Next-generation sequencing technologies and their implications for crop genetics and breeding
Using next-generation sequencing technologies it is possible to resequence entire plant genomes or sample 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 genetic variation are now so efficient and precise that thousands of variants can be tracked within large populations. In this review, we outline some important areas such as the large-scale development of molecular markers for linkage mapping, association mapping, wide crosses and alien introgression, epigenetic modifications, transcript profiling, population genetics and de novo genome/organellar genome assembly for which these technologies are expected to advance crop genetics and breeding, leading to crop improvement. [1]
Applications of single nucleotide polymorphisms in crop genetics
The discovery of single nucleotide polymorphisms (SNPs) and insertions/deletions, which are the basis of most differences between alleles, has been simplified by recent developments in sequencing technology. SNP discovery in many crop species, such as corn and soybean, is relatively straightforward because of their high level of intraspecific nucleotide diversity, and the availability of many gene and expressed sequence tag (EST) sequences. For these species, direct readout of SNP haplotypes is possible. Haplotype-based analysis is more informative than analysis based on individual SNPs, and has more power in analyzing association with phenotypes. The elite germplasm of some crops may have been subjected to bottlenecks relatively recently, increasing the amount of linkage disequilibrium (LD) present and facilitating the association of SNP haplotypes at candidate gene loci with phenotypes. Whole-genome scans may help identify genome regions that are associated with interesting phenotypes if sufficient LD is present. Technological improvements make the use of SNP and indel markers attractive for high-throughput use in marker-assisted breeding, EST mapping and the integration of genetic and physical maps. [2]
Association genetics in crop improvement
Increased availability of high throughput genotyping technology together with advances in DNA sequencing and in the development of statistical methodology appropriate for genome-wide association scan mapping in presence of considerable population structure contributed to the increased interest association mapping in plants. While most published studies in crop species are candidate gene-based, genome-wide studies are on the increase. New types of populations providing for increased resolution and power of detection of modest-size effects and for the analysis of epistatic interactions have been developed. Classical biparental mapping remains the method of choice for mapping the effects of alleles rare in germplasm collections, such as some disease resistance genes or alleles introgressed from exotic germplasm. [3]
Genetic Divergence in Sugarcane Genotypes
To assess genetic divergence of sugarcane germplasm, an experiment comprising 25 sugarcane genotypes was conducted at Sugar Crops Research Institute (SCRI), Mardan, Khyber Pakhtunkhwa, Pakistan, in quadruple lattice design during 2008-09. Among the 14 parameters evaluated, majority exhibited significant differences while some showed non-significant mean squares. The initial correlation matrix revealed medium to high correlations. Principal Component Analysis (PCA) showed that there were two principal components accounting for 88% of the total variation in the tested breeding material. The new components were named “Vigor”, and “Quality”. Principal Component Regression (PCR) indicated that these two accounted for 93.64% and 7.36% of variation in the yield, thus signifying the role of the “Vigor” Component. Cluster analysis using Ward’s method on the newly created variables using principal components revealed that there were 3 clusters at a linkage distance of 4.5. Cluster I and III had 11, and cluster II had 3 genotypes. Cluster I showed high mean values for Vigor Component while Cluster II for Quality Component and Cluster III showed genotypes with high mean yield. There was no correspondence of the clustering with the geographic location of the genotypes. It could be concluded from these analyses that there are two main components i.e. vigor, and quality accounting for maximum variation in yield. The genotypes in cluster I and II could be utilized as source for future selection or hybridization program for the improvement of these characters in sugarcane. [4]
Genetic Variation for Yield and Yield Components in Sole Soybean and Soybean/Celosia Inter-Crop in Makurdi (Southern Guinea Savanna Ecology), Nigeria
Aims: To determine genetic variation in soybean grown alone and in soybean/celosia intercrop system, and to provide information on the appropriate system to concentrate in a breeding programme.
Study Design: The experimental design was a randomized complete block design (RCBD) with three replications for each of the cropping systems in each of the years.
Place and Duration of Study: Field experiments were carried out at the Teaching and Research Farm of the Federal University of Agriculture, Makurdi, Nigeria, during the cropping seasons of 2006 and 2008.
Methodology: Twenty – five to thirty – nine improved varieties of soybean were evaluated in sole and soybean/celosia intercrop for each of the years. Data were taken on days to flowering, plant height, number of pods/plant, 100 – seed weight and grain yield for soybean in both cropping systems. Data were analysed using analysis of variance and components of genetic variation.
Results: Highly significant difference in varieties was observed for all the traits studied in the sole and intercrop soybean for each of the years with significantly higher grain yield in 2008 compared to 2006 in both the sole and intercrop soybean due to planting at the recommended planting date for soybean in 2008.Genetic variance was higher than error variance for all the traits in the sole soybean except 100 – seed weight in 2006. Genetic variance in the intercrop soybean was lower than the error variance for three out of the five traits studied leading to proportionally lower heritability estimate and genetic advance for almost all the traits in intercrop soybean compared to sole soybean system.
Conclusion: The evaluated varieties of soybean are genetically diverse. A faster progress in selection will be achieved in the sole soybean and should be adopted in the selection of soybean genotypes for soybean/celosia intercrop system. [5]
Reference
[1] Varshney, R.K., Nayak, S.N., May, G.D. and Jackson, S.A., 2009. Next-generation sequencing technologies and their implications for crop genetics and breeding. Trends in biotechnology, 27(9), pp.522-530.
[2] Rafalski, A., 2002. Applications of single nucleotide polymorphisms in crop genetics. Current opinion in plant biology, 5(2), pp.94-100.
[3] Rafalski, J.A., 2010. Association genetics in crop improvement. Current opinion in plant biology, 13(2), pp.174-180.
[4] Tahir, M., Rahman, H., Gul, R., Ali, A. and Khalid, M. (2012) “Genetic Divergence in Sugarcane Genotypes”, Journal of Experimental Agriculture International, 3(1), pp. 102-109. doi: 10.9734/AJEA/2013/2283.
[5] Ojo, G. O. S. and Odoba, A. (2017) “Genetic Variation for Yield and Yield Components in Sole Soybean and Soybean/Celosia Inter-Crop in Makurdi (Southern Guinea Savanna Ecology), Nigeria”, Journal of Experimental Agriculture International, 16(2), pp. 1-7. doi: 10.9734/JEAI/2017/29137.