ArticleBMC bioinformatics2006
Haplotype-based quantitative trait mapping using a clustering algorithm.
Article in BMC bioinformatics, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Detection of four polymorphisms in 5' upstream region of PNPLA2 gene and their associations with economic traits in pigs.Molecular biology reports · 2016Article
- Combined linkage and linkage disequilibrium QTL mapping in multiple families of maize (Zea mays L.) line crosses highlights complementarities between models based on parental haplotype and single locus polymorphism.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2013Article
- Variation in the IGF2 gene promoter region is associated with intramuscular fat content in porcine skeletal muscle.Molecular biology reports · 2012Article
- Combining an evolution-guided clustering algorithm and haplotype-based LRT in family association studies.BMC genetics · 2011Article
- Article
- Regulatory polymorphisms in the bovine Ankyrin 1 gene promoter are associated with tenderness and intramuscular fat content.BMC genetics · 2010Article
- A novel approach for haplotype-based association analysis using family data.BMC bioinformatics · 2010Article
- Linear models for joint association and linkage QTL mapping.Genetics, selection, evolution : GSE · 2009Article
- Data modeling as a main source of discrepancies in single and multiple marker association methods.BMC proceedings · 2009Article
- Local phylogeny mapping of quantitative traits: higher accuracy and better ranking than single-marker association in genomewide scans.Genetics · 2009Article
- Association mapping by generalized linear regression with density-based haplotype clustering.Genetic epidemiology · 2009Article
- Ontology-guided data preparation for discovering genotype-phenotype relationships.BMC bioinformatics · 2008Article
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3 authors.
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Abstract
backgroundWith the availability of large-scale, high-density single-nucleotide polymorphism (SNP) markers, substantial effort has been made in identifying disease-causing genes using linkage disequilibrium (LD) mapping by haplotype analysis of unrelated individuals. In addition to complex diseases, many continuously distributed quantitative traits are of primary clinical and health significance. However the development of association mapping methods using unrelated individuals for quantitative traits has received relatively less attention.
resultsWe recently developed an association mapping method for complex diseases by mining the sharing of haplotype segments (i.e., phased genotype pairs) in affected individuals that are rarely present in normal individuals. In this paper, we extend our previous work to address the problem of quantitative trait mapping from unrelated individuals. The method is non-parametric in nature, and statistical significance can be obtained by a permutation test. It can also be incorporated into the one-way ANCOVA (analysis of covariance) framework so that other factors and covariates can be easily incorporated. The effectiveness of the approach is demonstrated by extensive experimental studies using both simulated and real data sets. The results show that our haplotype-based approach is more robust than two statistical methods based on single markers: a single SNP association test (SSA) and the Mann-Whitney U-test (MWU). The algorithm has been incorporated into our existing software package called HapMiner, which is available from our website at http://www.eecs.case.edu/~jxl175/HapMiner.html.
conclusionFor QTL (quantitative trait loci) fine mapping, to identify QTNs (quantitative trait nucleotides) with realistic effects (the contribution of each QTN less than 10% of total variance of the trait), large samples sizes (>or= 500) are needed for all the methods. The overall performance of HapMiner is better than that of the other two methods. Its effectiveness further depends on other factors such as recombination rates and the density of typed SNPs. Haplotype-based methods might provide higher power than methods based on a single SNP when using tag SNPs selected from a small number of samples or some other sources (such as HapMap data). Rank-based statistics usually have much lower power, as shown in our study.
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