A Kinship-Based Modification of the Armitage Trend Test to Address Hidden Population Structure and Small Differential Genotyping Errors

Abstract

Background/AimsWe propose a modification of the well-known Armitage trend test to address the problems associated with hidden population structure and hidden relatedness in genome-wide case-control association studies.MethodsThe new test adopts beneficial traits from three existing testing strategies: the principal components, mixed model, and genomic control while avoiding some of their disadvantageous characteristics, such as the tendency of the principal components method to over-correct in certain situations or the failure of the genomic control approach to reorder the adjusted tests based on their degree of alignment with the underlying hidden structure. The new procedure is based on Gauss-Markov estimators derived from a straightforward linear model with an imposed variance structure proportional to an empirical relatedness matrix. Lastly, conceptual and analytical similarities to and distinctions from other approaches are emphasized throughout.ResultsOur simulations show that the power performance of the proposed test is quite promising compared to the considered competing strategies. The power gains are especially large when small differential differences between cases and controls are present; a likely scenario when public controls are used in multiple studies.ConclusionThe proposed modified approach attains high power more consistently than that of the existing commonly implemented tests. Its performance improvement is most apparent when small but detectable systematic differences between cases and controls exist.

Variance component model to account for sample structure in genome-wide association studies

“…There are several other methods that take into account pedigree-based or empirically estimated kinship matrices in the statistical test 39-42 . One of the key differences between these methods and the mixed model methods, including EMMAX, is that the mixed model methods have a procedure for estimating the contribution of the kinship matrix to the phenotypes, whereas the other methods do not.…”

New approaches to population stratification in genome-wide association studies

“…However, as an alternative to mixed models, genotypes can be modeled using a fixed set of phenotypes, a theoretically appealing approach that makes fewer assumptions about phenotypic covariance structure37–38. Simulations in the absence of unusually differentiated markers have shown that using the genotypic covariance matrix to account for both population and family structure can effectively control spurious associations under a variety of settings37 (ROADTRIPS; see Web Resources).…”

Evaluation of methods accounting for population structure with pedigree data and continuous outcomes

“…For unusually differentiated markers with allele frequency difference of 0.6 between the two populations, only EMMAX adjusting for PCs yielded a genomic control lambda parameter near 1.0. When the sample is composed of case-control data, recent methods such as ROADTRIPS [Rakovski and Stram, 2009;Thornton and McPeek, 2010], which model the genotypic covariance matrix to control for population and family structure has been shown to be advantageous.…”