Breeding for Biomass Yield in Switchgrass Using Surrogate Measures of Yield

Development of switchgrass (Panicum virgatum L.) as a dedicated biomass crop for conversion to energy requires substantial increases in biomass yield. Most efforts to breed for increased biomass yield are based on some form of indirect selection. The objective of this paper is to evaluate and compare the expected efficiency of several indirect measures of breeding value for improving sward-plot biomass yield of switchgrass. Sward-plot biomass yield, row-plot biomass, and spaced-plant biomass were measured on 144 half-sib families or their maternal parents from the WS4U-C2 breeding population of upland switchgrass. Heading date was also scored on row plots and anthesis date was scored on spaced plants. Use of any of these indirect selection criteria was expected to be less efficient than direct selection for biomass yield measured on sward plots, when expressed as genetic gain per year. Combining any of these indirect selection criteria with half-sib family selection for biomass yield resulted in increases in efficiency of 14 to 36%, but this could only be achieved at a very large cost of measuring phenotype on literally thousands of plants that would eventually have no chance of being selected because they were derived from inferior families. Genomic prediction methods offered the best solution to increase breeding efficiency by reducing average cycle time, increasing selection intensity, and placing selection pressure on all additive genetic variance within the population. Use of genomic selection methods is expected to double or triple genetic gains over field-based half-sib family selection

Additive Manufacturing of Ceramic Materials: a Performance Comparison of Catalysts for Monopropellant Thrusters

Switchgrass ( L.) is a promising herbaceous energy crop, but further gains in biomass yield and quality must be achieved to enable a viable bioenergy industry. Developing DNA markers can contribute to such progress, but depiction of genetic bases should be reliable, involving simple additive marker effects and also interactions with genetic backgrounds (e.g., ecotypes) or synergies with other markers. We analyzed plant height, C content, N content, and mineral concentration in a diverse panel consisting of 512 genotypes of upland and lowland ecotypes. We performed association analyses based on exome capture sequencing and tested 439,170 markers for marginal effects, 83,290 markers for marker × ecotype interactions, and up to 311,445 marker pairs for pairwise interactions. Analyses of pairwise interactions focused on subsets of marker pairs preselected on the basis of marginal marker effects, gene ontology annotation, and pairwise marker associations. Our tests identified 12 significant effects. Homology and gene expression information corroborated seven effects and indicated plausible causal pathways: flowering time and lignin synthesis for plant height; plant growth and senescence for C content and mineral concentration. Four pairwise interactions were detected, including three interactions preselected on the basis of pairwise marker correlations. Furthermore, a marker × ecotype interaction and a pairwise interaction were confirmed in an independent switchgrass panel. Our analyses identified reliable candidate variants for important bioenergy traits. Moreover, they exemplified the importance of interactive effects for depicting genetic bases and illustrated the usefulness of preselecting marker pairs for identifying pairwise marker interactions in association studies

Snowball Earth climate dynamics and Cryogenian geology-geobiology

Geological evidence indicates that grounded ice sheets reached sea level at all latitudes during two long-lived Cryogenian (58 and ≥5 My) glaciations. Combined uranium-lead and rhenium-osmium dating suggests that the older (Sturtian) glacial onset and both terminations were globally synchronous. Geochemical data imply that CO2 was 102 PAL (present atmospheric level) at the younger termination, consistent with a global ice cover. Sturtian glaciation followed breakup of a tropical supercontinent, and its onset coincided with the equatorial emplacement of a large igneous province. Modeling shows that the small thermal inertia of a globally frozen surface reverses the annual mean tropical atmospheric circulation, producing an equatorial desert and net snow and frost accumulation elsewhere. Oceanic ice thickens, forming a sea glacier that flows gravitationally toward the equator, sustained by the hydrologic cycle and by basal freezing and melting. Tropical ice sheets flow faster as CO2 rises but lose mass and become sensitive to orbital changes. Equatorial dust accumulation engenders supraglacial oligotrophic meltwater ecosystems, favorable for cyanobacteria and certain eukaryotes. Meltwater flushing through cracks enables organic burial and submarine deposition of airborne volcanic ash. The subglacial ocean is turbulent and well mixed, in response to geothermal heating and heat loss through the ice cover, increasing with latitude. Terminal carbonate deposits, unique to Cryogenian glaciations, are products of intense weathering and ocean stratification. Whole-ocean warming and collapsing peripheral bulges allow marine coastal flooding to continue long after ice-sheet disappearance. The evolutionary legacy of Snowball Earth is perceptible in fossils and living organisms

