The Flax Genome

Preface to the Series
Contents
1 Reference Genome Sequence of Flax
1.1 Introduction
1.2 Flax Genome Assemblies
1.2.1 The First Version of the Flax Genome Assembly for CDC Bethune
1.2.2 The Second Version of the Flax Genome Assembly for CDC Bethune: Chromosome-Level Pseudomolecules
1.2.3 Recent Assemblies Expanding the Representation to Both Morphotypes and to the Closest Wild Relative of Cultivated Flax
1.2.4 Quality Examination of Flax Genome Assemblies
1.3 Repeat Sequence
1.4 Gene Annotation
1.5 Non-coding RNAs
1.6 Chloroplast Genome
1.7 Concluding Remarks
Acknowledgements
References
2 Repeat DNA Sequences in Flax Genomes
2.1 Introduction
2.2 Types and Distribution of Repetitive DNA Sequences
2.3 Challenges in the Identification of Repeats Per Se
2.4 Repetitive Elements in Flax and Other Crop Species
2.5 Tools for the Identification of Repetitive Elements
2.5.1 RepeatMasker
2.5.2 RepeatModeler
2.5.3 LTR_Finder
2.5.4 LTRharvest
2.5.5 LTRAnnotator
2.5.6 LTR_Retriever
2.5.7 SINE_Scan
2.5.8 HelitronScanner
2.5.9 Miniature Inverted-Repeat Transposable Elements (MITE Tracker)
2.6 Case Study: A Comparative Analysis of Flax Genome TEs
2.7 Conclusion and Future Perspectives
References
3 Pale Flax (Linum Bienne): an Underexplored Flax Wild Relative
3.1 Introduction
3.2 Taxonomy
3.3 Biology
3.4 Domestication
3.5 Genetics
3.6 Genomics
3.7 Utilization
3.8 Conservation
3.9 Future Research
Acknowledgements
References
4 Flax Breeding
4.1 Taxonomy, Origin, Domestication, and Use of Flax
4.2 Flax Production in the World
4.3 Flax Harvested Area in the World
4.4 Flax Seed Yield Per Unit Area
4.5 Genetic Diversity
4.6 Breeding Strategy
4.6.1 Mass Selection Method
4.6.2 Pure-Line Selection Method
4.6.3 Pedigree Method
4.6.4 Bulk Method
4.6.5 Single-Seed Descent Method
4.6.6 Marker-Assisted Breeding
4.7 Conclusion and Future Perspectives
References
5 QTL Mapping: Strategy, Progress, and Prospects in Flax
5.1 Introduction
5.2 Linkage Map-Based QTL Mapping (LM)
5.2.1 Bi-Parental Populations
5.2.2 Linkage Map Construction
5.2.3 Statistical Models for Linkage Map-Based QTL Mapping
5.3 Association Mapping
5.3.1 Genetic Populations
5.3.2 Statistical Models for Association Mapping
5.3.3 Selection of Threshold for Significant Marker-Trait Associations
5.3.4 Post-QTN Identification Analysis
5.3.4.1 Haplotype Blocks (HBs)
5.3.4.2 QTN Effects
5.3.4.3 Favorable Alleles of QTNs
5.4 Meta-Analysis of GWAS and QTLs
5.4.1 Meta-GWAS
5.4.2 Meta-QTL Analysis
5.5 Candidate Gene Identification
5.6 QTL Mapping in Flax
5.6.1 Yield and Agronomic Traits
5.6.1.1 Yield
5.6.1.2 Seed Size
5.6.1.3 Flowering and Maturity
5.6.2 Fiber Traits
5.6.3 Seed Quality Traits
5.6.3.1 Protein and Oil Content
5.6.3.2 Iodine Value and Linolenic Acid Content
5.6.3.3 Seed Mucilage and Hull Content
5.6.3.4 Seed Coat Color
5.6.4 Abiotic Traits
5.6.4.1 Drought Tolerance
5.6.4.2 Salt Tolerance
5.6.5 Biotic Traits
5.6.5.1 Pasmo
5.6.5.2 Powdery Mildew (PM)
5.6.5.3 Fusarium Wilt (FW)
5.7 Perspectives
Acknowledgements
References
6 Genetics of Abiotic Stress in Flax
6.1 Introduction
6.2 Genetics and QTL Identification Studies in Flax
