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1
Bohlouli, Mehdi, Sadegh Alijani, Ardashir Nejati Javaremi, Sven König, and Tong Yin. "Genomic prediction by considering genotype × environment interaction using different genomic architectures." Annals of Animal Science 17, no. 3 (July 26, 2017): 683–701. http://dx.doi.org/10.1515/aoas-2016-0086.
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Abstract In this study, accuracies of genomic prediction across various scenarios were compared using single- trait and multiple-trait animal models to detect genotype × environment (G × E) interaction based on REML method. The simulated high and low linkage disequilibrium (HLD and LLD) genome consisted of 15,000 and 50,000 SNP chip applications with 300 and 600 QTLs controlling the trait of interest. The simulation was done to create the genetic correlations between the traits in 4 environments and heritabilities of the traits were 0.20, 0.25, 0.30 and 0.35 in environments 1, 2, 3 and 4, respectively. Two strategies were used to predict the accuracy of genomic selection for cows without phenotypes. In the first strategy, phenotypes for cows in three environments were kept as a training set and breeding values for all animals were estimated using three-trait model. In the second one, only 25, 50 or 75% of records in the fourth environment and all the records in the other three environments were used to predict GBV for non-phenotyped cows in the environment 4. For the first strategy, the highest accuracy of 0.695 was realized in scenario HLD with 600 QTL and 50K SNP chip for the fourth environment and the lowest accuracy of 0.495 was obtained in scenario LLD with 600QTL and 15K SNP chips for the first environment. Generally, the accuracy of prediction increased significantly (P<0.05) with increasing the number of markers, heritability and the genetic correlation between the traits, but no significant difference was observed between scenarios with 300 and 600 QTL. In comparison with models without G × E interaction, accuracies of the GBV for all environments increased when using multi-trait models. The results showed that the level of LD, number of animals in training set and genetic correlation across environments play important roles if G × E interaction exists. In conclusion, G × E interaction contributes to understanding variations of quantitative trait and increasing accuracy of genomic prediction. Therefore, the interaction should be taken into account in conducting selection in various environments or across different genotypes.
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Lozada, Dennis, and Arron Carter. "Insights into the Genetic Architecture of Phenotypic Stability Traits in Winter Wheat." Agronomy 10, no. 3 (March 7, 2020): 368. http://dx.doi.org/10.3390/agronomy10030368.
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Examining the architecture of traits through genomics is necessary to gain a better understanding of the genetic loci affecting important traits to facilitate improvement. Genomewide association study (GWAS) and genomic selection (GS) were implemented for grain yield, heading date, and plant height to gain insights into the genetic complexity of phenotypic stability of traits in a diverse population of US Pacific Northwest winter wheat. Analysis of variance using the Additive Main Effect and Multiplicative Interaction (AMMI) approach revealed significant genotype and genotype by environment interactions. GWAS identified 12 SNP markers distributed across 10 chromosomes affecting variation for both trait and phenotypic stability, indicating potential pleiotropic effects and signifying that similar genetic loci could be associated with different aspects of stability. The lack of stable and major effect loci affecting phenotypic variation supports the complexity of stability of traits. Accuracy of GS was low to moderate, between 0.14 and 0.66, indicating that phenotypic stability is under genetic control. The moderate to high correlation between trait and trait stability suggests the potential of simultaneous selection for trait and trait stability. Our results demonstrate the complex genetic architecture of trait stability and show the potential for improving stability in winter wheat using genomic-assisted approaches.
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Calus, M. P. L., D. P. Berry, G. Banos, Y. de Haas, and R. F. Veerkamp. "Genomic selection: the option for new robustness traits?" Advances in Animal Biosciences 4, no. 3 (July 2013): 618–25. http://dx.doi.org/10.1017/s2040470013000186.
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Genomic selection is rapidly becoming the state-of-the-art genetic selection methodology in dairy cattle breeding schemes around the world. The objective of this paper was to explore possibilities to apply genomic selection for traits related to dairy cow robustness. Deterministic simulations indicate that replacing progeny testing with genomic selection may favour genetic response for production traits at the expense of robustness traits, owing to a disproportional change in accuracies obtained across trait groups. Nevertheless, several options are available to improve the accuracy of genomic selection for robustness traits. Moreover, genomic selection opens up the opportunity to begin selection for new traits using specialised reference populations of limited size where phenotyping of large populations of animals is currently prohibitive. Reference populations for such traits may be nucleus-type herds, research herds or pooled data from (international) research experiments or research herds. The RobustMilk project has set an example for the latter approach, by collating international data for progesterone-based traits, feed intake and energy balance-related traits. Reference population design, both in terms of relatedness of the animals and variability in phenotypic performance, is important to optimise the accuracy of genomic selection. Use of indicator traits, combined with multi-trait genomic prediction models, can further contribute to improved accuracy of genomic prediction for robustness traits. Experience to date indicates that for newly recorded robustness traits that are negatively correlated with the main breeding goal, cow reference populations of ⩾10 000 are required when genotyping is based on medium- or high-density single-nucleotide polymorphism arrays. Further genotyping advances (e.g. sequencing) combined with post-genomics technologies will enhance the opportunities for (genomic) selection to improve cow robustness.
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Sunagar, Ramesh, and Manoj Kumar Pandey. "Genomic Approaches for Enhancing Yield and Quality Traits in Mustard (Brassica spp.): A Review of Breeding Strategies." Journal of Advances in Biology & Biotechnology 27, no. 6 (May 8, 2024): 174–85. http://dx.doi.org/10.9734/jabb/2024/v27i6877.
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Mustard, a vital oilseed crop, plays a significant role in global agriculture due to its versatile applications in food, feed, and biofuel industries. However, meeting the increasing demands for yield and quality traits poses a substantial challenge to mustard breeders. In response, genomic approaches have emerged as powerful tools to expedite mustard breeding programs by unraveling the genetic basis of key agronomic traits. This review provides a comprehensive overview of genomic strategies aimed at enhancing yield and quality traits in mustard. Beginning with an exploration of traditional breeding methods and their limitations, we delve into the advancements in genomics, including next-generation sequencing technologies, marker-assisted selection (MAS), and genome editing techniques. We discuss how these tools are leveraged to identify yield-related genes, quantitative trait loci (QTLs), and markers for efficient trait selection. Furthermore, we examine genomic approaches for improving oil content, nutritional profiles, and phytochemical composition, crucial for enhancing mustard quality. Case studies demonstrating the successful integration of genomics into breeding programs are highlighted, along with discussions on challenges such as regulatory concerns and technical hurdles. Finally, we outline future directions and the potential of genomic approaches to revolutionize mustard breeding, paving the way for sustainable crop improvement. This study offers valuable insights into the application of genomics in mustard breeding and underscores its importance in addressing the evolving needs of agriculture in the 21st century.
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Srivastava, Swati, Bryan Irvine Lopez, Sara de las Heras-Saldana, Jong-Eun Park, Dong-Hyun Shin, Han-Ha Chai, Woncheol Park, Seung-Hwan Lee, and Dajeong Lim. "Estimation of Genetic Parameters by Single-Trait and Multi-Trait Models for Carcass Traits in Hanwoo Cattle." Animals 9, no. 12 (December 2, 2019): 1061. http://dx.doi.org/10.3390/ani9121061.
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Hanwoo breed is preferred in South Korea because of the high standards in marbling and the palatability of its meat. Numerous studies have been conducted and are ongoing to increase the meat production and quality in this beef population. The aim of this study was to estimate and compare genetic parameters for carcass traits using BLUPF90 software. Four models were constructed, single trait pedigree model (STPM), single-trait genomic model (STGM), multi-trait pedigree model (MTPM), and multi-trait genomic model (MTGM), using the pedigree, phenotype, and genomic information of 7991 Hanwoo cattle. Four carcass traits were evaluated: Back fat thickness (BFT), carcass weight (CWT), eye muscle area (EMA), and marbling score (MS). Heritability estimates of 0.40 and 0.41 for BFT, 0.33 and 0.34 for CWT, 0.36 and 0.37 for EMA, and 0.35 and 0.38 for MS were obtained for the single-trait pedigree model and the multi-trait pedigree model, respectively, in Hanwoo. Further, the genomic model showed more improved results compared to the pedigree model, with heritability of 0.39 (CWT), 0.39 (EMA), and 0.46 (MS), except for 0.39 (BFT), which may be due to random events. Utilization of genomic information in the form of single nucleotide polymorphisms (SNPs) has allowed more capturing of the variance from the traits improving the variance components.
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Huang, Mao, Antonio Cabrera, Amber Hoffstetter, Carl Griffey, David Van Sanford, José Costa, Anne McKendry, Shiaoman Chao, and Clay Sneller. "Genomic selection for wheat traits and trait stability." Theoretical and Applied Genetics 129, no. 9 (June 4, 2016): 1697–710. http://dx.doi.org/10.1007/s00122-016-2733-z.
