Introducing Sparsity Into Selection Index Methodology With Applications To High Throughput Phenotyping And Genomic Prediction
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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.
Artificial Intelligence Applications In Specialty Crops
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Author : Yiannis Ampatzidis
language : en
Publisher: Frontiers Media SA
Release Date : 2022-03-02
Artificial Intelligence Applications In Specialty Crops written by Yiannis Ampatzidis 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 2022-03-02 with Science categories.
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
Genotyping By Sequencing For Crop Improvement
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Author : Humira Sonah
language : en
Publisher: John Wiley & Sons
Release Date : 2022-03-29
Genotyping By Sequencing For Crop Improvement written by Humira Sonah and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-29 with Science categories.
OGENOTYPING BY SEQUENCING FOR CROP IMPROVEMENT A thoroughly up-to-date exploration of genotyping-by-sequencing technologies and related methods in plant science In Genotyping by Sequencing for Crop Improvement, a team of distinguished researchers delivers an in-depth and current exploration of the latest advances in genotyping-by-sequencing (GBS) methods, the statistical approaches used to analyze GBS data, and its applications, including quantitative trait loci (QTL) mapping, genome-wide association studies (GWAS), and genomic selection (GS) in crop improvement. This edited volume includes insightful contributions on a variety of relevant topics, like advanced molecular markers, high-throughput genotyping platforms, whole genome resequencing, QTL mapping with advanced mapping populations, analytical pipelines for GBS analysis, and more. The distinguished contributors explore traditional and advanced markers used in plant genotyping in extensive detail, and advanced genotyping platforms that cater to unique research purposes are discussed, as is the whole-genome resequencing (WGR) methodology. The included chapters also examine the applications of these technologies in several different crop categories, including cereals, pulses, oilseeds, and commercial crops. Genotyping by Sequencing for Crop Improvement also offers: A thorough introduction to molecular marker techniques and recent advancements in the technology Comprehensive explorations of the genotyping of seeds while preserving their viability, as well as advances in genomic selection Practical discussions of opportunities and challenges relating to high throughput genotyping in polyploid crops In-depth examinations of recent advances and applications of GBS, GWAS, and GS in cereals, pulses, oilseeds, millets, and commercial crops Perfect for practicing plant scientists with an interest in genotyping-by-sequencing technology, Genotyping by Sequencing for Crop Improvement will also earn a place in the libraries of researchers and students seeking a one-stop reference on the foundational aspects of – and recent advances in – genotyping-by-sequencing, genome-wide association studies, and genomic selection.
A Differential Evolution Approach To Feature Selection In Genomic Prediction
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Author : Ian Whalen
language : en
Publisher:
Release Date : 2018
A Differential Evolution Approach To Feature Selection In Genomic Prediction written by Ian Whalen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Electronic dissertations categories.
The use of genetic markers has become widespread for prediction of genetic merit in agricultural applications and is a beginning to show promise for estimating propensity to disease in human medicine. This process is known as genomic prediction and attempts to model the mapping between an organism's genotype and phenotype. In practice, this process presents a challenging problem. Sequencing and recording phenotypic traits are often expensive and time consuming. This leads to datasets often having many more features than samples. Common models for genomic prediction often fall victim to overfitting due to the curse of dimensionality. In this domain, only a fraction of the markers that are present significantly affect a particular trait. Models that fit to non-informative markers are in effect fitting to statistical noise, leading to a decrease in predictive performance. Therefore, feature selection is desirable to remove markers that do not appear to have a significant effect on the trait being predicted. The method presented here uses differential evolution based search for feature selection. This study will characterize differential evolution's efficacy in feature selection for genomic prediction and present several extensions to the base search algorithm in an attempt to apply domain knowledge to guide the search toward better solutions.