Campus Units

Mechanical Engineering, Electrical and Computer Engineering, Plant Sciences Institute, Agronomy, Genetics and Genomics, Plant Biology, Molecular, Cellular and Developmental Biology, Bioinformatics and Computational Biology

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

5-20-2021

Journal or Book Title

The Plant Cell

DOI

10.1093/plcell/koab134

Abstract

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. By contrast, there is some evidence that trait measurement errors may be partially under genetic control. Consistent with this hypothesis, we observed substantial non-random, 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” vs. “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.

Comments

This is a manuscript of an article published as Zhou, Yan, Aaron Kusmec, Seyed Vahid Mirnezami, Lakshmi Attigala, Srikant Srinivasan, Talukder Zaki Jubery, James C. Schnable, Maria G. Salas Fernandez, Baskar Ganapathysubramanian, and Patrick S. Schnable. "Identification and utilization of genetic determinants of trait measurement errors in image-based, high-throughput phenotyping." The Plant Cell (2021). DOI: 10.1093/plcell/koab134. Posted with permission.

Copyright Owner

The Author(s)

Language

en

File Format

application/pdf

Published Version

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