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Eduardo, Iban, Pere Arús, Antonio José Monforte, Javier Obando, Juan Pablo Fernández-Trujillo, Juan Antonio Martínez, Antonio Luís Alarcón, Jose María Álvarez, and Esther van der Knaap. "Estimating the Genetic Architecture of Fruit Quality Traits in Melon Using a Genomic Library of Near Isogenic Lines." Journal of the American Society for Horticultural Science 132, no. 1 (January 2007): 80–89. http://dx.doi.org/10.21273/jashs.132.1.80.
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A melon (Cucumis melo L.) genomic library of near-isogenic lines derived from the cross between the Spanish cultivar Piel de Sapo and the exotic accession PI 161375 has been evaluated for fruit quality traits in four different locations. Traits evaluated were fruit weight, soluble solids content, maximum fruit diameter, fruit length, fruit shape index, ovary shape index, external color, and flesh color. Among these traits, soluble solids content showed the highest genotype × environment interaction, whereas genotype × environment interactions for fruit shape and fruit weight were low. Heritability was high for all traits except soluble solids content, with the highest value for fruit shape and ovary shape. Ten to 15 quantitative trait loci were detected for soluble solids content, fruit diameter, fruit length, and fruit shape; and four to five for ovary shape, external color, and flesh color. Depending on the trait, between 13% and 40% of the detected quantitative trait alleles from PI 161375 increased the trait, and between 60% and 87% of them decreased it, resulting in some PI 161375 alleles of interest for breeding. Most of the quantitative trait loci detected in previous experiments could be verified with the near-isogenic line population. Future studies with the melon near-isogenic line genomic library will provide a better understanding of the genetic control of melon fruit quality in a wider context related to agronomy, genetics, genomics and metabolomics studies.
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Fragomeni, Breno, Zulma Vitezica, Justine Liu, Yijian Huang, Kent Gray, Daniela Lourenco, and Ignacy Misztal. "209 Genomic selection for multiple maternal and growth traits in large white pigs using Single-Step GBLUP." Journal of Animal Science 97, Supplement_3 (December 2019): 42. http://dx.doi.org/10.1093/jas/skz258.084.
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Abstract The objective of this study was to implement a multi-trait genomic evaluation for maternal and growth traits in a swine population. Phenotypes for preweaning mortality, litter size, weaning weight, and average daily gain were available for 282K Large White pigs. The pedigree included 314k individuals, of which 35,731 were genotyped for 45K SNPs. Variance components were estimated in a multi-trait animal model without genomic information by AIREMLF90. Genomic breeding values were estimated using the genomic information by single-step GBLUP. The algorithm for proven and young (APY) was used to reduce computing time. Genetic correlation between proportion and the total number of preweaning deaths was 0.95. A strong, positive genetic correlation was also observed between weaning weight and average daily gain (r = 0.94). Conversely, the genetic correlations between mortality and growth traits were negative, with an average of -0.7. To avoid computations by expensive threshold models, preweaning mortality was transformed from a binary trait to two linear dam traits: proportion and a total number of piglets dead before weaning. Because of the high genetic correlations within groups of traits, inclusion of only one growth and one mortality trait in the model decreases computing time and allows for the inclusion of other traits. Reduction in computing time for the evaluation using APY was up to 20x, and no differences in EPD ranking were observed. The algorithm for proven and young improves the efficiency of genomic evaluation in swine without harming the quality of predictions. For this population, a binary trait of mortality can be replaced by a linear trait of the dam, resulting in a similar ranking for the selection candidates.
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Shabannejad, Morteza, Mohammad-Reza Bihamta, Eslam Majidi-Hervan, Hadi Alipour, and Asa Ebrahimi. "A classic approach for determining genomic prediction accuracy under terminal drought stress and well-watered conditions in wheat landraces and cultivars." PLOS ONE 16, no. 3 (March 5, 2021): e0247824. http://dx.doi.org/10.1371/journal.pone.0247824.
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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.
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Moeinizade, Saba, Aaron Kusmec, Guiping Hu, Lizhi Wang, and Patrick S. Schnable. "Multi-trait Genomic Selection Methods for Crop Improvement." Genetics 215, no. 4 (June 1, 2020): 931–45. http://dx.doi.org/10.1534/genetics.120.303305.
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Plant breeders make selection decisions based on multiple traits, such as yield, plant height, flowering time, and disease resistance. A commonly used approach in multi-trait genomic selection is index selection, which assigns weights to different traits relative to their economic importance. However, classical index selection only optimizes genetic gain in the next generation, requires some experimentation to find weights that lead to desired outcomes, and has difficulty optimizing nonlinear breeding objectives. Multi-objective optimization has also been used to identify the Pareto frontier of selection decisions, which represents different trade-offs across multiple traits. We propose a new approach, which maximizes certain traits while keeping others within desirable ranges. Optimal selection decisions are made using a new version of the look-ahead selection (LAS) algorithm, which was recently proposed for single-trait genomic selection, and achieved superior performance with respect to other state-of-the-art selection methods. To demonstrate the effectiveness of the new method, a case study is developed using a realistic data set where our method is compared with conventional index selection. Results suggest that the multi-trait LAS is more effective at balancing multiple traits compared with index selection.
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Espuela-Ortiz, Antonio, Elena Martin-Gonzalez, Paloma Poza-Guedes, Ruperto González-Pérez, and Esther Herrera-Luis. "Genomics of Treatable Traits in Asthma." Genes 14, no. 9 (September 20, 2023): 1824. http://dx.doi.org/10.3390/genes14091824.
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The astounding number of genetic variants revealed in the 15 years of genome-wide association studies of asthma has not kept pace with the goals of translational genomics. Moving asthma diagnosis from a nonspecific umbrella term to specific phenotypes/endotypes and related traits may provide insights into features that may be prevented or alleviated by therapeutical intervention. This review provides an overview of the different asthma endotypes and phenotypes and the genomic findings from asthma studies using patient stratification strategies and asthma-related traits. Asthma genomic research for treatable traits has uncovered novel and previously reported asthma loci, primarily through studies in Europeans. Novel genomic findings for asthma phenotypes and related traits may arise from multi-trait and specific phenotyping strategies in diverse populations.
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Baes, Christine F., Filippo Miglior, Flavio S. Schenkel, Ellen Goddard, Gerrit Kistemaker, Nienke van Staaveren, Ronaldo Cerri, Marc Andre A. Sirard, and Paul Stothard. "166 Livestock Resiliency: Concepts and Approaches." Journal of Animal Science 99, Supplement_3 (October 8, 2021): 89–90. http://dx.doi.org/10.1093/jas/skab235.159.
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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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Li, Wenjie, Wenqiang Li, Zichen Song, Zihao Gao, Kerui Xie, Yubing Wang, Bo Wang, et al. "Marker Density and Models to Improve the Accuracy of Genomic Selection for Growth and Slaughter Traits in Meat Rabbits." Genes 15, no. 4 (April 3, 2024): 454. http://dx.doi.org/10.3390/genes15040454.
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The selection and breeding of good meat rabbit breeds are fundamental to their industrial development, and genomic selection (GS) can employ genomic information to make up for the shortcomings of traditional phenotype-based breeding methods. For the practical implementation of GS in meat rabbit breeding, it is necessary to assess different marker densities and GS models. Here, we obtained low-coverage whole-genome sequencing (lcWGS) data from 1515 meat rabbits (including parent herd and half-sibling offspring). The specific objectives were (1) to derive a baseline for heritability estimates and genomic predictions based on randomly selected marker densities and (2) to assess the accuracy of genomic predictions for single- and multiple-trait linear mixed models. We found that a marker density of 50 K can be used as a baseline for heritability estimation and genomic prediction. For GS, the multi-trait genomic best linear unbiased prediction (GBLUP) model results in more accurate predictions for virtually all traits compared to the single-trait model, with improvements greater than 15% for all of them, which may be attributed to the use of information on genetically related traits. In addition, we discovered a positive correlation between the performance of the multi-trait GBLUP and the genetic correlation between the traits. We anticipate that this approach will provide solutions for GS, as well as optimize breeding programs, in meat rabbits.
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Guo, Jia, Jahangir Khan, Sumit Pradhan, Dipendra Shahi, Naeem Khan, Muhsin Avci, Jordan Mcbreen, et al. "Multi-Trait Genomic Prediction of Yield-Related Traits in US Soft Wheat under Variable Water Regimes." Genes 11, no. 11 (October 28, 2020): 1270. http://dx.doi.org/10.3390/genes11111270.
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The performance of genomic prediction (GP) on genetically correlated traits can be improved through an interdependence multi-trait model under a multi-environment context. In this study, a panel of 237 soft facultative wheat (Triticum aestivum L.) lines was evaluated to compare single- and multi-trait models for predicting grain yield (GY), harvest index (HI), spike fertility (SF), and thousand grain weight (TGW). The panel was phenotyped in two locations and two years in Florida under drought and moderately drought stress conditions, while the genotyping was performed using 27,957 genotyping-by-sequencing (GBS) single nucleotide polymorphism (SNP) makers. Five predictive models including Multi-environment Genomic Best Linear Unbiased Predictor (MGBLUP), Bayesian Multi-trait Multi-environment (BMTME), Bayesian Multi-output Regressor Stacking (BMORS), Single-trait Multi-environment Deep Learning (SMDL), and Multi-trait Multi-environment Deep Learning (MMDL) were compared. Across environments, the multi-trait statistical model (BMTME) was superior to the multi-trait DL model for prediction accuracy in most scenarios, but the DL models were comparable to the statistical models for response to selection. The multi-trait model also showed 5 to 22% more genetic gain compared to the single-trait model across environment reflected by the response to selection. Overall, these results suggest that multi-trait genomic prediction can be an efficient strategy for economically important yield component related traits in soft wheat.
