ArticleG3 (Bethesda, Md.)2016
Cross-Validation Without Doing Cross-Validation in Genome-Enabled Prediction.
Article in G3 (Bethesda, Md.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 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
25 citing papers in PubMed.
- AdaptiveGS: an explainable genomic selection framework based on adaptive stacking ensemble machine learning.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Rapid cycling genomic selection in maize landraces.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- On the ability of the LR method to detect bias when there is pedigree misspecification and lack of connectedness.Genetics, selection, evolution : GSE · 2024Article
- Ability of Genomic Prediction to Bi-Parent-Derived Breeding Population Using Public Data for Soybean Oil and Protein Content.Plants (Basel, Switzerland) · 2024Article
- Confidence intervals for validation statistics with data truncation in genomic prediction.Genetics, selection, evolution : GSE · 2024Article
- Inference about quantitative traits under selection: a Bayesian revisitation for the post-genomic era.Genetics, selection, evolution : GSE · 2022Article
- High accuracy of genome-enabled prediction of belowground and physiological traits in barley seedlings.G3 (Bethesda, Md.) · 2022Article
- Genomic Prediction: Progress and Perspectives for Rice Improvement.Methods in molecular biology (Clifton, N.J.) · 2022Review
- Modeling genetic differences of combined broiler chicken populations in single-step GBLUP.Journal of animal science · 2021Article
- Incorporation of parental phenotypic data into multi-omic models improves prediction of yield-related traits in hybrid rice.Plant biotechnology journal · 2021Article
- Article
- Genomic Prediction of Grain Yield in a Barley MAGIC Population Modeling Genotype per Environment Interaction.Frontiers in plant science · 2021Article
- Validation of single-step GBLUP genomic predictions from threshold models using the linear regression method: An application in chicken mortality.Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie · 2021Article
- Genome-enable prediction for health traits using high-density SNP panel in US Holstein cattle.Animal genetics · 2020Article
- Infrared Spectrometry as a High-Throughput Phenotyping Technology to Predict Complex Traits in Livestock Systems.Frontiers in genetics · 2020Review
- Radiomics Features Predict CIC Mutation Status in Lower Grade Glioma.Frontiers in oncology · 2020Article
- Pitfalls and Remedies for Cross Validation with Multi-trait Genomic Prediction Methods.G3 (Bethesda, Md.) · 2019Article
- Combining pedigree and genomic information to improve prediction quality: an example in sorghum.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2019Article
- High-frequency marker haplotypes in the genomic selection of dairy cattle.Journal of applied genetics · 2019Article
- On the Use of the Pearson Correlation Coefficient for Model Evaluation in Genome-Wide Prediction.Frontiers in genetics · 2019Article
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
Cross-validation of methods is an essential component of genome-enabled prediction of complex traits. We develop formulae for computing the predictions that would be obtained when one or several cases are removed in the training process, to become members of testing sets, but by running the model using all observations only once. Prediction methods to which the developments apply include least squares, best linear unbiased prediction (BLUP) of markers, or genomic BLUP, reproducing kernels Hilbert spaces regression with single or multiple kernel matrices, and any member of a suite of linear regression methods known as "Bayesian alphabet." The approach used for Bayesian models is based on importance sampling of posterior draws. Proof of concept is provided by applying the formulae to a wheat data set representing 599 inbred lines genotyped for 1279 markers, and the target trait was grain yield. The data set was used to evaluate predictive mean-squared error, impact of alternative layouts on maximum likelihood estimates of regularization parameters, model complexity, and residual degrees of freedom stemming from various strengths of regularization, as well as two forms of importance sampling. Our results will facilitate carrying out extensive cross-validation without model retraining for most machines employed in genome-assisted prediction of quantitative traits.
Indexed as
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.