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Title: Accuracy of genomic prediction in switchgrass (Panicum virgatum L.) improved by accounting for linkage disequilibrium

Journal Article · · G3
 [1];  [2];  [3];  [4];  [4];  [2];  [5]
  1. University of Wisconsin, Madison, WI, (United States). Department of Agronomy
  2. Michigan State University, East Lansing, MI, (United States). Department of Plant Biology; Department of Energy Great Lakes Bioenergy Research Center
  3. University of Wisconsin, Madison, WI, (United States). Department of Agronomy; Dept. of Energy Great Lakes Bioenergy Research Center
  4. University of Nebraska, Lincoln, NE, (United States). Grain, Forage, and Bioenergy Research Unit, Agricultural Research Service, United States Dept. of Agriculture
  5. University of Wisconsin, Madison, WI, (United States). Department of Agronomy; Agricultural Research Service; United States Department of Agriculture, Madison, WI, (United States)
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Switchgrass is a relatively high-yielding and environmentally sustainable biomass crop, but further genetic gains in biomass yield must be achieved to make it an economically viable bioenergy feedstock. Genomic selection (GS) is an attractive technology to generate rapid genetic gains in switchgrass, and meet the goals of a substantial displacement of petroleum use with biofuels in the near future. In this study, we empirically assessed prediction procedures for genomic selection in two different populations, consisting of 137 and 110 half-sib families of switchgrass, tested in two locations in the United States for three agronomic traits: dry matter yield, plant height, and heading date. Marker data were produced for the families’ parents by exome capture sequencing, generating up to 141,030 polymorphic markers with available genomic-location and annotation information. We evaluated prediction procedures that varied not only by learning schemes and prediction models, but also by the way the data were preprocessed to account for redundancy in marker information. More complex genomic prediction procedures were generally not significantly more accurate than the simplest procedure, likely due to limited population sizes. Nevertheless, a highly significant gain in prediction accuracy was achieved by transforming the marker data through a marker correlation matrix. Our results suggest that marker-data transformations and, more generally, the account of linkage disequilibrium among markers, offer valuable opportunities for improving prediction procedures in GS. Furthermore, some of the achieved prediction accuracies should motivate implementation of GS in switchgrass breeding programs.

Research Organization:
Univ. of California, Oakland, CA (United States)
Sponsoring Organization:
USDOE Office of Science (SC)
Grant/Contract Number:
AC02-05CH11231
OSTI ID:
1258157
Journal Information:
G3, Vol. 6, Issue 4; ISSN 2160-1836
Publisher:
Genetics Society of AmericaCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 23 works
Citation information provided by
Web of Science

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Cited By (8)

Genome-Wide Association and Genomic Prediction for Fry Color in Potato journal January 2020
Predictive ability of genomic selection models in a multi-population perennial ryegrass training set using genotyping-by-sequencing journal December 2017
Breeding progress and preparedness for mass-scale deployment of perennial lignocellulosic biomass crops switchgrass, miscanthus, willow and poplar journal October 2018
Genomic Prediction for Winter Survival of Lowland Switchgrass in the Northern USA journal June 2019
Benchmarking Parametric and Machine Learning Models for Genomic Prediction of Complex Traits journal November 2019
Genomic prediction accuracy for switchgrass traits related to bioenergy within differentiated populations journal July 2018
Extensions of BLUP Models for Genomic Prediction in Heterogeneous Populations: Application in a Diverse Switchgrass Sample journal March 2019
High-Throughput Genome-Wide Genotyping To Optimize the Use of Natural Genetic Resources in the Grassland Species Perennial Ryegrass (Lolium perenneL.) journal September 2020

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Related Subjects

59 BASIC BIOLOGICAL SCIENCES
genomic
selection
linkage
disequilibrium
exome capture
bioenergy
Panicum virgatum L
GenPred
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