6.2.1 Increasing Flax Genome Resources and Their Role in Abiotic Stress Study
6.2.1.1 Drought Stress
6.2.1.2 Salinity Stress
6.2.1.3 Heavy Metal Stress
6.3 Conclusions and Future Directions
References
7 QTL and Candidate Genes for Flax Disease Resistance
7.1 Introduction
7.2 Phenotypic Performance of Disease Resistance
7.2.1 Field Nurseries for Disease Resistance Phenotyping
7.2.2 Criteria for Field Nurseries for Disease Resistance Phenotyping
7.2.3 Broad-Sense Heritability of Flax Disease Traits
7.2.4 Flax Genetic Populations and Their Disease Resistance
7.3 Quantitative Trait Loci (QTLs) Associated with Flax Disease Resistance
7.3.1 Quantitative Trait Loci (QTLs) Associated with Pasmo Resistance
7.3.2 Quantitative Trait Loci (QTLs) Associated with Powdery Mildew Resistance
7.3.3 Quantitative Trait Loci (QTLs) Associated with Fusarium Wilt Resistance
7.4 Candidate Genes Co-localized with QTLs
7.4.1 Candidate Genes Associated with Pasmo (PAS) Resistance
7.4.2 Candidate Genes Associated with Powdery Mildew (PM) Resistance
7.4.3 Candidate Genes Associated with Fusarium Wilt Resistance
7.5 Conclusions
Acknowledgements
References
8 Key Stages of Flax Bast Fiber Development Through the Prism of Transcriptomics
8.1 Introduction
8.2 Intrusive Elongation
8.2.1 The Significance of Fiber Intrusive Elongation
8.2.2 Approaches to Reveal the Genes for Proteins Especially Important for a Fiber at Intrusive Growth Stage of Development
8.2.3 General Cell Physiology as Revealed by Transcriptomic Data
8.2.4 Cell Wall Rearrangement
8.2.5 Cytoskeleton
8.2.6 Regulation by Hormones, Transcription Factors, Kinases, and Other Regulatory Proteins
8.2.7 Other Genes Specifically Upregulated in Intrusively Growing Fibers
8.3 Tertiary Cell Wall Formation
8.3.1 Expression of CESA Genes and Genes for Putative Cofactors in Fibers Forming the Tertiary Cell Wall
8.3.2 Genes Encoding Proteins Associated with Rhamnogalacturonan I Metabolism
8.3.3 Other Genes Specifically Upregulated in Fibers with TCW
8.3.4 Transcription Factors Potentially Involved in TCW Formation
8.4 Pipeline Through Transcriptomics to Get Molecular Keys for Targeted Fiber Crop Improvement
8.5 MiRNA—The Potential Regulators of Gene Expression
8.6 Conclusions and Future Perspective
Acknowledgements
References
9 Metabolomics and Transcriptomics-Based Tools for Linseed Improvement
9.1 Introduction
9.2 Flax Metabolites and Metabolomics
9.2.1 Primary Metabolites
9.2.1.1 Lipids
9.2.1.2 Proteins
9.2.1.3 Starch
9.2.1.4 Cellulose and Lignin
9.2.2 Secondary Metabolites
9.2.2.1 Lignans
9.2.2.2 Cyanogenic Glucoside
9.2.3 Metabolomics Tools
9.2.3.1 Targeted Metabolomics
9.2.3.2 Untargeted Metabolomics
9.2.3.3 Analytical Chromatography Platforms and Tools
9.2.3.4 Spectroscopic Tools
Nuclear Magnetic Resonance (NMR) Spectroscopy
Synchrotron Light Source Spectroscopy
9.2.4 Metabolite Biosynthesis
9.2.4.1 Fatty Acids
9.2.4.2 Flax Lignans
9.2.4.3 Cyanogenic Glucosides
9.2.4.4 Interplay Between the Primary and Secondary Metabolite Pathways Leading to FA, Lignans, and CGs
9.3 Transcriptomics and Pathway Regulation
