Inference of missing SNPs and information quantity measurements for haplotype blocks | Zendy

S.-C. Su | Zendy; C.C. Jay Kuo | Zendy; Ting Chen | Zendy

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Inference of missing SNPs and information quantity measurements for haplotype blocks

Author(s) -

S.-C. Su,

C.C. Jay Kuo,

Ting Chen

Publication year - 2005

Publication title -

computer applications in the biosciences

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1460-2059

pISSN - 0266-7061

DOI - 10.1093/bioinformatics/bti261

Subject(s) - haplotype , inference , single nucleotide polymorphism , missing data , computer science , statistics , artificial intelligence , data mining , genetics , biology , mathematics , machine learning , genotype , gene

Missing data in genotyping single nucleotide polymorphism (SNP) spots are common. High-throughput genotyping methods usually have a high rate of missing data. For example, the published human chromosome 21 data by Patil et al. contains about 20% missing SNPs. Inferring missing SNPs using the haplotype block structure is promising but difficult because the haplotype block boundaries are not well defined. Here we propose a global algorithm to overcome this difficulty.

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