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2 results on '"Madison McCausland"'

1. Genetic Analysis of a Horizontal Resistance Locus BLMR2 in Brassica napus

Author

Qiang Zhang, Hanna Dandena, Madison McCausland, Huizhi Liu, Zheng Liu, Wen Xu, and Genyi Li

Subjects
Brassica napus, Leptosphaeria maculans, BLMR2, cytochrome p450, fine mapping, Plant culture, SB1-1110
Abstract

Leptosphaeria maculans causes blackleg disease in Brassica napus. The blackleg disease is mainly controlled by resistance genes in B. napus. Previous studies have shown that the blackleg resistant BLMR2 locus that conferred horizontal resistance under field conditions, is located on chromosome A10 of B. napus. The purpose of this study is to fine map this locus and hence identify a candidate gene underlying horizontal resistance. The spectrum of resistance to L. maculans isolates of the resistance locus BLMR2 was analyzed using near isogenic lines, resistant, and susceptible cultivars. The results showed that this locus was horizontally resistant to all isolates tested. Sequence characterized amplified regions (SCAR), simple sequence repeats (SSR), and single nucleotide polymorphism (SNP) markers were developed in the chromosome region of BLMR2 and a fine genetic map was constructed. Two molecular markers narrowed BLMR2 in a 53.37 kb region where six genes were annotated. Among the six annotated genes, BnaA10g11280D/BnaA10g11290D encoding a cytochrome P450 protein were predicted as the candidate of BLMR2. Based on the profiling of pathogen induced transcriptome, three expressed genes in the six annotated genes were identified while only cytochrome P450 showed upregulation. The candidate corresponds to the gene involved in the indole glucosinolate biosynthesis pathway and plant basal defense in Arabidopsis thaliana. The molecular markers identified in this study will allow the quick incorporation of the BLMR2 allele in rapeseed cultivars to enhance blackleg resistance.

Published
2021
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2. Genomic Prediction Accuracy of Seven Breeding Selection Traits Improved by QTL Identification in Flax

Author

Kyle Hauck, Chunfang Zheng, Sylvie Cloutier, Frank M. You, Scott Duguid, Madison McCausland, Samuel Lan, and Helen M. Booker

Subjects
0106 biological sciences, 0301 basic medicine, single nucleotide polymorphism (snp), flax, genome-wide association study (gwas), quantitative trait nucleotides (qtns), Quantitative Trait Loci, Combined use, Single-nucleotide polymorphism, Quantitative trait locus, Biology, Polymorphism, Single Nucleotide, 01 natural sciences, Article, Catalysis, genomic selection, Inorganic Chemistry, lcsh:Chemistry, 03 medical and health sciences, prediction accuracy, SNP, Physical and Theoretical Chemistry, Molecular Biology, lcsh:QH301-705.5, Spectroscopy, Selection (genetic algorithm), Genetics, Organic Chemistry, Haplotype, General Medicine, Computer Science Applications, Plant Breeding, 030104 developmental biology, quantitative trait loci (qtl), lcsh:Biology (General), lcsh:QD1-999, Genomic selection, Genome-Wide Association Study, Selective Breeding, 010606 plant biology & botany
Abstract

Molecular markers are one of the major factors affecting genomic prediction accuracy and the cost of genomic selection (GS). Previous studies have indicated that the use of quantitative trait loci (QTL) as markers in GS significantly increases prediction accuracy compared with genome-wide random single nucleotide polymorphism (SNP) markers. To optimize the selection of QTL markers in GS, a set of 260 lines from bi-parental populations with 17,277 genome-wide SNPs were used to evaluate the prediction accuracy for seed yield (YLD), days to maturity (DTM), iodine value (IOD), protein (PRO), oil (OIL), linoleic acid (LIO), and linolenic acid (LIN) contents. These seven traits were phenotyped over four years at two locations. Identification of quantitative trait nucleotides (QTNs) for the seven traits was performed using three types of statistical models for genome-wide association study: two SNP-based single-locus (SS), seven SNP-based multi-locus (SM), and one haplotype-block-based multi-locus (BM) models. The identified QTNs were then grouped into QTL based on haplotype blocks. For all seven traits, 133, 355, and 1,208 unique QTL were identified by SS, SM, and BM, respectively. A total of 1420 unique QTL were obtained by SS+SM+BM, ranging from 254 (OIL, LIO) to 361 (YLD) for individual traits, whereas a total of 427 unique QTL were achieved by SS+SM, ranging from 56 (YLD) to 128 (LIO). SS models alone did not identify sufficient QTL for GS. The highest prediction accuracies were obtained using single-trait QTL identified by SS+SM+BM for OIL (0.929 &plusmn, 0.016), PRO (0.893 &plusmn, 0.023), YLD (0.892 &plusmn, 0.030), and DTM (0.730 &plusmn, 0.062), and by SS+SM for LIN (0.837 &plusmn, 0.053), LIO (0.835 &plusmn, 0.049), and IOD (0.835 &plusmn, 0.041). In terms of the number of QTL markers and prediction accuracy, SS+SM outperformed other models or combinations thereof. The use of all SNPs or QTL of all seven traits significantly reduced the prediction accuracy of traits. The results further validated that QTL outperformed high-density genome-wide random markers, and demonstrated that the combined use of single and multi-locus models can effectively identify a comprehensive set of QTL that improve prediction accuracy, but further studies on detection and removal of redundant or false-positive QTL to maximize prediction accuracy and minimize the number of QTL markers in GS are warranted.

Published
2020

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