A Deterministic Sequential Monte Carlo Method for Haplotype Inference

• Published in: IEEE journal of selected topics in signal processing Vol. 2; no. 3; pp. 322 - 331
• Main Authors: Liang, Kuo-ching, Wang, Xiaodong
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2008; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Bioinformatics; Constraining; Deterministic sequential Monte Carlo (DSMC); Diseases; DNA; Genetics; genomic sequence; Genomics; haplotype block; haplotype inference; hidden Markov model (HMM); Hidden Markov models; Humans; Inference; Inference algorithms; Monte Carlo methods; Nucleotides; Polymorphism; Sequences; Signal processing; Signal processing algorithms
• ISSN: 1932-4553; 1941-0484
• DOI: 10.1109/JSTSP.2008.923842

Sets of single nucleotide polymorphisms (SNPs), or haplotypes, are widely used in the analysis of relationship between genetics and diseases. Due to the cost of obtaining exact haplotype pairs, genotypes which contain the unphased information corresponding to the haplotype pairs in the test subjects are used. Various haplotype inference algorithms have been proposed to resolve the unphased information. However, most existing algorithms are limited in different ways. For statistical algorithms, the limiting factors are often in terms of the number of SNPs allowed in the genotypes, or the number of subjects in the dataset. In this paper, we propose a deterministic sequential Monte Carlo-based haplotype inference algorithm which allows for larger datasets in terms of number of SNPs and number of subjects, while providing similar or better performance for datasets under various conditions.