ArticleBioinformatics (Oxford, England)2009
A method and program for estimating graphical models for linkage disequilibrium that scale linearly with the number of loci, and their application to gene drop simulation.
Article in Bioinformatics (Oxford, England), 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A method combining a random forest-based technique with the modeling of linkage disequilibrium through latent variables, to run multilocus genome-wide association studies.BMC bioinformatics · 2018Article
- Modelling and visualizing fine-scale linkage disequilibrium structure.BMC bioinformatics · 2013Article
- CrypticIBDcheck: an R package for checking cryptic relatedness in nominally unrelated individuals.Source code for biology and medicine · 2013Article
- Shared genomic segment analysis: the power to find rare disease variants.Annals of human genetics · 2012Article
- Pairwise shared genomic segment analysis in high-risk pedigrees: application to Genetic Analysis Workshop 17 exome-sequencing SNP data.BMC proceedings · 2011Article
- Visualization of pairwise and multilocus linkage disequilibrium structure using latent forests.PloS one · 2011Article
- Accuracy and computational efficiency of a graphical modeling approach to linkage disequilibrium estimation.Statistical applications in genetics and molecular biology · 2011Article
- Assessment of SNP streak statistics using gene drop simulation with linkage disequilibrium.Genetic epidemiology · 2010Article
Corrections and comments
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1 author.
Funding
Abstract
motivationEfficient models for genetic linkage disequilibrium (LD) are needed to enable appropriate statistical analysis of the dense, genome-wide single nucleotide polymorphism assays currently available.
resultsEstimation of graphical models for LD within a restricted class of decomposable models is shown to be possible using computer time and storage that scale linearly with the number of loci. Programs for estimation and for simulating from these models on a whole-genome basis are described and provided. AVAILABILITY: Java classes and source code for IntervalLD and GeneDrops are freely available over the internet at http://bioinformatics.med.utah.edu/~alun.
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