A strategy to identify representative maize core collections based on kernel properties

Risliawati, Andari; Suwarno, Willy Bayuardi; Lestari, Puji; TRIKOESOEMANINGTYAS, TRIKOESOEMANINGTYAS; SOBIR, SOBIR

doi:10.1007/s10722-022-01469-5

Cited by 7 publications

(5 citation statements)

References 37 publications

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“…The representativeness of type 1 and type 2 core collections based on A-NE and E-NE have been confirmed compared with those that were arbitrarily selected [32]. At present, core collections of strawberry (F. × ananassa) [12], hazelnut (Corylus avellana L.) [2], and other species [45,46] have been successfully constructed using this standard. Previous studies have included the establishment of the core collection of Chinese fir mainly based on geographic origin [21,47].…”

Section: Discussionmentioning

confidence: 89%

Construction of a Core Collection of Germplasms from Chinese Fir Seed Orchards

Duan

Wang

et al. 2023

Forests

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Chinese fir (Cunninghamia lanceolata (Lamb.) Hook) is one of the most important tree species for afforestation in China. First-, second-, and third-generation seed orchards of Chinese fir have been established successively, and rich germplasms have been accumulated in the process of genetic improvement. It is necessary to build a core collection of germplasms from Chinese fir seed orchards. In this work, we constructed core collections representing the genetic diversity of Chinese fir seed orchard resources based on SSR data. A total of 607 seed orchard materials from three generations were used to determine the best sampling method and intensity by comparing and analyzing nine methods for constructing core collections. Core Hunter’s multi-strategy optimizes allele coverage and the distance criterion under a 30% sampling intensity (weight: A–NE, 0.7; CV, 0.3 and E–NE, 0.5; CV, 0.5), which is superior to other strategies and was selected as the best method. The two core collections (A–NE&CV73, E–NE&CV55) constructed contained all the alleles of the whole collection and effectively limited the homology in the core collections; each core collection contained 182 accessions. Our findings could contribute greatly towards improving the management of genetic resources in Chinese fir seed orchards and provide elite materials for future studies.

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Section: Discussionmentioning

confidence: 89%

Construction of a Core Collection of Germplasms from Chinese Fir Seed Orchards

Duan

Wang

et al. 2023

Forests

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“…The upper threshold for emergence is just as high in the biennial and predominantly biennial groups, which demonstrates the advantage of evaluating this custom core collection more closely for agronomically important traits. Agromorphological data has been leveraged to create core collections in sweet potato ( Huamán et al., 1999 ), potato ( Huamán et al., 2000 ), groundnut ( Upadhyaya et al., 2003 ), pigeonpea ( Reddy et al., 2005 ), maize ( Malosetti & Abadie, 2001 ; Li et al., 2005 ; Risliawati et al., 2023 ), safflower ( Dwivedi et al., 2005 ), yam ( Girma et al., 2018 ), walnut ( Mahmoodi et al., 2019 ), pomegranate ( Razi et al., 2021 ), lentil ( Tripathi et al., 2022 ), and Indian mustard ( Nanjundan et al., 2022 ). Corak compared methods for creating custom core collections in a subset of 433 accessions from our study’s carrot diversity panel, and found that custom methods combined with representative methods built cores balanced for genetic representation and enriched for desirable phenotypes, though it is important to note that carrot has low population structure ( Corak et al., 2019 ; Corak, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Advancing utilization of diverse global carrot (Daucus carota L.) germplasm with flowering habit trait ontology

Loarca,

Liou,

Dawson

et al. 2024

Front. Plant Sci.

