%0 Journal Article
%T Statistical distributions of test statistics used for quantitative trait association mapping in structured populations
%A Simon Teyss¨¨dre
%A Jean-Michel Elsen
%A Anne Ricard
%J Genetics Selection Evolution
%D 2012
%I BioMed Central
%R 10.1186/1297-9686-44-32
%X The expectation and variance of the test statistics and their marginal expectations and variances according to the distribution of genotypes and estimators of variance components are given as a function of the relationship matrix and of the heritability of the polygenic effect. These formulae were used to compute type 1 error rate and power for any kind of relationship matrix between phenotyped and genotyped individuals for any level of heritability. For the regression method, type 1 error rate increased with the variability of relationships and with heritability, but decreased with the GRAMMAR method and was not affected with the FASTA and quantitative transmission/disequilibrium test methods.The formulae can be easily used to provide the correct threshold of type 1 error rate and to calculate the power when designing experiments or data collection protocols. The results concerning the efficacy of each method agree with simulation results in the literature but were generalized in this work. The power of the GRAMMAR method was equal to the power of the FASTA method at the same type 1 error rate. The power of the quantitative transmission/disequilibrium test was low. In conclusion, the FASTA method, which is very close to the full mixed model, is recommended in association mapping studies.Single Nucleotide Polymorphism (SNP) information has enabled the use of linkage disequilibrium to detect and localize loci affecting phenotypes. The first methods developed searched for disequilibrium between one or a few marker loci and loci responsible for disease susceptibility. Case¨Ccontrol designs were used [1]. Typically, data were analyzed to compare the frequency of marker alleles between healthy and diseased individuals, for instance using the relative risk criterion [2]. A similar approach for quantitative traits (including production traits in animals or plants) was to model the expectation of their distribution as a linear combination of marker genotype, allele or haplot
%U http://www.gsejournal.org/content/44/1/32