Using visual scores for genomic prediction of complex traits in breeding programs

Azevedo, Camila Ferreira; Ferrão, Luis Felipe Ventorim; Benevenuto, Juliana; de Resende, Marcos Deon Vilela; Nascimento, Moyses; Nascimento, Ana Carolina Campana; Munoz, Patricio R.

doi:10.1007/s00122-023-04512-w

Cited by 4 publications

(3 citation statements)

References 45 publications

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“…We considered such traits as normal traits, even if the assumption of normality was strongly violated. Recently, Azevedo et al (2023) showed that using a linear mixed model for GP of ordinal traits was robust but suboptimal. They advised using Bayesian Ordinal Regression Models, even though it is computationally demanding.…”

Section: Discussionmentioning

confidence: 99%

Enhancing grapevine breeding efficiency through genomic prediction and selection index

Brault,

Segura,

Roques

et al. 2024

G3: Genes, Genomes, Genetics

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Grapevine (Vitis vinifera) breeding reaches a critical point. New cultivars are released every year with resistance to powdery and downy mildews. However, the traditional process remains time-consuming, taking 20 to 25 years, and demands the evaluation of new traits to enhance grapevine adaptation to climate change. Until now, the selection process has relied on phenotypic data and a limited number of molecular markers for simple genetic traits such as resistance to pathogens, without a clearly defined ideotype and was carried out on a large scale. To accelerate the breeding process and address these challenges, we investigated the use of genomic prediction, a methodology using molecular markers to predict genotypic values. In our study, we focused on two existing grapevine breeding programs: Rosé wine and Cognac production. In these programs, several families were created through crosses of emblematic and inter-specific resistant varieties to powdery and downy mildews. 30 traits were evaluated for each program, using two genomic prediction methods: GBLUP (Genomic Best Linear Unbiased Predictor) and LASSO (Least Absolute Shrinkage Selection Operator). The results revealed substantial variability in predictive abilities across traits, ranging from 0 to 0.9. These discrepancies could be attributed to factors such as trait heritability and trait characteristics. Moreover, we explored the potential of across-population genomic prediction by leveraging other grapevine populations as training sets. Integrating genomic prediction allowed us to identify superior individuals for each program, using multivariate selection index method. The ideotype for each breeding program was defined collaboratively with representatives from the wine-growing sector.

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Section: Discussionmentioning

confidence: 99%

Enhancing grapevine breeding efficiency through genomic prediction and selection index

Brault,

Segura,

Roques

et al. 2024

G3: Genes, Genomes, Genetics

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“…Phenotypic plasticity has also been shown to play a key role in phenotypic variation in the expression of various key agronomic traits under different environmental conditions [20], which also motivates the genetic analysis of complex agronomic traits. Through innovative applications of genome-wide association studies (GWAS) [21][22][23], high-resolution genomic selection strategies [24][25][26], machine learning and bioinformatics tools [27], and the revolutionary advent of CRISPR-Cas9 gene editing technology [11,28,29], we have moved closer to unlocking the full potential of these critical crops. This Special Issue captures complex traits and molecular selection in annual crops and contains five articles, which I will briefly describe in the following paragraphs.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Complex Traits and Molecular Selection in Annual Crops

Shen

2024

Agronomy

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“…In this case, available methods involve 2-dimensional (2D) image analysis and 3D modeling, which all have the capability of producing quantitative traits. In many cases, the utilization of quantitative traits derived from these imaging approaches has proven to be more effective in genetics research and breeding compared to categorical traits [ 13 ]. Depending on the algorithm, some studies have focused on berry detection while others have focused only on whole cluster analysis.…”

Section: Introductionmentioning

confidence: 99%

Segment Anything for Comprehensive Analysis of Grapevine Cluster Architecture and Berry Properties

Torres-Lomas,

Lado-Bega,

Garcia-Zamora

et al. 2024

Plant Phenomics

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Grape cluster architecture and compactness are complex traits influencing disease susceptibility, fruit quality, and yield. Evaluation methods for these traits include visual scoring, manual methodologies, and computer vision, with the latter being the most scalable approach. Most of the existing computer vision approaches for processing cluster images often rely on conventional segmentation or machine learning with extensive training and limited generalization. The Segment Anything Model (SAM), a novel foundation model trained on a massive image dataset, enables automated object segmentation without additional training. This study demonstrates out-of-the-box SAM’s high accuracy in identifying individual berries in 2-dimensional (2D) cluster images. Using this model, we managed to segment approximately 3,500 cluster images, generating over 150,000 berry masks, each linked with spatial coordinates within their clusters. The correlation between human-identified berries and SAM predictions was very strong (Pearson’s r 2 = 0.96). Although the visible berry count in images typically underestimates the actual cluster berry count due to visibility issues, we demonstrated that this discrepancy could be adjusted using a linear regression model (adjusted R 2 = 0.87). We emphasized the critical importance of the angle at which the cluster is imaged, noting its substantial effect on berry counts and architecture. We proposed different approaches in which berry location information facilitated the calculation of complex features related to cluster architecture and compactness. Finally, we discussed SAM’s potential integration into currently available pipelines for image generation and processing in vineyard conditions.

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Using visual scores for genomic prediction of complex traits in breeding programs

Cited by 4 publications

References 45 publications

Enhancing grapevine breeding efficiency through genomic prediction and selection index

Enhancing grapevine breeding efficiency through genomic prediction and selection index

Analysis of Complex Traits and Molecular Selection in Annual Crops

Segment Anything for Comprehensive Analysis of Grapevine Cluster Architecture and Berry Properties

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