Animal and plant breeding is fast becoming one of the hot topics of the 21st Century. The ability to identify specific traits and use them to improve different species or breeds is important as we try to make sense of a rapidly growing world population, and to provide high quality food in a changing climate. Whether we are fine-tuning crops to handle drought, changing seasonal weather, repelling disease or breeding animals for higher quality meat or dairy there is a need to understand “what works better and where”.
Traditional breeding methods use the plant or animal’s physical characteristic (phenotype) observed in a set of environments to determine its breeding value (BV); this is often time consuming and inefficient. Many of the traits used to define a BV are only observable once the plant or animal has reached full maturity which will generate high costs with long time-to-market period.
As technology has moved on researchers were able to identify specific DNA regions (QTL) associated with a specific phenotypic trait. The locations of these QTLs were identified on linkage maps, and then the markers correlated, thus creating genetic markers. The problem with marker assisted technology is that for quantitative traits, it relies on a few QTLs to predict BV, and as such is limited in its predicting power since a lot of QTLs with small effects are all neglected.
The method of genomic selection to predict a genetic value attempts to circumvent some of these problems in defining BV. It combines marker data, phenotypic and pedigree data to try and improve the accuracy for predicting breeding and genotypic values. This process should speed up the ability to identify genetic gains and hence bring seeds and new animal breeds to market quicker.
Given the importance of efficiency and accuracy required in scientific studies designed to provide predictions, ASReml has been recognised as one of the most appropriate pieces of software to assist in the calculations. Using ASReml, we can easily do the following analysis necessary for the application of Genomic Selection:
1. Numerator Relationship Matrix (A)
2. Genomic Relationship Matrix (G)
3. Obtain G Matrix from Allele Frequencies
4. Regression Method to Obtain G Matrix
5. Normalized G matrix method (GN)
6. Calculating the Inverse of G
7. Combined Pedigree and Genomic Relationships (H)
8. Obtain G-1 matrix
9. Calculating the GBLUP Breeding Value
To read about methods of Genomic Selection see the following scholarly articles:
http://www.gsejournal.org/content/43/1/18 (Different models of genetic variation and their effect on genomic evaluation)
http://www.genetics.org/content/early/2012/01/23/genetics.111.137026 (Accuracy of Genomic \selection Methods in a Standard Data Set of Loblolly Pine (Pinus taeda L.)