Genomic prediction of regional-scale performance in switchgrass (Panicum virgatum) by accounting for genotype-by-environment variation and yield surrogate traits.

IF 2.1 3区 生物学 Q3 GENETICS & HEREDITY G3: Genes|Genomes|Genetics Pub Date : 2024-10-07 DOI:10.1093/g3journal/jkae159
Neal W Tilhou, Jason Bonnette, Arvid R Boe, Philip A Fay, Felix B Fritschi, Robert B Mitchell, Francis M Rouquette, Yanqi Wu, Julie D Jastrow, Michael Ricketts, Shelley D Maher, Thomas E Juenger, David B Lowry
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Abstract

Switchgrass is a potential crop for bioenergy or carbon capture schemes, but further yield improvements through selective breeding are needed to encourage commercialization. To identify promising switchgrass germplasm for future breeding efforts, we conducted multisite and multitrait genomic prediction with a diversity panel of 630 genotypes from 4 switchgrass subpopulations (Gulf, Midwest, Coastal, and Texas), which were measured for spaced plant biomass yield across 10 sites. Our study focused on the use of genomic prediction to share information among traits and environments. Specifically, we evaluated the predictive ability of cross-validation (CV) schemes using only genetic data and the training set (cross-validation 1: CV1), a subset of the sites (cross-validation 2: CV2), and/or with 2 yield surrogates (flowering time and fall plant height). We found that genotype-by-environment interactions were largely due to the north-south distribution of sites. The genetic correlations between the yield surrogates and the biomass yield were generally positive (mean height r = 0.85; mean flowering time r = 0.45) and did not vary due to subpopulation or growing region (North, Middle, or South). Genomic prediction models had CV predictive abilities of -0.02 for individuals using only genetic data (CV1), but 0.55, 0.69, 0.76, 0.81, and 0.84 for individuals with biomass performance data from 1, 2, 3, 4, and 5 sites included in the training data (CV2), respectively. To simulate a resource-limited breeding program, we determined the predictive ability of models provided with the following: 1 site observation of flowering time (0.39); 1 site observation of flowering time and fall height (0.51); 1 site observation of fall height (0.52); 1 site observation of biomass (0.55); and 5 site observations of biomass yield (0.84). The ability to share information at a regional scale is very encouraging, but further research is required to accurately translate spaced plant biomass to commercial-scale sward biomass performance.

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通过考虑基因型-环境变异和产量代用性状,从基因组学角度预测开关草(Panicum virgatum)在区域范围内的表现。
开关草是生物能源或碳捕集计划的潜在作物,但需要通过选择性育种进一步提高产量,以促进商业化。为了为未来的育种工作确定有前途的开关草种质,我们对来自 4 个开关草亚群(海湾、中西部、沿海和德克萨斯)的 630 个基因型的多样性面板进行了多地点和多性状基因组预测,并对 10 个地点的间隔植物生物量产量进行了测量。我们的研究重点是利用基因组预测在性状和环境之间共享信息。具体来说,我们评估了仅使用基因数据和训练集(交叉验证 1:CV1)、站点子集(交叉验证 2:CV2)和/或两个产量替代物(开花时间和秋季株高)的交叉验证(CV)方案的预测能力。我们发现,基因型与环境之间的相互作用主要是由于地点的南北分布造成的。产量代用指标与生物量产量之间的遗传相关性一般为正(平均株高 r=0.85;平均开花时间 r=0.45),且不因亚群或种植区域(北部、中部、南部)而异。基因组预测模型对仅使用遗传数据的个体的交叉验证预测能力为-0.02(CV1),但对使用训练数据中包含的一个、两个、三个、四个和五个地点的生物量表现数据的个体的交叉验证预测能力分别为 0.55、0.69、0.76、0.81 和 0.84(CV2)。为了模拟资源有限的育种计划,我们确定了以下模型的预测能力:一个地点的开花时间观测值(0.39)、一个地点的开花时间和秋季高度观测值(0.51)、一个地点的秋季高度观测值(0.52)、一个地点的生物量观测值(0.55)和五个地点的生物量产量观测值(0.84)。在区域范围内共享信息的能力非常令人鼓舞,但要将间隔植物生物量准确转化为商业规模的草地生物量表现,还需要进一步的研究。
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来源期刊
G3: Genes|Genomes|Genetics
G3: Genes|Genomes|Genetics GENETICS & HEREDITY-
CiteScore
5.10
自引率
3.80%
发文量
305
审稿时长
3-8 weeks
期刊介绍: G3: Genes, Genomes, Genetics provides a forum for the publication of high‐quality foundational research, particularly research that generates useful genetic and genomic information such as genome maps, single gene studies, genome‐wide association and QTL studies, as well as genome reports, mutant screens, and advances in methods and technology. The Editorial Board of G3 believes that rapid dissemination of these data is the necessary foundation for analysis that leads to mechanistic insights. G3, published by the Genetics Society of America, meets the critical and growing need of the genetics community for rapid review and publication of important results in all areas of genetics. G3 offers the opportunity to publish the puzzling finding or to present unpublished results that may not have been submitted for review and publication due to a perceived lack of a potential high-impact finding. G3 has earned the DOAJ Seal, which is a mark of certification for open access journals, awarded by DOAJ to journals that achieve a high level of openness, adhere to Best Practice and high publishing standards.
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