Dataset Information

Long-term comparison between index selection and optimal independent culling in plant breeding programs with genomic prediction.

ABSTRACT: In the context of genomic selection, we evaluated and compared breeding programs using either index selection or independent culling for recurrent selection of parents. We simulated a clonally propagated crop breeding program for 20 cycles using either independent culling or an economic index with two unfavourably correlated traits under selection. Cycle time from crossing to selection of parents was kept the same for both strategies. Both methods led to increasingly unfavourable genetic correlations between traits and, compared to independent culling, index selection led to larger changes in the genetic correlation between the two traits. When linkage disequilibrium was not considered, the two methods had similar losses of genetic diversity. Two independent culling approaches were evaluated, one using optimal culling levels and one using the same selection intensity for both traits. Optimal culling levels outperformed the same selection intensity even when traits had the same economic importance. Therefore, accurately estimating optimal culling levels is essential for maximizing gains when independent culling is performed. Once optimal culling levels are achieved, independent culling and index selection lead to comparable genetic gains.

SUBMITTER: Batista LG

PROVIDER: S-EPMC8109766 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Long-term comparison between index selection and optimal independent culling in plant breeding programs with genomic prediction.

Batista Lorena G LG Gaynor Robert Chris RC Margarido Gabriel R A GRA Byrne Tim T Amer Peter P Gorjanc Gregor G Hickey John M JM

PloS one 20210510 5

In the context of genomic selection, we evaluated and compared breeding programs using either index selection or independent culling for recurrent selection of parents. We simulated a clonally propagated crop breeding program for 20 cycles using either independent culling or an economic index with two unfavourably correlated traits under selection. Cycle time from crossing to selection of parents was kept the same for both strategies. Both methods led to increasingly unfavourable genetic correla ...[more]

PMID: 33970915

Similar Datasets

Project description:Allocating resources between population size and replication affects both genetic gain through phenotypic selection and quantitative trait loci detection power and effect estimation accuracy for marker-assisted selection (MAS). It is well known that because alleles are replicated across individuals in quantitative trait loci mapping and MAS, more resources should be allocated to increasing population size compared with phenotypic selection. Genomic selection is a form of MAS using all marker information simultaneously to predict individual genetic values for complex traits and has widely been found superior to MAS. No studies have explicitly investigated how resource allocation decisions affect success of genomic selection. My objective was to study the effect of resource allocation on response to MAS and genomic selection in a single biparental population of doubled haploid lines by using computer simulation. Simulation results were compared with previously derived formulas for the calculation of prediction accuracy under different levels of heritability and population size. Response of prediction accuracy to resource allocation strategies differed between genomic selection models (ridge regression best linear unbiased prediction [RR-BLUP], BayesCπ) and multiple linear regression using ordinary least-squares estimation (OLS), leading to different optimal resource allocation choices between OLS and RR-BLUP. For OLS, it was always advantageous to maximize population size at the expense of replication, but a high degree of flexibility was observed for RR-BLUP. Prediction accuracy of doubled haploid lines included in the training set was much greater than of those excluded from the training set, so there was little benefit to phenotyping only a subset of the lines genotyped. Finally, observed prediction accuracies in the simulation compared well to calculated prediction accuracies, indicating these theoretical formulas are useful for making resource allocation decisions.

Project description:Cowpea is one of the most important grain legumes in sub-Saharan Africa (SSA). It provides strong support to the livelihood of small-scale farmers through its contributions to their nutritional security, income generation and soil fertility enhancement. Worldwide about 6.5 million metric tons of cowpea are produced annually on about 14.5 million hectares. The low productivity of cowpea is attributable to numerous abiotic and biotic constraints. The abiotic stress factors comprise drought, low soil fertility, and heat while biotic constraints include insects, diseases, parasitic weeds, and nematodes. Cowpea farmers also have limited access to quality seeds of improved varieties for planting. Some progress has been made through conventional breeding at international and national research institutions in the last three decades. Cowpea improvement could also benefit from modern breeding methods based on molecular genetic tools. A number of advances in cowpea genetic linkage maps, and quantitative trait loci associated with some desirable traits such as resistance to Striga, Macrophomina, Fusarium wilt, bacterial blight, root-knot nematodes, aphids, and foliar thrips have been reported. An improved consensus genetic linkage map has been developed and used to identify QTLs of additional traits. In order to take advantage of these developments single nucleotide polymorphism (SNP) genotyping is being streamlined to establish an efficient workflow supported by genotyping support service (GSS)-client interactions. About 1100 SNPs mapped on the cowpea genome were converted by LGC Genomics to KASP assays. Several cowpea breeding programs have been exploiting these resources to implement molecular breeding, especially for MARS and MABC, to accelerate cowpea variety improvement. The combination of conventional breeding and molecular breeding strategies, with workflow managed through the CGIAR breeding management system (BMS), promises an increase in the number of improved varieties available to farmers, thereby boosting cowpea production and productivity in SSA.

