Correcting for cryptic relatedness by a regression-based genomic control method

Ting Yan,Yaning Yang,Bo Hou

doi:10.1186/1471-2156-10-78

Ting Yan, Yaning Yang + Show 1 more

Open Access

https://doi.org/10.1186/1471-2156-10-78

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Abstract

BackgroundGenomic control (GC) method is a useful tool to correct for the cryptic relatedness in population-based association studies. It was originally proposed for correcting for the variance inflation of Cochran-Armitage's additive trend test by using information from unlinked null markers, and was later generalized to be applicable to other tests with the additional requirement that the null markers are matched with the candidate marker in allele frequencies. However, matching allele frequencies limits the number of available null markers and thus limits the applicability of the GC method. On the other hand, errors in genotype/allele frequencies may cause further bias and variance inflation and thereby aggravate the effect of GC correction.ResultsIn this paper, we propose a regression-based GC method using null markers that are not necessarily matched in allele frequencies with the candidate marker. Variation of allele frequencies of the null markers is adjusted by a regression method.ConclusionThe proposed method can be readily applied to the Cochran-Armitage's trend tests other than the additive trend test, the Pearson's chi-square test and other robust efficiency tests. Simulation results show that the proposed method is effective in controlling type I error in the presence of population substructure.

Highlights

Genomic control (GC) method is a useful tool to correct for the cryptic relatedness in population-based association studies
We propose a regression-based genomic control (RGC) method that can be applied to association tests other than the additive trend test
RGC method In what follows, we propose a regression-based GC method to adjust for the frequency variability of null markers when the GC method is applied to the general trend tests and the Pearson chi-square test

Summary

Introduction

Genomic control (GC) method is a useful tool to correct for the cryptic relatedness in population-based association studies. When there is population stratification (PS) on allele frequencies, a direct method is to use family-based design [1,2,3,4,5] in which unaffected family members are chosen to match each case so that the association detected is truly due to the linkage between the candidate marker and the disease. This method is limited by the cost and the difficulty in recruiting family members. Patterson et al [9] proposed a principle components analysis method to (page number not for citation purposes)

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