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REDDY, S. N. K., PUNEET WALIA, and SANJEET SINGH SANDAL. "DIGITAL REVOLUTION IN PLANT BREEDING: A COMPREHENSIVE REVIEW OF METHODOLOGIES, TOOLS, APPLICATIONS, AND FUTURE PERSPECTIVES." Asian Journal of Microbiology, Biotechnology & Environmental Sciences 25, no. 04 (2023): 629–32. http://dx.doi.org/10.53550/ajmbes.2023.v25i04.003.
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Digital breeding integrates modern technologies like genomics, bioinformatics, and data analytics into conventional plant breeding. It accelerates the breeding process, improves selection effectiveness, and enhances crop yield and quality. High-throughput genotyping and phenotyping technologies enable efficient analysis of large populations and rapid characterization of plant traits. Genomic selection and data analytics aid in predicting breeding values and analyzing extensive data for trait improvement. Digital breeding applications include accelerated breeding cycles, trait-based breeding, disease resistance, stress tolerance, nutritional quality enhancement, remote sensing, yield prediction, multi-environment testing, and precision breeding and prospects involve integrating multiple omics technologies, developing precise phenotypic prediction models, and fostering data sharing and collaboration. Digital breeding can greatly improve breeding programs and address global food security challenges.Digital breeding integrates modern technologies like genomics, bioinformatics, and data analytics into conventional plant breeding. It accelerates the breeding process, improves selection effectiveness, and enhances crop yield and quality. High-throughput genotyping and phenotyping technologies enable efficient analysis of large populations and rapid characterization of plant traits. Genomic selection and data analytics aid in predicting breeding values and analyzing extensive data for trait improvement. Digital breeding applications include accelerated breeding cycles, trait-based breeding, disease resistance, stress tolerance, nutritional quality enhancement, remote sensing, yield prediction, multi-environment testing, and precision breeding and prospects involve integrating multiple omics technologies, developing precise phenotypic prediction models, and fostering data sharing and collaboration. Digital breeding can greatly improve breeding programs and address global food security challenges.
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Ma, Xiang, Ole F. Christensen, Hongding Gao, Ruihua Huang, Bjarne Nielsen, Per Madsen, Just Jensen, et al. "Prediction of breeding values for group-recorded traits including genomic information and an individually recorded correlated trait." Heredity 126, no. 1 (July 14, 2020): 206–17. http://dx.doi.org/10.1038/s41437-020-0339-3.
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AbstractRecords on groups of individuals could be valuable for predicting breeding values when a trait is difficult or costly to measure on single individuals, such as feed intake and egg production. Adding genomic information has shown improvement in the accuracy of genetic evaluation of quantitative traits with individual records. Here, we investigated the value of genomic information for traits with group records. Besides, we investigated the improvement in accuracy of genetic evaluation for group-recorded traits when including information on a correlated trait with individual records. The study was based on a simulated pig population, including three scenarios of group structure and size. The results showed that both the genomic information and a correlated trait increased the accuracy of estimated breeding values (EBVs) for traits with group records. The accuracies of EBV obtained from group records with a size 24 were much lower than those with a size 12. Random assignment of animals to pens led to lower accuracy due to the weaker relationship between individuals within each group. It suggests that group records are valuable for genetic evaluation of a trait that is difficult to record on individuals, and the accuracy of genetic evaluation can be considerably increased using genomic information. Moreover, the genetic evaluation for a trait with group records can be greatly improved using a bivariate model, including correlated traits that are recorded individually. For efficient use of group records in genetic evaluation, relatively small group size and close relationships between individuals within one group are recommended.
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Ofria, Charles, Wei Huang, and Eric Torng. "On the Gradual Evolution of Complexity and the Sudden Emergence of Complex Features." Artificial Life 14, no. 3 (July 2008): 255–63. http://dx.doi.org/10.1162/artl.2008.14.3.14302.
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Evolutionary theory explains the origin of complex organismal features through a combination of reusing and extending information from less-complex traits, and by needing to exploit only one of many unlikely pathways to a viable solution. While the appearance of a new trait may seem sudden, we show that the underlying information associated with each trait evolves gradually. We study this process using digital organisms, self-replicating computer programs that mutate and evolve novel traits, including complex logic operations. When a new complex trait first appears, its proper function immediately requires the coordinated operation of many genomic positions. As the information associated with a trait increases, the probability of its simultaneous introduction drops exponentially, so it is nearly impossible for a significantly complex trait to appear without reusing existing information. We show that the total information stored in the genome increases only marginally when a trait first appears. Furthermore, most of the information associated with a new trait is either correlated with existing traits or co-opted from traits that were lost in conjunction with the appearance of the new trait. Thus, while total genomic information increases incrementally, traits that require much more information can still arise during the evolutionary process.
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Reid, Kerry, Michael A. Bell, and Krishna R. Veeramah. "Threespine Stickleback: A Model System For Evolutionary Genomics." Annual Review of Genomics and Human Genetics 22, no. 1 (August 31, 2021): 357–83. http://dx.doi.org/10.1146/annurev-genom-111720-081402.
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The repeated adaptation of oceanic threespine sticklebacks to fresh water has made it a premier organism to study parallel evolution. These small fish have multiple distinct ecotypes that display a wide range of diverse phenotypic traits. Ecotypes are easily crossed in the laboratory, and families are large and develop quickly enough for quantitative trait locus analyses, positioning the threespine stickleback as a versatile model organism to address a wide range of biological questions. Extensive genomic resources, including linkage maps, a high-quality reference genome, and developmental genetics tools have led to insights into the genomic basis of adaptation and the identification of genomic changes controlling traits in vertebrates. Recently, threespine sticklebacks have been used as a model system to identify the genomic basis of highly complex traits, such as behavior and host–microbiome and host–parasite interactions. We review the latest findings and new avenues of research that have led the threespine stickleback to be considered a supermodel of evolutionary genomics.
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Negawo, Alemayehu Teressa, Meki Shehabu Muktar, Ricardo Alonso Sánchez Gutiérrez, Ermias Habte, Alice Muchugi, and Chris S. Jones. "A Genome-Wide Association Study of Biomass Yield and Feed Quality in Buffel Grass (Cenchrus ciliaris L.)." Agriculture 14, no. 2 (February 6, 2024): 257. http://dx.doi.org/10.3390/agriculture14020257.
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The development of modern genomic tools has helped accelerate genetic gains in the breeding program of food crops. More recently, genomic resources have been developed for tropical forages, providing key resources for developing new climate-resilient high-yielding forage varieties. In this study, we present a genome-wide association study for biomass yield and feed quality traits in buffel grass (Cenchrus ciliaris L. aka Pennisetum ciliare L.). Genome-wide markers, generated using the DArTSeq platform and mapped onto the Setaria italica reference genome, were used for the genome-wide association study. The results revealed several markers associated with biomass yield and feed quality traits. A total of 78 marker–trait associations were identified with R2 values ranging from 0.138 to 0.236. The marker–trait associations were distributed across different chromosomes. Of these associations, the most marker–trait associations (23) were observed on Chr9, followed by Chr5 with 12. The fewest number of marker–trait associations were observed on Chr4 with 2. In terms of traits, 17 markers were associated with biomass yield, 24 with crude protein, 26 with TDN, 14 with ADF, 10 with NDF and 6 with DMI. A total of 20 of the identified markers were associated with at least two traits. The identified marker–trait associations provide a useful genomic resource for the future improvement and breeding of buffel grass.
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Sanjay, Patil Shital, and Satya Prakash. "Advancements of Breeding and Genomics in Wheat (Triticum aestivum L.): Enhancing Yield and Nutritional Value for Sustainable Agriculture." Journal of Advances in Biology & Biotechnology 27, no. 5 (April 27, 2024): 863–75. http://dx.doi.org/10.9734/jabb/2024/v27i5848.
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Advancements in wheat breeding and genomics presently explores the genomic interventions driving focusing on quantitative trait loci (QTL) mapping, marker-assisted selection (MAS) and genomic selection (GS). QTL mapping emerges as a pivotal method for pinpointing markers linked with desirable traits, facilitating MAS. Furthermore, genomic selection (GS) holds immense potential for crop improvement. It also delves into the current landscape of MAS and explores various prospects of GS for wheat biofortification. Looking ahead, accelerated mapping studies combined with MAS and GS schemes are poised to further enhance wheat breeding efficiency. Dense molecular maps and a large set of ESTs (Expressed Sequence Tags) have enabled genome-wide identification of gene-rich and gene-poor regions, as well as QTL, including eQTL (Expression quantitative trait loci). Additionally, markers associated with major economic traits have facilitated MAS programs in some countries and enabled map-based cloning of several genes/QTL. Resources for functional genomics, such as TILLING and RNA interference (RNAi), alongside emerging approaches like epigenetics and association mapping, are further enriching wheat genomics research. In this review, we initially present cutting-edge genome-editing technologies in crop plants, with a specific focus on wheat, addressing both functional genomics and genetic enhancement. We subsequently delineate the utilization of additional technologies, including GWAS, high-throughput genotyping and phenotyping, speed breeding, and synthetic biology, within the context of wheat breeding. We assert that integrating genome editing with other molecular breeding strategies will significantly expedite the genetic enhancement of wheat, thus contributing to sustainable global production.