9.3.1 Transcriptomic Resources and Tools
9.3.1.1 Genomic Resources
9.3.1.2 Transcriptomic Platforms
9.3.2 Flax Transcriptomic
9.3.3 From Genome to Transcriptome to Metabolome to Phenotype
9.4 Concluding Remarks
Acknowledgements
References
10 Genome-Wide Prediction of Disease Resistance Gene Analogs in Flax
10.1 Introduction
10.2 Classification of Plant Resistance Gene Analogs
10.3 Experimental Methods for Resistance Gene Identification
10.3.1 Gene Cloning
10.3.2 QTL Mapping for Resistance Genes
10.4 Computational Methods for RGA Identification
10.4.1 RGAugury
10.4.1.1 Support for Docker and PodMan
10.4.1.2 Support for the RNL (RPW8)
10.4.2 Machine Learning Based RGA Annotation Pipelines
10.4.3 Comparison of RGA Identification Tools
10.5 RGA Profile of Flax
10.6 Conclusion and Perspective
References
11 Genome-Editing Tools for Flax Genetic Improvement
11.1 Introduction
11.2 Gene Editing
11.2.1 Definition and History
11.2.1.1 Definition
11.2.1.2 History
11.2.2 Gene-Editing Tools and Processes
11.2.2.1 Gene-Editing Tools
ZFNs
TALENs
CRISPR
11.2.2.2 CRISPR Gene-Editing Process
SgRNA Design
SgRNA and Cas9 Delivery
Cargo Systems Used with CRISPR
Delivery Methods Used with CRISPR
Gene-Editing Validation
11.2.3 Gene Editing in Plants
11.3 Gene Editing in Flax
11.3.1 Success Stories in Flax Genetic Transformation and Gene-Editing
11.3.2 Tools Paving the Road for Flax Gene Editing
11.3.3 Flax Genetic Resources
11.3.4 Flax Traits of Interest for Gene Editing
11.3.4.1 Agronomic Plant Traits
Seed Traits
Fiber Traits
Flax and Biotic and Abiotic Stresses
Flax Gene-Edited Traits Regulatory Frameworks
11.4 Concluding Remarks
Acknowledgements
References
12 Genomics Assisted Breeding Strategy in Flax
12.1 Introduction
12.2 Factors Affecting Genomic Prediction (GP) Efficiency
12.2.1 Genomic Prediction (GP) Models
12.2.2 Training Populations (TRPs) and Relatedness to the Test Populations (TPs)
12.2.3 Markers
12.3 Improving Predictive Ability by QTL Markers
12.3.1 QTL Identification by Single- and Multi-locus GWAS
12.3.2 Genomic Heritability ({{\varvec{h}}^{\bf 2})
12.3.3 A Case Study for Drought and Root Traits
12.4 A QTL-Based Genomic Selection (GS) Strategy
12.4.1 GS Modeling
12.4.2 Test Population (TP) and Genomic Selection (GS)
12.4.3 GS Evaluation
12.4.4 Cost of GS
12.5 Parent Evaluation and Cross-Prediction
12.5.1 Genomic Cross-Prediction for Flax Improvement
12.5.2 Future Based: Integrated Flax Breeding Improvement Strategy
12.6 Conclusions and Future Prospects
References
13 Flax Genomic Resources and Databases
13.1 Introduction
13.2 Flax Genomic Resources
13.2.1 Sequences
13.2.2 Molecular Markers
13.2.3 Genetic and Physical Maps
13.2.4 Genome Assemblies
13.2.5 Quantitative Trait Loci (QTLs)/Nucleotides (QTNs)
13.3 Phenotypic Evaluation of Flax Genetic Populations
13.4 Genomics and Breeding Databases
13.4.1 NCBI
13.4.2 Phytozome
13.4.3 Flax TILLING Platform
13.4.4 Canadian National Gene Bank Information System—GRIN-Global-CA
13.4.5 Flax Variety Databases
13.4.6 International Flax Database (IFDB)
13.4.7 FlaxDB: A Flax Genome and Breeding Database
13.5 Perspectives
Acknowledgements
References