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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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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. |
format | Online Article Text |
id | pubmed-9201771 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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