Applying Genome Sequencing Technologies to Crop Breeding
Author:R. D. Vekariya1*, A. G. Singh1 and Aashima Batheja2
1Dept. of Genetics and Plant Breeding, Navsari Agriculture University, Navsari (496390), India
2Dept. of Crop Improvement, CSK Himachal Pradesh Krishi Vishavidhalaya, Palampur, HP (176062), India
Next generation sequencing (NGS) technologies are being used to generate whole genome sequences for a wide range of crop species. These technologies provide a powerful and rapid tool for identifying the genetic basis of agriculturally important traits and for predicting the breeding value of individuals in a plant breeding population.
In 2012, the world population exceeded 7 billion people and is expected to continue growing. To feed this growing population and meet rising expectations regarding food quality, food production must increase by an estimated 70% by 2050. Recent fluctuate climatic make stable food production even more difficult and put pressure on fragile environments. There is, therefore, an urgent need to accelerate crop breeding improvements and to implement new management strategies that together can achieve sustainable yield increases without further expanding farmland or damaging the environment. To meet these future upcoming challenges, scientists are developing new and more efficient breeding strategies that integrate genomic technologies and high throughput phenotyping to better utilize natural and induced genetic variation. Rapid developments in next generation sequencing (NGS) technologies over the last decade have opened up many new opportunities and new plat form to explore the relationship between genotype and phenotype with greater resolution than ever before.
Why genome sequencing is more demanded for plant breeders?
Now a day NGS based new sequencing technology develop, so cost of sequencing has decreased, breeders have begun to utilize NGS with increasing regularity to sequence large populations of plants, increasing the resolution of gene and quantitative trait locus (QTL) discovery and providing the basis for modeling complex genotype-phenotype relationships at the whole-genome level. Specialized plant genetic stocks, such as bi-parental and multi-parent mapping populations, mutant populations, and immortalized collections of recombinant lines (RILs), have been generated to facilitate mapping and gene function analysis via association studies and QTL mapping in several crop species. Knowledge about the identification of marker liked trait and map location of agriculturally important genes/ QTL with tightly inked marker provides the basis for selection parents based on trait linked marker and marker-assisted selection (MAS) in plant breeding. NGS technologies have been available for a number of years and are widely used for de novo sequencing, whole genome sequencing (WGS), whole genome resequencing (WGRS), genotyping by sequencing (GBS) and transcriptome and epigenetic analysis (Varshney et al., 2009). Using next-generation sequencing technologies and their way of utilizing new NGS methodology helps to scientist for resequence entire plant genomes or entire transcriptomes more efficiently and economically and in greater depth than ever before. Rather than sequencing individual genomes, we envision the sequencing of hundreds or even thousands of related genomes to sample genetic diversity within and between germplasm pools. Identification and tracking of new genetic variation are now so efficient and precise that thousands of variants can be tracked within large populations using NGS based approaches.
Two main types of genomics-assisted breeding are (1) MAS and (2) GS. MAS, which includes marker-assisted back-crossing (MABC), uses molecular markers that map within specific genes or QTLs known to be associated with target traits or phenotypes to select individuals that carry favorable alleles for traits of interest and on the other hand Genome Selection (GS), uses all available marker data for a population as predictors of breeding value. Specifically, GS integrates marker data from a training population with phenotypic and, when available, pedigree data collected on the same population to generate a prediction model. The model outputs genomic estimated breeding values (GEBVs) for all genotyped individuals within a breeding population (Meuwissen et al. 2001). The use of NGS technologies provides genome-wide marker coverage at a very low cost per data point, allowing us to assess the inheritance of the entire genome with nucleotide-level precision. In the considering of genomics-assisted breeding applications, both MAS and GS have benefited tremendously from NGS technologies in crop improvement breeding. NGS is provide good plate form to breeders to easily looking for genotypic information with high efficiency and accuracy, which is relatively fast, cheap, and easy to generate and to inform them about the phenotypic potential of their materials. Both MAS and GS are attempts to do that, and they each have different strengths and limitations. The utility of each depends on the genetic architecture and heritability of the trait(s) involved in particular genotypes.
Gene and QTL Discovery
The application of MAS in plant breeding is predicated on the basis of prior knowledge about major-effect genes and QTLs that serve as the targets of selection. NGS technologies have proven useful in identifying these loci in diverse populations. In the following section, we discuss various approaches to gene and QTL discovery where the use of NGS enhances the efficiency and resolution of the mapping process.
