Format

Send to

Choose Destination
G3 (Bethesda). 2019 Mar 7;9(3):789-805. doi: 10.1534/g3.118.200969.

Extensions of BLUP Models for Genomic Prediction in Heterogeneous Populations: Application in a Diverse Switchgrass Sample.

Author information

1
Department of Agronomy, University of Wisconsin-Madison, Madison, WI 53706 gr226@cornell.edu.
2
Department of Agronomy, University of Wisconsin-Madison, Madison, WI 53706.
3
Agricultural Research Service, United States Department of Agriculture, Madison, WI 53706.

Abstract

Genomic prediction is a useful tool to accelerate genetic gain in selection using DNA marker information. However, this technology typically relies on standard prediction procedures, such as genomic BLUP, that are not designed to accommodate population heterogeneity resulting from differences in marker effects across populations. In this study, we assayed different prediction procedures to capture marker-by-population interactions in genomic prediction models. Prediction procedures included genomic BLUP and two kernel-based extensions of genomic BLUP which explicitly accounted for population heterogeneity. To model population heterogeneity, dissemblance between populations was either depicted by a unique coefficient (as previously reported), or a more flexible function of genetic distance between populations (proposed herein). Models under investigation were applied in a diverse switchgrass sample under two validation schemes: whole-sample calibration, where all individuals except selection candidates are included in the calibration set, and cross-population calibration, where the target population is entirely excluded from the calibration set. First, we showed that using fixed effects, from principal components or putative population groups, appeared detrimental to prediction accuracy, especially in cross-population calibration. Then we showed that modeling population heterogeneity by our proposed procedure resulted in highly significant improvements in model fit. In such cases, gains in accuracy were often positive. These results suggest that population heterogeneity may be parsimoniously captured by kernel methods. However, in cases where improvement in model fit by our proposed procedure is null-to-moderate, ignoring heterogeneity should probably be preferred due to the robustness and simplicity of the standard genomic BLUP model.

KEYWORDS:

GenPred; Genomic Prediction; Panicum virgatum; Shared Data Resources; kernel functions; marker-by-population interaction; population heterogeneity

PMID:
30651285
PMCID:
PMC6404615
DOI:
10.1534/g3.118.200969
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for HighWire Icon for PubMed Central
Loading ...
Support Center