Cargando…

Identification and utilization of genetic determinants of trait measurement errors in image-based, high-throughput phenotyping

The accuracy of trait measurements greatly affects the quality of genetic analyses. During automated phenotyping, trait measurement errors, i.e. differences between automatically extracted trait values and ground truth, are often treated as random effects that can be controlled by increasing populat...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhou, Yan, Kusmec, Aaron, Mirnezami, Seyed Vahid, Attigala, Lakshmi, Srinivasan, Srikant, Jubery, Talukder Z., Schnable, James C., Salas-Fernandez, Maria G., Ganapathysubramanian, Baskar, Schnable, Patrick S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8408462/
https://www.ncbi.nlm.nih.gov/pubmed/34015121
http://dx.doi.org/10.1093/plcell/koab134
_version_ 1783746829200916480
author Zhou, Yan
Kusmec, Aaron
Mirnezami, Seyed Vahid
Attigala, Lakshmi
Srinivasan, Srikant
Jubery, Talukder Z.
Schnable, James C.
Salas-Fernandez, Maria G.
Ganapathysubramanian, Baskar
Schnable, Patrick S.
author_facet Zhou, Yan
Kusmec, Aaron
Mirnezami, Seyed Vahid
Attigala, Lakshmi
Srinivasan, Srikant
Jubery, Talukder Z.
Schnable, James C.
Salas-Fernandez, Maria G.
Ganapathysubramanian, Baskar
Schnable, Patrick S.
author_sort Zhou, Yan
collection PubMed
description The accuracy of trait measurements greatly affects the quality of genetic analyses. During automated phenotyping, trait measurement errors, i.e. differences between automatically extracted trait values and ground truth, are often treated as random effects that can be controlled by increasing population sizes and/or replication number. In contrast, there is some evidence that trait measurement errors may be partially under genetic control. Consistent with this hypothesis, we observed substantial nonrandom, genetic contributions to trait measurement errors for five maize (Zea mays) tassel traits collected using an image-based phenotyping platform. The phenotyping accuracy varied according to whether a tassel exhibited “open” versus. “closed” branching architecture, which is itself under genetic control. Trait-associated SNPs (TASs) identified via genome-wide association studies (GWASs) conducted on five tassel traits that had been phenotyped both manually (i.e. ground truth) and via feature extraction from images exhibit little overlap. Furthermore, identification of TASs from GWASs conducted on the differences between the two values indicated that a fraction of measurement error is under genetic control. Similar results were obtained in a sorghum (Sorghum bicolor) plant height dataset, demonstrating that trait measurement error is genetically determined in multiple species and traits. Trait measurement bias cannot be controlled by increasing population size and/or replication number.
format Online
Article
Text
id pubmed-8408462
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Oxford University Press
record_format MEDLINE/PubMed
spelling pubmed-84084622021-09-02 Identification and utilization of genetic determinants of trait measurement errors in image-based, high-throughput phenotyping Zhou, Yan Kusmec, Aaron Mirnezami, Seyed Vahid Attigala, Lakshmi Srinivasan, Srikant Jubery, Talukder Z. Schnable, James C. Salas-Fernandez, Maria G. Ganapathysubramanian, Baskar Schnable, Patrick S. Plant Cell Research Articles The accuracy of trait measurements greatly affects the quality of genetic analyses. During automated phenotyping, trait measurement errors, i.e. differences between automatically extracted trait values and ground truth, are often treated as random effects that can be controlled by increasing population sizes and/or replication number. In contrast, there is some evidence that trait measurement errors may be partially under genetic control. Consistent with this hypothesis, we observed substantial nonrandom, genetic contributions to trait measurement errors for five maize (Zea mays) tassel traits collected using an image-based phenotyping platform. The phenotyping accuracy varied according to whether a tassel exhibited “open” versus. “closed” branching architecture, which is itself under genetic control. Trait-associated SNPs (TASs) identified via genome-wide association studies (GWASs) conducted on five tassel traits that had been phenotyped both manually (i.e. ground truth) and via feature extraction from images exhibit little overlap. Furthermore, identification of TASs from GWASs conducted on the differences between the two values indicated that a fraction of measurement error is under genetic control. Similar results were obtained in a sorghum (Sorghum bicolor) plant height dataset, demonstrating that trait measurement error is genetically determined in multiple species and traits. Trait measurement bias cannot be controlled by increasing population size and/or replication number. Oxford University Press 2021-05-20 /pmc/articles/PMC8408462/ /pubmed/34015121 http://dx.doi.org/10.1093/plcell/koab134 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of American Society of Plant Biologists. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) ), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Research Articles
Zhou, Yan
Kusmec, Aaron
Mirnezami, Seyed Vahid
Attigala, Lakshmi
Srinivasan, Srikant
Jubery, Talukder Z.
Schnable, James C.
Salas-Fernandez, Maria G.
Ganapathysubramanian, Baskar
Schnable, Patrick S.
Identification and utilization of genetic determinants of trait measurement errors in image-based, high-throughput phenotyping
title Identification and utilization of genetic determinants of trait measurement errors in image-based, high-throughput phenotyping
title_full Identification and utilization of genetic determinants of trait measurement errors in image-based, high-throughput phenotyping
title_fullStr Identification and utilization of genetic determinants of trait measurement errors in image-based, high-throughput phenotyping
title_full_unstemmed Identification and utilization of genetic determinants of trait measurement errors in image-based, high-throughput phenotyping
title_short Identification and utilization of genetic determinants of trait measurement errors in image-based, high-throughput phenotyping
title_sort identification and utilization of genetic determinants of trait measurement errors in image-based, high-throughput phenotyping
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8408462/
https://www.ncbi.nlm.nih.gov/pubmed/34015121
http://dx.doi.org/10.1093/plcell/koab134
work_keys_str_mv AT zhouyan identificationandutilizationofgeneticdeterminantsoftraitmeasurementerrorsinimagebasedhighthroughputphenotyping
AT kusmecaaron identificationandutilizationofgeneticdeterminantsoftraitmeasurementerrorsinimagebasedhighthroughputphenotyping
AT mirnezamiseyedvahid identificationandutilizationofgeneticdeterminantsoftraitmeasurementerrorsinimagebasedhighthroughputphenotyping
AT attigalalakshmi identificationandutilizationofgeneticdeterminantsoftraitmeasurementerrorsinimagebasedhighthroughputphenotyping
AT srinivasansrikant identificationandutilizationofgeneticdeterminantsoftraitmeasurementerrorsinimagebasedhighthroughputphenotyping
AT juberytalukderz identificationandutilizationofgeneticdeterminantsoftraitmeasurementerrorsinimagebasedhighthroughputphenotyping
AT schnablejamesc identificationandutilizationofgeneticdeterminantsoftraitmeasurementerrorsinimagebasedhighthroughputphenotyping
AT salasfernandezmariag identificationandutilizationofgeneticdeterminantsoftraitmeasurementerrorsinimagebasedhighthroughputphenotyping
AT ganapathysubramanianbaskar identificationandutilizationofgeneticdeterminantsoftraitmeasurementerrorsinimagebasedhighthroughputphenotyping
AT schnablepatricks identificationandutilizationofgeneticdeterminantsoftraitmeasurementerrorsinimagebasedhighthroughputphenotyping