A Rapid Association Test Procedure Robust under Different Genetic Models Accounting for Population Stratification

Abstract

Objective: For genome-wide association studies (GWAS) using case-control data with stratification, a commonly used association test is the generalized Armitage (GA) trend test implemented in the software EIGENSTRAT. The GA trend test uses principal component analysis to correct for population stratification. It usually assumes an additive disease model and can have high power when the underlying disease model is additive or multiplicative, but may have relatively low power when the underlying disease model is recessive or dominant. The purpose of this paper is to provide a test procedure for GWAS with increased power over the GA trend test under the recessive and dominant models, while maintaining the power of the GA trend test under the additive and multiplicative models. Methods: We extend a Hardy-Weinberg disequilibrium (HWD) trend test for a homogeneous population to account for population stratification, and then propose a robust association test procedure for GWAS that incorporates information from the extended HWD trend test into the GA trend test. Results and Conclusions: Our simulation studies and application of our method to a GWAS data set indicate that our proposed method can achieve the purpose described above.

Identifying novel associations in GWAS by hierarchical Bayesian latent variable detection of differentially misclassified phenotypes

“…The genetic model with 30 disease-associated SNPs out of total 10,000 preserves the characteristic sparsity of “true signal” in GWAS datasets, while keeping the dataset size manageable for rigorous simulations. Number of samples, number of SNPs, number of disease-associated SNPs, and effect sizes were set in accordance with precedence in literature[106-108], where in particular, effect sizes were selected to ensure genotype-specific disease odds ratio remained realistic (in the range: 1-3)[83-85] (S3 Fig). For each simulated true disease phenotype Y’ , differential misclassification was introduced at varying degrees by switching a fraction of randomly selected controls to cases.…”

A Generalized Sequential Bonferroni Procedure for GWAS in Admixed Populations Incorporating Admixture Mapping Information into Association Tests

“…From Holm (1979), we can see that, to control the FWER of the GSB procedure, the condition “weights are independent of the p-values of the tests” is a sufficient but not necessary condition. Our simulation studies [24] showed that under some situations, even the weights were weakly correlated with the p-values, the original GSB procedure of Holm (1979) still controlled the FWER well. We will show below by simulation studies that the GSB procedure for GWAS can control the FWER well under the null hypothesis H 02 .…”

“…In our previous contribution [24], we proposed an adjusted p-value P genome for each marker for GSB procedure, which is used to compare sequentially with the nominal FWER level α (see also Westfall and Yong (1993), page 64–65). For comparison with the T local test, we calculated a new p-value at each SNP for the smooth-GSB procedure that was equal to the adjusted p-value P genome divided by number of test SNPs ( m ).…”

“… 1 Chromosome number; 2, 3 The allele frequencies in HapMap CEU and YRI populations, respectively; 4 P-value calculated as the adjusted p-value divided by the number of tests (see [24]). …”

Genetics of Alzheimer´S Disease

“…In light of these evidence, several methods have been developed to detect HWD. The most used method is chi square, however this statistic only must be used in homogeneous population [186]. Other approach is detect the intrapoblational variance (F is ), where F is > 0 means a homozygous excess, whereas F is < 0 means heterozygous deficit [187].…”