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1.
J Integr Plant Biol ; 65(1): 117-132, 2023 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-36218273

RESUMEN

Advances in plant phenotyping technologies are dramatically reducing the marginal costs of collecting multiple phenotypic measurements across several time points. Yet, most current approaches and best statistical practices implemented to link genetic and phenotypic variation in plants have been developed in an era of single-time-point data. Here, we used time-series phenotypic data collected with an unmanned aircraft system for a large panel of soybean (Glycine max (L.) Merr.) varieties to identify previously uncharacterized loci. Specifically, we focused on the dissection of canopy coverage (CC) variation from this rich data set. We also inferred the speed of canopy closure, an additional dimension of CC, from the time-series data, as it may represent an important trait for weed control. Genome-wide association studies (GWASs) identified 35 loci exhibiting dynamic associations with CC across developmental stages. The time-series data enabled the identification of 10 known flowering time and plant height quantitative trait loci (QTLs) detected in previous studies of adult plants and the identification of novel QTLs influencing CC. These novel QTLs were disproportionately likely to act earlier in development, which may explain why they were missed in previous single-time-point studies. Moreover, this time-series data set contributed to the high accuracy of the GWASs, which we evaluated by permutation tests, as evidenced by the repeated identification of loci across multiple time points. Two novel loci showed evidence of adaptive selection during domestication, with different genotypes/haplotypes favored in different geographic regions. In summary, the time-series data, with soybean CC as an example, improved the accuracy and statistical power to dissect the genetic basis of traits and offered a promising opportunity for crop breeding with quantitative growth curves.


Asunto(s)
Estudio de Asociación del Genoma Completo , Glycine max , Mapeo Cromosómico , Glycine max/genética , Factores de Tiempo , Fitomejoramiento , Fenotipo , Polimorfismo de Nucleótido Simple
2.
Plant Commun ; : 101010, 2024 Jun 25.
Artículo en Inglés | MEDLINE | ID: mdl-38918950

RESUMEN

A genome-wide association study (GWAS) identifies trait-associated loci, but identifying the causal genes can be a bottleneck, due in part to slow decay of linkage disequilibrium (LD). A transcriptome-wide association study (TWAS) addresses this issue by identifying gene expression-phenotype associations or integrating gene expression quantitative trait loci with GWAS results. Here, we used self-pollinated soybean (Glycine max [L.] Merr.) as a model to evaluate the application of TWAS to the genetic dissection of traits in plant species with slow LD decay. We generated RNA sequencing data for a soybean diversity panel and identified the genetic expression regulation of 29 286 soybean genes. Different TWAS solutions were less affected by LD and were robust to the source of expression, identifing known genes related to traits from different tissues and developmental stages. The novel pod-color gene L2 was identified via TWAS and functionally validated by genome editing. By introducing a new exon proportion feature, we significantly improved the detection of expression variations that resulted from structural variations and alternative splicing. As a result, the genes identified through our TWAS approach exhibited a diverse range of causal variations, including SNPs, insertions or deletions, gene fusion, copy number variations, and alternative splicing. Using this approach, we identified genes associated with flowering time, including both previously known genes and novel genes that had not previously been linked to this trait, providing insights complementary to those from GWAS. In summary, this study supports the application of TWAS for candidate gene identification in species with low rates of LD decay.

3.
Front Plant Sci ; 13: 1012293, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-36589058

RESUMEN

The estimation of yield parameters based on early data is helpful for agricultural policymakers and food security. Developments in unmanned aerial vehicle (UAV) platforms and sensor technology help to estimate yields efficiency. Previous studies have been based on less cultivars (<10) and ideal experimental environments, it is not available in practical production. Therefore, the objective of this study was to estimate the yield parameters of soybean (Glycine max (L.) Merr.) under lodging conditions using RGB information. In this study, 17 time point data throughout the soybean growing season in Nanchang, Jiangxi Province, China, were collected, and the vegetation index, texture information, canopy cover, and crop height were obtained by UAV-image processing. After that, partial least squares regression (PLSR), logistic regression (Logistic), random forest regression (RFR), support vector machine regression (SVM), and deep learning neural network (DNN) were used to estimate the yield parameters. The results can be summarized as follows: (1) The most suitable time point to estimate the yield was flowering stage (48 days), which was when most of the soybean cultivars flowered. (2) The multiple data fusion improved the accuracy of estimating the yield parameters, and the texture information has a high potential to contribute to the estimation of yields, and (3) The DNN model showed the best accuracy of training (R2=0.66 rRMSE=32.62%) and validation (R2=0.50, rRMSE=43.71%) datasets. In conclusion, these results provide insights into both best estimate period selection and early yield estimation under lodging condition when using remote sensing.

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