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Goddardt, M. E. "Animal breeding in the (post-) genomic era." Animal Science 76, no. 3 (June 2003): 353–65. http://dx.doi.org/10.1017/s1357729800058586.
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AbstractOne of the benefits of the genomics revolution for animal production will be knowledge of genes that can be used to select more profitable livestock. Although it is possible to use genetic markers linked to genes of economic importance, tests for the genes themselves will be much more successful. Consequently finding genes of economic importance to livestock will be a major research aim for the future. Most traits of economic importance are quantitative traits affected by many genes. Mutations at many genes (e.g. 500) and at many positions within a gene (e.g. 1000 coding and non-coding bases) can affect a typical quantitative trait. The effect of these mutations on phenotype is usually small (e.g. 0·1 standard deviation) but occasionally large. Many mutations are lost from the population through genetic drift and selection, so that polymorphisms exist at only a subset of the relevant genes (e.g. 100 genes). Finding these genes, that have relatively small effects, is more difficult than finding genes for a classical Mendellian trait but, as the genomic tools become more powerful, it is becoming feasible and some successes have already occurred. The standard approach is to map a quantitative trait loci (QTL) to a chromosome region using linkage and linkage disequilibrium. Then test polymorphisms in positional candidate genes for an effect on the trait. Tools such as genomic sequence, EST collections and comparative maps make this approach feasible. Candidate genes can be selected based on functional data such as gene expression obtained from microarrays. At present the gain in rate of genetic improvement from use of DNA-based tests for QTL is small, because selection without them is already quite accurate, not enough QTL have been identified and genotyping is too expensive. However, in the future, with many QTL identified and inexpensive genotyping combined with decreased generation intervals, large gains are possible.
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Stalder, Kenneth J. "296 Awardee Talk: The Genetics of Sow Longevity." Journal of Animal Science 98, Supplement_4 (November 3, 2020): 28. http://dx.doi.org/10.1093/jas/skaa278.050.
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Abstract Sow longevity is a key productivity indicator trait that has real economic and welfare importance for commercial swine farms globally. The average parity at culling is 3.8 parities. Reports indicate that it takes 3 to 4 parities before a sow “pays for herself.” Research groups around the world have reported heritabilities estimates for sow longevity traits ranging from 0.05 to 0.35. Estimate differences result from the animal population under evaluation, the trait being evaluated, and the methodology employed to obtain the genetic parameter estimate. Because sow longevity is measured at the end of the sow’s productive life, indicator traits like age at first farrowing, leg conformation, and other traits are utilized in gilt selection programs. The genetic correlations between sow longevity and lifetime production traits range have been reported to range from 0.64 to 0.94, suggesting that selection will improve sow longevity. Genetic markers have been identified that affect both sow longevity and other indicator traits. Selection to improved sow longevity still requires phenotypes. Future technologies, e.g. CT scans, digital images, and automated disease detection, will provide additional phenotypes. Continued hardware, software, and molecular developments will improve selection accuracy for sow longevity traits and related traits. Research is needed to evaluate the impact that non-additive genetic effects have on sow longevity and other fitness-related traits. Sow longevity seems to be an ideal trait to employ genomic selection in order to make more rapid trait improvements because it is measured late in life, it is sex-limited, and the trait is not directly measured on nucleus animals. In conclusion, sow longevity and related traits have sufficient heritability and variation to improve through traditional and genomic enhanced selection methods. Selection programs employing effective genomic selection programs will be more effective in improving sow longevity trait and related traits and ultimately economical return.
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O’Brien, Anna M., Chandra N. Jack, Maren L. Friesen, and Megan E. Frederickson. "Whose trait is it anyways? Coevolution of joint phenotypes and genetic architecture in mutualisms." Proceedings of the Royal Society B: Biological Sciences 288, no. 1942 (January 13, 2021): 20202483. http://dx.doi.org/10.1098/rspb.2020.2483.
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Evolutionary biologists typically envision a trait’s genetic basis and fitness effects occurring within a single species. However, traits can be determined by and have fitness consequences for interacting species, thus evolving in multiple genomes. This is especially likely in mutualisms, where species exchange fitness benefits and can associate over long periods of time. Partners may experience evolutionary conflict over the value of a multi-genomic trait, but such conflicts may be ameliorated by mutualism’s positive fitness feedbacks. Here, we develop a simulation model of a host–microbe mutualism to explore the evolution of a multi-genomic trait. Coevolutionary outcomes depend on whether hosts and microbes have similar or different optimal trait values, strengths of selection and fitness feedbacks. We show that genome-wide association studies can map joint traits to loci in multiple genomes and describe how fitness conflict and fitness feedback generate different multi-genomic architectures with distinct signals around segregating loci. Partner fitnesses can be positively correlated even when partners are in conflict over the value of a multi-genomic trait, and conflict can generate strong mutualistic dependency. While fitness alignment facilitates rapid adaptation to a new optimum, conflict maintains genetic variation and evolvability, with implications for applied microbiome science.
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Edwards, M. D., C. W. Stuber, and J. F. Wendel. "Molecular-Marker-Facilitated Investigations of Quantitative-Trait Loci in Maize. I. Numbers, Genomic Distribution and Types of Gene Action." Genetics 116, no. 1 (May 1, 1987): 113–25. http://dx.doi.org/10.1093/genetics/116.1.113.
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ABSTRACT Individual genetic factors which underlie variation in quantitative traits of maize were investigated in each of two F2 populations by examining the mean trait expressions of genotypic classes at each of 17–20 segregating marker loci. It was demonstrated that the trait expression of marker locus classes could be interpreted in terms of genetic behavior at linked quantitative trait loci (QTLs). For each of 82 traits evaluated, QTLs were detected and located to genomic sites. The numbers of detected factors varied according to trait, with the average trait significantly influenced by almost two-thirds of the marked genomic sites. Most of the detected associations between marker loci and quantitative traits were highly significant, and could have been detected with fewer than the 1800–1900 plants evaluated in each population. The cumulative, simple effects of marker-linked regions of the genome explained between 8 and 40% of the phenotypic variation for a subset of 25 traits evaluated. Single marker loci accounted for between 0.3% and 16% of the phenotypic variation of traits. Individual plant heterozygosity, as measured by marker loci, was significantly associated with variation in many traits. The apparent types of gene action at the QTLs varied both among traits and between loci for given traits, although overdominance appeared frequently, especially for yield-related traits. The prevalence of apparent overdominance may reflect the effects of multiple QTLs within individual marker-linked regions, a situation which would tend to result in overestimation of dominance. Digenic epistasis did not appear to be important in determining the expression of the quantitative traits evaluated. Examination of the effects of marked regions on the expression of pairs of traits suggests that genomic regions vary in the direction and magnitudes of their effects on trait correlations, perhaps providing a means of selecting to dissociate some correlated traits. Marker-facilitated investigations appear to provide a powerful means of examining aspects of the genetic control of quantitative traits. Modifications of the methods employed herein will allow examination of the stability of individual gene effects in varying genetic backgrounds and environments.
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Mehrban, Hossein, Masoumeh Naserkheil, Deuk Hwan Lee, Chungil Cho, Taejeong Choi, Mina Park, and Noelia Ibáñez-Escriche. "Genomic Prediction Using Alternative Strategies of Weighted Single-Step Genomic BLUP for Yearling Weight and Carcass Traits in Hanwoo Beef Cattle." Genes 12, no. 2 (February 12, 2021): 266. http://dx.doi.org/10.3390/genes12020266.
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The weighted single-step genomic best linear unbiased prediction (GBLUP) method has been proposed to exploit information from genotyped and non-genotyped relatives, allowing the use of weights for single-nucleotide polymorphism in the construction of the genomic relationship matrix. The purpose of this study was to investigate the accuracy of genetic prediction using the following single-trait best linear unbiased prediction methods in Hanwoo beef cattle: pedigree-based (PBLUP), un-weighted (ssGBLUP), and weighted (WssGBLUP) single-step genomic methods. We also assessed the impact of alternative single and window weighting methods according to their effects on the traits of interest. The data was comprised of 15,796 phenotypic records for yearling weight (YW) and 5622 records for carcass traits (backfat thickness: BFT, carcass weight: CW, eye muscle area: EMA, and marbling score: MS). Also, the genotypic data included 6616 animals for YW and 5134 for carcass traits on the 43,950 single-nucleotide polymorphisms. The ssGBLUP showed significant improvement in genomic prediction accuracy for carcass traits (71%) and yearling weight (99%) compared to the pedigree-based method. The window weighting procedures performed better than single SNP weighting for CW (11%), EMA (11%), MS (3%), and YW (6%), whereas no gain in accuracy was observed for BFT. Besides, the improvement in accuracy between window WssGBLUP and the un-weighted method was low for BFT and MS, while for CW, EMA, and YW resulted in a gain of 22%, 15%, and 20%, respectively, which indicates the presence of relevant quantitative trait loci for these traits. These findings indicate that WssGBLUP is an appropriate method for traits with a large quantitative trait loci effect.
