ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2009
TreeQA: quantitative genome wide association mapping using local perfect phylogeny trees.
Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 23 citations in OpenAlex.
- Article
- New Rodent Population Models May Inform Human Health Risk Assessment and Identification of Genetic Susceptibility to Environmental Exposures.Environmental health perspectives · 2017Review
- Comparing performance of non-tree-based and tree-based association mapping methods.BMC proceedings · 2016Article
- Explaining evolution via constrained persistent perfect phylogeny.BMC genomics · 2014Article
- Using ancestral information to detect and localize quantitative trait loci in genome-wide association studies.BMC bioinformatics · 2013Article
- Mapping quantitative trait loci onto a phylogenetic tree.Genetics · 2012Article
- Genome-wide association mapping of loci for antipsychotic-induced extrapyramidal symptoms in mice.Mammalian genome : official journal of the International Mammalian Genome Society · 2012Article
- HTreeQA: Using Semi-Perfect Phylogeny Trees in Quantitative Trait Loci Study on Genotype Data.G3 (Bethesda, Md.) · 2012Article
- Moving toward System Genetics through Multiple Trait Analysis in Genome-Wide Association Studies.Frontiers in genetics · 2012Article
- 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
The goal of genome wide association (GWA) mapping in modern genetics is to identify genes or narrow regions in the genome that contribute to genetically complex phenotypes such as morphology or disease. Among the existing methods, tree-based association mapping methods show obvious advantages over single marker-based and haplotype-based methods because they incorporate information about the evolutionary history of the genome into the analysis. However, existing tree-based methods are designed primarily for binary phenotypes derived from case/control studies or fail to scale genome-wide. In this paper, we introduce TreeQA, a quantitative GWA mapping algorithm. TreeQA utilizes local perfect phylogenies constructed in genomic regions exhibiting no evidence of historical recombination. By efficient algorithm design and implementation, TreeQA can efficiently conduct quantitative genom-wide association analysis and is more effective than the previous methods. We conducted extensive experiments on both simulated datasets and mouse inbred lines to demonstrate the efficiency and effectiveness of TreeQA.
Indexed as
Identifiers
19209719PMC2739990W2138775195What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.