scholarly journals Genomic Selection in Sugarcane: Current Status and Future Prospects

2021 ◽  
Vol 12 ◽  
Author(s):  
Channappa Mahadevaiah ◽  
Chinnaswamy Appunu ◽  
Karen Aitken ◽  
Giriyapura Shivalingamurthy Suresha ◽  
Palanisamy Vignesh ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Genomic Prediction ◽  
Gene Bank ◽  
Genomic Diversity ◽  
Cane Yield ◽  
Genetic Base ◽  
Genome Wide ◽  
Breeding Programmes ◽  
Base Broadening ◽  
Genetic Base Broadening

Sugarcane is a C4 and agro-industry-based crop with a high potential for biomass production. It serves as raw material for the production of sugar, ethanol, and electricity. Modern sugarcane varieties are derived from the interspecific and intergeneric hybridization between Saccharum officinarum, Saccharum spontaneum, and other wild relatives. Sugarcane breeding programmes are broadly categorized into germplasm collection and characterization, pre-breeding and genetic base-broadening, and varietal development programmes. The varietal identification through the classic breeding programme requires a minimum of 12–14 years. The precise phenotyping in sugarcane is extremely tedious due to the high propensity of lodging and suckering owing to the influence of environmental factors and crop management practices. This kind of phenotyping requires data from both plant crop and ratoon experiments conducted over locations and seasons. In this review, we explored the feasibility of genomic selection schemes for various breeding programmes in sugarcane. The genetic diversity analysis using genome-wide markers helps in the formation of core set germplasm representing the total genomic diversity present in the Saccharum gene bank. The genome-wide association studies and genomic prediction in the Saccharum gene bank are helpful to identify the complete genomic resources for cane yield, commercial cane sugar, tolerances to biotic and abiotic stresses, and other agronomic traits. The implementation of genomic selection in pre-breeding, genetic base-broadening programmes assist in precise introgression of specific genes and recurrent selection schemes enhance the higher frequency of favorable alleles in the population with a considerable reduction in breeding cycles and population size. The integration of environmental covariates and genomic prediction in multi-environment trials assists in the prediction of varietal performance for different agro-climatic zones. This review also directed its focus on enhancing the genetic gain over time, cost, and resource allocation at various stages of breeding programmes.

scholarly journals Genomic prediction using low density marker panels in aquaculture: performance across species, traits, and genotyping platforms

2019 ◽  
Author(s):  
Christina Kriaridou ◽  
Smaragda Tsairidou ◽  
Ross D. Houston ◽  
Diego Robledo
Keyword(s):  
Genomic Selection ◽  
Genomic Prediction ◽  
Species Traits ◽  
Breeding Values ◽  
Genotyping Platform ◽  
Genome Wide ◽  
Breeding Programmes ◽  
Cost Efficient ◽  
Snp Panels ◽  
Different Characteristics

ABSTRACTGenomic selection increases the rate of genetic gain in breeding programmes, which results in significant cumulative improvements in commercially important traits such as disease resistance. Genomic selection currently relies on collecting genome-wide genotype data accross a large number of individuals which requires substantial economic investment. However, global aquaculture production predominantly occurs in small and medium sized enterprises for whom this technology can be prohibitively expensive. For genomic selection to benefit these aquaculture sectors more cost-efficient genotyping is necessary. In this study the utility of low and medium density SNP panels (ranging from 100 to 9000 SNPs) to accurate predict breeding values was tested and compared in four aquaculture datasets with different characteristics (species, genome size, genotyping platform, family number and size, total population size, and target trait). A consistent pattern of genomic prediction accuracy was observed across species, with little or no reduction until SNP density was reduced below 1,000 SNPs. Below this SNP density, heritability estimates and genomic prediction accuracies tended to be lower and more variable (93 % of maximum accuracy achieved with 1,000 SNPs, 89 % with 500 SNPs, and 70% with 100 SNPs). Now that a multitude of studies have highlighted the benefits of genomic over pedigree-based prediction of breeding values in aquaculture species, the results of the current study highlight that these benefits can be achieved at lower SNP densities and at lower cost, raising the possibility of a broader application of genetic improvement in smaller and more fragmented aquaculture settings.

scholarly journals A sorghum Practical Haplotype Graph facilitates genome-wide imputation and cost-effective genomic prediction

