Evaluating the impact of modeling the family effect for clonal selection in potato-breeding programs
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ABSTRACT:
SUBMITTER: Martins V
PROVIDER: S-EPMC10642306 | biostudies-literature | 2023 Jan
REPOSITORIES: biostudies-literature
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Project description:Different cross-selection (CS) methods incorporating genomic selection (GS) have been used in diploid species to improve long-term genetic gain and preserve diversity. However, their application to heterozygous and autotetraploid crops such as potato (Solanum tuberosum L.) is lacking so far. The objectives of our study were to (i) assess the effects of different CS methods and the incorporation of GS and genetic variability monitoring on both short- and long-term genetic gains compared to strategies using phenotypic selection (PS); (ii) evaluate the changes in genetic variability and the efficiency of converting diversity into genetic gain across different CS methods; and (iii) investigate the interaction effects between different genetic architectures and CS methods on long-term genetic gain. In our simulation results, implementing GS with optimal selected proportions had increased short- and long-term genetic gain compared to any PS strategy. The CS method considering additive and dominance effects to predict progeny mean based on simulated progenies (MEGV-O) achieved the highest long-term genetic gain among the assessed mean-based CS methods. Compared to MEGV-O and usefulness criteria (UC), the linear combination of UC and genome-wide diversity (called EUCD) maintained the same level of genetic gain but resulted in higher diversity and a lower number of fixed QTLs. Moreover, EUCD had a relatively high degree of efficiency in converting diversity into genetic gain. However, choosing the most appropriate weight to account for diversity in EUCD depends on the genetic architecture of the target trait and the breeder's objectives. Our results provide breeders with concrete methods to improve their potato breeding programs.
| S-EPMC11835509 | biostudies-literature
Project description:Biochemical markers for cold-induced sweetening (CIS) resistance were tested for their stability over years and their use in selection of parents for crossing to achieve high selection efficiency in potato breeding programs. Two regulatory enzymes directly associated with reducing sugar (RS) accumulation during potato tubers cold storage were tested as a predictor for CIS resistance. These enzymes were studied in 33 potato clones from various breeding programs over four years. Clones with the presence of A-II isozymes of UDP-glucose pyrophosphorylase (UGPase) and low activity of vacuolar acid invertase (VAcInv) enzyme had increased resistance to cold-induced sweetening (CIS). Depending on the levels of these enzymes, clones were divided into class A, class B and class C. Clones categorized as class A had average RS of 0.73 mg per g FW after six months at 5.5°C storage. Class B and C had average RS of 1.15 and 3.80 mg per g FW respectively. The enzyme activity was closely associated with RS accumulation over long-term cold storage. The biochemical markers were found to be stable over the years. Repeated-measure analysis showed 75% chance of maintaining class from one year to the next and a 25% chance of switching, No clone switched between class A and class C, even across all four years. Application of these biochemical markers can identify clones with CIS resistance early in the selection process. Biochemical markers were used to select parents for crossing and six families were established. Results showed that with both parents from class A, 95% of their offspring had desirable glucose levels and chip color, which dropped to 52% when one parent was from class A and other from class B. These results suggest that two regulatory enzymes, i.e., UGPase and VAcInv, can be used as stable prognostic biochemical markers for CIS resistance for precise parent selection resulting in progenies with significantly higher percentage of clones with acceptable processing quality.
| S-EPMC6938367 | biostudies-literature
Project description:Cultivating resistant varieties of potato is the most effective and environmentally sound method of protecting potato crops against pests and diseases. Potato cyst nematodes (PCN) are major nematode pests causing severe constraints in potato production worldwide. There are five pathotypes of Globodrea rostochiensis (Ro1-Ro5) and three of G. pallida Pa1-Pa3. Cultivation of potato varieties with broad nematode resistance may influence the growth of the wide spectrum of PCN pathotypes, but there is limited availability of such varieties on the market. The use of molecular markers allows for the effective selection of resistant genotypes at early stages of breeding. However, the impact of early selection for nematode resistance on the agronomic value of the final selected clones is a cause of concern for potato breeders. This study investigates the relationships between the presence of the combined resistance genes H1, Gro1-4 and GpaVvrn , which confer resistance to the nematodes, and certain agricultural traits. Clones with broad nematode resistance conferred by the genes H1, Gro1-4 and GpaVvrn presented yields and tuber morphology traits similar to those of the clones without identified resistance genes.
| S-EPMC8784344 | biostudies-literature
Project description:BackgroundTest-trace-isolate programs are an essential part of coronavirus disease 2019 (COVID-19) control that offer a more targeted approach than many other nonpharmaceutical interventions. Effective use of such programs requires methods to estimate their current and anticipated impact.Methods and findingsWe present a mathematical modeling framework to evaluate the expected reductions in the reproductive number, R, from test-trace-isolate programs. This framework is implemented in a publicly available R package and an online application. We evaluated the effects of completeness in case detection and contact tracing and speed of isolation and quarantine using parameters consistent with COVID-19 transmission (R0: 2.5, generation time: 6.5 days). We show that R is most sensitive to changes in the proportion of cases detected in almost all scenarios, and other metrics have a reduced impact when case detection levels are low (<30%). Although test-trace-isolate programs can contribute substantially to reducing R, exceptional performance across all metrics is needed to bring R below one through test-trace-isolate alone, highlighting the need for comprehensive control strategies. Results from this model also indicate that metrics used to evaluate performance of test-trace-isolate, such as the proportion of identified infections among traced contacts, may be misleading. While estimates of the impact of test-trace-isolate are sensitive to assumptions about COVID-19 natural history and adherence to isolation and quarantine, our qualitative findings are robust across numerous sensitivity analyses.ConclusionsEffective test-trace-isolate programs first need to be strong in the "test" component, as case detection underlies all other program activities. Even moderately effective test-trace-isolate programs are an important tool for controlling the COVID-19 pandemic and can alleviate the need for more restrictive social distancing measures.
| S-EPMC8121292 | biostudies-literature
Project description:Rice breeding programs in Hokkaido over the past 100 years have dramatically increased productivity and improved the eating quality of rice. Commercial varieties with high yield and good eating quality, such as Kirara 397, Hoshinoyume, and Nanatsuboshi, have been continuously registered since 1990. Furthermore, varieties with better eating quality using Wx1-1, which reduces amylose content to improve the taste of sticky rice, such as Oborozuki and Yumepirika, were registered in 2006 and 2008, respectively. However, to the best of our knowledge the genomic changes associated with these improvements have not been determined. Better understanding of the relationships between DNA sequences and agricultural traits could facilitate rice breeding programs in Hokkaido. Marker-assisted selection (MAS), which can select the plants with chromosomal regions tagged with DNA markers for desirable traits, is an advanced technology to manage genetic improvements. Here, we summarize the current states of MAS in rice breeding programs in Hokkaido before huge data sets of genome sequences using next-generation sequencing technology come into practical use in rice breeding programs.
| S-EPMC6776137 | biostudies-literature
Project description:Predictive breeding approaches, like phenomic or genomic selection, have the potential to increase the selection gain for potato breeding programs which are characterized by very large numbers of entries in early stages and the availability of very few tubers per entry in these stages. The objectives of this study were to (i) explore the capabilities of phenomic prediction based on drone-derived multispectral reflectance data in potato breeding by testing different prediction scenarios on a diverse panel of tetraploid potato material from all market segments and considering a broad range of traits, (ii) compare the performance of phenomic and genomic predictions, and (iii) assess the predictive power of mixed relationship matrices utilizing weighted SNP array and multispectral reflectance data. Predictive abilities of phenomic prediction scenarios varied greatly within a range of - 0.15 and 0.88 and were strongly dependent on the environment, predicted trait, and considered prediction scenario. We observed high predictive abilities with phenomic prediction for yield (0.45), maturity (0.88), foliage development (0.73), and emergence (0.73), while all other traits achieved higher predictive ability with genomic compared to phenomic prediction. When a mixed relationship matrix was used for prediction, higher predictive abilities were observed for 20 out of 22 traits, showcasing that phenomic and genomic data contained complementary information. We see the main application of phenomic selection in potato breeding programs to allow for the use of the principle of predictive breeding in the pot seedling or single hill stage where genotyping is not recommended due to high costs.
| S-EPMC10917832 | biostudies-literature
Project description:In species with long life cycles and discontinuous availability of individuals to reproduction, implementing a long-term captive breeding program can be difficult or impossible. In such cases, managing diversity among familiar groups instead of individuals could become a suitable approach to avoid inbreeding and increase the possibility to accomplish a breeding scheme. This is the case of several sturgeon species including the Adriatic sturgeon, whose recovery depends on the management of a few captive stocks directly descended from the same group of wild parents. In the present study, relatedness among 445 potential breeders was inferred with a novel software for pedigree reconstruction in tetraploids ("BreedingSturgeons"). This information was used to plan a breeding scheme considering familiar groups as breeding units and identifying mating priorities. A two-step strategy is proposed: a short-term breeding program, relying on the 13 remaining F0 individuals of certain wild origin; and a long-term plan based on F1 families. Simulations to evaluate the loss of alleles in the F2 generation under different pairing strategies and assess the number of individuals to breed, costs and logistical aquaculture constraints were performed. The strategy proposed is transferable to the several other tetraploid sturgeon species on the brink of extinction.
| S-EPMC4214717 | biostudies-literature
Project description:Key messageEarly generation genomic selection is superior to conventional phenotypic selection in line breeding and can be strongly improved by including additional information from preliminary yield trials. The selection of lines that enter resource-demanding multi-environment trials is a crucial decision in every line breeding program as a large amount of resources are allocated for thoroughly testing these potential varietal candidates. We compared conventional phenotypic selection with various genomic selection approaches across multiple years as well as the merit of integrating phenotypic information from preliminary yield trials into the genomic selection framework. The prediction accuracy using only phenotypic data was rather low (r = 0.21) for grain yield but could be improved by modeling genetic relationships in unreplicated preliminary yield trials (r = 0.33). Genomic selection models were nevertheless found to be superior to conventional phenotypic selection for predicting grain yield performance of lines across years (r = 0.39). We subsequently simplified the problem of predicting untested lines in untested years to predicting tested lines in untested years by combining breeding values from preliminary yield trials and predictions from genomic selection models by a heritability index. This genomic assisted selection led to a 20% increase in prediction accuracy, which could be further enhanced by an appropriate marker selection for both grain yield (r = 0.48) and protein content (r = 0.63). The easy to implement and robust genomic assisted selection gave thus a higher prediction accuracy than either conventional phenotypic or genomic selection alone. The proposed method took the complex inheritance of both low and high heritable traits into account and appears capable to support breeders in their selection decisions to develop enhanced varieties more efficiently.
| S-EPMC5263211 | biostudies-literature
Project description:Colorectal cancer (CRC) is among the deadliest cancers worldwide, with metastasis being the main cause of patient mortality. During CRC progression the complex tumor ecosystem changes in its composition at virtually every stage. However, clonal dynamics and associated niche-dependencies at these stages are unknown. Hence, it is of importance to utilize models that faithfully recapitulate human CRC to define its clonal dynamics. We used an optical barcoding approach in mouse-derived organoids (MDOs) that revealed niche-dependent clonal selection. Our findings highlight that clonal selection is controlled by a site-specific niche, which critically contributes to cancer heterogeneity and has implications for therapeutic intervention.
| S-EPMC9454531 | biostudies-literature
Project description:Researchers have used quantitative genetics to map cotton fiber quality and agronomic performance loci, but many alleles may be population or environment-specific, limiting their usefulness in a pedigree selection, inbreeding-based system. Here, we utilized genotypic and phenotypic data on a panel of 80 important historical Upland cotton (Gossypium hirsutum L.) lines to investigate the potential for genomics-based selection within a cotton breeding program’s relatively closed gene pool. We performed a genome-wide association study (GWAS) to identify alleles correlated to 20 fiber quality, seed composition, and yield traits and looked for a consistent detection of GWAS hits across 14 individual field trials. We also explored the potential for genomic prediction to capture genotypic variation for these quantitative traits and tested the incorporation of GWAS hits into the prediction model. Overall, we found that genomic selection programs for fiber quality can begin immediately, and the prediction ability for most other traits is lower but commensurate with heritability. Stably detected GWAS hits can improve prediction accuracy, although a significance threshold must be carefully chosen to include a marker as a fixed effect. We place these results in the context of modern public cotton line-breeding and highlight the need for a community-based approach to amass the data and expertise necessary to launch US public-sector cotton breeders into the genomics-based selection era.
| S-EPMC9182660 | biostudies-literature