A Hybrid Method For The Implementation Of Genomic Selection Based On Fst Prioritized Single Nucleotide Polymorphisms
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A Hybrid Method For The Implementation Of Genomic Selection Based On Fst Prioritized Single Nucleotide Polymorphisms
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Author : Sajjad Toghiani
language : en
Publisher:
Release Date : 2018
A Hybrid Method For The Implementation Of Genomic Selection Based On Fst Prioritized Single Nucleotide Polymorphisms written by Sajjad Toghiani and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
Availability of high-density (HD) marker panels provides an opportunity to improve the accuracy of genomic selection (GS). Unfortunately, using HD panels resulted in no significant increase in the accuracy of GS. This lack of improvement in accuracy is more likely due to the limitations of current GS methods rather than the uselessness of HD data. Increasing variants in association models caused a reduction in statistical power. Increase in the number of genotyped animals complicated the inversion of the genomic relationship matrix. Thus, reducing the number of variants and eliminating the inversion of genomic relationship matrix are required for the full benefit from HD marker data. We proposed fixation index (FST) to prioritize SNPs for GS. To validate the usefulness of FST, a trait with heritability of 0.4 under different SNP densities was simulated. Prioritized top 2.5% markers were able to tag most significant QTL and to increase functional genomic similarity. The latter could be used as a decision-making or selection tool. In spite of being able to prioritize markers in linkage disequilibrium with relevant QTL, the latter explained only a portion of the genetic variance. This is the case because small effects QTL are often not tagged with the prioritized SNPs. These small effect QTL could be tracked, however, by a polygenic component. Thus, a hybrid model was proposed that included the prioritized SNPs and a polygenic component in the association model. The proposed approach was evaluated based on simulated data of a trait with heritability of 0.1 and 0.4 and a real data of weaning weight in beef cattle. Using only genotyped animals, the hybrid model outperformed BayesB, BayesC and GBLUP when the prioritized 2.5% SNPs were used in the association model. The hybrid model was extended to accommodate non-genotyped animals. It outperformed ssGBLUP method using simulated data under both heritability scenarios. Although the results of the evaluation are likely to depend on the data generating process including the genetic complexity of the trait, the hybrid model seemed to be competitive compared to current methods. Furthermore, its computational costs in terms of CPU time and peak memory are limited.
Application Of Genomics In Livestock Populations Under Selection Or Conservation
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Author : Anupama Mukherjee
language : en
Publisher: Frontiers Media SA
Release Date : 2024-02-06
Application Of Genomics In Livestock Populations Under Selection Or Conservation written by Anupama Mukherjee and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-06 with Science categories.
Index Medicus
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Author :
language : en
Publisher:
Release Date : 2004
Index Medicus written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Medicine categories.
Vols. for 1963- include as pt. 2 of the Jan. issue: Medical subject headings.
Harnessing Genebanks High Throughput Phenotyping And Genotyping Of Crop Wild Relatives And Landraces
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Author : Andrés J. Cortés
language : en
Publisher: Frontiers Media SA
Release Date : 2023-04-06
Harnessing Genebanks High Throughput Phenotyping And Genotyping Of Crop Wild Relatives And Landraces written by Andrés J. Cortés and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-06 with Science categories.
Marine Ecosystem Restoration Mer Challenges And New Horizons
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Author : Brian Silliman
language : en
Publisher: Frontiers Media SA
Release Date : 2023-10-23
Marine Ecosystem Restoration Mer Challenges And New Horizons written by Brian Silliman and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-23 with Science categories.