2019 ◽  
Author(s):  
Sarah E. Jensen ◽  
Jean Rigaud Charles ◽  
Kebede Muleta ◽  
Peter Bradbury ◽  
Terry Casstevens ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Genomic Prediction ◽  
Sequence Data ◽  
Input Sequence ◽  
Genotyping By Sequencing ◽  
Cost Effective ◽  
Genome Wide ◽  
Variant Information ◽  
Sequencing Platforms ◽  
Low Coverage

AbstractSuccessful management and utilization of increasingly large genomic datasets is essential for breeding programs to increase genetic gain and accelerate cultivar development. To help with data management and storage, we developed a sorghum Practical Haplotype Graph (PHG) pangenome database that stores all identified haplotypes and variant information for a given set of individuals. We developed two PHGs in sorghum, one with 24 individuals and another with 398 individuals, that reflect the diversity across genic regions of the sorghum genome. 24 founders of the Chibas sorghum breeding program were sequenced at low coverage (0.01x) and processed through the PHG to identify genome-wide variants. The PHG called SNPs with only 5.9% error at 0.01x coverage - only 3% lower than its accuracy when calling SNPs from 8x coverage sequence. Additionally, 207 progeny from the Chibas genomic selection (GS) training population were sequenced and processed through the PHG. Missing genotypes in the progeny were imputed from the parental haplotypes available in the PHG and used for genomic prediction. Mean prediction accuracies with PHG SNP calls range from 0.57-0.73 for different traits, and are similar to prediction accuracies obtained with genotyping-by-sequencing (GBS) or markers from sequencing targeted amplicons (rhAmpSeq). This study provides a proof of concept for using a sorghum PHG to call and impute SNPs from low-coverage sequence data and also shows that the PHG can unify genotype calls from different sequencing platforms. By reducing the amount of input sequence needed, the PHG has the potential to decrease the cost of genotyping for genomic selection, making GS more feasible and facilitating larger breeding populations that can capture maximum recombination. Our results demonstrate that the PHG is a useful research and breeding tool that can maintain variant information from a diverse group of taxa, store sequence data in a condensed but readily accessible format, unify genotypes from different genotyping methods, and provide a cost-effective option for genomic selection for any species.

Genetic base-broadening of cacao for precocity and cropping efficiency

2016 ◽  
Vol 15 (6) ◽  
pp. 548-557 ◽  
Author(s):  
Francis Kwame Padi ◽  
Atta Ofori ◽  
Abraham Akpertey
Keyword(s):  
West Africa ◽  
Improvement Strategy ◽  
Cross Sectional ◽  
Genetic Groups ◽  
Genetic Base ◽  
Productivity Increase ◽  
Base Broadening ◽  
Bean Yield ◽  
Variety Performance ◽  
Genetic Base Broadening

AbstractIntroduction of clones from genetic groups that are underrepresented in the pedigree of commercial cacao varieties in West Africa represents an important aspect of cacao improvement strategy of broadening the genetic base to overcome current yield stagnation of the crop. The objective of the present study was to determine the combining abilities of more recently introduced cacao clones for yield and cropping efficiency in the early bearing years. Seven recently introduced clones were crossed as males to five clones commonly used in the seed gardens in Ghana using a North Carolina II design. The 35 F1 varieties and one commercial variety were evaluated in the field from June 2010 to March 2015 for four traits: increase in trunk cross-sectional area in the juvenile, and in the pod-bearing phases, bean yield and cropping efficiency. Though both GCA and SCA variances were significant for all traits, the ratios of GCA:SCA were much smaller than unity, indicating the importance of non-additive effects in the control of the traits. Among the set of clones therefore, prediction of F1 variety performance cannot be based on the GCA or per se (average) performance of the clones. Six varieties were more precocious, and eight had higher cropping efficiencies than the standard variety. Bean yields ranged from 0.74 to 1.05 t/ha/year in the fourth and fifth years after planting among the top six varieties. The study provides evidence of the large potential for productivity increase through the use of cacao clones beyond Pound's early introductions into West Africa.

scholarly journals A classic approach for determining genomic prediction accuracy under terminal drought stress and well-watered conditions in wheat landraces and cultivars

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0247824
Author(s):  
Morteza Shabannejad ◽  
Mohammad-Reza Bihamta ◽  
Eslam Majidi-Hervan ◽  
Hadi Alipour ◽  
Asa Ebrahimi
Keyword(s):  
Drought Stress ◽  
Genomic Selection ◽  
Bread Wheat ◽  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Terminal Drought ◽  
Genome Wide ◽  
Best Linear Unbiased ◽  
Terminal Drought Stress ◽  
Trait Associations