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Lozada, Dennis N., and Arron H. Carter. "Genomic Selection in Winter Wheat Breeding Using a Recommender Approach." Genes 11, no. 7 (July 11, 2020): 779. http://dx.doi.org/10.3390/genes11070779.
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Achieving optimal predictive ability is key to increasing the relevance of implementing genomic selection (GS) approaches in plant breeding programs. The potential of an item-based collaborative filtering (IBCF) recommender system in the context of multi-trait, multi-environment GS has been explored. Different GS scenarios for IBCF were evaluated for a diverse population of winter wheat lines adapted to the Pacific Northwest region of the US. Predictions across years through cross-validations resulted in improved predictive ability when there is a high correlation between environments. Using multiple spectral traits collected from high-throughput phenotyping resulted in better GS accuracies for grain yield (GY) compared to using only single traits for predictions. Trait adjustments through various Bayesian regression models using genomic information from SNP markers was the most effective in achieving improved accuracies for GY, heading date, and plant height among the GS scenarios evaluated. Bayesian LASSO had the highest predictive ability compared to other models for phenotypic trait adjustments. IBCF gave competitive accuracies compared to a genomic best linear unbiased predictor (GBLUP) model for predicting different traits. Overall, an IBCF approach could be used as an alternative to traditional prediction models for important target traits in wheat breeding programs.
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Eteqadi, Bahareh, Seyed A. Rafat, Sadegh Alijani, Sven König, and Mehdi Bohlouli. "Genomic evaluation of binary traits in dairy cattle by considering genotype × environment interactions." Spanish Journal of Agricultural Research 20, no. 1 (March 2022): e0401-e0401. http://dx.doi.org/10.5424/sjar/2022201-17417.
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Aim of study: To assess genotype by environment (G×E) interaction via single- and multi-trait animal models for binary traits in dairy cattle. Area of study: University of Tabriz, Tabriz, Iran. Material and methods: Phenotypic and genomic data were simulated considering a binary trait in four environments as different correlated traits. Heritabilities of 0.05, 0.10, 0.15, and 0.20 were considered to mimic the genetic variation of the binary trait in different environments. Eight scenarios resulted from combining the number of QTLs (60 or 300), LD level (high or low), and incidence of the binary trait (10% or 30%) were simulated to compare the accuracy of predictions. For all scenarios, 1667 markers per chromosome (depicting a 50K SNP chip) were randomly spaced over 30 chromosomes. Multi-trait animal models were applied to take account of G×E interaction and to predict the genomic breeding value in different environments. Prediction accuracies obtained from the single- and multi-trait animal models were compared. Main results: In the models with G×E interaction, the largest accuracy of 0.401 was obtained in high LD scenario with 60 QTLs, and incidence of 30% for the fourth environment. The lowest accuracy of 0.190 was achieved in low LD scenario with 300 QTLs and incidence of 10% for the first environment. Research highlights: Genomic selection with high prediction accuracy can be possible by considering the G×E interaction during the genetic improvement programs in dairy cattle
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Smith, Timothy P. "226 Genomics in animal agriculture: current technologies and applications." Journal of Animal Science 97, Supplement_3 (December 2019): 55–56. http://dx.doi.org/10.1093/jas/skz258.113.
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Abstract The early impact of genomic research on animal agriculture was relatively modest, as it proved difficult to translate quantitative trait loci mapping to industrial application. Fortunately, developments in technology have facilitated the application of genomics to animal agriculture, which has led to more substantial impacts on many commercially produced animal species. A brief look back on the history of genomic research will be presented, followed by an overview of recent developments in genomic technologies. Examples of application of genomic research, focusing on beef cattle and comparative genomics with other bovinae specie, and the current status of some new genomic resources emerging for sheep, pigs, and goats, will also be presented.
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Thudi, Mahendar, Pooran M. Gaur, Lakshmanan Krishnamurthy, Reyazul R. Mir, Himabindu Kudapa, Asnake Fikre, Paul Kimurto, et al. "Genomics-assisted breeding for drought tolerance in chickpea." Functional Plant Biology 41, no. 11 (2014): 1178. http://dx.doi.org/10.1071/fp13318.
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Terminal drought is one of the major constraints in chickpea (Cicer arietinum L.), causing more than 50% production losses. With the objective of accelerating genetic understanding and crop improvement through genomics-assisted breeding, a draft genome sequence has been assembled for the CDC Frontier variety. In this context, 544.73 Mb of sequence data were assembled, capturing of 73.8% of the genome in scaffolds. In addition, large-scale genomic resources including several thousand simple sequence repeats and several million single nucleotide polymorphisms, high-density diversity array technology (15 360 clones) and Illumina GoldenGate assay genotyping platforms, high-density genetic maps and transcriptome assemblies have been developed. In parallel, by using linkage mapping approach, one genomic region harbouring quantitative trait loci for several drought tolerance traits has been identified and successfully introgressed in three leading chickpea varieties (e.g. JG 11, Chefe, KAK 2) by using a marker-assisted backcrossing approach. A multilocation evaluation of these marker-assisted backcrossing lines provided several lines with 10–24% higher yield than the respective recurrent parents.Modern breeding approaches like marker-assisted recurrent selection and genomic selection are being deployed for enhancing drought tolerance in chickpea. Some novel mapping populations such as multiparent advanced generation intercross and nested association mapping populations are also being developed for trait mapping at higher resolution, as well as for enhancing the genetic base of chickpea. Such advances in genomics and genomics-assisted breeding will accelerate precision and efficiency in breeding for stress tolerance in chickpea.
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Ramzan, Faisal, Mehmet Gültas, Hendrik Bertram, David Cavero, and Armin Otto Schmitt. "Combining Random Forests and a Signal Detection Method Leads to the Robust Detection of Genotype-Phenotype Associations." Genes 11, no. 8 (August 5, 2020): 892. http://dx.doi.org/10.3390/genes11080892.
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Genome wide association studies (GWAS) are a well established methodology to identify genomic variants and genes that are responsible for traits of interest in all branches of the life sciences. Despite the long time this methodology has had to mature the reliable detection of genotype–phenotype associations is still a challenge for many quantitative traits mainly because of the large number of genomic loci with weak individual effects on the trait under investigation. Thus, it can be hypothesized that many genomic variants that have a small, however real, effect remain unnoticed in many GWAS approaches. Here, we propose a two-step procedure to address this problem. In a first step, cubic splines are fitted to the test statistic values and genomic regions with spline-peaks that are higher than expected by chance are considered as quantitative trait loci (QTL). Then the SNPs in these QTLs are prioritized with respect to the strength of their association with the phenotype using a Random Forests approach. As a case study, we apply our procedure to real data sets and find trustworthy numbers of, partially novel, genomic variants and genes involved in various egg quality traits.
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Wijma, Robert, Daniel J. Weigel, Natascha Vukasinovic, Dianelys Gonzalez-Peña, Shaileen P. McGovern, Brenda C. Fessenden, Anthony K. McNeel, and Fernando A. Di Croce. "Genomic Prediction for Abortion in Lactating Holstein Dairy Cows." Animals 12, no. 16 (August 15, 2022): 2079. http://dx.doi.org/10.3390/ani12162079.
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Abortion in dairy cattle causes great economic losses due to reduced animal health, increase in culling rates, reduction in calf production, and milk yield, among others. Although the etiology of abortions can be of various origins, previous research has shown a genetic component. The objectives of this study were to (1) describe the development of the genomic prediction for cow abortions in lactating Holstein dairy cattle based on producer-recorded data and ssGBLUP methodology and (2) evaluate the efficacy of genomic predictions for cow abortions in commercial herds of US Holstein cows using data from herds that do not contribute phenotypic information to the evaluation. We hypothesized that cows with greater genomic predictions for cow abortions (Z_Abort STA) would have a reduced incidence of abortion. Phenotypic data on abortions, pedigree, and genotypes were collected directly from commercial dairy producers upon obtaining their permission. Abortion was defined as the loss of a confirmed pregnancy after 42 and prior to 260 days of gestation, treated as a binary outcome (0, 1), and analyzed using a threshold model. Data from a different subset of animals were used to test the efficacy of the prediction. The additive genetic variance for the cow abortion trait (Z_Abort) was 0.1235 and heritability was 0.0773. For all animals with genotypes (n = 1,662,251), mean reliability was 42%, and genomic predicted transmitting abilities (gPTAs) ranged from −8.8 to 12.4. Z_Abort had a positive correlation with cow and calf health traits and reproductive traits, and a negative correlation with production traits. Z_Abort effectively identified cows with a greater or lesser risk of abortion (16.6% vs. 11.0% for the worst and best genomics groups, respectively; p < 0.0001). The inclusion of cow abortion genomic predictions in a multi-trait selection index would allow dairy producers and consultants to reduce the incidence of abortion and to select high-producing, healthier, and more profitable cows.