Orbitally forced ice sheet fluctuations during the Marinoan Snowball Earth glaciation

Two global glaciations occurred during the Neoproterozoic. Snowball Earth theory posits that these were terminated after millions of years of frigidity when initial warming from rising atmospheric CO2 concentrations was amplified by the reduction of ice cover and hence a reduction in planetary albedo. This scenario implies that most of the geological record of ice cover was deposited in a brief period of melt-back. However, deposits in low palaeo-latitudes show evidence of glacial–interglacial cycles. Here we analyse the sedimentology and oxygen and sulphur isotopic signatures of Marinoan Snowball glaciation deposits from Svalbard, in the Norwegian High Arctic. The deposits preserve a record of oscillations in glacier extent and hydrologic conditions under uniformly high atmospheric CO2 concentrations. We use simulations from a coupled three-dimensional ice sheet and atmospheric general circulation model to show that such oscillations can be explained by orbital forcing in the late stages of a Snowball glaciation. The simulations suggest that while atmospheric CO2 concentrations were rising, but not yet at the threshold required for complete melt-back, the ice sheets would have been sensitive to orbital forcing. We conclude that a similar dynamic can potentially explain the complex successions observed at other localities

Breeding for Biomass Yield in Switchgrass Using Surrogate Measures of Yield

Development of switchgrass (Panicum virgatum L.) as a dedicated biomass crop for conversion to energy requires substantial increases in biomass yield. Most efforts to breed for increased biomass yield are based on some form of indirect selection. The objective of this paper is to evaluate and compare the expected efficiency of several indirect measures of breeding value for improving sward-plot biomass yield of switchgrass. Sward-plot biomass yield, row-plot biomass, and spaced-plant biomass were measured on 144 half-sib families or their maternal parents from the WS4U-C2 breeding population of upland switchgrass. Heading date was also scored on row plots and anthesis date was scored on spaced plants. Use of any of these indirect selection criteria was expected to be less efficient than direct selection for biomass yield measured on sward plots, when expressed as genetic gain per year. Combining any of these indirect selection criteria with half-sib family selection for biomass yield resulted in increases in efficiency of 14 to 36%, but this could only be achieved at a very large cost of measuring phenotype on literally thousands of plants that would eventually have no chance of being selected because they were derived from inferior families. Genomic prediction methods offered the best solution to increase breeding efficiency by reducing average cycle time, increasing selection intensity, and placing selection pressure on all additive genetic variance within the population. Use of genomic selection methods is expected to double or triple genetic gains over field-based half-sib family selection

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

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

Accuracy of Genomic Prediction in Switchgrass (\u3ci\u3ePanicum virgatum\u3c/i\u3e L.) Improved by Accounting for Linkage Disequilibrium

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. Some of the achieved prediction accuracies should motivate implementation of GS in switchgrass breeding programs

Accuracy of Genomic Prediction in Switchgrass (Panicum virgatum L.) Improved by Accounting for Linkage Disequilibrium

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. Some of the achieved prediction accuracies should motivate implementation of GS in switchgrass breeding programs

The snowball Earth aftermath: Exploring the limits of continental weathering processes

International audienc

genotype_means.zip

compressed zip file containing estimated genotype means in NAP (genotype_means-NAP.csv) or SAP (genotype_means-SAP.csv