Reliable and fast estimation of recombination rates by convergence diagnosis and parallel Markov Chain Monte Carlo

• Main Authors: Guo, Jing, Jain, Ritika, Yang, Peng, Fan, Rui, Kwoh, Chee Keong, Zheng, Jie
• Other Authors: School of Computer Engineering
• Format: Article
• Language: English
• Published: 2013
• Subjects: DRNTU::Engineering::Computer science and engineering::Theory of computation::Analysis of algorithms and problem complexity
• Online Access: https://hdl.handle.net/10356/80080; http://hdl.handle.net/10220/17663
• Institution: Nanyang Technological University

Genetic recombination is an essential event during the process of meiosis resulting in an exchange of segments between paired chromosomes. Estimating recombination rate is crucial for understanding evolution. Experimental methods are normally difficult and limited to small scale estimations. Thus statistical methods using population genetic data are important for large-scale analysis. LDhat is an extensively used statistical method using rjMCMC algorithm to predict recombination rates. Due to the complexity of rjMCMC scheme, LDhat may take a long time to generate results for large SNP data. In addition, rjMCMC parameters should be manually defined in the original program that directly impact results. To address these issues, we designed an improved algorithm based on LDhat implementing MCMC convergence diagnostic algorithms to automatically predict values of parameters and monitor the mixing process. Then parallel computation methods were employed to further accelerate the new program. The new algorithms have been tested on ten samples from HapMap phase 2 datasets. The results were compared with previous code and showed nearly identical outputs, however our new methods achieved significant acceleration proving that they are more efficient and reliable for the estimation of recombination rates. The stand-alone package is freely available for download at the link below.