Modeling linkage disequilibrium between a polymorphic marker locus and a locus affecting complex dichotomous traits in natural populations.
Linkage disequilibrium is an important topic in evolutionary and population genetics. An issue yet to be settled is the theory required to extend the linkage disequilibrium analysis to complex traits. In this study, we present theoretical analysis and methods for detecting or estimating linkage disequilibrium (LD) between a polymorphic marker locus and any one of the loci affecting a complex dichotomous trait on the basis of samples randomly or selectively collected from natural populations. Statistical properties of these methods were investigated and their powers were compared analytically or by use of Monte Carlo simulations. The results show that the disequilibrium may be detected with a power of 80% by using phenotypic records and marker genotype when both the trait and marker variants are common (30%) and the LD is relatively high (40-100% of the theoretical maximum). The maximum-likelihood approach provides accurate estimates of the model parameters as well as detection of linkage disequilibrium. The likelihood method is preferred for its higher power and reliability in parameter estimation. The approaches developed in this article are also compared to those for analyzing a continuously distributed quantitative trait. It is shown that a larger sample size is required for the dichotomous trait model to obtain the same level of power in detecting linkage disequilibrium as the continuous trait analysis. Potential use of these estimates in mapping the trait locus is also discussed.
Luo
Z W
ZW
School of Biosciences, The University of Birmingham, Edgbaston, Birmingham B15 2TT, England. z.luo@bham.ac.uk
Wu
C I
CI
D000483:Alleles; D019143:Evolution, Molecular; D014644:Genetic Variation; D006801:Humans; D016013:Likelihood Functions; D015810:Linkage Disequilibrium; D008957:Models, Genetic; D009010:Monte Carlo Method; D011110:Polymorphism, Genetic; D019655:Quantitative Trait, Heritable; D012044:Regression Analysis