Skip to main navigation Skip to search Skip to main content

Predicting Genomic Selection Accuracy from Hetergeneous Sources

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We predict genomic selection accuracy from a heterogeneous reference population that contains close relatives, herd- or flock mates and individuals from the wider population, using an established theory. The various sources of information were modeled as different and independent reference populations with different effective sizes. We show that information on close relatives can have a substantial effect on genomic prediction accuracy. We also show the increase of the genomic prediction accuracy to be less reliant on higher marker density or total reference population size when there are more closely related individuals to predict from. Conversely, the value of close relatives is smaller when the total reference population size is larger. Our modelling is useful to assess the value of a population reference versus a breeder's own reference, based on own animals genotyped.
Original languageEnglish
Title of host publicationProceedings of the Association for the Advancement of Animal Breeding and Genetics
EditorsKim Bunter, Tim Byrne, Hans Daetwyler, Susanne Hermesch, Kathryn Kemper, James Kijas, David Nation, Wayne Pitchford, Suzanne Rowe, Matt Shaffer, Alison van Eenennaam
Place of PublicationArmidale, Australia
PublisherAssociation for the Advancement of Animal Breeding and Genetics (AAABG)
Pages161-164
Volume21
ISBN (Print)9780646945545
Publication statusPublished - 2015
EventAAABG 2015: 21st Conference of the Association for the Advancement of Animal Breeding and Genetics - Lorne, Australia
Duration: 28 Sept 201530 Sept 2015

Conference

ConferenceAAABG 2015: 21st Conference of the Association for the Advancement of Animal Breeding and Genetics
CityLorne, Australia
Period28/09/1530/09/15

Keywords

  • Animal Breeding

Fingerprint

Dive into the research topics of 'Predicting Genomic Selection Accuracy from Hetergeneous Sources'. Together they form a unique fingerprint.

Cite this