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

Authors

Yan Zhou, Iowa State University
Aaron Kusmec, Iowa State University
Seyed Vahid Mirnezami, Iowa State University
Lakshmi Attigala, Iowa State University
Srikant Srinivasan, Iowa State University
Talukder Zaki Jubery, Iowa State University,
James Schnable, University of Nebraska-LincolnFollow
Maria G. Salas Fernandez, Iowa State University,
Baskar Ganapathysubramanian, Iowa State University,
Patrick S. Schnable, Iowa State UniversityFollow

Date of this Version

2021

Citation

Published in The Plant Cell (2021). DOI: 10.1093/plcell/koab134.

Comments

© The Author(s) (2021) . Published by Oxford University Press on behalf of American Society of Plant Biologists. This is an Open Access article distributed under the terms of the Creative Commons Attribution-Non-Commercial-NoDerivs licence (

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.