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Guo, Gang, Fuping Zhao, Yachun Wang, Yuan Zhang, Lixin Du, and Guosheng Su. "Comparison of single-trait and multiple-trait genomic prediction models." BMC Genetics 15, no. 1 (2014): 30. http://dx.doi.org/10.1186/1471-2156-15-30.
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Yan, Chao, Ping Lin, Tao Lyu, Zhikang Hu, Zhengqi Fan, Xinlei Li, Xiaohua Yao, Jiyuan Li, and Hengfu Yin. "Unraveling the Roles of Regulatory Genes during Domestication of Cultivated Camellia: Evidence and Insights from Comparative and Evolutionary Genomics." Genes 9, no. 10 (October 10, 2018): 488. http://dx.doi.org/10.3390/genes9100488.
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With the increasing power of DNA sequencing, the genomics-based approach is becoming a promising resolution to dissect the molecular mechanism of domestication of complex traits in trees. Genus Camellia possesses rich resources with a substantial value for producing beverage, ornaments, edible oil and more. Currently, a vast number of genetic and genomic research studies in Camellia plants have emerged and provided an unprecedented opportunity to expedite the molecular breeding program. In this paper, we summarize the recent advances of gene expression and genomic resources in Camellia species and focus on identifying genes related to key economic traits such as flower and fruit development and stress tolerances. We investigate the genetic alterations and genomic impacts under different selection programs in closely related species. We discuss future directions of integrating large-scale population and quantitative genetics and multiple omics to identify key candidates to accelerate the breeding process. We propose that future work of exploiting the genomic data can provide insights related to the targets of domestication during breeding and the evolution of natural trait adaptations in genus Camellia.
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Wu, Chong. "Multi-trait Genome-Wide Analyses of the Brain Imaging Phenotypes in UK Biobank." Genetics 215, no. 4 (June 15, 2020): 947–58. http://dx.doi.org/10.1534/genetics.120.303242.
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Many genetic variants identified in genome-wide association studies (GWAS) are associated with multiple, sometimes seemingly unrelated, traits. This motivates multi-trait association analyses, which have successfully identified novel associated loci for many complex diseases. While appealing, most existing methods focus on analyzing a relatively small number of traits, and may yield inflated Type 1 error rates when a large number of traits need to be analyzed jointly. As deep phenotyping data are becoming rapidly available, we develop a novel method, referred to as aMAT (adaptive multi-trait association test), for multi-trait analysis of any number of traits. We applied aMAT to GWAS summary statistics for a set of 58 volumetric imaging derived phenotypes from the UK Biobank. aMAT had a genomic inflation factor of 1.04, indicating the Type 1 error rate was well controlled. More important, aMAT identified 24 distinct risk loci, 13 of which were ignored by standard GWAS. In comparison, the competing methods either had a suspicious genomic inflation factor or identified much fewer risk loci. Finally, four additional sets of traits have been analyzed and provided similar conclusions.
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Persa, Reyna, Arthur Bernardeli, and Diego Jarquin. "Prediction Strategies for Leveraging Information of Associated Traits under Single- and Multi-Trait Approaches in Soybeans." Agriculture 10, no. 8 (July 22, 2020): 308. http://dx.doi.org/10.3390/agriculture10080308.
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The availability of molecular markers has revolutionized conventional ways to improve genotypes in plant and animal breeding through genome-based predictions. Several models and methods have been developed to leverage the genomic information in the prediction context to allow more efficient ways to screen and select superior genotypes. In plant breeding, usually, grain yield (yield) is the main trait to drive the selection of superior genotypes; however, in many cases, the information of associated traits is also routinely collected and it can potentially be used to enhance the selection. In this research, we considered different prediction strategies to leverage the information of the associated traits ([AT]; full: all traits observed for the same genotype; and partial: some traits observed for the same genotype) under an alternative single-trait model and the multi-trait approach. The alternative single-trait model included the information of the AT for yield prediction via the phenotypic covariances while the multi-trait model jointly analyzed all the traits. The performance of these strategies was assessed using the marker and phenotypic information from the Soybean Nested Association Mapping (SoyNAM) project observed in Nebraska in 2012. The results showed that the alternative single-trait strategy, which combines the marker and the information of the AT, outperforms the multi-trait model by around 12% and the conventional single-trait strategy (baseline) by 25%. When no information on the AT was available for those genotypes in the testing sets, the multi-trait model reduced the baseline results by around 6%. For the cases where genotypes were partially observed (i.e., some traits observed but not others for the same genotype), the multi-trait strategy showed improvements of around 6% for yield and between 2% to 9% for the other traits. Hence, when yield drives the selection of superior genotypes, the single-trait and multi-trait genomic prediction will achieve significant improvements when some genotypes have been fully or partially tested, with the alternative single-trait model delivering the best results. These results provide empirical evidence of the usefulness of the AT for improving the predictive ability of prediction models for breeding applications.
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McINTYRE, LAUREN M., CYNTHIA J. COFFMAN, and R. W. DOERGE. "Detection and localization of a single binary trait locus in experimental populations." Genetical Research 78, no. 1 (August 2001): 79–92. http://dx.doi.org/10.1017/s0016672301005092.
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The advancements made in molecular technology coupled with statistical methodology have led to the successful detection and location of genomic regions (quantitative trait loci; QTL) associated with quantitative traits. Binary traits (e.g. susceptibility/resistance), while not quantitative in nature, are equally important for the purpose of detecting and locating significant associations with genomic regions. Existing interval regression methods used in binary trait analysis are adapted from quantitative trait analysis and the tests for regression coefficients are tests of effect, not detection. Additionally, estimates of recombination that fail to take into account varying penetrance perform poorly when penetrance is incomplete. In this work a complete probability model for binary trait data is developed allowing for unbiased estimation of both penetrance and recombination between a genetic marker locus and a binary trait locus for backcross and F2 experimental designs. The regression model is reparameterized allowing for tests of detection. Extensive simulations were conducted to assess the performance of estimation and testing in the proposed parameterization. The proposed parameterization was compared with interval regression via simulation. The results indicate that our parameterization shows equivalent estimation capabilities, requires less computational effort and works well with only a single marker.
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Hernandez, Christopher O., Lindsay E. Wyatt, and Michael R. Mazourek. "Genomic Prediction and Selection for Fruit Traits in Winter Squash." G3: Genes|Genomes|Genetics 10, no. 10 (August 19, 2020): 3601–10. http://dx.doi.org/10.1534/g3.120.401215.
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Improving fruit quality is an important but challenging breeding goal in winter squash. Squash breeding in general is resource-intensive, especially in terms of space, and the biology of squash makes it difficult to practice selection on both parents. These restrictions translate to smaller breeding populations and limited use of greenhouse generations, which in turn, limit genetic gain per breeding cycle and increases cycle length. Genomic selection is a promising technology for improving breeding efficiency; yet, few studies have explored its use in horticultural crops. We present results demonstrating the predictive ability of whole-genome models for fruit quality traits. Predictive abilities for quality traits were low to moderate, but sufficient for implementation. To test the use of genomic selection for improving fruit quality, we conducted three rounds of genomic recurrent selection in a butternut squash (Cucurbita moschata) population. Selections were based on a fruit quality index derived from a multi-trait genomic selection model. Remnant seed from selected populations was used to assess realized gain from selection. Analysis revealed significant improvement in fruit quality index value and changes in correlated traits. This study is one of the first empirical studies to evaluate gain from a multi-trait genomic selection model in a resource-limited horticultural crop.
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Canal, Guilherme Bravim, Cynthia Aparecida Valiati Barreto, Francine Alves Nogueira de Almeida, Iasmine Ramos Zaidan, Diego Pereira do Couto, Camila Ferreira Azevedo, Moysés Nascimento, Marcia Flores da Silva Ferreira, and Adésio Ferreira. "Single and multi-trait genomic prediction for agronomic traits in Euterpe edulis." PLOS ONE 18, no. 4 (April 7, 2023): e0275407. http://dx.doi.org/10.1371/journal.pone.0275407.
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Popularly known as juçaizeiro, Euterpe edulis has been gaining prominence in the fruit growing sector and has demanded the development of superior genetic materials. Since it is a native species and still little studied, the application of more sophisticated techniques can result in higher gains with less time. Until now, there are no studies that apply genomic prediction for this crop, especially in multi-trait analysis. In this sense, this study aimed to apply new methods and breeding techniques for the juçaizeiro, to optimize this breeding program through the application of genomic prediction. This data consisted of 275 juçaizeiro genotypes from a population of Rio Novo do Sul-ES, Brazil. The genomic prediction was performed using the multi-trait (G-BLUP MT) and single-trait (G-BLUP ST) models and the selection of superior genotypes was based on a selection index. Similar results for predictive ability were observed for both models. However, the G-BLUP ST model provided greater selection gains when compared to the G-BLUP MT. For this reason, the genomic estimated breeding values (GEBVs) from the G-BLUP ST, were used to select the six superior genotypes (UFES.A.RN.390, UFES.A.RN.386, UFES.A.RN.080, UFES.A.RN.383, UFES.S.RN.098, and UFES.S.RN.093). This was intended to provide superior genetic materials for the development of seedlings and implantation of productive orchards, which will meet the demands of the productive, industrial and consumer market.
