Redesigning Animal Agriculture

76 K. Prayaga and A. Reverter
to data from the Australian Beef CRC,
Reverter et al. (2005b) engineered a gene
network for bovine skeletal muscle with 102
genes, constructed by relating the genes surveyed
in five Beef CRC studies with musclespecific
genes identified from the National
Cancer Institute, Cancer Genome Anatomy
Project, SAGE database. This method is currently
being extended to the construction of
gene networks in bovine skeletal muscle and
adipose tissue, based on gene-expression
profile data with 822 genes from the entire
set of Beef CRC experiments comprising 147
hybridizations across nine experiments and
47 conditions. Furthermore, our approaches
are being applied to gene expression data
from other species and conditions as they
become available. These include the identification
of changes in the network topology
in the presence of fleece rot in Merino
sheep.
At the same time and with the advent of
robust bioinformatics approaches, it is anticipated
that biotechnology laboratories will
not be inclined to undertake new in vitro,
let alone in vivo, experiments unless their
computational/mathematical models anticipate
a high chance of success. This is further
exacerbated by the raise of ‘Reduce, Refine,
Replace’ – the Three Rs – as the basic tenets
of research and other policies concerning
the use of animals in scientific testing and
animal experimentation.
The availability of genomics data on
a large scale has led to the development of
bioinformatics inferential algorithms for statistical
prediction of causal molecular pathways.
However, these potentially powerful
algorithms are limited by our inability to
evaluate their accuracy, as we do not know
the true biological network with which to
compare them and experimenters cannot
physically perform in reasonable time the
multiple gene knockouts (or other types of
interventions) necessary to test the predicted
networks systematically. Nevertheless, the
study of the basic principles involved in
the dynamics and topological changes in
genomes as a result of a given perturbation is
gaining momentum. Given: (i) the potentially
large number of gene-to-gene interactions
resulting from epistatic effects; (ii) the inher-
ent stochastic nature of the individual main
effects of each gene in the network; and (iii)
the fact that most gene knockouts result in
little or no phenotypic change; it follows that
successful (i.e. directed to a desired biological
outcome) testing of postulated hypotheses
about desired biological outcomes becomes a
non-trivial task and/or too complex to allow
for an exact solution. Furthermore, given a
well-defined genetic model, postulating biologically
meaningful hypotheses could be a
task prone to large Type III error rates (i.e.
correctly rejecting the null hypothesis, but
incorrectly attributing the cause).
The advent of new high-throughput
genetic technologies has brought the potential
to drive the prediction of future performance
using vastly improved frameworks, most
importantly via integrative approaches. In its
broad sense, such a shift toward biologi cal integration
is the approach advocated in systems
biology. The ultimate vision of our systems
biology endeavours is to provide a comprehensive
integration of data-driven knowledge
of genetic and environmental architectures to
develop functional models that provide qualitative
description of the subject matter with
predictive power (Hwang et al., 2005a, b).
A Venn diagram representation of how
this integration is taking place is presented in
Fig. 5.1. Within its core, three distinct types of
data are clearly distinguishable: (i) Phenotype
and pedigree (the basis for genetic evaluation
and parameter estimation); (ii) Phenotype and
marker (the basis for the simplest of the marker
association studies); and (iii) Gene (protein or
metabolite) expression (the basis for differential
expression and differential connectivity
studies). While attempts exist to integrate any
two of these three types of data with mixed
results, a great deal of research is still required
to successfully integrate the three domains.
Hence, although we are moving in the
right direction in the pursuit of precision animal
breeding, the target itself is highly dynamic
in nature. As we are learning more about the
biology of traits under polygenic inheritance,
it is becoming very obvious that we can only
make incremental advances and precision
animal breeding has to include and integrate
information from all possible sources such as
phenotype, genotype, and gene expression.