A Maximum Likelihood Based Genetic Algorithm for Inferring Haplotypes from Genotypes
A haplotype is a set of single nucleotide polymorphisms (SNPs) from a given chromosome, and provides valuable information about complex diseases. Current practices that the inferring of large scale SNP haplotypes from raw SNP data (genotypes) using computational approaches has gained a lot of attention, but it presents a grand challenges as it is inherently a NP-Hard problem. In this paper, we propose a heuristic approach, Genetic Algorithm (GA) model for the haplotypes inference method, based on the maximum-likelihood estimates of haplotype frequencies under the assumption of Hardy-Weinberg proportions. The goal of the genetic algorithm method is to obtain high prediction accuracy within a reasonable computing time. The performance of our model was evaluated on both simulated datasets and real datasets, and these results are promising, indicating that our model is a potential computational tool for haplotype inferences.
Haplotype Inference Genetic Algorithm Haplotypes Genotypes SNP sites
Priyadarshini Lakshminarasimhan Robert Marmelstein Mary Devito Dongsheng Che Qi Liu
Department of Computer Science,East Stroudsburg University,PA 18301, USA College of Life Science and Biotechnology,Tongji University,Shanghai, 200092, P.R.China
国际会议
上海
英文
92-96
2010-06-22（万方平台首次上网日期，不代表论文的发表时间）