Agriculture Reference
In-Depth Information
the large phenotyped and genotyped reference
populations that will be required to realize the
full potential of GS. This strengthens the case for
government and industry investment in GS ini-
tiatives (Hume
et al
., 2011). For example, it
might be possible to measure methane produc-
tion on large numbers of cattle or sheep in a
research herd in order to calibrate the GS predic-
tion equation for wider application by breeders.
Initial studies suggest there is a positive relation-
ship between efficiency and reduced methane
emissions (Zhou
et al
., 2009). For disease sur-
veillance, it might be possible to integrate GS
approaches using the phenotypes that are col-
lected as part of routine government-funded dis-
ease surveillance (e.g. collection of case-control
samples from mortalities on a given farm). These
approaches may ultimately reveal a sub-set of
markers of sufficiently large effect that they can
be cost-effectively combined into a DNA test with
a reduced number of markers. This would
decrease the cost per test, although the accuracy
of trait prediction would likely also be decreased.
In the future, selection on genetic markers is
likely to become more common. As with other
selection criteria, DNA-informed selection deci-
sions should be based on the overall effect of the
different genotypes on maximizing the selection
response per unit time.
Although on the surface GS may arouse
less public opposition because it utilizes natu-
rally occurring genetic variation, some associ-
ated applications to reduce generation interval
that are enabled by GS may be seen as contrary
to animal welfare. These include the use of germ
line approaches to shorten the generation inter-
val, such as the harvest of oocytes from calves
that are still
in utero
(Georges and Massey, 1991),
or an approach where breeding is essentially
done in the laboratory using GS to predict the
EBV of cells derived from
in vitro
meiosis events
(Haley and Visscher, 1998). Such animal breed-
ing scenarios are largely hypothetical, but anal-
ogous manipulations in the world of plant
breeding have met with great success.
In vitro
sexual recombination in combination with GS
could rapidly accelerate the rate of genetic pro-
gress, and may also serve as a new way of gener-
ating genetic diversity (Hume
et al
., 2011). As
with all of the genetic technologies discussed in
this chapter, there is a need to weight the use of
technologies or genetic resources that accelerate
the rate of genetic gain against any potential
negative impact such use may have on compet-
ing sustainability goals.
Sustainable BO need to include both pro-
duction and functional traits including disease
resistance, reproduction, longevity and well-
being to optimize the result of genetic improve-
ment programmes. Genomic technologies may
offer new opportunities to expand the pheno-
types available as selection criteria for functional
and other novel traits (e.g. feed efficiency, meth-
ane production) thereby enabling their inclusion
in future BO. In addition, BO need to also account
for the NMV associated with environmental,
genetic diversity, ethical and social considera-
tions. The weighting given to these different con-
siderations will necessarily vary between
countries and regions given the differing produc-
tion environments, local cultural and social con-
ditions, food security concerns and uncertainty
about future circumstances. Having regionally
appropriate BO will help to maintain genetic
diversity among breeding stocks throughout the
world. Ultimately, developing sustainable BO will
require a nuanced balance among often conflict-
ing animal welfare aspirations, social and envi-
ronmental concerns, food safety, public health,
genetic variability and production demands.
References
Blackburn, H.D. (2004) Development of national animal genetic resource programs.
Reproduction, Fertility
and Development
16, 27-32.
Caballero, A. and Toro, M.A. (2002) Analysis of genetic diversity for the management of conserved subdi-
vided populations.
Conservation Genetics
3, 289-299.
Capper, J.L., Cady, R.A. and Bauman, D.E. (2009) The environmental impact of dairy production: 1944
compared with 2007.
Journal of Animal Science
87, 2160-2167.
Cheng, H.W. (2010) Breeding of tomorrow's chickens to improve well-being.
Poultry Science
89, 805-813.
