Assessing the Effect of Phenotyping Scoring Systems and SNP Calling and Filtering Parameters on Detection of QTL Associated with Reaction of Brassica napus to Sclerotinia sclerotiorum

Abstract

The polyploid nature of canola (Brassica napus) represents a challenge for the accurate identification of single-nucleotide polymorphisms (SNPs) and the detection of quantitative trait loci (QTL). In this study, combinations of eight phenotyping scoring systems and six SNP calling and filtering parameters were evaluated for their efficiency in detection of QTL associated with response to Sclerotinia stem rot, caused by Sclerotinia sclerotiorum, in two doubled haploid canola mapping populations. Most QTL were detected in lesion length, relative areas under the disease progress curve (rAUDPC) for lesion length, and binomial-plant mortality data sets. Binomial data derived from lesion size were less efficient in QTL detection. Inclusion of additional phenotypic sets to the analysis increased the numbers of significant QTL by 2.3-fold; however, the continuous data sets were more efficient. Between two filtering parameters used to analyze genotyping-by-sequencing data, imputation of missing data increased QTL detection in one population with a high level of missing data but not in the other. Inclusion of segregation-distorted SNPs increased QTL detection but did not impact their R 2 values significantly. In all, 12 of 16 detected QTL were on chromosomes A02 and C01, and the rest were on A07, A09, and C03. Marker A02-7594120, associated with a QTL on chromosome A02, was detected in both populations. Results of this study suggest that the impact of genotypic variant calling and filtering parameters may be population dependent while deriving additional phenotyping scoring systems such as rAUDPC datasets and mortality binary may improve QTL detection efficiency. [Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

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