Genome-wide association studies (GWAS): Genome-wide association studies utilize association mapping, also known as linkage disequilibrium (LD) mapping, to map QTLs by taking advantage of historic LD to identify statistically significant phenotype-genotype associations. GWAS have been successfully performed in several crop plants, including maize, rice, wheat, soybean, sorghum and foxtail millet. The use of NGS in the context of GWAS makes it possible to genotype larger populations of plants with a higher density of markers than was previously possible, and this contributes increased mapping resolution. Simultaneously developments in NGS technologies and their new approaches, specialized mapping populations have also been developed that significantly enhance the power and efficiency of GWAS. Nested association mapping (NAM) and Multi-parent advanced generation inter-cross (MAGIC) used to shuffle the genetic background among a set of diverse parental lines and increase recombination, and consequently the mapping resolution of QTL. Both types of population have been successfully developed and used to identify QTLs for a number of traits in diverse crop species.
High Resolution Genetic Mapping and Candidate Gene Identification: NGS-based approaches, including sequencing-based mapping (SbM), can be used in combination with bulked segregant analysis (BSA) and modifications thereof to help speed the identification of candidate genes (Huang et al., 2009). NGS-based approaches that involve whole genome sequencing can improve the power of BSA and are being widely used in many plant species today (Varshney et al., 2014). MutMap is a method based on WGRS of pooled DNA samples from the phenotypic extremes of a segregating population derived from a cross between a mutant of interest and wild type line. Abe and colleagues (2012) utilized this strategy to identify causal SNPs in a gene (OsCAO1) for the pale green leaf mutant in rice, and results were validated transgenically.
TILLING/Eco-TILLING by Sequencing: Targeting-induced local lesions in genomes (TILLING) is a reverse genetics approach for the rapid discovery and mapping of induced causal mutation responsible for traits of interest. Eco-TILLING is a method that uses TILLING techniques to identify natural mutations in individuals (Wang et al., 2012). TILLING populations have been developed for several crop plants, such as rice, wheat, sorghum, oat, Brassica, chickpea and pearl millet and used to identify useful alleles (Varshney et al., 2014).
Marker-Assisted Selection as a Breeding Practice : The oldest and most widely used type of genomics-assisted breeding is MAS. Identifying a gene or genomic region (QTL) that is responsible for a trait of interest is only an initial step in using MAS in a crop improvement program. Once found, the next step is to introgression the identified gene or genomic region(s) into an adapted crop line(s) using markers to identify the offspring that carry the most favorable combination of alleles. Both genotyping arrays and NGS approaches have been successfully used to introgress target loci into elite varieties to improve performance. Simply inherited traits commonly targeted for MAS include disease and insect resistance, abiotic stress tolerance and grain quality (Varshney et al., 2014).
Exploitation of genome sequencing in crop improvement: The development of improved breeding lines for commercial crop cultivation has traditionally been a time consuming and expensive task. With the deployment of genomics-assisted breeding, the generation of such lines is destined to become easier and faster, if also more expensive in the short term. To meet the demands of the human population and increasing volatility of the climate, we must accelerate the pace of our current breeding practices and apply genomics based selection approaches. Selection based on NGS allows marker discovery, marker validation, and genotyping itself to occur simultaneously, as we have discussed and NGS-generated data, including the many forms of GBS, will become the way of the future in crop improvement area. As evident from the above examples, NGS can have significant implications for crop genetics and breeding. The development of large-scale genomic resources, including transcript and sequence data, molecular markers and genetic and physical maps, is significant, in addition to other potential applications. Transcriptome and genome sequencing (both resequencing and de novo) using NGS technology is increasing for crop plants. The use of NGS technologies has already led to a quantum leap in the amount of genomic data available for crops for which not many genomic resources were previously available, such as chickpea and pigeon pea. For instance, large scale development of molecular markers using NGS can facilitate linkage mapping and WGS-based association genetics that are of practical use for MAS in marker deficient crops.
Varshney RK, Nayak SN, May GD, Jackson SA (2009). Next generation sequencing technologies and the application for crop genetics and breeding. Trends Biotechnol, 27: 522–530.
Meuwissen THE, Hayes BJ, Goddard ME (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics, 157:1819–1829.
Huang X, Feng Q, Qian Q, Zhao Q, Wang L, et al. (2009). High-throughput genotyping by whole-genome resequencing. Genome Res., 19:1068–1076.
Varshney RK., Terauchi R., Susan R. M. (2014). Harvesting the promising fruits of genomics: applying genome sequencing technologies to crop breeding. PLOS Biology, 12:1-8.
Abe A, Kosugi S, Yoshida K, Natsume S, Takagi H, et al. (2012). Genome sequencing reveals agronomically important loci in rice using MutMap. Nat Biotechnol., 30: 174–178.
Wang TL, Uauy C, Robson F, Till B (2012). TILLING in extremis. Plant Biotechnol. J., 10: 761–772.
About Author / Additional Info:
I am currently pursuing Ph.D. in Genetics and Plant Breeding at Navsari Agricultural University, Navsari, Gujarat. I have also worked Molecular breeding in my MSc and PhD research programme for 2 years.