Plant breeding relied upon utilization of natural and mutant induced genetic variation followed by efficient selection by using suitable breeding methods. Nowadays, genomics provides breeders with a new set of tools and techniques that allow study of the whole genome, and which represents a paradigm shift, by facilitating the direct study of the genotype and its relationship with the phenotype. The combination of conventional breeding techniques with genomic tools and approaches drive to a new genomics-based plant breeding. One of the main pillars of genomic breeding is the development of high-throughput DNA sequencing technologies, collectively known as next generation sequencing (NGS) methods.

Genomic Tools For Plant Breeding

Genome and Transcriptome Sequencing

The availability of the whole genome sequence of a crop is of great utility for plant breeding, given the high cost of whole genome sequencing. Therefore transcriptome sequencing has been a cheaper alternative. The cDNA sequences (expressed sequence tags, ESTs) provide relevant information about the genes expressed in a certain tissue or organ, at a given stage of development and under particular environmental conditions. ESTs sequencing projects do not provide information about non-coding sequences. The field of genomics has changed with the arrival of NGS technologies.


NGS technologies are facilitating sequencing projects, but have brought new challenges, as millions of short DNA reads have to be analysed and assembled. Also, genetic maps, genotypes, or expression information at a genomic scale have to be processed in order to obtain the relevant biological information. Two of the most common analyses carried out on these NGS reads are genome assembly, annotation and mapping. The most commonly used assemblers are Roche's 454 Gsassembler, Celera Assembler, and Mira.


TILLING (Targeting Induced Local Lesions in Genomes) is a method in molecular biology that allows directed identification of mutations in a specific gene. In order to facilitate the identification of the accessions of interest in these collections, a genetic reverse approach has been used. TILLING is able to identify all allelic variants of a DNA region present in an artificial mutant collection. A similar procedure called ecotype TILLING (EcoTILLING) can be used to identify allelic variants for targeting genes in natural collections. These two methods are based on the use of endonucleases, such as CEL I or Endo I, that recognize and cut mismatches in the double helix of DNA. Since the TILLING and EcoTILLING techniques identify all allelic variants for a certain genomic region, the phenotypic characterization effort can be concentrated in a reduced number of accessions with different variants. Obviously, the success of the identification of variation useful for breeding programmes will depend on the right selection of target genes. The availability of sequences coming from NGS sequencing projects and the information provided by gene expression studies is significantly increasing the number and quality of candidates for TILLING and EcoTILLING studies.

Construction of High Density Genetic Maps

The high-density map construction involves the location of hundreds or even thousand markers in the different linkage groups. In these maps the coverage should be very high and no large gaps must be present. NGS technologies and high-throughput genotyping platforms have allowed the improvement of genetic maps by increasing markers density. Several works include the integration of new markers, basically SNPs derived from re-sequencing studies, into previously developed genetic maps, both in diploid and polyploidy species. Golden Gate has been the most widely used platform. Restriction-site associated DNA (RAD) is a kind of marker which detects genetic variation adjacent to restriction enzyme cleavage sites across a target genome. These markers are produced after NGS sequencing of genomic libraries obtained after digestion with different restriction enzymes.

Identification of Molecular Markers Linked to Single Genes and QTLs

NGS and high-resolution maps have led to a significant improvement in the identification of molecular markers linked to specific genes and QTLs. The most important advantage comes from the dense genome coverage, which allows the identification of markers closely linked to any target genomic region, with the advantages that this tight linkage provides. QTL detection has traditionally been conducted by linkage mapping. NGS technologies are significantly contributing to increase accuracy in detection of QTLs. They allow increases in many orders of magnitude of the number of markers mapped, ensuring high mapping resolution, and also aid in the development of mapping populations, such as RILs, NILs, CSSLs, and for QTLs detection. These populations have conventionally been constructed and genotyped using a limited number of markers. QTL detection based on the linkage analysis method has the disadvantage that the number of recombination events is limited to the generations needed to develop the mapping population. Association mapping or linkage disequilibrium (LD) mapping is a new powerful approach to map complex traits. This method identifies genetic loci associated with phenotypic trait variation in a collection of individuals. Association mapping uses the natural diversity, which represents many more recombination events occurred in the history of the population, providing better resolution.

Breeding by Design

The possibility to predict the outcome of a set of crosses on the basis of molecular markers information is known as ‘breeding by design’. The process includes three steps: mapping loci involved in all agronomically relevant traits, assessment of the allelic variation at those loci, and, finally, breeding by design.

Genomic Selection

MAS require the identification of markers associated to the traits of interest. This represents one of the weaknesses of traditional MAS approaches. Nevertheless, MAS can also be applied eluding this step, using an approach known as genomic (or genome-wide) selection. Genomic selection is based on simultaneous estimation of effects on phenotype of all loci, haplotypes, and markers available. Genomic selection requires the availability of phenotypic and genotypic data for the reference population.

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