Project description:Key message Optimal cross selection increases long-term genetic gain of two-part programs with rapid recurrent genomic selection. It achieves this by optimising efficiency of converting genetic diversity into genetic gain through reducing the loss of genetic diversity and reducing the drop of genomic prediction accuracy with rapid cycling. This study evaluates optimal cross selection to balance selection and maintenance of genetic diversity in two-part plant breeding programs with rapid recurrent genomic selection. The two-part program reorganises a conventional breeding program into a population improvement component with recurrent genomic selection to increase the mean value of germplasm and a product development component with standard methods to develop new lines. Rapid recurrent genomic selection has a large potential, but is challenging due to genotyping costs or genetic drift. Here we simulate a wheat breeding program for 20 years and compare optimal cross selection against truncation selection in the population improvement component with one to six cycles per year. With truncation selection we crossed a small or a large number of parents. With optimal cross selection we jointly optimised selection, maintenance of genetic diversity, and cross allocation with AlphaMate program. The results show that the two-part program with optimal cross selection delivered the largest genetic gain that increased with the increasing number of cycles. With four cycles per year optimal cross selection had 78% (15%) higher long-term genetic gain than truncation selection with a small (large) number of parents. Higher genetic gain was achieved through higher efficiency of converting genetic diversity into genetic gain; optimal cross selection quadrupled (doubled) efficiency of truncation selection with a small (large) number of parents. Optimal cross selection also reduced the drop of genomic selection accuracy due to the drift between training and prediction populations. In conclusion optimal cross selection enables optimal management and exploitation of population improvement germplasm in two-part programs.

Project description:The usefulness of genomic prediction (GP) for many animal and plant breeding programs has been highlighted for many studies in the last 20 years. In maize breeding programs, mostly dedicated to delivering more highly adapted and productive hybrids, this approach has been proved successful for both large- and small-scale breeding programs worldwide. Here, we present some of the strategies developed to improve the accuracy of GP in tropical maize, focusing on its use under low budget and small-scale conditions achieved for most of the hybrid breeding programs in developing countries. We highlight the most important outcomes obtained by the University of São Paulo (USP, Brazil) and how they can improve the accuracy of prediction in tropical maize hybrids. Our roadmap starts with the efforts for germplasm characterization, moving on to the practices for mating design, and the selection of the genotypes that are used to compose the training population in field phenotyping trials. Factors including population structure and the importance of non-additive effects (dominance and epistasis) controlling the desired trait are also outlined. Finally, we explain how the source of the molecular markers, environmental, and the modeling of genotype-environment interaction can affect the accuracy of GP. Results of 7 years of research in a public maize hybrid breeding program under tropical conditions are discussed, and with the great advances that have been made, we find that what is yet to come is exciting. The use of open-source software for the quality control of molecular markers, implementing GP, and envirotyping pipelines may reduce costs in an efficient computational manner. We conclude that exploring new models/tools using high-throughput phenotyping data along with large-scale envirotyping may bring more resolution and realism when predicting genotype performances. Despite the initial costs, mostly for genotyping, the GP platforms in combination with these other data sources can be a cost-effective approach for predicting the performance of maize hybrids for a large set of growing conditions.

Project description:BackgroundFamily-based selective breeding programs typically employ both between-family and within-family selection in aquaculture. However, these programs may exhibit a reduced genetic gain in the presence of a genotype by environment interactions (G × E) when employing biosecurity-based breeding schemes (BS), compared to non-biosecurity-based breeding schemes (NBS). Fortunately, genomic selection shows promise in improving genetic gain by taking within-family variance into account. Stochastic simulation was employed to evaluate genetic gain and G × E trends in BS for improving the body weight of L. vannamei, considering selective genotyping strategies for test group (TG) at a commercial farm environment (CE), the number individuals of the selection group (SG) genotyped at nucleus breeding center (NE), and varying levels of G × E.ResultsThe loss of genetic gain in BS ranged from 9.4 to 38.9% in pedigree-based selection and was more pronounced when G × E was stronger, as quantified by a lower genetic correlation for body weight between NE and CE. Genomic selection, particularly with selective genotyping of TG individuals with extreme performance, effectively offset the loss of genetic gain. With a genetic correlation of 0.8, genotyping 20 SG individuals in each candidate family achieved 93.2% of the genetic gain observed for NBS. However, when the genetic correlation fell below 0.5, the number of genotyped SG individuals per family had to be increased to 50 or more. Genetic gain improved by on average 9.4% when the number of genotyped SG individuals rose from 20 to 50, but the increase in genetic gain averaged only 2.4% when expanding from 50 to 80 individuals genotyped. In addition, the genetic correlation decreased by on average 0.13 over 30 generations of selection when performing BS and the genetic correlation fluctuated across generations.ConclusionsGenomic selection can effectively compensate for the loss of genetic gain in BS due to G × E. However, the number of genotyped SG individuals and the level of G × E significantly affected the extra genetic gain from genomic selection. A family-based BS selective breeding program should monitor the level of G × E and genotyping 50 SG individuals per candidate family to minimize the loss of genetic gain due to G × E, unless the level of G × E is confirmed to be low.

Dataset Information

Long-term comparison between index selection and optimal independent culling in plant breeding programs with genomic prediction.

Publications

Long-term comparison between index selection and optimal independent culling in plant breeding programs with genomic prediction.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets