The topic of this lecture is a genetic
parameters in animal breeding.
The lecture is part of module 3,
Animal Breeding.
The creation of this presentation was
supported by the Erasmus plus KA2
grant, if the project ISAGREED,
innovation of content
and structure of study programs in the
field of management of animal genetics
and food resources using digitalization.
Genetic parameters are statistical values
that define the genetic
potential and the heritability of traits
in a population of animals.
Heritability is expressed as a population
statistical parameter
as the heritability coefficient. This
coefficient measures
the proportion of total phenotypic
variability in a trait that can be
attributed
to genetic variability. This
proportion can range from zero
to 1. Another genetic
parameter is genetic correlations,
which quantify the extent to which genes
influencing 1 trait
simultaneously affect another
trait. The value of genetic
correlations range from -1 to
+1. Positive genetic
correlations indicate that improvement
in one trait will lead to improvement
in another trait. Negative
genetic correlations indicate
a trait of between traits, where
improvement in one trait may lead to
a decrease in another.
The last genetic parameter
is repeatability coefficient R,
which estimates the genetic
in repeated traits through an
individual's lifetimes.
Methods for estimating genetic
parameters
Require knowledge of phenotypic values
which are measured for production
traits and quantification of kinship
relationships which determine
shared gene proportions. These
relationships can be determined using
pedigree data or currently using
genomic data.
How do we estimate them?We must use
statistical methods, particularly
linear models such as regression
analysis, analysis of variants.
Covariance analysis and correlation
analysis. The goal is
always to estimate the genetic and
environmental components of variance.
This genetic variance further divided
into 3 main components
where the additive genetic variance is of
most interest.
Among the oldest methods
for estimating genetic parameters is the
analysis of variance.
Using least square method, balanced
data is advantageous for this method.
But for unbalanced data, modified
Henderson's method must be used, which
are included
in various software packages like SAS,
RRAY, et cetera.
In practice, maximum likelihood methods
or its various REML variants
are more commonly used
as they provide the best estimates. Even
for unbalanced data,
Bayesian methods are still
used in researchareas.
We will describe the use of utilityvalues
in families to estimate heritability.
Phenotypic variances between families
and within families are always
calculated if the variance
between families is low. Then the
variance within families is high.
In this case, a high variability value is
calculated from these
traits. Conversely, if the
variance within families
is high and the variance between families
is low, then the low heritability
value is estimated. This is because
the variance of within families
or among individuals within the family.
is more influenced
by environmental variances. On
the other hand, the variances between
families
is more influenced by genetic variances.
It follows
the rule that
total variance
of trait in population is caused by
variance between families, which is
the same as the covariance within
families and variance within
families.
The SCEM
of such an experiment can be seen in the
following picture. We have three
families, where each family consists of
father who has many offspring
with multiple mothers. Herotypic
values are measured in these
offspring. We can assess the variability
within offspring along with thevariability
between groups of related individuals
according to fathers or
according to fathers and mothers
simultaneously. We can get
group
half SIPs with 25% common
genes or we
can also assess groups of full
SIPs with 50%genes.
This model is best applied using
analysis of variants. Analysis
of variants can detect important sources,
effects that contribute to differences
between individuals.
In our case,Those will be the parents
father.
their contribution to the total variance.
The main ways
to determine this variance is to derive
the sum of square of deviations
from the mean and degrees of freedom.
It is important
to have individuals in groups with the
same degree of relatedness. For example,
group of half-sips according to
father or the parent-child
relationship. It holds that
covarianceor similarity
between family members,
is equal to the variance
component between groups.
Ifwe use the model of the half-SIPs
families, then the variance between
half-SIPs
families by pattern now is
equal to the covariance between
the half-SIPs, which is equal to
quarter to additive genetic variance.
The variance in half-shapes families is
equal to residual variances,
which is generally referred to as
environmental variances.
The heritability is then equal to
the ratio of additive genetic
variance to the total phenotypic
variances, which is equal to 4
times
to paternal variance or the variance
between half-shapes families
to the total phenotypic variance.
Wecan see the simplified design of the
experiment in this family.
father has one offspring with one mother.
Therefore, we
evaluate the variability between
offspring by groups according
to fathers. Then it naturally
follows that the additive genetic
is equal to four times the paternal
variance, SIRE
variance. We can estimate the
paternal SARE variance using
analysis of variance.
Thismodel is called the paternal model or
SARE model. It is one-factor
analysis
of variance where the only fixed effect
in the equation is the effect of
fathers. It is assumed that
fathers and mothers are unrelated,
randomly paired, and not affected by
selection.
Another assumption is that we have a
balancer design. This means that the
P fathers
are paired with N mothers and they have
one offspring.
In the resulting table of the new
paternal model,
we see three rows. The last row is
the total phenotypic variance.
The first and the second rowsExpress the
estimation of variance between
and within families. The
degrees of freedom DF
formulas for calculating sum of square or
deviations from the mean according
to the source of variability and the
calculation of the mean square of
variance as
the ratio sum of squares and
degrees of freedom are expressed
here. Statistics and here
and it is and in the last
column. The genetic part begins.
The environmental variance
is directly equal to the variance within
families,residual variances.
In the variance between families, between
groups according to fathers,
MSA includes genetic
variance.
The variance among fathers,
is equal to the residual variance plus.
N0 times
the genetic variances. N0 is the
weighted number of offspring per
father. Because the residual
variance is directly equal to
the within family variances, we can
directly calculate genetic variance asthe
difference between MSA, it is
paternal variance, minus
residual variance, divided by
N0.
Furthermore,We calculate the intraclass
correlation coefficient, rho,
as the ratio of genetic variance to the
total phenotypic variance.
The estimate of heritability coefficient
is obtained by multiplying
this intraclass correlation coefficient
by four,
because we calculate genetic variance
based on half sip groups. And
we know that half sip have a
quarter genetic resemblance.
For correct interpretation
of heritability estimate results,
it is unnecessary to calculate
its standard error. Its value,
SVC, from the formula
depends mainly on the number of half-CB
groups, P, and the weighted
number of offsprings per father.
What is the importance of estimating
heritability?
An estimated high heritability suggests
that the phenotypic variance of the trait
inthe population is largely influenced by
genetic variability. Breeding
programs can focus on traits with
high heritability and thus achieve
faster genetic progress, genetic gain.
Inconclusion, understanding genetic
parameters is crucial for efficient
animal breeding. Heritability
genetic correlation, and
advancements such as GWAS and
genomic selection allow targeted
improvement of traits to increase
productivity and profitability in animal
production.
Thanks for your attention.