Traditional Method Of Genomic Selection

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Genomic selection Traditional method of genetic improvement of livestock using information on phenotypes and pedigrees to predict breeding values has been very successful, however, breeding values could be predict more accurately by using information on variation in DNA sequence between animals. Research on marker assisted selection (MAS) is very extensive but has limited implementation and increases in genetic gain is small (Dekkers, 2004). Goddard and Hayes (2002) showed that the factors governing the additional gains from MAS are the accuracy of the existing estimated breeding values (EBV). If at all, the accuracy is high, there will be little gain. However, where traditional selection is most difficult, e.g. traits displayed only in …show more content…

Using simulation, they showed that the breeding value could be predicted with an accuracy of 0.85 from marker data alone. The major limitation to the implementation of genomic selection has been the large number of markers required and the cost of genotyping these markers (Grapes et al. 2004). Recently both these limitations have been overcome in most livestock species following the sequencing of the livestock genomes, the subsequent availability of hundreds of thousands of single nucleotide polymorphisms (SNP). As a result of these developments there are many livestock breeding companies planning to implement genomic selection in the near future (Grapes et al. 2004). Statistical analysis to calculate EBV from genome-wide DNA markers it is convenient to think of the process in three steps:
i. Use the markers to deduce the genotype of each animal at each QTL. ii. Estimate the effects of each QTL genotype on the trait. iii. Sum all the QTL effects for selection candidates to obtain their genomic EBV (GEBV).
As the markers are unlikely to be evenly spaced, and due to the variable nature of the LD, we could still not expect that all QTL would have an SNP in complete LD with them. This suggests that we need denser markers than are currently available. The technology to achieve this is available (Parks et al. 2007). An additional problem arises if we wish to estimate the effect of each marker across more than

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