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1 April 2020 Linkage mapping and whole-genome predictions in canola (Brassica napus) subjected to differing temperature treatments
Chadwick B. Koscielny, Stuart W. Gardner, Frank Technow, Robert W. Duncan
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Abstract

Canola (Brassica napus L.) is grown on >8 Mha in Canada and is sensitive to high temperatures; therefore, research on breeding methodologies to improve heat-stress tolerance is warranted. This study utilised a doubled-haploid population created from two parents (PB36 and PB56) that differed in their ability to set seed following growth at high temperatures. The experiment was designed to identify potential quantitative trait loci (QTLs) responsible for conferring tolerance to increased temperatures, and to utilise this population as a test case for evaluating the prospects of whole-genome prediction. The population was phenotyped in a split-plot, randomised complete block experimental design at three locations with two planting-date treatments. The first planting date was during the normal planting period (control), and the second planting was timed to experience increased average temperatures (1.7°C, 2.0°C and 1.2°C) and increased number of days with maximum temperatures above the critical temperature of 29.5°C (4, 12 and 3 days). The stress treatment reduced yield on average by 16.7%. There were 66 QTLs discovered across the nine traits collected. Given the quantitative nature of the traits collected, the ability to use whole-genome prediction was investigated. The prediction accuracies ranged from 0.14 (yield) to 0.66 (1000-seed weight). Prediction had higher accuracy within the stress treatment than within the control treatment for seven of the nine traits, demonstrating that phenotyping within a stress environment can provide valuable data for whole-genome predictions.

© CSIRO 2020 Open Access CC BY-NC-ND
Chadwick B. Koscielny, Stuart W. Gardner, Frank Technow, and Robert W. Duncan "Linkage mapping and whole-genome predictions in canola (Brassica napus) subjected to differing temperature treatments," Crop and Pasture Science 71(3), 229-238, (1 April 2020). https://doi.org/10.1071/CP19387
Received: 4 October 2019; Accepted: 25 December 2019; Published: 1 April 2020
KEYWORDS
breeding methods
climate change
predictive breeding
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