Ø DH technology enables the efficient fixation of desirable alleles and the removal of deleterious ones, enhancing the efficiency of breeding programs.
Ø DH lines have applications in hybrid seed production, allowing for the creation of pure, uniform parental lines for hybrid crosses. The production of DH lines can be achieved through various methods, including anther culture, microspore culture, and embryo rescue techniques.
Ø DH technology has revolutionized plant breeding by providing a rapid and efficient means to generate homozygous lines with predictable genetic backgrounds, accelerating the pace of crop improvement.
Ø Chromosome walking is a molecular technique used to identify and isolate adjacent DNA sequences along a chromosome in plants. It involves starting with a known DNA sequence, often a genetic marker or gene of interest, and iteratively identifying adjacent DNA fragments to "walk" along the chromosome.
Ø The process of chromosome walking relies on techniques such as DNA hybridization, PCR (Polymerase Chain Reaction), and DNA sequencing.
Ø Chromosome walking allows researchers to map and characterize specific regions of plant genomes, including gene-rich regions, regulatory elements, and structural variants. This technique is particularly useful in identifying genes associated with important agronomic traits, such as disease resistance, yield, and stress tolerance.
Ø Chromosome walking can be employed in both forward genetics (starting with a phenotype and identifying associated genes) and reverse genetics (starting with a known gene and identifying its function or regulatory elements).
Ø By systematically moving along the chromosome, chromosome walking enables the identification of neighbouring genes and genetic elements that may be involved in the same biological pathway or regulatory network.
Ø Chromosome walking has been instrumental in cloning genes of interest in plants, leading to advancements in crop breeding, genetic engineering, and fundamental research. This technique requires careful design and optimization of primers, probes, and screening methods to efficiently isolate and sequence adjacent DNA fragments.
Ø With the advent of high-throughput sequencing technologies, chromosome walking has become more efficient and scalable, allowing for the rapid characterization of large genomic regions in plants.
Ø Indirect selection indices have been developed for efficient phenotypic selection of target traits in plant breeding.
Ø Genetic models, such as the best linear unbiased prediction (BLUP), aid in predicting phenotypic performance or target traits.
Ø Molecular markers enable effective capture of genetic variation across the genome, facilitating marker-trait associations.
Ø Genomic selection (GS) predicts complex traits from genotypic data using a model constructed with genotyped and phenotyped training populations.
Ø Multiomics information, including genomics, phenomics, and enviromics, can improve predictive breeding but requires handling multidimensional big data and integrating information from multiple sources.
Ø Enviromic data from multi-environmental trials can be utilized more in predictive breeding to understand crop phenotypes better. The environmental component can be treated comparably with genotype and phenotype for improved predictive breeding.
Ø Fully informative genotype, phenotype, and environment data enhance genomic prediction models.
Ø Despite advancements, there remains a genotype-phenotype gap in translating data into meaningful knowledge.
Ø Multiomic data have been utilized in predictive breeding through various models and indices, enabling integrated modeling and prediction.
Ø AI increases the probability of identifying favorable genotypes and has been used by multinational seed enterprises for predictive breeding.
Ø An integrated genomic-enviromic prediction (iGEP) strategy, driven by big data and AI, offers potential for smart breeding in the future.
Ø Evaluation of genetic gain in plant breeding is crucial, with formulas like DG = ih2sp/t for phenotypic selection (PS) and DG = irAsA/t for genomic selection (GS).
Ø Predictive breeding accelerates the breeding process by producing plants with desired traits adapted to changing environments, thereby reducing cycle time.
Ø The history of plant breeding is delineated into four stages, with the fourth stage aiming to combine all favorable alleles/haplotypes into optimal combinations, facilitated by smart breeding.
Ø Smart breeding utilizes both traditional breeding methods and big data, integrating past, present, and future data to improve breeding outcomes.
Ø Modern breeding programs encounter challenges in managing big data from various sources, requiring processing for computer-based analysis, including machine learning (ML).
