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ARS Home » Pacific West Area » Corvallis, Oregon » Horticultural Crops Disease and Pest Management Research Unit » Research » Research Project #441012

Research Project: Genomic Prediction for Quantitative Resistance to Root Lesion Nematode in Raspberry

Location: Horticultural Crops Disease and Pest Management Research Unit

Project Number: 2072-22000-046-006-G
Project Type: Grant

Start Date: Sep 1, 2021
End Date: Oct 31, 2024

Implement and validate genomic prediction as a plant breeding tool to increase the resistance of raspberry cultivars to the root lesion nematode (RLN), Pratylenchus penetrans.

In the fall, we will quantify the plant RLN populations (Zasada et al. 2015). Roots will be dug with a shovel on either side of the plant. In the lab, roots will be rinsed for RLN extraction under intermittent mist for 5 days. Roots from each sample will be placed in a drier for 7 days prior to measuring dry root weight. RLN will be counted using a microscope and RLN resistance phenotypes expressed as the number of RLN/g root mass. In spring, 300 genotypes in four single-plant replicates and seedling populations will be established in fumigated soil with a grower cooperator. Half of the clonal replicates and seedlings will be inoculated with mixed stage RLN individuals, obtained by mist collection from raspberry roots from an infested field (Zasada and Moore, 2014). The inoculum delivered will be approximately 1,000 nematodes/plant in 5 ml of water into the root system. Immature leaf tissue will be collected from each plant for DNA extraction. DNA samples will be submitted for molecular marker genotyping using a single-nucleotide polymorphism (SNP) array containing roughly 8,000 red raspberry marker probes. We will use the resulting molecular marker genotypes and RLN phenotypes to calculate genomic estimated breeding values (GEBVs) for non-genotyped individuals using the whole-genome regression methods employed by Pincot et al. (2020). We will determine the accuracy of the genomic prediction models using Monte Carlo cross-validation (MCCV) by randomly splitting accessions into training (80%) and validation (20%) subsets and comparing GEBVs to phenotypic data collected in the field (Poland & Rutkoski, 2016). We will also perform a genome-wide association analysis to identify any potential large-effect resistance genes in the clonally replicated genotypes.