Efficient whole-genome association mapping using local phylogenies for unphased genotype data

Motivation: Recent advances in genotyping technology has made data acquisition for whole-genome association study cost effective, and a current active area of research is developing efficient methods to analyze such large-scale datasets. Most sophisticated association mapping methods that are curren...

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Bibliographic Details
Published inBioinformatics Vol. 24; no. 19; pp. 2215 - 2221
Main Authors Ding, Zhihong, Mailund, Thomas, Song, Yun S.
Format Journal Article
LanguageEnglish
Published Oxford Oxford University Press 01.10.2008
Oxford Publishing Limited (England)
Subjects
Animals
Biological and medical sciences
Chromosome Mapping - methods
Computer Simulation
Fundamental and applied biological sciences. Psychology
General aspects
Genomics - methods
Genotype
Haplotypes
Humans
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
Original Papers
Phylogeny
Genotype
Phylogeny
Genome
Bioinformatics
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Summary:Motivation: Recent advances in genotyping technology has made data acquisition for whole-genome association study cost effective, and a current active area of research is developing efficient methods to analyze such large-scale datasets. Most sophisticated association mapping methods that are currently available take phased haplotype data as input. However, phase information is not readily available from sequencing methods and inferring the phase via computational approaches is time-consuming, taking days to phase a single chromosome. Results: In this article, we devise an efficient method for scanning unphased whole-genome data for association. Our approach combines a recently found linear-time algorithm for phasing genotypes on trees with a recently proposed tree-based method for association mapping. From unphased genotype data, our algorithm builds local phylogenies along the genome, and scores each tree according to the clustering of cases and controls. We assess the performance of our new method on both simulated and real biological datasets. Availability The software described in this article is available at http://www.daimi.au.dk/~mailund/Blossoc and distributed under the GNU General Public License. Contact:mailund@birc.au.dk
Bibliography:To whom correspondence should be addressed.
Associate Editor: Martin Bishop
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ArticleID:btn406
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content type line 23
ISSN:1367-4803
1460-2059
1367-4811
DOI:10.1093/bioinformatics/btn406