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Biennial vegetable crops are challenging to breed due to long breeding cycle times. At the same time, it is important to preserve a strong biennial growth habit, avoiding premature flowering that renders the crop unmarketable. Gene banks carry important genetic variation which may be essential to improve crop resilience, but these collections are underutilized due to lack of characterization for key traits like bolting tendency for biennial vegetable crops. Due to concerns about introducing undesirable traits such as premature flowering into elite germplasm, many accessions may not be considered for other key traits that benefit growers, leaving crops more vulnerable to pests, diseases, and abiotic stresses. In this study, we develop a method for characterizing flowering to identify accessions that are predominantly biennial, which could be incorporated into biennial breeding programs without substantially increasing the risk of annual growth habits. This should increase the use of these accessions if they are also sources of other important traits such as disease resistance. We developed the CarrotOmics flowering habit trait ontology and evaluated flowering habit in the largest (N=695), and most diverse collection of cultivated carrots studied to date. Over 80% of accessions were collected from the Eurasian supercontinent, which includes the primary and secondary centers of carrot diversity. We successfully identified untapped genetic diversity in biennial carrot germplasm (n=197 with 0% plants flowering) and predominantly-biennial germplasm (n=357 with <15% plants flowering). High broad-sense heritability for flowering habit (0.81 < H2< 0.93) indicates a strong genetic component of this trait, suggesting that these carrot accessions should be consistently biennial. Breeders can select biennial plants and eliminate annual plants from a predominantly biennial population. The establishment of the predominantly biennial subcategory nearly doubles the availability of germplasm with commercial potential and accounts for 54% of the germplasm collection we evaluated. This subcollection is a useful source of genetic diversity for breeders. This method could also be applied to other biennial vegetable genetic resources and to introduce higher levels of genetic diversity into commercial cultivars, to reduce crop genetic vulnerability. We encourage breeders and researchers of biennial crops to optimize this strategy for their particular crop.

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“…This percentage corresponds to the average size for core collection development, which is 10%, which is considered to represent the entire collection [33,34]. Previous research on maize core collection development at the same genebank reported that a similar number (11.7%) had retained the entire collection's maximum diversity [35].…”

Section: Linear Discriminant Analysismentioning

confidence: 99%

Enhancing Maize Germplasm Selection for Genebanks: A Decision Support System Combining Shannon-Weaver Diversity Index and Machine Learning

Adnan,

Suryana,

Aziz

et al. 2024

IJDNE

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While core collections offer various advantages, achieving a balance between representing genetic diversity and ensuring the practical manageability of the entire genebank collection is crucial. This study investigated the utility of the Shannon-Weaver diversity index and machine learning as a support system for the acceptability of new maize accessions into the genebank collection. This study examined 1279 maize germplasm accessions from the Agricultural Genebank Indonesia. The maize germplasm collection was divided into two parts. The first part, namely subset A, contains 600 accessions, which acted as original collections and were randomly selected for calculating the diversity values in each kernel character using the Shannon-Weaver diversity index. The second part, subset B, consisting of 679 accessions, was used to determine whether each accession was similar or different from subset A using an Excel macro-based application built by the authors. Principal component analysis (PCA) and Mahalanobis distances were used in the first step to identify outliers in data points with nine independent variables, namely kernel type, kernel color, presence of white cap, mottled type, kernel upper surface shape, kernel weight, kernel length, width, and thickness. Seventeen outlier samples, detected through PCA and Mahalanobis distances, were intentionally excluded from the dataset to ensure the integrity of subsequent machine learning analyses. The subset B was then divided into two parts to perform conventional and novel machine learning, i.e., linear discriminant analysis (LDA) and Tabnet. The results of this study show that the model's accuracy of LDA and TabNet were 89.36% and 86.4%, respectively. By integrating the Shannon-Weaver diversity index and machine learning methodologies, this research offers a comprehensive decision support system for guiding the acceptance of new maize accessions. The proposed system implies optimizing genebank strategies for the integrity and adaptability of maize germplasm collections.

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A strategy to identify representative maize core collections based on kernel properties

Cited by 7 publications

References 37 publications

Construction of a Core Collection of Germplasms from Chinese Fir Seed Orchards

Construction of a Core Collection of Germplasms from Chinese Fir Seed Orchards

Advancing utilization of diverse global carrot (Daucus carota L.) germplasm with flowering habit trait ontology

Enhancing Maize Germplasm Selection for Genebanks: A Decision Support System Combining Shannon-Weaver Diversity Index and Machine Learning

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