Biomass yield improvement in switchgrass through genomic prediction of flowering time
Abstract The seasonal timing of the transition from vegetative to reproductive growth has a major impact on biomass accumulation in switchgrass. Late‐flowering switchgrass cultivars produce greater biomass, a critical trait for sustainable bioenergy production. Genomic prediction (GP) may allow rapi...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-09-01
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| Series: | GCB Bioenergy |
| Subjects: | |
| Online Access: | https://doi.org/10.1111/gcbb.12983 |
| _version_ | 1811322741904637952 |
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| author | Neal W. Tilhou Michael D. Casler |
| author_facet | Neal W. Tilhou Michael D. Casler |
| author_sort | Neal W. Tilhou |
| collection | DOAJ |
| description | Abstract The seasonal timing of the transition from vegetative to reproductive growth has a major impact on biomass accumulation in switchgrass. Late‐flowering switchgrass cultivars produce greater biomass, a critical trait for sustainable bioenergy production. Genomic prediction (GP) may allow rapid selection of late‐flowering individuals with reduced time and expense for field evaluations. To evaluate GP, two flowering time traits (heading date and anthesis date) were collected on 1532 genotypes from four breeding populations: Midwest, Gulf, Atlantic, and Hybrid. These were sequenced using genotype‐by‐sequencing (530,792 single‐nucleotide polymorphisms). Predictive ability of single‐trait and multi‐trait models were evaluated by cross‐validation, by prediction of a progeny trial (n = 122), and through prediction of yield performance in a parallel experiment (n = 52). Predictive ability was not improved by sharing information among breeding groups. Overall, multi‐trait models provided an advantage during cross‐validation, but a smaller advantage during progeny prediction. Within populations, GP resulted in lower per‐cycle progress than previously reported field evaluations (3.1 vs. 5.0 day−1 cycle−1). However, GP cycles are potentially much faster than field evaluations. When directly predicting biomass yield, the Hybrid training population had a predictive ability of 0.54–0.63. This reinforces the strong linkage between biomass yields in swards and flowering time. These results highlight the value of GP for rapid yield improvement in switchgrass, particularly in a breeding program designed to share information between biomass yield trials and low‐cost flowering time evaluations. |
| first_indexed | 2024-04-13T13:41:27Z |
| format | Article |
| id | doaj.art-9d4b31821893413aade0e5e8e35be714 |
| institution | Directory Open Access Journal |
| issn | 1757-1693 1757-1707 |
| language | English |
| last_indexed | 2024-04-13T13:41:27Z |
| publishDate | 2022-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | GCB Bioenergy |
| spelling | doaj.art-9d4b31821893413aade0e5e8e35be7142022-12-22T02:44:38ZengWileyGCB Bioenergy1757-16931757-17072022-09-011491023103410.1111/gcbb.12983Biomass yield improvement in switchgrass through genomic prediction of flowering timeNeal W. Tilhou0Michael D. Casler1Department of Agronomy University of Wisconsin Madison Wisconsin USADepartment of Agronomy University of Wisconsin Madison Wisconsin USAAbstract The seasonal timing of the transition from vegetative to reproductive growth has a major impact on biomass accumulation in switchgrass. Late‐flowering switchgrass cultivars produce greater biomass, a critical trait for sustainable bioenergy production. Genomic prediction (GP) may allow rapid selection of late‐flowering individuals with reduced time and expense for field evaluations. To evaluate GP, two flowering time traits (heading date and anthesis date) were collected on 1532 genotypes from four breeding populations: Midwest, Gulf, Atlantic, and Hybrid. These were sequenced using genotype‐by‐sequencing (530,792 single‐nucleotide polymorphisms). Predictive ability of single‐trait and multi‐trait models were evaluated by cross‐validation, by prediction of a progeny trial (n = 122), and through prediction of yield performance in a parallel experiment (n = 52). Predictive ability was not improved by sharing information among breeding groups. Overall, multi‐trait models provided an advantage during cross‐validation, but a smaller advantage during progeny prediction. Within populations, GP resulted in lower per‐cycle progress than previously reported field evaluations (3.1 vs. 5.0 day−1 cycle−1). However, GP cycles are potentially much faster than field evaluations. When directly predicting biomass yield, the Hybrid training population had a predictive ability of 0.54–0.63. This reinforces the strong linkage between biomass yields in swards and flowering time. These results highlight the value of GP for rapid yield improvement in switchgrass, particularly in a breeding program designed to share information between biomass yield trials and low‐cost flowering time evaluations.https://doi.org/10.1111/gcbb.12983climate changecrop yieldflowering timegenomic predictiongrass breedingswitchgrass |
| spellingShingle | Neal W. Tilhou Michael D. Casler Biomass yield improvement in switchgrass through genomic prediction of flowering time GCB Bioenergy climate change crop yield flowering time genomic prediction grass breeding switchgrass |
| title | Biomass yield improvement in switchgrass through genomic prediction of flowering time |
| title_full | Biomass yield improvement in switchgrass through genomic prediction of flowering time |
| title_fullStr | Biomass yield improvement in switchgrass through genomic prediction of flowering time |
| title_full_unstemmed | Biomass yield improvement in switchgrass through genomic prediction of flowering time |
| title_short | Biomass yield improvement in switchgrass through genomic prediction of flowering time |
| title_sort | biomass yield improvement in switchgrass through genomic prediction of flowering time |
| topic | climate change crop yield flowering time genomic prediction grass breeding switchgrass |
| url | https://doi.org/10.1111/gcbb.12983 |
| work_keys_str_mv | AT nealwtilhou biomassyieldimprovementinswitchgrassthroughgenomicpredictionoffloweringtime AT michaeldcasler biomassyieldimprovementinswitchgrassthroughgenomicpredictionoffloweringtime |