Genetic improvement of trees has always been a challenge, especially because of the long generation time and complex genomes. Developments in the DNA marker based technology have revolutionized plant breeding practices over the years. Molecular breeding based on DNA markers was termed as Marker Assisted Selection (MAS). By utilising markers closely linked to desirable traits one could exploit the allelic variations underlying the traits of interest. Prior identification of markers in association with the trait was a prerequisit for MAS. One of the major limitations of MAS came from the polygenic inheritance of traits. It did not consider small-effect genes underlying complex polygenic traits, thus limiting its usage to large effect alleles with known association to markers.
In 2001 using a simulation approach Meuwissen et al. 2001 came up with an alternative to overcome the limitatons of MAS. They proposed that selection based on breeding values which were estimated using highly dense markers could considerably enhance the genetic gain in both animals and plants. This genome wide selection method or Genomic Selection (GS) could overcome the shortcomings of MAS by extensive usage of markers without prior knowlegde of association to desirable trait. It employed larger number of markers spanning across the whole genome with the aim of covering the total genetic variance, which included rare alleles and genes with small effects as well. The vast coverage of markers would simultaneoulsy ensure LD of the QTLs of desired traits with at least one marker. Owing to the recent advancements in the next generation sequencing (NGS) technology the cost of generating innumerable markers and genotyping have been dramatically reduced making GS highly lucrative for breeders.
GS involves a reference or training population that includes large number of individuals that are phenotyped for target traits and genotyped using a whole range of markers covering the entire genome, in order to detect SNPs associated with the desired trait with higher accuracy. This data is used to creat a prediction model so that the differences at phenotypic level can be resolved by markers analysed. Using this model genomic estimated breeding value (GEBV) is calculated to predict the genetic gain. The phenotype can be predicted using genotypic data of the breeding material.
Heffner E. L. et al. 2009. Genomic selection for crop improvement. Crop science 49: 1-12. DOI: 10.2135/cropsci2008.08.0512. This paper presents a review on GS methods and the promises it holds for the future. A review of limitations of MAS is followed by in-depth description of GS methods and their advantages over MAS.
Perez-de-Castro A. M. et al. 2012. Application of genomic tools in plant breeding. Current genomics 13 (3): 179-195. The advancements in genomic technology are opening new fronts for plant breeding. This review introduces the latest genomic tools and their implementation in the field of plant breeding. It is an excellent read for those who are new to this field.
Goddard M.E. and Hayes B. J. 2007. Genomic selection. Journal of animal breeding and genetics. 124: 323-330. This paper presents the statistical analysis for calculating estimated breeding value from genome-wide DNA markers in a three step process. It further discusses the implementation of GS and its advantages over MAS and implications for future breeding programmes.
Jannink J-L. et al. 2010. Genomic selection in plant breeding: from theory to practice. Briefings in functional genomics 9(2): 166-177. DOI:10.1093/bfgp/elq001. This paper reviews different aspects of genomic selection (GS) and presents a summary on theoretical, simulation and empirical studies in context of GS. This paper also suggests training population design and management of short and long term gains.
Harfouche et al. 2011. Accelerating the domestication of forest trees in a changing world. Trends in Plant Sciences 17(2):64-72. DOI: http://dx.doi.org/10.1016/j.tplants.2011.11.005. This paper gives an overview of modern tree breeding approaches and how these could accelerate forest tree breeding and domestication. It explains the applicability of Genomic Selection (GS), Genome Wide Association Studies (GWAS) and Genetic Engineering (GE) and the advantages they offer over Marker Assisted Selection (MAS). It also describes the applications of Next Generation Sequencing (NGS) and “omics” research in tree improvement.
Grattapaglia D. and Resende M. D. V. 2011. Genomic selection in forest tree breeding. Tree genetics and genomes 7:241-255. DOI 10.1007/s11295-010-0328-4. This paper explains the genome wide selection approach. The authors present the results of the analysis of the effect of four parameters namely: linkage disequilibrium between markers used and the QTL; number of individuals in the training set; trait heritability; number of QTLs and their distribution, on the prediction accuracy using a deterministic approach.
Poland J. A. and Rife T. W. 2012. Genotyping by Sequencing for plant breeding and genetics. The Plant Genome 5(3): 92-102. DOI: 10.3835/plantgenome2012.05.0005. New prospects of GBS (genotyping by sequencing) are highlighted in this review. Along with various areas of application for GS e.g., construction of genetic maps: from mapping of single genes to whole genome association mapping; combining SNP marker discovery and genotyping at the same time, this would be very useful for plants as a de novo approach especially for organisms with large and complex genomes.
Generation of thousands of markers at a low cost by GBS (genotyping by sequencing) can provide rapid, adaptable and cost effective genotyping platforms that are in a position to achieve high density genotypes which will be highly useful for GS. GBS showed better prediction accuracy as compared to hybridization-based markers in wheat genome (Poland et al. 2012).
Desta Z. A. and Ortiz R. 2014. Genomic selection: genome-wide prediction in plant improvement. Trends in plant science 19(9): 592-601. DOI:10.1016/j.tplants.2014.05.006. This review compares the genomic estimated breeding value and prediction accuracy between genomic selection and other plant breeding methods.