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Association Studies and Genomic Prediction for Genetic Improvements in Agriculture

To feed the fast growing global population with sufficient food using limited global resources, it is urgent to develop and utilize cutting-edge technologies and improve efficiency of agricultural production. In this review, we specifically introduce the concepts, theories, methods, applications and...

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Detalles Bibliográficos
Autores principales: Zhang, Qianqian, Zhang, Qin, Jensen, Just
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9201771/
https://www.ncbi.nlm.nih.gov/pubmed/35720549
http://dx.doi.org/10.3389/fpls.2022.904230
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author Zhang, Qianqian
Zhang, Qin
Jensen, Just
author_facet Zhang, Qianqian
Zhang, Qin
Jensen, Just
author_sort Zhang, Qianqian
collection PubMed
description To feed the fast growing global population with sufficient food using limited global resources, it is urgent to develop and utilize cutting-edge technologies and improve efficiency of agricultural production. In this review, we specifically introduce the concepts, theories, methods, applications and future implications of association studies and predicting unknown genetic value or future phenotypic events using genomics in the area of breeding in agriculture. Genome wide association studies can identify the quantitative genetic loci associated with phenotypes of importance in agriculture, while genomic prediction utilizes individual genetic value to rank selection candidates to improve the next generation of plants or animals. These technologies and methods have improved the efficiency of genetic improvement programs for agricultural production via elite animal breeds and plant varieties. With the development of new data acquisition technologies, there will be more and more data collected from high-through-put technologies to assist agricultural breeding. It will be crucial to extract useful information among these large amounts of data and to face this challenge, more efficient algorithms need to be developed and utilized for analyzing these data. Such development will require knowledge from multiple disciplines of research.
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spelling pubmed-92017712022-06-17 Association Studies and Genomic Prediction for Genetic Improvements in Agriculture Zhang, Qianqian Zhang, Qin Jensen, Just Front Plant Sci Plant Science To feed the fast growing global population with sufficient food using limited global resources, it is urgent to develop and utilize cutting-edge technologies and improve efficiency of agricultural production. In this review, we specifically introduce the concepts, theories, methods, applications and future implications of association studies and predicting unknown genetic value or future phenotypic events using genomics in the area of breeding in agriculture. Genome wide association studies can identify the quantitative genetic loci associated with phenotypes of importance in agriculture, while genomic prediction utilizes individual genetic value to rank selection candidates to improve the next generation of plants or animals. These technologies and methods have improved the efficiency of genetic improvement programs for agricultural production via elite animal breeds and plant varieties. With the development of new data acquisition technologies, there will be more and more data collected from high-through-put technologies to assist agricultural breeding. It will be crucial to extract useful information among these large amounts of data and to face this challenge, more efficient algorithms need to be developed and utilized for analyzing these data. Such development will require knowledge from multiple disciplines of research. Frontiers Media S.A. 2022-06-02 /pmc/articles/PMC9201771/ /pubmed/35720549 http://dx.doi.org/10.3389/fpls.2022.904230 Text en Copyright © 2022 Zhang, Zhang and Jensen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Zhang, Qianqian
Zhang, Qin
Jensen, Just
Association Studies and Genomic Prediction for Genetic Improvements in Agriculture
title Association Studies and Genomic Prediction for Genetic Improvements in Agriculture
title_full Association Studies and Genomic Prediction for Genetic Improvements in Agriculture
title_fullStr Association Studies and Genomic Prediction for Genetic Improvements in Agriculture
title_full_unstemmed Association Studies and Genomic Prediction for Genetic Improvements in Agriculture
title_short Association Studies and Genomic Prediction for Genetic Improvements in Agriculture
title_sort association studies and genomic prediction for genetic improvements in agriculture
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9201771/
https://www.ncbi.nlm.nih.gov/pubmed/35720549
http://dx.doi.org/10.3389/fpls.2022.904230
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