ArticleBMC proceedings2010
Genome-wide selection by mixed model ridge regression and extensions based on geostatistical models.
Article in BMC proceedings, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
7 citing papers in PubMed.
- Genomic selection across multiple breeding cycles in applied bread wheat breeding.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2016Article
- Kernel-based whole-genome prediction of complex traits: a review.Frontiers in genetics · 2014Review
- Comparisons of single-stage and two-stage approaches to genomic selection.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2013Article
- Article
- Predicting genetic values: a kernel-based best linear unbiased prediction with genomic data.Genetics · 2011Article
- Pre-selection of markers for genomic selection.BMC proceedings · 2011Article
- Comparison of analyses of the QTLMAS XIII common dataset. I: genomic selection.BMC proceedings · 2010Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe success of genome-wide selection (GS) approaches will depend crucially on the availability of efficient and easy-to-use computational tools. Therefore, approaches that can be implemented using mixed models hold particular promise and deserve detailed study. A particular class of mixed models suitable for GS is given by geostatistical mixed models, when genetic distance is treated analogously to spatial distance in geostatistics.
methodsWe consider various spatial mixed models for use in GS. The analyses presented for the QTL-MAS 2009 dataset pay particular attention to the modelling of residual errors as well as of polygenetic effects.
resultsIt is shown that geostatistical models are viable alternatives to ridge regression, one of the common approaches to GS. Correlations between genome-wide estimated breeding values and true breeding values were between 0.879 and 0.889. In the example considered, we did not find a large effect of the residual error variance modelling, largely because error variances were very small. A variance components model reflecting the pedigree of the crosses did not provide an improved fit.
conclusionsWe conclude that geostatistical models deserve further study as a tool to GS that is easily implemented in a mixed model package.
Identifiers
What OpenQuestion holds
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.