ArticleBMC bioinformatics2013
Using ancestral information to detect and localize quantitative trait loci in genome-wide association studies.
Article in BMC bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 30 citations in OpenAlex.
- Clade distillation for genome-wide association studies.Genetics · 2026Article
- Tree-based QTL mapping with expected local genetic relatedness matrices.American journal of human genetics · 2023Article
- Tree-based QTL mapping with expected local genetic relatedness matrices.bioRxiv : the preprint server for biology · 2023Article
- Article
- Hierarchical Modelling of Haplotype Effects on a Phylogeny.Frontiers in genetics · 2020Article
- Incorporating prior knowledge induced from stochastic differential equations in the classification of stochastic observations.EURASIP journal on bioinformatics & systems biology · 2016Article
- Comparing performance of non-tree-based and tree-based association mapping methods.BMC proceedings · 2016Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn mammalian genetics, many quantitative traits, such as blood pressure, are thought to be influenced by specific genes, but are also affected by environmental factors, making the associated genes difficult to identify and locate from genetic data alone. In particular, the application of classical statistical methods to single nucleotide polymorphism (SNP) data collected in genome-wide association studies has been especially challenging. We propose a coalescent approach to search for SNPs associated with quantitative traits in genome-wide association study (GWAS) data by taking into account the evolutionary history among SNPs.
resultsWe evaluate the performance of the new method using simulated data, and find that it performs at least as well as existing methods with an increase in performance in the case of population structure. Application of the methodology to a real data set consisting of high-density lipoprotein cholesterol measurements in mice shows the method performs well for empirical data, as well.
conclusionsBy combining methods from stochastic processes and phylogenetics, this work provides an innovative avenue for the development of new statistical methodology in the analysis of GWAS data.
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Registered trials
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