scholarly journals Genomic Selection in Tropical Forage Grasses: Current Status and Future Applications

2021 ◽  
Vol 12 ◽  
Author(s):  
Rosangela M. Simeão ◽  
Marcos D. V. Resende ◽  
Rodrigo S. Alves ◽  
Marco Pessoa-Filho ◽  
Ana Luisa S. Azevedo ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Current Status ◽  
World Population ◽  
Genome Wide Association Studies ◽  
Primary Role ◽  
Forage Grasses ◽  
Breeding Programs ◽  
Genetic Gains ◽  
Tropical Forage ◽  
Forage Species

The world population is expected to be larger and wealthier over the next few decades and will require more animal products, such as milk and beef. Tropical regions have great potential to meet this growing global demand, where pasturelands play a major role in supporting increased animal production. Better forage is required in consonance with improved sustainability as the planted area should not increase and larger areas cultivated with one or a few forage species should be avoided. Although, conventional tropical forage breeding has successfully released well-adapted and high-yielding cultivars over the last few decades, genetic gains from these programs have been low in view of the growing food demand worldwide. To guarantee their future impact on livestock production, breeding programs should leverage genotyping, phenotyping, and envirotyping strategies to increase genetic gains. Genomic selection (GS) and genome-wide association studies play a primary role in this process, with the advantage of increasing genetic gain due to greater selection accuracy, reduced cycle time, and increased number of individuals that can be evaluated. This strategy provides solutions to bottlenecks faced by conventional breeding methods, including long breeding cycles and difficulties to evaluate complex traits. Initial results from implementing GS in tropical forage grasses (TFGs) are promising with notable improvements over phenotypic selection alone. However, the practical impact of GS in TFG breeding programs remains unclear. The development of appropriately sized training populations is essential for the evaluation and validation of selection markers based on estimated breeding values. Large panels of single-nucleotide polymorphism markers in different tropical forage species are required for multiple application targets at a reduced cost. In this context, this review highlights the current challenges, achievements, availability, and development of genomic resources and statistical methods for the implementation of GS in TFGs. Additionally, the prediction accuracies from recent experiments and the potential to harness diversity from genebanks are discussed. Although, GS in TFGs is still incipient, the advances in genomic tools and statistical models will speed up its implementation in the foreseeable future. All TFG breeding programs should be prepared for these changes.

scholarly journals A Semi-Automated SNP-Based Approach for Contaminant Identification in Biparental Polyploid Populations of Tropical Forage Grasses

2021 ◽  
Vol 12 ◽  
Author(s):  
Felipe Bitencourt Martins ◽  
Aline Costa Lima Moraes ◽  
Alexandre Hild Aono ◽  
Rebecca Caroline Ulbricht Ferreira ◽  
Lucimara Chiari ◽  
...  
Keyword(s):  
Principal Component ◽  
Genotyping By Sequencing ◽  
Snp Markers ◽  
Correct Identification ◽  
Forage Grasses ◽  
Mendelian Segregation ◽  
Breeding Programs ◽  
Tropical Forage ◽  
Genotypic Analysis ◽  
Shiny App

Artificial hybridization plays a fundamental role in plant breeding programs since it generates new genotypic combinations that can result in desirable phenotypes. Depending on the species and mode of reproduction, controlled crosses may be challenging, and contaminating individuals can be introduced accidentally. In this context, the identification of such contaminants is important to avoid compromising further selection cycles, as well as genetic and genomic studies. The main objective of this work was to propose an automated multivariate methodology for the detection and classification of putative contaminants, including apomictic clones (ACs), self-fertilized individuals, half-siblings (HSs), and full contaminants (FCs), in biparental polyploid progenies of tropical forage grasses. We established a pipeline to identify contaminants in genotyping-by-sequencing (GBS) data encoded as allele dosages of single nucleotide polymorphism (SNP) markers by integrating principal component analysis (PCA), genotypic analysis (GA) measures based on Mendelian segregation, and clustering analysis (CA). The combination of these methods allowed for the correct identification of all contaminants in all simulated progenies and the detection of putative contaminants in three real progenies of tropical forage grasses, providing an easy and promising methodology for the identification of contaminants in biparental progenies of tetraploid and hexaploid species. The proposed pipeline was made available through the polyCID Shiny app and can be easily coupled with traditional genetic approaches, such as linkage map construction, thereby increasing the efficiency of breeding programs.