Worldwide, marine ecosystems have been lost and degraded due to anthropogenic disturbances. For example, oyster reefs have declined by at least ∼85%, coral reefs by ∼19%, seagrasses by ∼29%, North American salt marshes by ∼42%, and mangroves by ∼35% from the early 19th century. Deepwater reefs and deep-sea vents are not immune and have also been reduced in extent in many areas. Factors driving these losses include habitat destruction, pollution, invasive species, overfishing, trawling, mining and, more recently, climate change effects, such as ocean warming, species range changes and acidification. These habitat declines are occurring at a time when marine waters are being used at or near their maximum productive capacity to meet the contemporary needs of an ever-increasing human population. Because coastal and marine ecosystems generate some of the richest biodiversity hotspots on Earth, and provide critical ecosystem services, including storm protection, fisheries production, and carbon storage, over 1 billion US dollars have been spent globally in an attempt to halt and reverse observed declines. Early conservation efforts aimed at protecting these valuable and threatened habitats focused on reducing human impacts and physical stressors. However, with habitat degradation continuing and sometimes increasing in rate, it is now clear conservation alone will not be sufficient to protect and reestablish coastal ecosystems. Habitat restoration, although in existence for many decades, has recently been elevated as a new primary strategy to stem and even reverse coastal habitat loss. The call for increasing investment in restoration efforts has emerged with significant advances in propagule rearing and dispersion of habitat-forming organisms (e.g., oysters, seagrasses, corals). In addition, restoration resources are increasingly allocated by governments and/or large corporations with the aim to, for example, fix past landscape engineering efforts that had unintended environmental consequences. Such investments are being made to (i) provide jobs for those unemployed during economic downturns, (ii) restore ecosystems destroyed by natural disasters and stressors, (iii) increase coastal defense in response to increased frequency of intense storms, and/or (iv) compensate for pollution-and development-driven habitat degradation. Conservation practitioners have traditionally been skeptical to invest heavily in restoration at large-scales because of the high cost per area (10,000-5,000,000 US$/ha for coastal vs. 500-5,000 US$/ha for terrestrial systems) to replant coastal ecosystems and/or the high chance that the restored ecosystems will not live long (e.g. outplanted corals). For restoration to be effective and employed as a primary method of coastal conservation at relevant scales, we must improve its efficiency, lower costs and rapidly share and incorporate advances. One crucial step will be to identify when and where restoration attempts have been carried out according to state-of-art ecological theory and gauge their success. Another is generating synthesis studies that focus both within and across ecosystems to identify efficiencies, adaptations and innovations. Work that shows theoretical and methodological innovations in specific ecosystems as well as across systems will be critical to pushing all fields of MER forward. Although there is rapidly increasing interest and investment, the field of marine ecosystem restoration is just beginning to undergo synthesis. Therefore, the aim of this Research Topic is to bring together research contributions to help address this synthesis need, provide a spotlight for recent innovations, enhance our understanding of successful methods in marine ecosystem restoration and promote integration of ecological, sociological and engineering theory into restoration practices.
Genomic Selection Lessons Learned And Perspectives
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Author : Johannes W. R. Martini
language : en
Publisher: Frontiers Media SA
Release Date : 2022-09-15
Genomic Selection Lessons Learned And Perspectives written by Johannes W. R. Martini and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-15 with Science categories.
Genomic selection (GS) has been the most prominent topic in breeding science in the last two decades. The continued interest is promoted by its huge potential impact on the efficiency of breeding. Predicting a breeding value based on molecular markers and phenotypic values of relatives may be used to manipulate three parameters of the breeder's equation. First, the accuracy of the selection may be improved by predicting the genetic value more reliably when considering the records of relatives and the realized genomic relationship. Secondly, genotyping and predicting may be more cost effective than comprehensive phenotyping. Resources can instead be allocated to increasing population sizes and selection intensity. The third, probably most important factor, is time. As shown in dairy cattle breeding, reducing cycle time by crossing selection candidates earlier may have the strongest impact on selection gain. Many different prediction models have been used, and different ways of using predicted values in a breeding program have been explored. We would like to address the questions: i. How did GS change breeding schemes of different crops in the last 20 years? ii. What was the impact on realized selection gain? iii. What would be the best structure of a crop-specific breeding scheme to exploit the full potential of GS? iv. What is the potential of hybrid prediction, epistasis effect models, deep learning methods and other extensions of the standard prediction of additive effects? v. What are the long-term effects of GS? vi. Can predictive breeding approaches also be used to harness genetic resources from germplasm banks in a more efficient way to adapt current germplasm to new environmental challenges? This Research Topic welcomes submissions of Original Research papers, Opinions, Perspectives, Reviews, and Mini-Reviews related to these themes: 1. Genomic selection: statistical methodology 2. The (optimal) use of GS in breeding schemes 3. Practical experiences with GS (selection gain, long-term effects, negative side effects) 4. Predictive approaches to harness genetic resources Concerning point 1): If an original research paper compares different methods empirically without theoretical considerations on when one or the other method should be better, the methods should be compared with at least five different data sets. The data sets should differ either in crop, genotyping method or its source, for instance from a breeding program or gene bank accessions. Concerning point 2): Manuscripts addressing the use of GS in breeding schemes should illustrate breeding schemes that are run in practice. General ideas about schemes that may be run in the future may be considered as 'Perspective' articles. Conflict of Interest statements: - Topic Editor Valentin Wimmer is affiliated to KWS SAAT SE & Co. KGaA, Germany. - Topic Editor Brian Gardunia is affiliated to Bayer Crop Sciences and has a collaboration with AbacusBio, and is an author on patents with Bayer Crop Sciences. The other Topic Editors did not disclose any conflicts of interest. Image credit: CIMMYT, reproduced under the CC BY-NC-SA 2.0 license
Optimum Strategies To Implement Genomic Selection In Hybrid Breeding
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Author : José J. Marulanda
language : en
Publisher:
Release Date : 2023
Optimum Strategies To Implement Genomic Selection In Hybrid Breeding written by José J. Marulanda and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
Introducing Sparsity Into Selection Index Methodology With Applications To High Throughput Phenotyping And Genomic Prediction
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Author : Marco Antonio Lopez Cruz
language : en
Publisher:
Release Date : 2020
Introducing Sparsity Into Selection Index Methodology With Applications To High Throughput Phenotyping And Genomic Prediction written by Marco Antonio Lopez Cruz and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Electronic dissertations categories.
