Evidence map›Paper›PMID 18704696›Full record

ArticleGenetica2009

Genomic selection: prediction of accuracy and maximisation of long term response.

Mike Goddard

Abstract read
PubMed Publisher
In one paragraph

Article in Genetica, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 493 papers.

0numbers the graph read from it
0cells of the map it votes in
493citing 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

493 citing papers in PubMed.

  1. Article
  2. Review
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  5. Review
  6. Better Multi-Breed Genomic Predictions for Tropical Bull Fertility Using a Breed-Adjusted Genomic Relationship Matrix.Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie · 2026
    Article
  7. Article
  8. Article
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  12. Review
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  15. Genomic Prediction Ability for Novel Profitability Traits Using Different Models in Nelore Cattle.Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie · 2026
    Article
  16. Article
  17. Article
  18. Article
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  20. Article

433 more citing papers are in PubMed but not listed here.

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.

Mike GoddardDepartment of Primary Industries, University of Melbourne, Victoria, Australia. mike.goddard@nre.vic.gov.au

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Genomic selection refers to the use of dense markers covering the whole genome to estimate the breeding value of selection candidates for a quantitative trait. This paper considers prediction of breeding value based on a linear combination of the markers. In this case the best estimate of each marker's effect is the expectation of the effect conditional on the data. To calculate this requires a prior distribution of marker effects. If the marker effects are normally distributed with constant variance, BLUP can be used to calculate the estimated effects of the markers and hence the estimated breeding value (EBV). In this case the model is equivalent to a conventional animal model in which the relationship matrix among the animals is estimated from the markers instead of the pedigree. The accuracy of the EBV can approach 1.0 but a very large amount of data is required. An alternative model was investigated in which only some markers have non-zero effects and these effects follow a reflected exponential distribution. In this case the expected effect of a marker is a non-linear function of the data such that apparently small effects are regressed back almost to zero and consequently these markers can be deleted from the model. The accuracy in this case is considerably higher than when marker effects are normally distributed. If genomic selection is practiced for several generations the response declines in a manner that can be predicted from the marker allele frequencies. Genomic selection is likely to lead to a more rapid decline in the selection response than phenotypic selection unless new markers are continually added to the prediction of breeding value. A method to find the optimum index to maximise long term selection response is derived. This index varies the weight given to a marker according to its frequency such that markers where the favourable allele has low frequency receive more weight in the index.

Indexed as

AnimalsBreedingGenetic MarkersGenomicsModels, GeneticSensitivity and SpecificityTime FactorsGenetic Markers

Identifiers

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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.