scholarly journals Molecular advances on agricultural crop improvement to meet current cultivating demands

2019 ◽  
Vol 12 (2) ◽  
pp. 39-60
Author(s):  
T. Margaritopoulou ◽  
D. Milioni
Keyword(s):  
Genomic Selection ◽  
Quantitative Traits ◽  
Seed Quality ◽  
Association Studies ◽  
Crop Improvement ◽  
Genome Wide Association Studies ◽  
Whole Genome ◽  
Agricultural Crop ◽  
Marker Assisted Breeding ◽  
Breeding Programs

Abstract Sunflower, maize and potato are among the world’s principal crops. In order to improve various traits, these crops have been genetically engineered to a great extent. Even though molecular markers for simple traits such as, fertility, herbicide tolerance or specific pathogen resistance have been successfully used in marker-assisted breeding programs for years, agronomical important complex quantitative traits like yield, biotic and abiotic stress resistance and seed quality content are challenging and require whole genome approaches. Collections of genetic resources for these crops are conserved worldwide and represent valuable resources to study complex traits. Nowadays technological advances and the availability of genome sequence have made novel approaches on the whole genome level possible. Molecular breeding, including both transgenic approach and marker-assisted breeding have facilitated the production of large amounts of markers for high density maps and allowed genome-wide association studies and genomic selection in sunflower, maize and potato. Marker-assisted selection related to hybrid performance has shown that genomic selection is a successful approach to address complex quantitative traits and to facilitate speeding up breeding programs in these crops in the future.

In vitro regeneration from leaf-base segments in three genotypes of Urochloa spp.

2018 ◽  
Vol 69 (5) ◽  
pp. 527 ◽  
Author(s):  
Diliane Harumi Yaguinuma ◽  
Luciana Midori Takamori ◽  
Adriana Mendonça de Oliveira ◽  
Luiz Gonzaga Esteves Vieira ◽  
Alessandra Ferreira Ribas
Keyword(s):  
In Vitro Regeneration ◽  
Forage Grasses ◽  
Leaf Base ◽  
Breeding Programs ◽  
Seed Formation ◽  
Regenerated Plants ◽  
Tropical Forage ◽  
Half Strength ◽  
The Media

The key agricultural species of Urochloa P.Beauv. (signal grass), important as tropical forage grasses, are characterised by asexual seed formation (apomixis), and this presents a challenge for breeding programs. Biotechnological approaches could be an option to develop improved cultivars. We evaluated the regenerative potential from three commercial genotypes, U. brizantha cv. Marandu, U. decumbens cv. Basilisk and U. ruziziensis cv. Ruziziensis, by using leaf-base segments as explants. We tested two auxins (2,4-D and picloram) and one cytokinin (TDZ) at four concentrations (1, 2, 3 and 4 mg L–1). Seeds were scarified, peeled and disinfected before inoculation on half-strength MS media in the dark for 14 days. Leaf-base explants were sectioned in thin slices and inoculated into the media. We analysed the number of primary calluses, number of calluses with shoots clusters and the average of regenerated plants. The lowest concentration of auxins tested (1 mg L–1) yielded the highest number of regenerated plants for Marandú and Basilisk, whereas the optimum for Ruziziensis was 2 mg L–1. Medium with higher concentrations of TDZ (4 mg L–1) was required to produce high frequency of plants for all genotypes. Explants cultured on media with TDZ produced very few calluses. These results indicate that the auxins and cytokinin tested can induce plant regeneration from Urochloa leaf-base segments, and may be used to produce transgenic plants in genetic transformation studies.

scholarly journals Current status of genomic evaluation

2020 ◽  
Vol 98 (4) ◽  
Author(s):  
Ignacy Misztal ◽  
Daniela Lourenco ◽  
Andres Legarra
Keyword(s):  
Genomic Selection ◽  
Genomic Data ◽  
Large Data ◽  
Single Step ◽  
Current Status ◽  
Accurate Estimation ◽  
Small Data ◽  
Data Sets ◽  
Genome Wide Association Studies ◽  
Double Counting

