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0:00
Hello, this is Daniel Gianola
from the University of Wisconsin-Madison
and Technical
University of Munich, Germany.
And I will start discussing
the second part of the presentation
concentrating on the impact
of "Genomics in Animal Breeding".
0:16
So now we're about
to enter the genomic era,
but until we actually had
a massive number
of molecular markers,
we were using linear models,
using phenotypes
and pairing research inputs.
But in the mean time,
the Edinburgh school kept working
on the basic foundations
of quantitative genetics.
And I would like to mention
Alan Robertson and William Hill
from the University of Edinburgh
that did considerable work
on the quantitative genetics
of small populations.
What would be the fate
of favorable alleles
in the selection process? And
what would be the probability
of fixation? That's the first formula.
And then we have a series of curves
that show the chance
of fixation of recessive alleles
as a function of population size
and selection intensity.
So we were working
with this sort of black box,
blind statistical models,
but at the same time
there were persons
such as Hill and Robertson
working on the underlying theory
that we would need at some point.
And the time came
when genomics began to be applied
in animal and plant breeding.
1:32
Let me state briefly
what the genomic era has meant
from an animal breeding perspective.
1:39
First,
let's talk about genomic data
and more specifically
about one type of genomic data
that I already mentioned,
called the SNPs.
So in this diagram, we have
randomly grown individuals
with a pair of chromosomes,
one and two.
And here you see
the double helixes.
And if you observe
the sequential basis,
these letters A's, T's, C's, and G's.
You see that the two chromosomes
are the same
except in that part, it's a SNP,
where in one chromosome,
we have a C paired with G,
whereas, in the other chromosome
we have a T paired with A.
And that is called a polymorphism
in the population.
And millions of such polymorphisms
have been discovered.
This gave the impetus to the idea
that perhaps we should use
all the genomic variability
in prediction models.