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Singh, Gurnoor, Arnold Kuzniar, Matthijs Brouwer, Carlos Martinez-Ortiz, Christian W. B. Bachem, Yury M. Tikunov, Arnaud G. Bovy, and Richard G. F. Visser and Richard Finkers. "Linked Data Platform for Solanaceae Species." Applied Sciences 10, no. 19 (September 28, 2020): 6813. http://dx.doi.org/10.3390/app10196813.
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Genetics research is increasingly focusing on mining fully sequenced genomes and their annotations to identify the causal genes associated with traits (phenotypes) of interest. However, a complex trait is typically associated with multiple quantitative trait loci (QTLs), each comprising many genes, that can positively or negatively affect the trait of interest. To help breeders in ranking candidate genes, we developed an analytical platform called pbg-ld that provides semantically integrated geno- and phenotypic data on Solanaceae species. This platform combines both unstructured data from scientific literature and structured data from publicly available biological databases using the Linked Data approach. In particular, QTLs were extracted from tables of full-text articles from the Europe PubMed Central (PMC) repository using QTLTableMiner++ (QTM), while the genomic annotations were obtained from the Sol Genomics Network (SGN), UniProt and Ensembl Plants databases. These datasets were transformed into Linked Data graphs, which include cross-references to many other relevant databases such as Gramene, Plant Reactome, InterPro and KEGG Orthology (KO). Users can query and analyze the integrated data through a web interface or programmatically via the SPARQL and RESTful services (APIs). We illustrate the usability of pbg-ld by querying genome annotations, by comparing genome graphs, and by two biological use cases in Jupyter Notebooks. In the first use case, we performed a comparative genomics study using pbg-ld to compare the difference in the genetic mechanism underlying tomato fruit shape and potato tuber shape. In the second use case, we developed a seamlessly integrated workflow that uses genomic data from pbg-ld knowledge graphs and prioritization pipelines to predict candidate genes within QTL regions for metabolic traits of tomato.
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Shaibu, Abdulwahab S., Clay Sneller, Babu N. Motagi, Jackline Chepkoech, Mercy Chepngetich, Zainab L. Miko, Adamu M. Isa, Hakeem A. Ajeigbe, and Sanusi G. Mohammed. "Genome-Wide Detection of SNP Markers Associated with Four Physiological Traits in Groundnut (Arachis hypogaea L.) Mini Core Collection." Agronomy 10, no. 2 (February 1, 2020): 192. http://dx.doi.org/10.3390/agronomy10020192.
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In order to integrate genomics in breeding and development of drought-tolerant groundnut genotypes, identification of genomic regions/genetic markers for drought surrogate traits is essential. We used 3249 diversity array technology sequencing (DArTSeq) markers for a genetic analysis of 125 ICRISAT groundnut mini core collection evaluated in 2015 and 2017 for genome-wide marker-trait association for some physiological traits and to determine the magnitude of linkage disequilibrium (LD). Marker-trait association (MTA) analysis, probability values, and percent variation modelled by the markers were calculated using the GAPIT package via the KDCompute interface. The LD analysis showed that about 36% of loci pairs were in significant LD (p < 0.05 and r2 > 0.2) and 3.14% of the pairs were in complete LD. The MTAs studies revealed 20 significant MTAs (p < 0.001) with 11 markers. Four MTAs were identified for leaf area index, 13 for canopy temperature, one for chlorophyll content and two for normalized difference vegetation index. The markers explained 20.8% to 6.6% of the phenotypic variation observed. Most of the MTAs identified on the A subgenome were also identified on the respective homeologous chromosome on the B subgenome. This could be due to a common ancestor of the A and B genome which explains the linkage detected between markers lying on different chromosomes. The markers identified in this study can serve as useful genomic resources to initiate marker-assisted selection and trait introgression of groundnut for drought tolerance after further validation.
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Zhu, Xintian, Hans Peter Maurer, Mario Jenz, Volker Hahn, Arno Ruckelshausen, Willmar L. Leiser, and Tobias Würschum. "The performance of phenomic selection depends on the genetic architecture of the target trait." Theoretical and Applied Genetics 135, no. 2 (November 22, 2021): 653–65. http://dx.doi.org/10.1007/s00122-021-03997-7.
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Abstract Key message The phenomic predictive ability depends on the genetic architecture of the target trait, being high for complex traits and low for traits with major QTL. Abstract Genomic selection is a powerful tool to assist breeding of complex traits, but a limitation is the costs required for genotyping. Recently, phenomic selection has been suggested, which uses spectral data instead of molecular markers as predictors. It was shown to be competitive with genomic prediction, as it achieved predictive abilities as high or even higher than its genomic counterpart. The objective of this study was to evaluate the performance of phenomic prediction for triticale and the dependency of the predictive ability on the genetic architecture of the target trait. We found that for traits with a complex genetic architecture, like grain yield, phenomic prediction with NIRS data as predictors achieved high predictive abilities and performed better than genomic prediction. By contrast, for mono- or oligogenic traits, for example, yellow rust, marker-based approaches achieved high predictive abilities, while those of phenomic prediction were very low. Compared with molecular markers, the predictive ability obtained using NIRS data was more robust to varying degrees of genetic relatedness between the training and prediction set. Moreover, for grain yield, smaller training sets were required to achieve a similar predictive ability for phenomic prediction than for genomic prediction. In addition, our results illustrate the potential of using field-based spectral data for phenomic prediction. Overall, our result confirmed phenomic prediction as an efficient approach to improve the selection gain for complex traits in plant breeding.
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Tsuruta, S., I. Misztal, I. Aguilar, and T. J. Lawlor. "Multiple-trait genomic evaluation of linear type traits using genomic and phenotypic data in US Holsteins." Journal of Dairy Science 94, no. 8 (August 2011): 4198–204. http://dx.doi.org/10.3168/jds.2011-4256.
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Abraham, Abin, Abigail L. LaBella, John A. Capra, and Antonis Rokas. "Mosaic patterns of selection in genomic regions associated with diverse human traits." PLOS Genetics 18, no. 11 (November 7, 2022): e1010494. http://dx.doi.org/10.1371/journal.pgen.1010494.
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Natural selection shapes the genetic architecture of many human traits. However, the prevalence of different modes of selection on genomic regions associated with variation in traits remains poorly understood. To address this, we developed an efficient computational framework to calculate positive and negative enrichment of different evolutionary measures among regions associated with complex traits. We applied the framework to summary statistics from >900 genome-wide association studies (GWASs) and 11 evolutionary measures of sequence constraint, population differentiation, and allele age while accounting for linkage disequilibrium, allele frequency, and other potential confounders. We demonstrate that this framework yields consistent results across GWASs with variable sample sizes, numbers of trait-associated SNPs, and analytical approaches. The resulting evolutionary atlas maps diverse signatures of selection on genomic regions associated with complex human traits on an unprecedented scale. We detected positive enrichment for sequence conservation among trait-associated regions for the majority of traits (>77% of 290 high power GWASs), which included reproductive traits. Many traits also exhibited substantial positive enrichment for population differentiation, especially among hair, skin, and pigmentation traits. In contrast, we detected widespread negative enrichment for signatures of balancing selection (51% of GWASs) and absence of enrichment for evolutionary signals in regions associated with late-onset Alzheimer’s disease. These results support a pervasive role for negative selection on regions of the human genome that contribute to variation in complex traits, but also demonstrate that diverse modes of evolution are likely to have shaped trait-associated loci. This atlas of evolutionary signatures across the diversity of available GWASs will enable exploration of the relationship between the genetic architecture and evolutionary processes in the human genome.
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Zonaed Siddiki, A. M. A. M., Gous Miah, Md Sirazul Islam, Mahadia Kumkum, Meheadi Hasan Rumi, Abdul Baten, and Mohammad Alamgir Hossain. "Goat Genomic Resources: The Search for Genes Associated with Its Economic Traits." International Journal of Genomics 2020 (August 21, 2020): 1–13. http://dx.doi.org/10.1155/2020/5940205.
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Goat plays a crucial role in human livelihoods, being a major source of meat, milk, fiber, and hides, particularly under adverse climatic conditions. The goat genomics related to the candidate gene approach is now being used to recognize molecular mechanisms that have different expressions of growth, reproductive, milk, wool, and disease resistance. The appropriate literature on this topic has been reviewed in this article. Several genetic characterization attempts of different goats have reported the existence of genotypic and morphological variations between different goat populations. As a result, different whole-genome sequences along with annotated gene sequences, gene function, and other genomic information of different goats are available in different databases. The main objective of this review is to search the genes associated with economic traits in goats. More than 271 candidate genes have been discovered in goats. Candidate genes influence the physiological pathway, metabolism, and expression of phenotypes. These genes have different functions on economically important traits. Some genes have pleiotropic effect for expression of phenotypic traits. Hence, recognizing candidate genes and their mutations that cause variations in gene expression and phenotype of an economic trait can help breeders look for genetic markers for specific economic traits. The availability of reference whole-genome assembly of goats, annotated genes, and transcriptomics makes comparative genomics a useful tool for systemic genetic upgradation. Identification and characterization of trait-associated sequence variations and gene will provide powerful means to give positive influences for future goat breeding program.