Abstract Early application of genomic selection relied on SNP estimation with phenotypes or de-regressed proofs (DRP). Chips of 50k SNP seemed sufficient for an accurate estimation of SNP effects. Genomic estimated breeding values (GEBV) were composed of an index with parent average, direct genomic value, and deduction of a parental index to eliminate double counting. Use of SNP selection or weighting increased accuracy with small data sets but had minimal to no impact with large data sets. Efforts to include potentially causative SNP derived from sequence data or high-density chips showed limited or no gain in accuracy. After the implementation of genomic selection, EBV by BLUP became biased because of genomic preselection and DRP computed based on EBV required adjustments, and the creation of DRP for females is hard and subject to double counting. Genomic selection was greatly simplified by single-step genomic BLUP (ssGBLUP). This method based on combining genomic and pedigree relationships automatically creates an index with all sources of information, can use any combination of male and female genotypes, and accounts for preselection. To avoid biases, especially under strong selection, ssGBLUP requires that pedigree and genomic relationships are compatible. Because the inversion of the genomic relationship matrix (G) becomes costly with more than 100k genotyped animals, large data computations in ssGBLUP were solved by exploiting limited dimensionality of genomic data due to limited effective population size. With such dimensionality ranging from 4k in chickens to about 15k in cattle, the inverse of G can be created directly (e.g., by the algorithm for proven and young) at a linear cost. Due to its simplicity and accuracy, ssGBLUP is routinely used for genomic selection by the major chicken, pig, and beef industries. Single step can be used to derive SNP effects for indirect prediction and for genome-wide association studies, including computations of the P-values. Alternative single-step formulations exist that use SNP effects for genotyped or for all animals. Although genomics is the new standard in breeding and genetics, there are still some problems that need to be solved. This involves new validation procedures that are unaffected by selection, parameter estimation that accounts for all the genomic data used in selection, and strategies to address reduction in genetic variances after genomic selection was implemented.

scholarly journals An Automated SNP-Based Approach for Contaminant Identification in Biparental Polyploid Populations of Tropical Forage Grasses

2021 ◽  
Author(s):  
Felipe Bitencourt Martins ◽  
Aline da Costa Lima Moraes ◽  
Alexandre Hild Aono ◽  
Rebecca Caroline Ulbricht Ferreira ◽  
Lucimara Chiari ◽  
...  
Keyword(s):  
Principal Component ◽  
Genotyping By Sequencing ◽  
Snp Markers ◽  
Correct Identification ◽  
Forage Grasses ◽  
Mendelian Segregation ◽  
Breeding Programs ◽  
Tropical Forage ◽  
Genotypic Analysis ◽  
Shiny App

Artificial hybridization plays a fundamental role in plant breeding programs since it generates new genotypic combinations that can result in desirable phenotypes. Depending on the species and mode of reproduction, controlled crosses may be challenging, and contaminating individuals can be introduced accidentally. In this context, the identification of such contaminants is important to avoid compromising further selection cycles, as well as genetic and genomic studies. The main objective of this work was to propose an automated multivariate methodology for the detection and classification of putative contaminants, including apomictic clones, self-fertilized individuals, half-siblings and full contaminants, in biparental polyploid progenies of tropical forage grasses. We established a pipeline to identify contaminants in genotyping-by-sequencing (GBS) data encoded as allele dosages of single nucleotide polymorphism (SNP) markers by integrating principal component analysis (PCA), genotypic analysis (GA) measures based on Mendelian segregation and clustering analysis (CA). The combination of these methods allowed the correct identification of all contaminants in all simulated progenies and the detection of putative contaminants in three real progenies of tropical forage grasses, providing an easy and promising methodology for the identification of contaminants in biparental progenies of tetraploid and hexaploid species. The proposed pipeline was made available through the polyCID Shiny app and can be easily coupled with traditional genetic approaches, such as linkage map construction, thereby increasing the efficiency of breeding programs.

