Genomic Selection in Plant Breeding

Newell, Mark A.; Jannink, Jean‐Luc

doi:10.1007/978-1-4939-0446-4_10

Cited by 325 publications

(27 citation statements)

References 20 publications

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“…GEBV is a prediction model that combines the phenotypic data with marker and pedigree data in order to increase the accuracy of prediction. As compared to MAS, GEBV is dependent on all markers including major and minor marker effects [221]. In this technique, genetic markers having the ability to cover the whole genome are selected and utilized in a way that all QTLs are in LD with at least a single marker [222].…”

Section: Genomic Selection (Gs): a Step Forward From Masmentioning

confidence: 99%

DNA molecular markers in plant breeding: current status and recent advancements in genomic selection and genome editing

Nadeem

Nawaz

Shahid

et al. 2017

Biotechnology & Biotechnological Equipment

542

298

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With the development of molecular marker technology in the 1980s, the fate of plant breeding has changed. Different types of molecular markers have been developed and advancement in sequencing technologies has geared crop improvement. To explore the knowledge about molecular markers, several reviews have been published in the last three decades; however, all these reviews were meant for researchers with advanced knowledge of molecular genetics. This review is intended to be a synopsis of recent developments in molecular markers and their applications in plant breeding and is devoted to early researchers with a little or no knowledge of molecular markers. The progress made in molecular plant breeding, genetics, genomic selection and genome editing has contributed to a more comprehensive understanding of molecular markers and provided deeper insights into the diversity available for crops and greatly complemented breeding stratagems. Genotyping-by-sequencing and association mapping based on next-generation sequencing technologies have facilitated the identification of novel genetic markers for complex and unstructured populations. Altogether, the history, the types of markers, their application in plant sciences and breeding, and some recent advancements in genomic selection and genome editing are discussed.

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Section: Genomic Selection (Gs): a Step Forward From Masmentioning

confidence: 99%

DNA molecular markers in plant breeding: current status and recent advancements in genomic selection and genome editing

Nadeem

Nawaz

Shahid

et al. 2017

Biotechnology & Biotechnological Equipment

542

298

View full text Add to dashboard Cite

show abstract

“…In contrast to GWAS, GS does not test for significance but rather uses genomewide marker data to calculate the GEBV. More genetic variation could be captured in GS compared with marker-assisted selection as all markers are used in estimating breeding values [20]. In GS, a prediction model is first trained using a training population that has both genotypic and phenotypic information [21].…”

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confidence: 99%

Insights into the Genetic Architecture of Phenotypic Stability Traits in Winter Wheat

Lozada

Carter

2020

Agronomy

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Examining the architecture of traits through genomics is necessary to gain a better understanding of the genetic loci affecting important traits to facilitate improvement. Genomewide association study (GWAS) and genomic selection (GS) were implemented for grain yield, heading date, and plant height to gain insights into the genetic complexity of phenotypic stability of traits in a diverse population of US Pacific Northwest winter wheat. Analysis of variance using the Additive Main Effect and Multiplicative Interaction (AMMI) approach revealed significant genotype and genotype by environment interactions. GWAS identified 12 SNP markers distributed across 10 chromosomes affecting variation for both trait and phenotypic stability, indicating potential pleiotropic effects and signifying that similar genetic loci could be associated with different aspects of stability. The lack of stable and major effect loci affecting phenotypic variation supports the complexity of stability of traits. Accuracy of GS was low to moderate, between 0.14 and 0.66, indicating that phenotypic stability is under genetic control. The moderate to high correlation between trait and trait stability suggests the potential of simultaneous selection for trait and trait stability. Our results demonstrate the complex genetic architecture of trait stability and show the potential for improving stability in winter wheat using genomic-assisted approaches.Agronomy 2020, 10, 368 2 of 15 the strength of linkage disequilibrium (i.e., the non-random association of alleles at multiple loci) between markers and functional polymorphism across diverse germplasm [10]. In wheat, GWAS has been conducted in different traits such as grain yield, yield components (thousand kernel weight and kernel number), Fusarium head blight resistance, snow mold tolerance, plant height, and heading date [11][12][13][14][15][16]. Compared with biparental mapping, GWAS results in a higher mapping resolution as it exploits the majority of the recombination histories of the individuals belonging to the diverse population used [17,18]. The main drawback of GWAS, nonetheless, is that it might not able to capture the causative and rare variants (loci) with small effects, and therefore would be a special case of missing heritability, i.e., the portion of genetic variance that cannot be explained by all significant loci [19]. In this case, genomic selection (GS) is seen as a complementary approach to GWAS.GS uses genomewide markers to estimate the marker effects and calculate genomic estimated breeding values (GEBV), which represents the genetic "worth" or merit of an individual. A high GEBV for grain yield, for example, would mean that a line is predicted to have better grain yield compared with others. In contrast to GWAS, GS does not test for significance but rather uses genomewide marker data to calculate the GEBV. More genetic variation could be captured in GS compared with marker-assisted selection as all markers are used in estimating breeding values [20]. In GS, a prediction model ...

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“…Such approaches, however, will require highly efficient plant transformation and regeneration processes for lentil (Akcay et al, 2009). Densely distributed genome-wide markers will also support the use of genomic selection strategies (Meuwissen et al, 2001; Newell and Jannink, 2014), in which the genetic merits of individual genotypes within a breeding program are predicted on the basis of a genomic estimated breeding value (GEBV) derived from the summation of contributory gene effects across the genome.…”

Section: Future Prospects For Resistance Breeding In the Genomic Eramentioning

confidence: 99%

Molecular Breeding for Ascochyta Blight Resistance in Lentil: Current Progress and Future Directions

et al. 2017

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Lentil (Lens culinaris Medik.) is a diploid (2n = 2x = 14), self-pollinating, cool-season, grain legume that is cultivated worldwide and is highly valuable due to its high protein content. However, lentil production is constrained by many factors including biotic stresses, majority of which are fungal diseases such as ascochyta blight (AB), fusarium wilt, rust, stemphylium blight, anthracnose, and botrytis gray mold. Among various diseases, AB is a major -problem in many lentil-producing countries and can significantly reduce crop production. Breeding for AB resistance has been a priority for breeding programs across the globe and consequently, a number of resistance sources have been identified and extensively exploited. In order to increase the efficiency of combining genes from different genetic backgrounds, molecular genetic tools can be integrated with conventional breeding methods. A range of genetic linkage maps have been generated based on DNA-based markers, and quantitative trait loci (QTLs) for AB resistance have been identified. Molecular markers linked to these QTLs may potentially be used for efficient pyramiding of the AB disease resistance genes. Significant genomic resources have been established to identify and characterize resistance genes, including an integrated genetic map, expressed sequence tag libraries, gene based markers, and draft genome sequences. These resources are already being utilized for lentil crop improvement, to more effectively select for disease resistance, as a case study of the Australian breeding program will show. The combination of genomic resources, effective molecular genetic tools and high resolution phenotyping tools will improve the efficiency of selection for ascochyta blight resistance and accelerate varietal development of global lentil breeding programs.

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Genomic Selection in Plant Breeding

Cited by 325 publications

References 20 publications

DNA molecular markers in plant breeding: current status and recent advancements in genomic selection and genome editing

DNA molecular markers in plant breeding: current status and recent advancements in genomic selection and genome editing

Insights into the Genetic Architecture of Phenotypic Stability Traits in Winter Wheat

Molecular Breeding for Ascochyta Blight Resistance in Lentil: Current Progress and Future Directions

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