Evidence map›Paper›PMID 23742095›Full record

ArticleBMC bioinformatics2013

Modelling and visualizing fine-scale linkage disequilibrium structure.

David Edwards

Abstract read
In one paragraph

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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

David EdwardsDepartment of Molecular Biology and Genetics, Centre for Quantitative Genetics and Genomics, Blichers Allé 20, Tjele 8830, Denmark. David.Edwards@agrsci.dk

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDetailed study of genetic variation at the population level in humans and other species is now possible due to the availability of large sets of single nucleotide polymorphism data. Alleles at two or more loci are said to be in linkage disequilibrium (LD) when they are correlated or statistically dependent. Current efforts to understand the genetic basis of complex phenotypes are based on the existence of such associations, making study of the extent and distribution of linkage disequilibrium central to this endeavour. The objective of this paper is to develop methods to study fine-scale patterns of allelic association using probabilistic graphical models.

resultsAn efficient, linear-time forward-backward algorithm is developed to estimate chromosome-wide LD models by optimizing a penalized likelihood criterion, and a convenient way to display these models is described. To illustrate the methods they are applied to data obtained by genotyping 8341 pigs. It is found that roughly 20% of the porcine genome exhibits complex LD patterns, forming islands of relatively high genetic diversity.

conclusionsThe proposed algorithm is efficient and makes it feasible to estimate and visualize chromosome-wide LD models on a routine basis.

Indexed as

Linkage DisequilibriumModels, StatisticalAlgorithmsAllelesAnimalsComputer GraphicsGenetic VariationGenotyping TechniquesHaplotypesPolymorphism, Single NucleotideSwine

Identifiers

PMID23742095
PMCPMC3683336

What OpenQuestion holds

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Registered trials

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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.