Genomic selection: genome-wide prediction in plant improvement

Desta, Zeratsion Abera; Ortíz, Rodomiro

doi:10.1016/j.tplants.2014.05.006

Cited by 567 publications

(457 citation statements)

References 86 publications

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“…Usually, the increase in the number of individuals in the TP increases the prediction accuracy of the genetic value (Desta and Ortiz, 2014). However, despite the increase in accuracy, when more than 600 individuals was used in the TP, this increment was very low, making it almost null for traits with a heritability of 80-99% in the present study.…”

Section: Training Population Size Versus Estimated Genetic Valuementioning

confidence: 57%

Determination of the optimal number of markers and individuals in a training population necessary for maximum prediction accuracy in F<sub>2</sub> populations by using genomic selection models

Peixoto

Cruz

2016

Genet. Mol. Res.

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ABSTRACT. Genomic selection is a useful technique to assist breeders in selecting the best genotypes accurately. Phenotypic selection in the F 2 generation presents with low accuracy as each genotype is represented by one individual; thus, genomic selection can increase selection accuracy at this stage of the breeding program. This study aimed to establish the optimal number of individuals required to compose the training population and to establish the amount of markers necessary to obtain the maximum accuracy by genomic selection methods in F 2 populations. F 2 populations with 1000 individuals were simulated, and six traits were simulated with different heritability values (5, 20, 40, 60, 80 and 99%). Ridge regression best linear unbiased prediction was used in all analyses. Genomic selection models were set by varying the number of individuals in the training population (2 to 1000 individuals) and markers (2 to 3060 markers). Phenotypic accuracy, genotypic accuracy, genetic variance, residual variance, and heritability were evaluated. Greater the number of individuals in the training population, higher was the accuracy; the values of genotypic and residual variances and heritability were close to the optimum value. Higher the heritability of the trait, higher is the number of markers necessary to obtain maximum accuracy, ranging from 200 for the trait with 5% heritability to 900 for the trait with 99% heritability. Therefore, genomic selection models for prediction in F 2 populations must consist of 200 to 900 markers of major effect on the trait and more than 600 individuals in the training population.

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Section: Training Population Size Versus Estimated Genetic Valuementioning

confidence: 57%

Determination of the optimal number of markers and individuals in a training population necessary for maximum prediction accuracy in F<sub>2</sub> populations by using genomic selection models

Peixoto

Cruz

2016

Genet. Mol. Res.

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“…This approach should be particularly appealing for outcrossing longlived species like forest trees, for which establishing common gardens is expensive and time consuming. In addition, for taxa with extremely large genomes, such as conifers (Birol et al 2013;Nystedt et al 2013;Neale et al 2014), the use of a candidate gene strategy seems an interesting and feasible costefficient alternative to genome-wide selection (e.g., , for which millions of markers evenly distributed across the genome might be necessary to have a good predictive power (e.g., Desta and Ortiz 2014). In this context, the adequate preselection of candidate Climate PC genes becomes a fundamental step toward capturing a large part of the adaptive/phenotypic variance that needs to be used to perform such predictions.…”

Section: Discussionmentioning

confidence: 99%

Ecological and association genetics in two Mediterranean pine species

Quilón¹

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“…Therefore, the GEBV have this potential ability to capture more of the genetic variation for the particular trait under selection (Newell and Jannink, 2014). Due to its more ability to detect the markers based on whole-genome predictions and to generate more accurate results, GS can replace phenotypic selection or marker-assisted breeding protocols (Desta and Ortiz, 2014). However, the combination of these methods may result in better conclusion.…”

Section: Genomic Selectionmentioning

confidence: 99%

Genomic and transcriptomic approaches toward plant selection

Jazayeri¹,

Torres

2017

JSR

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Abstract-Omics era has opened a new window to biology. Genomics and transcriptomics are two well-known fields by which plant selection and breeding are fulfilled more easily and accurately. They provide useful information about genes, transcripts, their functions those are the principal data for other subsequent approaches. Reference genomes of various plants are available and facilitate genome-based studies. The complex of genomic, transcriptomic data and the findings from variant methods like QTLs (quantitative trait loci), SNPs (single nucleotide polymorphism), CNVs (copy number variant), resequencing, GBS (genome-by-sequencing) are extremely important for plant selection in terms of price and time. The new workflows are routinely using different approaches and mixing them based on the genomic/transcriptomic information in their subsequent steps. They, however, are validated during the whole process toward screening genotypes possessing agronomically important desired trait. SNP-Seq presented hereinafter is a new approach for analyzing plants toward selection and screening by SNP sequencing in various genotypes simultaneously. It can accelerate the cycle of plant selection from genotypes to phenotypes in a reverse engineering way.Keywords:-Genomics, Omics, Plant Selection, Plant Improvement, TranscriptomicsResumen-La era Omica ha abierto una nueva ventana a la biología. La genómica y la transcriptómica son dos campos conocidos, con los cuales, la selección y el mejoramiento de plantas se cumplen con mayor facilidad y precisión. Proporcionan informaciónútil sobre los genes, las transcripciones, sus funciones y sirven como datos primordiales para otros enfoques posteriores. Los genomas de referencia de varias plantas han sido secuenciados, y están disponibles, facilitando así el acceso a informaciónómica indispensable para llevar a cabo estudios basados en estos mismos genomas. El total de datos genómicos, transcriptómicos y los hallazgos de métodos variantes que van desde QTL (rasgo cuantitativo), PSN (polimorfismo de un solo nucleótido), NCV (número de copias variante), GBS (genoma por secuencia) son extremadamente importantes para la selección y el mejoramiento de plantas en términos de precio y tiempo. Los nuevos flujos de trabajo utilizan diferentes enfoques basados en la información genómica / transcriptómica en pasos posteriores mezclándolos y se validan durante todo el proceso para seleccionar genotipos que posean un rasgo deseado agronómicamente importante. SNP-Seq, que se presenta en este artículo, es un nuevo enfoque para analizar las plantas hacia la selección y la detección mediante secuenciación de SNP en varios genotipos simultáneamente. Este proceso puede acelerar el ciclo de selección de plantas desde los genotipos a los fenotipos en una forma de ingeniería inversa.

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Genomic selection: genome-wide prediction in plant improvement

Cited by 567 publications

References 86 publications

Determination of the optimal number of markers and individuals in a training population necessary for maximum prediction accuracy in F<sub>2</sub> populations by using genomic selection models

Determination of the optimal number of markers and individuals in a training population necessary for maximum prediction accuracy in F<sub>2</sub> populations by using genomic selection models

Ecological and association genetics in two Mediterranean pine species

Genomic and transcriptomic approaches toward plant selection

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