Evidence map›Paper›PMID 27529480›Full record

ArticlePloS one2016

An Upper Bound for Accuracy of Prediction Using GBLUP.

Emre Karaman, Hao Cheng, Mehmet Z Firat, Dorian J Garrick, Rohan L Fernando

Abstract read
In one paragraph

Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

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

38 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Genomic Diversity of U.S. Katahdin Hair Sheep.Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie · 2025
    Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. A comparison of methods for training population optimization in genomic selection.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023
    Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Predicting the accuracy of genomic predictions.Genetics, selection, evolution : GSE · 2021
    Article
  17. Article
  18. Article
  19. Article
  20. Article
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

5 authors.

Emre KaramanDepartment of Animal Science, Faculty of Agriculture, Akdeniz University, 07059 Antalya, Turkey.
Hao ChengDepartment of Animal Science, Iowa State University, 50011 Ames, Iowa, United States of America.
Mehmet Z FiratDepartment of Animal Science, Faculty of Agriculture, Akdeniz University, 07059 Antalya, Turkey.
Dorian J GarrickDepartment of Animal Science, Iowa State University, 50011 Ames, Iowa, United States of America.
Rohan L FernandoDepartment of Animal Science, Iowa State University, 50011 Ames, Iowa, United States of America.

Funding

Factors Affecting Prediction Accuracy of Complex Human Traits and DiseasesR01GM099992 · NIGMS · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI DE LOS CAMPOS, GUSTAVO · 2012 to 2015
$1.0M
NIGMS NIH HHS R01 GM099992
6 · The paper itself

Abstract

This study aims at characterizing the asymptotic behavior of genomic prediction R2 as the size of the reference population increases for common or rare QTL alleles through simulations. Haplotypes derived from whole-genome sequence of 85 Caucasian individuals from the 1,000 Genomes Project were used to simulate random mating in a population of 10,000 individuals for at least 100 generations to create the LD structure in humans for a large number of individuals. To reduce computational demands, only SNPs within a 0.1M region of each of the first 5 chromosomes were used in simulations, and therefore, the total genome length simulated was 0.5M. When the genome length is 30M, to get the same genomic prediction R2 as with a 0.5M genome would require a reference population 60 fold larger. Three scenarios were considered varying in minor allele frequency distributions of markers and QTL, for h2 = 0.8 resembling height in humans. Total number of markers was 4,200 and QTL were 70 for each scenario. In this study, we considered the prediction accuracy in terms of an estimability problem, and thereby provided an upper bound for reliability of prediction, and thus, for prediction R2. Genomic prediction methods GBLUP, BayesB and BayesC were compared. Our results imply that for human height variable selection methods BayesB and BayesC applied to a 30M genome have no advantage over GBLUP when the size of reference population was small (<6,000 individuals), but are superior as more individuals are included in the reference population. All methods become asymptotically equivalent in terms of prediction R2, which approaches genomic heritability when the size of the reference population reaches 480,000 individuals.

Indexed as

AllelesGenomicsHaplotypesHumansLinear ModelsModels, GeneticPhenotypePolymorphism, Single NucleotideQuantitative Trait LociReproducibility of ResultsSample Size

Identifiers

PMID27529480
PMCPMC4986954

What OpenQuestion holds

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LicenceCC BY
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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.