Literature DB >> 23600820

Whole genome identity-by-descent determination.

Hadi Sabaa1, Zhipeng Cai, Yining Wang, Randy Goebel, Stephen Moore, Guohui Lin.   

Abstract

High-throughput single nucleotide polymorphism genotyping assays conveniently produce genotype data for genome-wide genetic linkage and association studies. For pedigree datasets, the unphased genotype data is used to infer the haplotypes for individuals, according to Mendelian inheritance rules. Linkage studies can then locate putative chromosomal regions based on the haplotype allele sharing among the pedigree members and their disease status. Most existing haplotyping programs require rather strict pedigree structures and return a single inferred solution for downstream analysis. In this research, we relax the pedigree structure to contain ungenotyped founders and present a cubic time whole genome haplotyping algorithm to minimize the number of zero-recombination haplotype blocks. With or without explicitly enumerating all the haplotyping solutions, the algorithm determines all distinct haplotype allele identity-by-descent (IBD) sharings among the pedigree members, in linear time in the total number of haplotyping solutions. Our algorithm is implemented as a computer program iBDD. Extensive simulation experiments using 2 sets of 16 pedigree structures from previous studies showed that, in general, there are trillions of haplotyping solutions, but only up to a few thousand distinct haplotype allele IBD sharings. iBDD is able to return all these sharings for downstream genome-wide linkage and association studies.

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Year:  2013        PMID: 23600820     DOI: 10.1142/S0219720013500029

Source DB:  PubMed          Journal:  J Bioinform Comput Biol        ISSN: 0219-7200            Impact factor:   1.122


  2 in total

1.  Cloud computing for detecting high-order genome-wide epistatic interaction via dynamic clustering.

Authors:  Xuan Guo; Yu Meng; Ning Yu; Yi Pan
Journal:  BMC Bioinformatics       Date:  2014-04-10       Impact factor: 3.169

2.  JS-MA: A Jensen-Shannon Divergence Based Method for Mapping Genome-Wide Associations on Multiple Diseases.

Authors:  Xuan Guo
Journal:  Front Genet       Date:  2020-10-30       Impact factor: 4.599

  2 in total

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