Redesigning Animal Agriculture

tions underlying the desirable phenotypes
of various production, reproduction and
adaptation traits. Although hundreds of
QTL are map ped in livestock species, very
few causative mutations are identified. One
notable exception is that of the DGAT1 locus
contributing to fat composition in milk followed
by functional confirmation of the
effect of a missense mutation (Grisart et al.,
2002, 2004). These are termed as the quantitative
trait nucleotides (QTN) signifying the
actual nucleotide affecting the quantitative
trait of interest.
Another tool that needs to be exploited
is the field of comparative genomics, or
comparative analyses of genomes. This is
one of the ways of dissecting the genetic
basis of pheno typic variation across species.
The power of this tool is enhanced by
the availability of whole genome sequences
for the livestock species. These comparative
studies can direct research to species-specific
chromosomal regions that could not be
revealed by focusing on studying individual
genomes. Womack (2005) remarked that
identification of QTL for disease resistance
in livestock is the next big frontier, leading
to the understanding of host–pathogen
interaction and subsequent improvement of
both animal and human health. In the tropics,
however, resistance not only to endemic
diseases but also to natural tropical stressors
such as ticks, endo-parasites and heat is
Precision Animal Breeding 73
Table 5.2. Map status and genomic resource for livestock species (Source: Womack, 2005).
Cow Pig Sheep Horse Buffalo Goat
Chromosome
Mapped markers
60 38 54 64 50 60
Synteny 1800 1000 250 500 60 –
Linkage 2500 2700 1500 480 – 350
Cytogenic 300 300 850 450 300 350
RH
Resources
3200 3000 300 800 30 –
EST sequence 450k 300k 20k 30k – –
cDNA libraries 80 100 12 15 – –
BAC libraries 3 5 3 3 – –
Microarrays 3 2 – – – –
Whole genome sequence 6× * – – – –
Note:
*Whole genome sequencing of pig genome begun in 2005
essential to achieve desired production and
health targets of farm animals.
QTL mapping
While the information on molecular genetic
markers has revolutionized the biological
research in animals, there have been equally
important developments in statistical methods,
aided by the availability of increased computing
power. Detection of QTL requires accurate
phenotypes for traits of interest from preferably
a structured population of animals, genotypic
data on those animals followed by statistical
analyses correlating phenotypic and genotypic
data. Traditional animal breeding relied on the
performance recording for selection decisions.
Later developments emphasized the importance
of pedigree recording. This enables animal
breeders to exploit the recent advances
in molecular genetics assisting them in the
mapping of genes affecting traits of economic
importance. The recent developments in polymorphic
DNA markers (microsatelites) as well
as SNPs led to the onus on statistical developments
in analysing this information.
A comprehensive review of the development
of statistical methods dealing with
QTL analysis was given by Rocha et al.
(2002). A summary of this review and important
references are given in Table 5.3.