Dominant markers in an F 2 population or a hybrid population have much less linkage information in repulsion phase than in coupling phase. Linkage analysis produces two separate complementary marker linkage maps that have little use in disease association analysis and breeding. There is a need to develop efficient statistical methods and computational algorithms to construct or merge a complete linkage dominant marker maps. The key for doing so is to efficiently estimate recombination fractions between dominant markers in repulsion phases. We proposed an expectation least square (ELS) algorithm and binomial analysis of three-point gametes (BAT) for estimating gamete frequencies from F 2 dominant and codominant marker data, respectively. The results obtained from simulated and real genotype datasets showed that the ELS algorithm was able to accurately estimate frequencies of gametes and outperformed the EM algorithm in estimating recombination fractions between dominant loci and recovering true linkage maps of 6 dominant loci in coupling and unknown linkage phases. Our BAT method also had smaller variances in estimation of two-point recombination fractions than the EM algorithm. ELS is a powerful method for accurate estimation of gamete frequencies in dominant three-locus system in an F 2 population and BAT is a computationally efficient and fast method for estimating frequencies of three-point codominant gametes.

MOTIVATION: Chromatin immunoprecipitation (ChIP) coupled with tiling microarray (chip) experiments have been used in a wide range of biological studies such as identification of transcription factor binding sites and investigation of DNA methylation and histone modification. Hidden Markov models are widely used to model the spatial dependency of ChIP-chip data. However, parameter estimation for these models is typically either heuristic or suboptimal, leading to inconsistencies in their applications. To overcome this limitation and to develop an efficient software, we propose a hidden ferromagnetic Ising model for ChIP-chip data analysis. RESULTS: We have developed a simple, but powerful Bayesian hierarchical model for ChIP-chip data via a hidden Ising model. Metropolis within Gibbs sampling algorithm is used to simulate from the posterior distribution of the model parameters. The proposed model naturally incorporates the spatial dependency of the data, and can be used to analyze data with various genomic resolutions and sample sizes. We illustrate the method using three publicly available datasets and various simulated datasets, and compare it with three closely related methods, namely TileMap HMM, tileHMM and BAC. We find that our method performs as well as TileMap HMM and BAC for the high-resolution data from Affymetrix platform, but significantly outperforms the other three methods for the low-resolution data from Agilent platform. Compared with the BAC method which also involves MCMC simulations, our method is computationally much more efficient. AVAILABILITY: A software called iChip is freely available at http://www.bioconductor.org/. CONTACT: [email protected].