The application of pangenomics and machine learning in genomic selection in plants

Abstract Genomic selection approaches have increased the speed of plant breeding, leading to growing crop yields over the last decade. However, climate change is impacting current and future yields, resulting in the need to further accelerate breeding efforts to cope with these changing conditions....

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Philipp E. Bayer, Jakob Petereit, Monica Furaste Danilevicz, Robyn Anderson, Jacqueline Batley, David Edwards
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Plant culture SB1-1110 Genetics QH426-470
Acceso en línea:	https://doaj.org/article/3973acaa6835469697e3153b1e4701f5
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:3973acaa6835469697e3153b1e4701f5
record_format	dspace
spelling	oai:doaj.org-article:3973acaa6835469697e3153b1e4701f52021-12-05T07:50:11ZThe application of pangenomics and machine learning in genomic selection in plants1940-337210.1002/tpg2.20112https://doaj.org/article/3973acaa6835469697e3153b1e4701f52021-11-01T00:00:00Zhttps://doi.org/10.1002/tpg2.20112https://doaj.org/toc/1940-3372Abstract Genomic selection approaches have increased the speed of plant breeding, leading to growing crop yields over the last decade. However, climate change is impacting current and future yields, resulting in the need to further accelerate breeding efforts to cope with these changing conditions. Here we present approaches to accelerate plant breeding and incorporate nonadditive effects in genomic selection by applying state‐of‐the‐art machine learning approaches. These approaches are made more powerful by the inclusion of pangenomes, which represent the entire genome content of a species. Understanding the strengths and limitations of machine learning methods, compared with more traditional genomic selection efforts, is paramount to the successful application of these methods in crop breeding. We describe examples of genomic selection and pangenome‐based approaches in crop breeding, discuss machine learning‐specific challenges, and highlight the potential for the application of machine learning in genomic selection. We believe that careful implementation of machine learning approaches will support crop improvement to help counter the adverse outcomes of climate change on crop production.Philipp E. BayerJakob PetereitMonica Furaste DanileviczRobyn AndersonJacqueline BatleyDavid EdwardsWileyarticlePlant cultureSB1-1110GeneticsQH426-470ENThe Plant Genome, Vol 14, Iss 3, Pp n/a-n/a (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Plant culture SB1-1110 Genetics QH426-470
spellingShingle	Plant culture SB1-1110 Genetics QH426-470 Philipp E. Bayer Jakob Petereit Monica Furaste Danilevicz Robyn Anderson Jacqueline Batley David Edwards The application of pangenomics and machine learning in genomic selection in plants
description	Abstract Genomic selection approaches have increased the speed of plant breeding, leading to growing crop yields over the last decade. However, climate change is impacting current and future yields, resulting in the need to further accelerate breeding efforts to cope with these changing conditions. Here we present approaches to accelerate plant breeding and incorporate nonadditive effects in genomic selection by applying state‐of‐the‐art machine learning approaches. These approaches are made more powerful by the inclusion of pangenomes, which represent the entire genome content of a species. Understanding the strengths and limitations of machine learning methods, compared with more traditional genomic selection efforts, is paramount to the successful application of these methods in crop breeding. We describe examples of genomic selection and pangenome‐based approaches in crop breeding, discuss machine learning‐specific challenges, and highlight the potential for the application of machine learning in genomic selection. We believe that careful implementation of machine learning approaches will support crop improvement to help counter the adverse outcomes of climate change on crop production.
format	article
author	Philipp E. Bayer Jakob Petereit Monica Furaste Danilevicz Robyn Anderson Jacqueline Batley David Edwards
author_facet	Philipp E. Bayer Jakob Petereit Monica Furaste Danilevicz Robyn Anderson Jacqueline Batley David Edwards
author_sort	Philipp E. Bayer
title	The application of pangenomics and machine learning in genomic selection in plants
title_short	The application of pangenomics and machine learning in genomic selection in plants
title_full	The application of pangenomics and machine learning in genomic selection in plants
title_fullStr	The application of pangenomics and machine learning in genomic selection in plants
title_full_unstemmed	The application of pangenomics and machine learning in genomic selection in plants
title_sort	application of pangenomics and machine learning in genomic selection in plants
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/3973acaa6835469697e3153b1e4701f5
work_keys_str_mv	AT philippebayer theapplicationofpangenomicsandmachinelearningingenomicselectioninplants AT jakobpetereit theapplicationofpangenomicsandmachinelearningingenomicselectioninplants AT monicafurastedanilevicz theapplicationofpangenomicsandmachinelearningingenomicselectioninplants AT robynanderson theapplicationofpangenomicsandmachinelearningingenomicselectioninplants AT jacquelinebatley theapplicationofpangenomicsandmachinelearningingenomicselectioninplants AT davidedwards theapplicationofpangenomicsandmachinelearningingenomicselectioninplants AT philippebayer applicationofpangenomicsandmachinelearningingenomicselectioninplants AT jakobpetereit applicationofpangenomicsandmachinelearningingenomicselectioninplants AT monicafurastedanilevicz applicationofpangenomicsandmachinelearningingenomicselectioninplants AT robynanderson applicationofpangenomicsandmachinelearningingenomicselectioninplants AT jacquelinebatley applicationofpangenomicsandmachinelearningingenomicselectioninplants AT davidedwards applicationofpangenomicsandmachinelearningingenomicselectioninplants
_version_	1718372548861755392

The application of pangenomics and machine learning in genomic selection in plants

Ejemplares similares