Evidence map›Paper›PMID 35764921›Full record

ArticleGenetics, selection, evolution : GSE2022

Impact of genomic preselection on subsequent genetic evaluations with ssGBLUP using real data from pigs.

Ibrahim Jibrila, Jeremie Vandenplas, Jan Ten Napel, Rob Bergsma, Roel F Veerkamp, Mario P L Calus

Open access · goldAbstract read
In one paragraph

Article in Genetics, selection, evolution : GSE, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.6field-weighted citation impact, top 29% of its field
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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. 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

6 authors at 2 institutions in 1 country.

Ibrahim JibrilaAnimal Breeding and Genomics Group, Wageningen University & Research, PO Box 338, 6700 AH, Wageningen, The Netherlands. ibrahim.jibrila@gmail.com.ORCID http://orcid.org/0000-0002-5683-1263
Jeremie VandenplasAnimal Breeding and Genomics Group, Wageningen University & Research, PO Box 338, 6700 AH, Wageningen, The Netherlands.ORCID http://orcid.org/0000-0002-2554-072X
Jan Ten NapelAnimal Breeding and Genomics Group, Wageningen University & Research, PO Box 338, 6700 AH, Wageningen, The Netherlands.ORCID http://orcid.org/0000-0002-1918-9080
Rob BergsmaTopigs Norsvin Research Center B.V., Schoenaker 6, 6641 SZ, Beuningen, The Netherlands.ORCID http://orcid.org/0000-0002-8254-5535
Roel F VeerkampAnimal Breeding and Genomics Group, Wageningen University & Research, PO Box 338, 6700 AH, Wageningen, The Netherlands.ORCID http://orcid.org/0000-0002-5240-6534
Mario P L CalusAnimal Breeding and Genomics Group, Wageningen University & Research, PO Box 338, 6700 AH, Wageningen, The Netherlands.ORCID http://orcid.org/0000-0002-3213-704X
Wageningen University & Research · NLTopigs Norsvin (Netherlands) · NL

Funding

ministerie van economische zaken Food project 16022ministerie van economische zaken TKI Agri
6 · The paper itself

Abstract

backgroundEmpirically assessing the impact of preselection on genetic evaluation of preselected animals requires comparing scenarios that take different approaches into account, including scenarios without preselection. However, preselection is almost always performed in animal breeding programs, so it is difficult to have a dataset without preselection. Hence, most studies on preselection have used simulated datasets, and have concluded that genomic estimated breeding values (GEBV) from subsequent single-step genomic best linear unbiased prediction (ssGBLUP) evaluations are unbiased. The aim of this study was to investigate the impact of genomic preselection (GPS) on accuracy and bias in subsequent ssGBLUP evaluations, using data from a commercial pig breeding program.

methodsWe used data on average daily gain during performance testing, average daily gain throughout life, backfat thickness, and loin depth from one sire line and one dam line of pigs. As these traits have different weights in the breeding goals of the two lines, we analyzed the lines separately. For each line, we implemented a reference GPS scenario that kept all available data, against which the next two scenarios were compared. We then implemented two other scenarios with additional layers of GPS by removing all animals without progeny either (i) only in the validation generation, or (ii) in all generations. We conducted subsequent ssGBLUP evaluations for each GPS scenario, using all the data remaining after implementing the GPS scenario. Accuracy and bias were computed by comparing GEBV against progeny yield deviations of validation animals.

resultsResults for all traits and in both lines showed a marginal loss in accuracy due to the additional layers of GPS. Average accuracies across all GPS scenarios in the two lines were 0.39, 0.47, 0.56, and 0.60, for average daily gain during performance testing and throughout life, backfat thickness, and loin depth, respectively. Biases were largely absent, and when present, did not differ greatly between the GPS scenarios.

conclusionsWe conclude that the impact of preselection on accuracy and bias in subsequent ssGBLUP evaluations of selection candidates in pigs is generally minimal. We expect this conclusion to apply for other animal breeding programs as well, since preselection of any type or intensity generally has the same effect in animal breeding programs.

Indexed as

GenomeModels, GeneticAnimalsBiasGenomicsPhenotypeSwine

Identifiers

PMID35764921
PMCPMC9238012
OpenAlexW4283652821

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.