Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/28967
Title: Estimation of genomic prediction accuracy from reference populations with varying degrees of relationship
Contributor(s): Hong Lee, S  (author); Clark, Sam  (author)orcid ; van der Werf, Julius H J  (author)orcid 
Publication Date: 2017-12-21
Open Access: Yes
DOI: 10.1371/journal.pone.0189775
Handle Link: https://hdl.handle.net/1959.11/28967
Abstract: Genomic prediction is emerging in a wide range of fields including animal and plant breeding, risk prediction in human precision medicine and forensic. It is desirable to establish a theoretical framework for genomic prediction accuracy when the reference data consists of information sources with varying degrees of relationship to the target individuals. A reference set can contain both close and distant relatives as well as `unrelated' individuals from the wider population in the genomic prediction. The various sources of information were modeled as different populations with different effective population sizes (Nₑ). Both the effective number of chromosome segments (Mₑ) and Nₑ are considered to be a function of the data used for prediction. We validate our theory with analyses of simulated as well as real data, and illustrate that the variation in genomic relationships with the target is a predictor of the information content of the reference set. With a similar amount of data available for each source, we show that close relatives can have a substantially larger effect on genomic prediction accuracy than lesser related individuals. We also illustrate that when prediction relies on closer relatives, there is less improvement in prediction accuracy with an increase in training data or marker panel density. We release software that can estimate the expected prediction accuracy and power when combining different reference sources with various degrees of relationship to the target, which is useful when planning genomic prediction (before or after collecting data) in animal, plant and human genetics.
Publication Type: Journal Article
Grant Details: ARC/DP160102126
Source of Publication: PLoS One, 12(12), p. 1-22
Publisher: Public Library of Science (PLoS)
Place of Publication: United States of America
ISSN: 1932-6203
Field of Research (FOR): 070201 Animal Breeding
060412 Quantitative Genetics (incl. Disease and Trait Mapping Genetics)
060408 Genomics
Socio-Economic Objective (SEO): 830399 Livestock Raising not elsewhere classified
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article
School of Environmental and Rural Science

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