# Use of remote sensing for linkage mapping and genomic prediction for common rust resistance in maize

## TL;DR

This study compares remote sensing and visual scoring for evaluating maize resistance to common rust, finding that both methods identify the same key genetic region.

## Contribution

Demonstrates the potential of remote sensing to streamline high-throughput phenotyping for disease resistance in maize.

## Key findings

- Visual scores showed higher heritability and data quality compared to remote sensing indices.
- Both methods identified the same genomic region on chromosome 10 associated with common rust resistance.
- Remote sensing can reduce evaluation costs and increase trialing capacities for disease resistance.

## Abstract

Breeding for disease resistance is a central component of strategies implemented to mitigate biotic stress impacts on crop yield. Conventionally, genotypes of a plant population are evaluated through a labor-intensive process of assigning visual scores (VS) of susceptibility (or resistance) by specifically trained staff, which limits manageable volumes and repeatability of evaluation trials. Remote sensing (RS) tools have the potential to streamline phenotyping processes and to deliver more standardized results at higher through-put. Here, we use a two-year evaluation trial of three newly developed biparental populations of maize doubled haploid lines (DH) to compare the results of genomic analyses of resistance to common rust (CR) when phenotyping is either based on conventional VS or on RS-derived (vegetation) indices. As a general observation, for each population × year combination, the broad sense heritability of VS was greater than or very close to the maximum heritability across all RS indices. Moreover, results of linkage mapping as well as of genomic prediction (GP), suggest that VS data was of a higher quality, indicated by higher −logp values in the linkage studies and higher predictive abilities for genomic prediction. Nevertheless, despite the qualitative differences between the phenotyping methods, each successfully identified the same genomic region on chromosome 10 as being associated with disease resistance. This region is likely related to the known CR resistance locus Rp1. Our results indicate that RS technology can be used to streamline genetic evaluation processes for foliar disease resistance in maize. In particular, RS can potentially reduce costs of phenotypic evaluations and increase trialing capacities.

•Remote sensing (RS) allows for high-throughput phenotyping in genetic evaluations.•RS and visual scores (VS) from trained staff identified the same genomic regions.•Data quality of VS was higher than that of vegetation indices obtained from RS.•An index more specific to disease symptoms may improve data quality further.

Remote sensing (RS) allows for high-throughput phenotyping in genetic evaluations.

RS and visual scores (VS) from trained staff identified the same genomic regions.

Data quality of VS was higher than that of vegetation indices obtained from RS.

An index more specific to disease symptoms may improve data quality further.