Dried biomass samples were also analyzed using prediction equations of NIRS at the Noble Foundation and for lignin content, S/G ratio, and sugar release characteristics at the NREL. Phenotypic data on plant height, tillering ability, regrowth, flowering time, and biomass yield were collected. All the progenies, founder parents, F1 parents (n=2350) were evaluated in replicated field trials at Ardmore, OK and Knoxville, TN. The switchgrass NAM population consists of a total of 2000 genotypes from 15 families. Progenies form each family were randomly selected to develop the NAM population. Ten genotypes from each of the 15 F1 families were then chain crossed. To develop a NAM population of switchgrass, 15 highly diverse genotypes with specific characteristics were selected from a diversity panel and crossed to a recurrent parent, AP13, a more » genotype selected for whole genome sequencing and parent of a mapping population. The nested association mapping (NAM) analysis combines the best features of both bi-parental and association analyses and can provide high power and high resolution in QTL detection and will ensure significant improvements in biomass yield and quality. Understanding the genetic basis of quantitative traits is essential to facilitate genome-enabled breeding programs.
Switchgrass (Panicum virgatum L.) is a C4 grass with high biomass yield potential and a model species for bioenergy feedstock development. of California, Oakland, CA (United States) Sponsoring Org.: USDOE Office of Science (SC) OSTI Identifier: 1258157 Grant/Contract Number: AC02-05CH11231 Resource Type: Journal Article: Accepted Manuscript Journal Name: G3 Additional Journal Information: Journal Volume: 6 Journal Issue: 4 Journal ID: ISSN 2160-1836 Publisher: Genetics Society of America Country of Publication: United States Language: English Subject: 59 BASIC BIOLOGICAL SCIENCES genomic selection linkage disequilibrium exome capture bioenergy Panicum virgatum L GenPred shared data = , Research Service United States Department of Agriculture, Madison, WI, (United States) University of Wisconsin, Madison, WI, (United States).Grain, Forage, and Bioenergy Research Unit, Agricultural Research Service, United States Dept. University of Nebraska, Lincoln, NE, (United States).of Energy Great Lakes Bioenergy Research Center Department of Plant Biology Department of Energy Great Lakes Bioenergy Research Center Michigan State University, East Lansing, MI, (United States).Furthermore, some of the achieved prediction accuracies should motivate implementation of GS in switchgrass breeding programs. Our results more » suggest that marker-data transformations and, more generally, the account of linkage disequilibrium among markers, offer valuable opportunities for improving prediction procedures in GS.
Nevertheless, a highly significant gain in prediction accuracy was achieved by transforming the marker data through a marker correlation matrix.
More complex genomic prediction procedures were generally not significantly more accurate than the simplest procedure, likely due to limited population sizes. 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. 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. 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. 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. 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.