The effects of demography and long-term selection on the accuracy of genomic prediction with sequence data

MacLeod, Iona M., Hayes, Ben J. and Goddard, Michael E. (2014) The effects of demography and long-term selection on the accuracy of genomic prediction with sequence data. Genetics, 198 4: 1671-1684. doi:10.1534/genetics.114.168344


Author MacLeod, Iona M.
Hayes, Ben J.
Goddard, Michael E.
Title The effects of demography and long-term selection on the accuracy of genomic prediction with sequence data
Journal name Genetics   Check publisher's open access policy
ISSN 1943-2631
Publication date 2014-12-01
Sub-type Article (original research)
DOI 10.1534/genetics.114.168344
Open Access Status Not yet assessed
Volume 198
Issue 4
Start page 1671
End page 1684
Total pages 14
Place of publication Bethesda, United States
Publisher Genetics Society of America
Language eng
Formatted abstract
The use of dense SNPs to predict the genetic value of an individual for a complex trait is often referred to as “genomic selection” in livestock and crops, but is also relevant to human genetics to predict, for example, complex genetic disease risk. The accuracy of prediction depends on the strength of linkage disequilibrium (LD) between SNPs and causal mutations. If sequence data were used instead of dense SNPs, accuracy should increase because causal mutations are present, but demographic history and long-term negative selection also influence accuracy. We therefore evaluated genomic prediction, using simulated sequence in two contrasting populations: one reducing from an ancestrally large effective population size (Ne) to a small one, with high LD common in domestic livestock, while the second had a large constant-sized Ne with low LD similar to that in some human or outbred plant populations. There were two scenarios in each population; causal variants were either neutral or under long-term negative selection. For large Ne, sequence data led to a 22% increase in accuracy relative to ∼600K SNP chip data with a Bayesian analysis and a more modest advantage with a BLUP analysis. This advantage increased when causal variants were influenced by negative selection, and accuracy persisted when 10 generations separated reference and validation populations. However, in the reducing Ne population, there was little advantage for sequence even with negative selection. This study demonstrates the joint influence of demography and selection on accuracy of prediction and improves our understanding of how best to exploit sequence for genomic prediction.
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: Queensland Alliance for Agriculture and Food Innovation
 
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