Future perspectives and
conclusions
Knowledge of the genes underling the expression of a trait
will allow researchers to search for novel combinations of
alleles to make further improvements in desired traits, as
the most beneficial alleles and allele combinations may not
occur in the production populations. Breeding schemes
could then be designed to test novel combinations of
alleles at different loci. Some of these novel allele
combinations may result in the improvement of particular
traits beyond that which would be possible by selecting
phenotypically superior animals within a population. It is
therefore important to maintain a diverse range of genetic
backgrounds to provide sources of variation.
The behaviour of genes (including major genes) that
control a trait is likely to be dependent on the genetic
background. The myostatin allele found in Belgian Blue
cattle is also found in other breeds; however, the
phenotype associated with the allele is variable between
the breeds. This suggests that there are genes at other loci
in the genome that act to modify the phenotypic
expression of the major gene. Thus, information is
required not only on the major genes that control a trait,
but also on the interactions between genes. Further
information on gene interactions may be obtained from
gene expression studies. Gene expression micro-arrays that
allow the expression patterns of many thousands of genes
to be assayed simultaneously have now been produced for
the majority of livestock species. Compiling information
on expression patterns in different tissues and species
could reveal co-regulated physiological pathways
(i.e. pathways that are regulated by common genes or sets
of genes) that are currently not known.
Information on the genes that control commercially
important traits is only just emerging from the numerous
studies that are underway. For those genes that have been
identified, the level of variation within the genes between
individuals or populations is not known, nor is the effect
of specific variations on phenotypes. As discussed above in
the context of double muscling, the effects of even wellcharacterised
variations that are associated with a major
phenotype can vary depending on the genetic background.
It is therefore premature to start using DNA-based
selection widely. However, some DNA tests for specific
polymorphisms are being offered commercially, e.g. the GeneSTAR tests for tenderness (based on variations in the
calpastatin gene) and marbling (based on variations in the
thyroglobulin gene), and the Ingenity test for fat deposition
(based on variations in the leptin gene). These tests can be
used by breeders and evaluated in their populations.
Variations in phenotypes may arise from functional
differences within the coding regions of genes, which
change the structure of the protein that the genes code for,
or from differences in the DNA sequences that regulate the
expression of the gene. The coding regions of genes can
generally be identified, but the role of the non-coding
regions is poorly understood. A project is underway to
sequence the bovine genome, and sequences are now
available for the genomes of several different species.
Comparison of the coding and non-coding regions of the
genome across species is a further approach that is used to
identify functionally significant variations in genes. It will also help to identify regulatory elements in non-coding
regions that may account for some of the variation that is
seen in particular traits, such as found for fat deposition
controlled by the IGF2 gene in pigs or the Callipyge
phenotype in sheep. Livestock species are an excellent
model for studies on gene function and regulation because
of the vast genetic diversity that is present and the
extended families that are available to dissect specific
regions of the genome by studying recombination
throughout generations. What is urgently required are
populations that have had phenotypes well recorded for a
wide range of traits, so that the wealth of genetic
information that is being produced from recent advances
in genomics technology can be linked to phenotypic
variation and used to explore genome function.