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Islam, Md S., Per McCord, Quentin D. Read, Lifang Qin, Alexander E. Lipka, Sushma Sood, James Todd, and Marcus Olatoye. "Accuracy of Genomic Prediction of Yield and Sugar Traits in Saccharum spp. Hybrids." Agriculture 12, no. 9 (September 10, 2022): 1436. http://dx.doi.org/10.3390/agriculture12091436.
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Genomic selection (GS) has been demonstrated to enhance the selection process in breeding programs. The objectives of this study were to experimentally evaluate different GS methods in sugarcane hybrids and to determine the prospect of GS in future breeding approaches. Using sugar and yield-related trait data from 432 sugarcane clones and 10,435 single nucleotide polymorphisms (SNPs), a study was conducted using seven different GS models. While fivefold cross-validated prediction accuracy differed by trait and by crop cycle, there were only small differences in prediction accuracy among the different models. Prediction accuracy was on average 0.20 across all traits and crop cycles for all tested models. Utilizing a trait-assisted GS model, we could effectively predict the fivefold cross-validated genomic estimated breeding value of ratoon crops using both SNPs and trait values from the plant cane crop. We found that the plateau of prediction accuracy could be achieved with 4000 to 5000 SNPs. Prediction accuracy did not decline with decreasing size of the training population until it was reduced below 60% (259) to 80% (346) of the original number of clones. Our findings suggest that GS is possibly a new direction for improving sugar and yield-related traits in sugarcane.
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Budhlakoti, Neeraj, Dwijesh Chandra Mishra, Anil Rai, S. B. Lal, Krishna Kumar Chaturvedi, and Rajeev Ranjan Kumar. "A Comparative Study of Single-Trait and Multi-Trait Genomic Selection." Journal of Computational Biology 26, no. 10 (October 1, 2019): 1100–1112. http://dx.doi.org/10.1089/cmb.2019.0032.
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Beier, Sara, Johannes Werner, Thierry Bouvier, Nicolas Mouquet, and Cyrille Violle. "Trait-trait relationships and tradeoffs vary with genome size in prokaryotes." Frontiers in Microbiology 13 (October 21, 2022). http://dx.doi.org/10.3389/fmicb.2022.985216.
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We report genomic traits that have been associated with the life history of prokaryotes and highlight conflicting findings concerning earlier observed trait correlations and tradeoffs. In order to address possible explanations for these contradictions we examined trait–trait variations of 11 genomic traits from ~18,000 sequenced genomes. The studied trait–trait variations suggested: (i) the predominance of two resistance and resilience-related orthogonal axes and (ii) at least in free living species with large effective population sizes whose evolution is little affected by genetic drift an overlap between a resilience axis and an oligotrophic-copiotrophic axis. These findings imply that resistance associated traits of prokaryotes are globally decoupled from resilience related traits and in the case of free-living communities also from traits associated with resource availability. However, further inspection of pairwise scatterplots showed that resistance and resilience traits tended to be positively related for genomes up to roughly five million base pairs and negatively for larger genomes. Genome size distributions differ across habitats and our findings therefore point to habitat dependent tradeoffs between resistance and resilience. This in turn may preclude a globally consistent assignment of prokaryote genomic traits to the competitor - stress-tolerator - ruderal (CSR) schema that sorts species depending on their location along disturbance and productivity gradients into three ecological strategies and may serve as an explanation for conflicting findings from earlier studies. All reviewed genomic traits featured significant phylogenetic signals and we propose that our trait table can be applied to extrapolate genomic traits from taxonomic marker genes. This will enable to empirically evaluate the assembly of these genomic traits in prokaryotic communities from different habitats and under different productivity and disturbance scenarios as predicted via the resistance-resilience framework formulated here.
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Farooq, Muhammad, Aalt D. J. van Dijk, Harm Nijveen, Mark G. M. Aarts, Willem Kruijer, Thu-Phuong Nguyen, Shahid Mansoor, and Dick de Ridder. "Prior Biological Knowledge Improves Genomic Prediction of Growth-Related Traits in Arabidopsis thaliana." Frontiers in Genetics 11 (January 20, 2021). http://dx.doi.org/10.3389/fgene.2020.609117.
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Prediction of growth-related complex traits is highly important for crop breeding. Photosynthesis efficiency and biomass are direct indicators of overall plant performance and therefore even minor improvements in these traits can result in significant breeding gains. Crop breeding for complex traits has been revolutionized by technological developments in genomics and phenomics. Capitalizing on the growing availability of genomics data, genome-wide marker-based prediction models allow for efficient selection of the best parents for the next generation without the need for phenotypic information. Until now such models mostly predict the phenotype directly from the genotype and fail to make use of relevant biological knowledge. It is an open question to what extent the use of such biological knowledge is beneficial for improving genomic prediction accuracy and reliability. In this study, we explored the use of publicly available biological information for genomic prediction of photosynthetic light use efficiency (ΦPSII) and projected leaf area (PLA) inArabidopsis thaliana. To explore the use of various types of knowledge, we mapped genomic polymorphisms to Gene Ontology (GO) terms and transcriptomics-based gene clusters, and applied these in a Genomic Feature Best Linear Unbiased Predictor (GFBLUP) model, which is an extension to the traditional Genomic BLUP (GBLUP) benchmark. Our results suggest that incorporation of prior biological knowledge can improve genomic prediction accuracy for both ΦPSIIand PLA. The improvement achieved depends on the trait, type of knowledge and trait heritability. Moreover, transcriptomics offers complementary evidence to the Gene Ontology for improvement when used to define functional groups of genes. In conclusion, prior knowledge about trait-specific groups of genes can be directly translated into improved genomic prediction.
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Skovbjerg, Cathrine Kiel, Deepti Angra, Tom Robertson-Shersby-Harvie, Jonathan Kreplak, Gabriel Keeble-Gagnère, Sukhjiwan Kaur, Wolfgang Ecke, et al. "Genetic analysis of global faba bean diversity, agronomic traits and selection signatures." Theoretical and Applied Genetics 136, no. 5 (April 19, 2023). http://dx.doi.org/10.1007/s00122-023-04360-8.
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Abstract Key message We identified marker-trait associations for key faba bean agronomic traits and genomic signatures of selection within a global germplasm collection. Abstract Faba bean (Vicia faba L.) is a high-protein grain legume crop with great potential for sustainable protein production. However, little is known about the genetics underlying trait diversity. In this study, we used 21,345 high-quality SNP markers to genetically characterize 2678 faba bean genotypes. We performed genome-wide association studies of key agronomic traits using a seven-parent-MAGIC population and detected 238 significant marker-trait associations linked to 12 traits of agronomic importance. Sixty-five of these were stable across multiple environments. Using a non-redundant diversity panel of 685 accessions from 52 countries, we identified three subpopulations differentiated by geographical origin and 33 genomic regions subjected to strong diversifying selection between subpopulations. We found that SNP markers associated with the differentiation of northern and southern accessions explained a significant proportion of agronomic trait variance in the seven-parent-MAGIC population, suggesting that some of these traits were targets of selection during breeding. Our findings point to genomic regions associated with important agronomic traits and selection, facilitating faba bean genomics-based breeding.
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Lozada-Soto, Emmanuel André, Daniela Lourenco, Christian Maltecca, Justin Fix, Clint Schwab, Caleb Shull, and Francesco Tiezzi. "Genotyping and phenotyping strategies for genetic improvement of meat quality and carcass composition in swine." Genetics Selection Evolution 54, no. 1 (June 7, 2022). http://dx.doi.org/10.1186/s12711-022-00736-4.
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Abstract Background Meat quality and composition traits have become valuable in modern pork production; however, genetic improvement has been slow due to high phenotyping costs. Combining genomic information with multi-trait indirect selection based on cheaper indicator traits is an alternative for continued cost-effective genetic improvement. Methods Data from an ongoing breeding program were used in this study. Phenotypic and genomic information was collected on three-way crossbred and purebred Duroc animals belonging to 28 half-sib families. We applied different methods to assess the value of using purebred and crossbred information (both genomic and phenotypic) to predict expensive-to-record traits measured on crossbred individuals. Estimation of multi-trait variance components set the basis for comparing the different scenarios, together with a fourfold cross-validation approach to validate the phenotyping schemes under four genotyping strategies. Results The benefit of including genomic information for multi-trait prediction depended on the breeding goal trait, the indicator traits included, and the source of genomic information. While some traits benefitted significantly from genotyping crossbreds (e.g., loin intramuscular fat content, backfat depth, and belly weight), multi-trait prediction was advantageous for some traits even in the absence of genomic information (e.g., loin muscle weight, subjective color, and subjective firmness). Conclusions Our results show the value of using different sources of phenotypic and genomic information. For most of the traits studied, including crossbred genomic information was more beneficial than performing multi-trait prediction. Thus, we recommend including crossbred individuals in the reference population when these are phenotyped for the breeding objective.