title: Correcting for cryptic relatedness by a regression-based genomic control method.
abstract: BACKGROUND
  Genomic 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.
  
  
  RESULTS
  In 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.
  
  
  CONCLUSIONS
  The 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.
authors: Department of Statistics and Finance, University of Science and Technology of China, Hefei, Anhui 230026, PR China. sunroom@mail.ustc.edu.cn
authors: Ting
authors: T
authors: Yan
authors: Bo
authors: B
authors: Hou
authors: Yaning
authors: Y
authors: Yang
mesh_terms: D000483:Alleles; D005819:Genetic Markers; D005821:Genetic Techniques; D015894:Genome, Human; D005838:Genotype; D006801:Humans; D012044:Regression Analysis
chemical_list: D005819:Genetic Markers