The present study aimed to improve the accuracy of genomic prediction of 16 agronomic traits in a diverse bread wheat (Triticum aestivum L.) germplasm under terminal drought stress and well-watered conditions in semi-arid environments. An association panel including 87 bread wheat cultivars and 199 landraces from Iran bread wheat germplasm was planted under two irrigation systems in semi-arid climate zones. The whole association panel was genotyped with 9047 single nucleotide polymorphism markers using the genotyping-by-sequencing method. A number of 23 marker-trait associations were selected for traits under each condition, whereas 17 marker-trait associations were common between terminal drought stress and well-watered conditions. The identified marker-trait associations were mostly single nucleotide polymorphisms with minor allele effects. This study examined the effect of population structure, genomic selection method (ridge regression-best linear unbiased prediction, genomic best-linear unbiased predictions, and Bayesian ridge regression), training set size, and type of marker set on genomic prediction accuracy. The prediction accuracies were low (-0.32) to moderate (0.52). A marker set including 93 significant markers identified through genome-wide association studies with P values ≤ 0.001 increased the genomic prediction accuracy for all traits under both conditions. This study concluded that obtaining the highest genomic prediction accuracy depends on the extent of linkage disequilibrium, the genetic architecture of trait, genetic diversity of the population, and the genomic selection method. The results encouraged the integration of genome-wide association study and genomic selection to enhance genomic prediction accuracy in applied breeding programs.

scholarly journals Global Wild Annual Lens Collection: A Potential Resource for Lentil Genetic Base Broadening and Yield Enhancement

PLoS ONE ◽  
2014 ◽  
Vol 9 (9) ◽  
pp. e107781 ◽  
Author(s):  
Mohar Singh ◽  
Ishwari Singh Bisht ◽  
Sandeep Kumar ◽  
Manoranjan Dutta ◽  
Kailash Chander Bansal ◽  
...  
Keyword(s):  
Yield Enhancement ◽  
Genetic Base ◽  
Base Broadening ◽  
Potential Resource ◽  
Genetic Base Broadening

scholarly journals The impact of genetic relationship between training and validation populations on genomic prediction accuracy in Atlantic salmon

2021 ◽  
Author(s):  
Clemence Fraslin ◽  
Jose Yanez ◽  
Diego Robledo ◽  
Ross D. Houston
Keyword(s):  
Body Weight ◽  
Atlantic Salmon ◽  
Genomic Selection ◽  
Genetic Relationship ◽  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Genetic Relationships ◽  
Sea Lice ◽  
Breeding Programmes ◽  
The Impact

The potential of genomic selection to improve production traits has been widely demonstrated in many aquaculture species. Atlantic salmon breeding programmes typically consist of sibling testing schemes, where traits that cannot be measured on the selection candidates are measured on the candidates' siblings (such as disease resistance traits). While annual testing on close relatives is effective, it is expensive due to high genotyping and phenotyping costs. Therefore, accurate prediction of breeding values in distant relatives could significantly reduce the cost of genomic selection. The aims of this study were (i) to evaluate the impact of decreasing the genomic relationship between the training and validation populations on the accuracy of genomic prediction for two key target traits; body weight and resistance to sea lice; and (ii) to assess the interaction of genetic relationship with SNP density, a major determinant of genotyping costs. Phenotype and genotype data from two year classes of a commercial breeding population of Atlantic salmon were used. The accuracy of genomic predictions obtained within a year class was similar to that obtained combining the data from the two year classes for sea lice count (0.49 - 0.48) and body weight (0.63 - 0.61), but prediction accuracy was close to zero when the prediction was performed across year groups. Systematically reducing the relatedness between the training and validation populations within a year class resulted in decreasing accuracy of genomic prediction; when the training and validation populations were set up to contain no relatives with genomic relationships >0.3, the accuracies fell from 0.48 to 0.27 for sea lice count and from 0.63 to 0.29 for body weight. Lower relatedness between training and validation populations also tended to result in highly biased predictions. No clear interaction between decreasing SNP density and relatedness between training and validation population was found. These results confirm the importance of genetic relationships between training and selection candidate populations in salmon breeding programmes, and suggests that prediction across generations using existing approaches would severely compromise the efficacy of genomic selection.

Sign in / Sign up

Export Citation Format

Share Document