47 Genomic Studies of Reproductive Performance in American Mink

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 26-27
Author(s):  
Shafagh Valipour ◽  
Karim Karimi ◽  
Younes Miar
Keyword(s):  
Genetic Correlation ◽  
Genomic Selection ◽  
Reproductive Performance ◽  
Genetic Parameters ◽  
Association Studies ◽  
American Mink ◽  
Reproductive Traits ◽  
Gestation Length ◽  
Genome Wide Association Studies ◽  
Breeding Programs

Abstract Improving reproductive efficiency is one of the main breeding goals in mink farming. Understanding the genetics of reproductive performance is essential for the development of effective breeding programs in mink. The objectives of this study are to 1) estimate the phenotypic and genetic parameters for litter sizes (LS), mortality rate at birth. (MB) and gestation-length (GL) traits; 2) perform genome-wide association studies (GWAS) for these reproductive traits; 3) implement GWAS results in the selection of mink for reproductive performance; 4) explore the potential for genomic selection in mink. The detailed reproductive performance on 3,500 female mink has been collected at the Canadian Center for Fur Animal Research at Dalhousie University (Truro, NS, Canada), in which, 1,000 of them will be genotyped with Affymetrix 50k SNP panel. A series of univariate and bivariate analyses were implemented in ASREML software to estimate the genetic and phenotypic parameters. Heritability estimates (±SE) were low-to-moderate, ranged from 0.06±0.02 for total number born to 0.23±0.03 for GL. High positive genetic correlations (±SE) were observed between LS traits, ranged from 0.59±0.18 to 0.85±0.11. There was a moderate genetic correlation (±SE) between MB and total number of kits born (0.46±0.15). However, MB had a favorable strong negative genetic correlation (±SE) with the number of weaned kits (–0.75±0.16). These estimated genetic parameters can be incorporated into Canadian mink breeding programs. Considering the low-to-moderate heritability of reproduction traits, the availability of the mink reference genome and genotyping panel will provide opportunities to accelerate mink breeding through genomics. The results of this project will contribute significantly to the current genetic knowledge of reproductive traits and identify the opportunities for genetic improvement through the application of genomics. The overall project aim is to develop a cost-effective, low-density panel of markers for the implementation of genomic selection for reproductive performance in mink.

166 Livestock Resiliency: Concepts and Approaches

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 89-90
Author(s):  
Christine F Baes ◽  
Filippo Miglior ◽  
Flavio S Schenkel ◽  
Ellen Goddard ◽  
Gerrit Kistemaker ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Large Scale ◽  
Association Studies ◽  
Genetic Correlations ◽  
Performance Testing ◽  
Breeding Value ◽  
Estimation Methods ◽  
Genome Wide Association Studies ◽  
Breeding Programs ◽  
Fertility Traits

Abstract Genetic improvement of health, welfare, efficiency, and fertility traits is challenging due to expensive and fuzzy phenotypes, the polygenic nature of traits, antagonistic genetic correlations to production traits and low heritabilities. Nevertheless, many organizations have introduced large-scale genetic evaluations for such traits in routine selection indexes. Medium and high-density arrays can be applied in genomic selection strategies to improve breeding value accuracy, and also in genome-wide association studies (GWAS) to identify causative mutations responsible for economically important traits. Genomic information is particularly helpful when traits have low heritability. The objective here is to provide a framework for including health, welfare, efficiency, and fertility traits taken from large-scale genetic and genomic analyses and identifying areas of potential improvement in terms of trait definition and performance testing. General tendencies between trait groups confirmed that a number of moderate unfavourable correlations (+/-0.20 or higher) exist between economically important trait complexes and health, welfare, and fertility traits. A number of trait complexes were identified in which “closer-to-biology” phenotypes could provide clear improvements to routine genetic and genomic selection programs. Here we outline development of these phenotypes and describe their collection. While conventional variance component estimation methods have underpinned the genomic component of some traits of economic interest, performance testing for health, welfare, efficiency, and fertility traits remains an elusive goal for breeding programs. Although our results are encouraging, there is much to be done in terms of trait definition and obtaining better measures of physiological parameters for wide-scale application in breeding programs. Close collaboration between veterinarians, physiologists, and geneticists is necessary to attain meaningful advancement in such areas. We would like to acknowledge the support and funding from all national and international partners involved in the RDGP project through the Large Scale Applied Research Project program from Genome Canada

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