Research in plant and animal breeding has been largely focused on the development of methods for a more efficient selection by altering the factors that affect genetic progress: selection intensity, selection accuracy, genetic variance, and length of the breeding cycle. Most of the breeding efforts have been primarily towards increasing selection accuracy and reducing the breeding cycle.Genomic selection has been successfully adopted by many public and private breeding organizations. Over years, these institutions have developed and accumulated large volumes of genomic data linked to phenotypes from multiple populations and multiple generations. This data abundance offers the opportunity to revolutionize genetic research. However, these data sets are also increasingly heterogeneous, with many subpopulations and multiple generations represented in the data. This translates into potentially heterogeneous allele frequencies and different LD patterns, thus leading to SNP-effect heterogeneity.Genomic selection methods were developed with reference to homogeneous populations in which SNP-effects are assumed constant across the whole population. These methods are not necessarily optimal for the contemporary available data sets for model training. Therefore, a first focus of this dissertation is on developing novel methods that can leverage the large-scale of modern data sets while coping with the heterogeneity and complexity of this type of data.In recent years, there have also been important advances in high-throughput phenotyping (HTP) technologies that can generate large volumes of data at multiple time-points of a crop. Examples of this include hyper-spectral imaging technologies that can capture the reflectance of electromagnetic power by crops at potentially thousands of wavelengths. The integration of HTP in genetic evaluations represents a great opportunity to further advance plant breeding; however, the high-dimensional nature of HTP data poses important challenges. Therefore, a second focus of this dissertation is on the development of a novel approach to efficiently incorporate HTP data for breeding values prediction.Thus, this dissertation aims to contribute novel methods that can improve the accuracy of genomic prediction by optimizing the use of large, potentially heterogeneous, genomic data sets and by enabling the integration of HTP data. We present a novel statistical approach that combines the standard selection index methodology with variable-selection methods commonly used in machine learning and statistics, and developed software to implement the method. Our approach offers solutions to both genomic selection with potentially highly heterogeneous genomic data sets, and the integration of HTP in genetic evaluations.
Admixture Dynamics Natural Selection And Diseases In Admixed Populations
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Author : Wenfei Jin
language : en
Publisher: Springer
Release Date : 2015-10-20
Admixture Dynamics Natural Selection And Diseases In Admixed Populations written by Wenfei Jin and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-10-20 with Medical categories.
In this thesis, Dr. Jin presents the distribution of ancestral chromosomal segments in the admixed genome, which could provide the information needed to explore population admixture dynamics. The author derives accurate population histories of African Americans and Mexicans using genome-wide single nucleotide polymorphisms (SNPs) data. Mapping the genetic background facilitates the study of natural selection in the admixed population, and the author identifies the signals of selection in African Americans since their African ancestors left for America. He further demonstrates that many of the selection signals were associated with African American-specific high-risk diseases such as prostate cancer and hypertension, suggesting an important role these disease-related genes might have played in adapting to their new environment. Lastly, the author reveals the complexity of natural selection in shaping human susceptibility to disease. The thesis significantly advances our understanding of the recent population admixture, adaptation to local environment and its health implications.
Optimal Designs In Genomic Selection
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Author : Josafhat Salinas Ruiz
language : en
Publisher:
Release Date : 2015
Optimal Designs In Genomic Selection written by Josafhat Salinas Ruiz and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
Recently, many plant and animal breeders have been using genome-wide genetic markers and statistical methods to aid with selection of genetic material. These methods, termed genomic selection (GS), make selections based on estimates of breeding values obtained from a prediction model computed from phenotypic and genomic data of a training population. The precision of the predictions strongly depends on the genetic diversity of the training population (TP). The objectives of this research were (1) To present a new method for creating a TP that maximizes genetic diversity using either the most important genomic markers or the first few principal components (PCs) of the genomic data as inputs into A, D, and V optimal design algorithms; and (2) To evaluate the average predictive ability of the A, D, and V optimal TPs and compare their predictabilities with TPs based on random sampling, the commonly used approach. Using data from the University of Nebraska red winter wheat breeding program, results showed that when created the TP using either the most significant markers or the first PCs, the gain of the average predictive ability was higher in all optimal designs compared with random sampling with an average increase by 13.425% over random sampling. In addition, it was estimated that genetic gains of selection can be increased by 2.8348 and 3.3538 times when using the p1 significant markers and the first p1 PCs compared with the genetic gain of 1.8306 random sampling TP, respectively.