ArticleBioinformatics (Oxford, England)2008
Efficient whole-genome association mapping using local phylogenies for unphased genotype data.
Article in Bioinformatics (Oxford, England), 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- New Genetic Approaches to AD: Lessons from APOE-TOMM40 Phylogenetics.Current neurology and neuroscience reports · 2016Review
- HTreeQA: Using Semi-Perfect Phylogeny Trees in Quantitative Trait Loci Study on Genotype Data.G3 (Bethesda, Md.) · 2012Article
- Local genealogies in a linear mixed model for genome-wide association mapping in complex pedigreed populations.PloS one · 2011Article
- Genome-wide compatible SNP intervals and their properties.The 2010 ACM International Conference on Bioinformatics and Computational Biology : ACM-BCB 2010 : Niagara Falls, New York, U.S.A., August 2-4, 2010. ACM International Conference on Bioinformatics and Computational Biology (1st : 2010 :... · 2010Article
- Global haplotype partitioning for maximal associated SNP pairs.BMC bioinformatics · 2009Article
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Authors and funding
3 authors.
Funding
Abstract
motivationRecent 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.
resultsIn 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.
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