative confounders traditionally included in genetic as...

In linkage disequilibrium mapping of genetic variants causally associated with phenotypes, spurious associations can potentially be generated by any of a variety of types of population structure. However, mathematical theory of the production of spurious associations has largely been restricted to population structure models that involve the sampling of individuals from a collection of discrete...

To avoid inflated type I error and reduced power in genetic association studies, it is necessary to adjust properly for population stratification and known/unknown subject relatedness. It would be interesting to compare the performance of a principal component-based approach with a linear mixed model. Furthermore, with the availability of genome-wide sequencing data, the question of whether it ...

For analysis of case-control genetic association studies, it has recently been shown that gene-environment independence in the population can be leveraged to increase efficiency for estimating gene-environment interaction effects in comparison with the standard prospective analysis. However, for the special case in which data on the binary phenotype and genetic and environmental risk factors ca...

The International HapMap Project was designed to create a genome-wide database of patterns of human genetic variation, with the expectation that these patterns would be useful for genetic association studies of common diseases. This expectation has been amply fulfilled with just the initial output of genome-wide association studies, identifying nearly 100 loci for nearly 40 common diseases and ...

A topical question in genetic association studies is the optimal use of the information provided by genotyped single-nucleotide polymorphisms (SNPs) in order to detect the role of a candidate gene in a multifactorial disease. We propose a strategy called "combination test" that tests the association between a quantitative trait and all possible phased combinations of various numbers of SNPs. We...

Recent advances in next-generation sequencing technologies facilitate the detection of rare variants, making it possible to uncover the roles of rare variants in complex diseases. As any single rare variants contain little variation, association analysis of rare variants requires statistical methods that can effectively combine the information across variants and estimate their overall effect. ...

Subgroup and stratification analyses have been widely applied in genetic association studies to compare the effects of different factors or control for the effects of the confounding variables associated with a disease. However, studies have not systematically provided application standards and computing methods for stratification analyses. Based on the Mantel-Haenszel and Inverse-Variant appro...

The need for analysis of multiple responses arises from many applications. In behavioral science, for example, comorbidity is a common phenomenon where multiple disorders occur in the same person. The advantage of jointly analyzing multiple correlated responses has been examined and documented. Due to the difficulties of modeling multiple responses, nonparametric tests such as generalized Kenda...

recent studies have provided evidence that common genetic variations could account for a proportion of leukemia in adult or children. to evaluate the contribution of candidate gene association studies to the understanding of genetic susceptibility to acute lymphoblastic leukemia we conducted a systematic review from published studies. the polymorphisms of genes encoding carcinogen-metabolizing ...