Abstract
Genome-wide association studies (GWAS) comprise a powerful tool for mapping genes of complex traits. However, an inflation of the test statistic can occur because of population substructure or cryptic relatedness, which could cause spurious associations. If information on a large number of genetic markers is available, adjusting the analysis results by using the method of genomic control (GC) is possible. GC was originally proposed to correct the Cochran-Armitage additive trend test. For non-additive models, correction has been shown to depend on allele frequencies. Therefore, usage of GC is limited to situations where allele frequencies of null markers and candidate markers are matched. In this work, we extended the capabilities of the GC method for non-additive models, which allows us to use null markers with arbitrary allele frequencies for GC. Analytical expressions for the inflation of a test statistic describing its dependency on allele frequency and several population parameters were obtained for recessive, dominant, and over-dominant models of inheritance. We proposed a method to estimate these required population parameters. Furthermore, we suggested a GC method based on approximation of the correction coefficient by a polynomial of allele frequency and described procedures to correct the genotypic (two degrees of freedom) test for cases when the model of inheritance is unknown. Statistical properties of the described methods were investigated using simulated and real data. We demonstrated that all considered methods were effective in controlling type 1 error in the presence of genetic substructure. The proposed GC methods can be applied to statistical tests for GWAS with various models of inheritance. All methods developed and tested in this work were implemented using R language as a part of the GenABEL package.
Highlights
Genome-wide association studies (GWAS) are a powerful tool for mapping genes of complex traits
We demonstrated by simulations and the analysis of real data that the proposed genomic control (GC) methods (VIFGC and polynomial’’ GC (PGC)) could be used for the correction of non-additive test statistics in the context of GWAS assuming different models of inheritance
Note that even after the most advanced analysis model is used, some residual inflation may be expected. This residual inflation is usually corrected by the GC, because even minor inflation still can lead to much increased false positive rate in GWAS
Summary
Genome-wide association studies (GWAS) are a powerful tool for mapping genes of complex traits. Standard statistical methods used for GWAS, such as linear regression, assume that the correlation between a phenotype and a genotypic marker exists because of the marker itself or a strong linkage disequilibrium with the causative locus. This assumption holds when the sample consists of representatives of one panmictic population. In GWAS, the genetic substructure of the studied samples is among the most important confounders. If the analysis is not accounted for confounding by population substructure, the test statistic is inflated [1], which makes its statistical interpretation difficult and may lead to false-positive findings
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