Evidence map›Paper›PMID 31730478›Full record

ArticleGenetics, selection, evolution : GSE2019

Optimized grouping to increase accuracy of prediction of breeding values based on group records in genomic selection breeding programs.

Thinh T Chu, John W M Bastiaansen, Peer Berg, Hans Komen

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Article in Genetics, selection, evolution : GSE, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Thinh T ChuCenter for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark. Chu.Thinh@mbg.au.dk.ORCID http://orcid.org/0000-0002-7226-3454
John W M BastiaansenAnimal Breeding and Genomics, Wageningen University & Research, 6709 PG, Wageningen, The Netherlands.
Peer BergCenter for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark.
Hans KomenAnimal Breeding and Genomics, Wageningen University & Research, 6709 PG, Wageningen, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPhenotypic records of group means or group sums are a good alternative to individual records for some difficult to measure, but economically important traits such as feed efficiency or egg production. Accuracy of predicted breeding values based on group records increases with increasing relationships between group members. The classical way to form groups with more closely-related animals is based on pedigree information. When genotyping information is available before phenotyping, its use to form groups may further increase the accuracy of prediction from group records. This study analyzed two grouping methods based on genomic information: (1) unsupervised clustering implemented in the STRUCTURE software and (2) supervised clustering that models genomic relationships.

resultsUsing genomic best linear unbiased prediction (GBLUP) models, estimates of the genetic variance based on group records were consistent with those based on individual records. When genomic information was available to constitute the groups, genomic relationship coefficients between group members were higher than when random grouping of paternal half-sibs and of full-sibs was applied. Grouping methods that are based on genomic information resulted in higher accuracy of genomic estimated breeding values (GEBV) prediction compared to random grouping. The increase was ~ 1.5% for full-sibs and ~ 11.5% for paternal half-sibs. In addition, grouping methods that are based on genomic information led to lower coancestry coefficients between the top animals ranked by GEBV. Of the two proposed methods, supervised clustering was superior in terms of accuracy, computation requirements and applicability. By adding surplus genotyped offspring (more genotyped offspring than required to fill the groups), the advantage of supervised clustering increased by up to 4.5% compared to random grouping of full-sibs, and by 14.7% compared to random grouping of paternal half-sibs. This advantage also increased with increasing family sizes or decreasing genome sizes.

conclusionsThe use of genotyping information for grouping animals increases the accuracy of selection when phenotypic group records are used in genomic selection breeding programs.

Indexed as

Models, GeneticAnimalsBiasBreedingChickensGenome-Wide Association StudyGenotypePedigreePhenotypeUnsupervised Machine Learning

Identifiers

PMID31730478
PMCPMC6858773

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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.