Designing Genomic Solutions for Abiotic Traits in Flax (Linum usitatissimum L.)
Creators: name: Khan, Nadeem
Publisher: name: Université d'Ottawa / University of Ottawa
Year: 2022
Subjects: Flax (Linum usitatissimum L.)
Cadmium (Cd) accumulation
Genome-wide association study (GWAS)
Quantitative trait loci (QTLs)
Quantitative trait nucleotides (QTNs)
Genomic selection (GS)
Genomic cross prediction
Candidate genes
ATP-binding cassette (ABC) transporters
Heavy metal associated (HMA) genes

description: Flax (Linum usitatissimum L.) is a self-pollinated crop widely cultivated for fiber and oil production. Flaxseed is renowned for its health attributes but the presence of compounds, such as the heavy metal cadmium (Cd), is undesirable. Genomic studies in flax have produced large amounts of data in the last 15 years, providing useful resources to improve the genetic of this crop using genomics-based technologies and strategies. The goal of this thesis is therefore to capitalize on these advances to address the Cd problem and to propose solutions to improve breeding efficiencies. To find genomic-based solutions to Cd content, to the currently low breeding efficiency and to abiotic stress resistance in flax, this study utilized four major strategies: (1) genomic cross prediction, (2) gene family identification, (3) genome-wide association study (GWAS) and (4) genomic selection (GS). Characterization of the ATP-binding cassette (ABC) transporter and heavy metal associated (HMA) gene families was performed using the flax genome sequence. A total of 198 ABC transporter and 12 HMA genes were identified in the flax genome, of which nine were orthologous to Cd-associated genes in Arabidopsis, rice and maize. A transcriptomic analysis of eight tissues provided some support towards the functional annotation of these genes and confirmed the expression of these ABC transporter and HMA genes in flax seeds and other tissues. A diversity panel of 168 flax accessions was grown in the field at multiple locations and years and the seed content of 24 heavy metals (HMs) was measured. The panel was also sequenced and a single nucleotide polymorphism (SNP) dataset of nearly 43,000 SNPs was defined. A GWAS was conducted using these genotypic and phenotypic data and a total of 355 non-redundant quantitative trait nucleotides (QTNs) were identified for ten of the 24 metal contents. Overall, a total of 24 major and 331 minor effect QTNs were detected, including 11 that were pleiotropic. After allelic tests, 108 non-redundant QTNs were retained for eight of the ten metals and ranging from one for copper (Cu) to 70 for strontium (Sr). A total of 20 candidate genes for HM accumulation were identified at 12 of the 24 major QTN loci, of which five belonged to the ABC transporter family. Many of the metal contents, including Cd, appeared to be controlled by many genes of small effects; hence, GS is better suited than marker-assisted selection for application in breeding. To test this, predictive ability using ten GS statistical models was evaluated using trait-specific QTN and the random genome-wide 43K SNP datasets. Significantly higher predictive abilities were observed from the GS models built with the dataset made of QTNs associated with metal contents (70-80%) compared to that of the 43K dataset (10-25%). This study showed the feasibility of using GS to improve the predictive ability of polygenic traits such as metal content in seeds. GS can be applied in early generation selection to accelerate the improvement of abiotic stress resistance and either select low-Cd lines or discard high-Cd lines. These findings validate the use of a QTL-based strategy as a highly effective method for improving the efficiency of predictive ability of GS for highly complex traits such as resistance or tolerance to HM accumulation. Identification of both large and minor effect QTNs and/or pleiotropic effects hold potential for flax breeding improvement. Candidate gene functional validation can be performed using methods such as genome editing or targeting induced local lesions in genomes (TILLING).