Empirical significance values for linkage analysis: trait simulation using posterior model distributions from MCMC oligogenic segregation analysis

Abstract

Variance-components (VC) linkage analysis is a powerful model-free method for assessing linkage, but the distribution of VC logarithm of the odds ratio (LOD) scores may deviate substantially from the assumed asymptotic distribution. Typically, the null distribution of the VC-LOD score and other linkage statistics has been estimated by generating new genotype data independently of the trait data, and computing a linkage statistic for many such marker-simulated data sets. However, marker simulation is susceptible to errors in the assumed marker and map model and is computationally intensive. Here, we describe a method for generating posterior distributions of linkage statistics through simulation of trait data based on the original sample and on results from an initial scan using a Bayesian Markov-chain Monte Carlo (MCMC) approach for oligogenic segregation analysis. We use samples of oligogenic trait models taken from the posterior distribution to generate new samples of trait data, which were paired with the original marker data for analysis. Empirical P-values obtained from trait and marker simulation were similar when derived for several strong linkage signals from published linkage scans, and for analysis of data with a known, simulated, trait model. Furthermore, trait simulation produces the expected null distribution of VC-LOD scores and is computationally fast when marker identity-by-descent estimates from the original data could be reused. These results suggest that trait simulation gives valid estimates of statistical significance of linkage signals. Finally, these results also demonstrate the feasibility of obtaining empirical significance levels for evaluating Bayesian oligogenic linkage signals with either marker or trait simulation.