ArticleBMC genetics2005
Fine mapping--19th century style.
Article in BMC genetics, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed.
- Association mapping by generalized linear regression with density-based haplotype clustering.Genetic epidemiology · 2009Article
- Association mapping with single-feature polymorphisms.Genetics · 2006Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
backgroundThere is great interest in the use of computationally intensive methods for fine mapping of marker data. In this paper we develop methods based upon ideas originally proposed 100 years ago in the context of spatial clustering.
methodsWe use spatial clustering of haplotypes as a low-dimensional surrogate for the unobserved genealogy underlying a set of genotype data. In doing so we hope to avoid the computational complexity inherent in explicitly modelling details of the ancestry of the sample, while at the same time capturing the key correlations induced by that ancestry at a much lower computational cost.
resultsWe benchmark our methods using the simulated Genetic Analysis Workshop 14 data, using 100 replicates of 4 phenotypes to indicate the power of our method. When a functional mutation relating to a trait is actually present, we find evidence for that mutation in 97 out of 100 replicates, on average.
conclusionOur results show that our method has the ability to accurately infer the location of functional mutations from unphased genotype data.
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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.