Dataset Information

Genotyping crossing parents and family bulks can facilitate cost-efficient genomic prediction strategies in small-scale line breeding programs.

ABSTRACT:

Key message

Genomic relationship matrices based on mid-parent and family bulk genotypes represent cost-efficient alternatives to full genomic prediction approaches with individually genotyped early generation selection candidates. The routine usage of genomic selection for improving line varieties has gained an increasing popularity in recent years. Harnessing the benefits of this approach can, however, be too costly for many small-scale breeding programs, as in most genomic breeding strategies several hundred or even thousands of lines have to be genotyped each year. The aim of this study was thus to compare a full genomic prediction strategy using individually genotyped selection candidates with genomic predictions based on genotypes obtained from pooled DNA of progeny families as well as genotypes inferred from crossing parents. A population of 722 wheat lines representing 63 families tested in more than 100 multi-environment trials during 2010-2019 was for this purpose employed to conduct an empirical study, which was supplemented by a simulation with genotypic data from further 3855 lines. A similar or higher prediction ability was achieved for grain yield, protein yield, and the protein content when using mid-parent or family bulk genotypes in comparison with pedigree selection in the empirical across family prediction scenario. The difference of these methods with a full genomic prediction strategy became furthermore marginal if pre-existing phenotypic data of the selection candidates was already available. Similar observations were made in the simulation, where the usage of individually genotyped lines or family bulks was generally preferable with smaller family sizes. The proposed methods can thus be regarded as alternatives to full genomic or pedigree selection strategies, especially when pedigree information is limited like in the exchange of germplasm between breeding programs.

SUBMITTER: Michel S

PROVIDER: S-EPMC8081688 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundEach country manages access to anticancer drugs differently due to variations in the structure and financing of the health system, but a summary of the various strategies used is absent. This study aimed to review and summarize financing strategies implemented across countries to facilitate access to high-cost anticancer drugs.MethodsWe conducted a systematic review of articles referenced in PubMed, Embase, and Web of Science through May 12, 2021. Articles published in the English language from 2000 that describe strategies implemented in different countries to facilitate access to high-cost anticancer drugs were included. Letters, news articles, and proposed strategies were excluded. Quality assessment was not performed as we aimed to summarize the strategies. Data were analyzed by thematic analysis. A review protocol was registered at PROSPERO (CRD42018068616).ResultsThe review included 204 studies from 176 countries. Three themes of financing strategies were identified: (1) Basic pharmaceutical reimbursement and pricing policies, (2) Alternative funding strategies specific to high-cost drugs, and (3) Financial assistance for individual patients. Access in most countries depends mainly on basic pharmaceutical reimbursement policies (165 of 176 countries). Apart from that, high-income countries (HICs) tended to use funding strategies targeting high-cost drugs (72% of HICs vs 0%-24% of the rest), such as managed entry agreements (MEAs) or dedicated funds for high-cost drugs. In contrast, lower-income countries tended to implement financial assistance programs for cancer patients as a tool to increase access (32% of HICs vs 62%-79% of the rest).ConclusionMany countries have implemented a combination of strategies to increase access to high-cost anticancer drugs. Most low- and middle-income countries utilized placement of anticancer drugs on a national list of essential medicines and patient assistance programs (PAPs) to facilitate access, while many HICs implemented a broader range of strategies.

Project description:BackgroundTheory-based methods to support the uptake of evidence-based practices (EBPs) are critical to improving mental health outcomes. Implementation strategy costs can be substantial, and few have been rigorously evaluated. The purpose of this study is to conduct a cost-effectiveness analysis to identify the most cost-effective approach to deploying implementation strategies to enhance the uptake of Life Goals, a mental health EBP.MethodsWe used data from a previously conducted randomized trial to compare the cost-effectiveness of Replicating Effective Programs (REP) combined with external and/or internal facilitation among sites non-responsive to REP. REP is a low-level strategy that includes EBP packaging, training, and technical assistance. External facilitation (EF) involves external expert support, and internal facilitation (IF) augments EF with protected time for internal staff to support EBP implementation. We developed a decision tree to assess 1-year costs and outcomes for four implementation strategies: (1) REP only, (2) REP+EF, (3) REP+EF add IF if needed, (4) REP+EF/IF. The analysis used a 1-year time horizon and assumed a health payer perspective. Our outcome was quality-adjusted life years (QALYs). The economic outcome was the incremental cost-effectiveness ratio (ICER). We conducted deterministic and probabilistic sensitivity analysis (PSA).ResultsOur results indicate that REP+EF add IF is the most cost-effective option with an ICER of $593/QALY. The REP+EF/IF and REP+EF only conditions are dominated (i.e., more expensive and less effective than comparators). One-way sensitivity analyses indicate that results are sensitive to utilities for REP+EF and REP+EF add IF. The PSA results indicate that REP+EF, add IF is the optimal strategy in 30% of iterations at the threshold of $100,000/QALY.ConclusionsOur results suggest that the most cost-effective implementation support begins with a less intensive, less costly strategy initially and increases as needed to enhance EBP uptake. Using this approach, implementation support resources can be judiciously allocated to those clinics that would most benefit. Our results were not robust to changes in the utility measure. Research is needed that incorporates robust and relevant utilities in implementation studies to determine the most cost-effective strategies. This study advances economic evaluation of implementation by assessing costs and utilities across multiple implementation strategy combinations.Trial registrationClinicalTrials.gov Identifier: NCT02151331 , 05/30/2014.

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:Resilience is the capacity of an animal to be minimally affected by disturbances or to rapidly return to the state pertained before exposure to a disturbance. However, indicators for general resilience to environmental disturbances have not yet been defined, and perhaps therefore resilience is not yet included in breeding goals. The current developments on big data collection give opportunities to determine new resilience indicators based on longitudinal data, which can aid to incorporate resilience in animal breeding goals. The objectives of this paper were: (1) to define resilience indicator traits based on big data, (2) to define economic values for resilience, and (3) to show the potential to improve resilience of livestock through inclusion of resilience in breeding goals. Resilience might be measured based on deviations from expected production levels over a period of time. Suitable resilience indicators could be the variance of deviations, the autocorrelation of deviations, the skewness of deviations, and the slope of a reaction norm. These (new) resilience indicators provide opportunity to include resilience in breeding programs. Economic values of resilience indicators in the selection index can be calculated based on reduced costs due to labor and treatments. For example, when labor time is restricted, the economic value of resilience increases with an increasing number of animals per farm, and can become as large as the economic value of production. This shows the importance of including resilience in breeding goals. Two scenarios were described to show the additional benefit of including resilience in breeding programs. These examples showed that it is hard to improve resilience with only production traits in the selection index, but that it is possible to greatly improve resilience by including resilience indicators in the selection index. However, when health-related traits are already present in the selection index, the effect is smaller. Nevertheless, inclusion of resilience indicators in the selection index increases the response in the breeding goal and resilience, which results in less labor-demanding, and thus easier-to-manage livestock. Current developments on massive collection of data, and new phenotypes based on these data, offer exciting opportunities to breed for improved resilience of livestock.

Dataset Information

Genotyping crossing parents and family bulks can facilitate cost-efficient genomic prediction strategies in small-scale line breeding programs.

Key message

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets