Biometrical Approaches in Genetics and Breeding of Farm Animals

Josef
Přibyl, Jana Přibylová, Luboš
Vostrý

Genetic work with farm animals is
organized on a state level and is regulated by low. Farmers are
organized in Association, which are responsible for breeding programs.
Breeds are continuously selected and improved to the demands of
farmers. Base for selection is regular production recording in herds,
which
covers approx. 95% of dairy cows (350,000 cows), 10% of beef breeds
cows (22,000 cows),
10% of sheep (25,000 ewes) and 5% of pigs (5,000 sows) and their
progeny. Data are
centralized a stored several decades. Data included production,
evidence of animal
and farm and pedigree. Data are regularly several times in a year BLUP
evaluated by
authorized organization (Plemdat) and distributed to farmers.
Procedures of
evaluation are regularly internationally validated. In dairy cattle are
results –
estimated breeding value (EBV) of sires from participating countries
delivered to the global
evaluation in Interbull, because many sires have progeny in several
countries. A global
evaluation is by MACE method and to the country is returned new EBVs of
all
globally used sires, converted into national scale of each country.
Research is under
mentioned conditions. The methodology is based on analysis of surveys.
Basic
procedure is Mixed Linear Model. Files cover several hundred thousand
till several tens of
millions of observations. Evaluation is in several steps. Editing –
elimination of
unreliable values and classes with small number of observations.
Creation of effects and
pedigree files. Tuning of models with fixed effects (LSM). Construction
of
mixed model.
Calculation of variance components (REML, Gibbs Sampling) on a fitted
data.
Implanting of variance components into BLUP for evaluation of all
animals. Different models for BLUP are used, ST/MT, Animal, Maternal.

Elements
of Experimental Design in
Plant Variety Testing

David
Hampel, Jiří Hartmann, Martin
Tláskal

Realization of an experiment is affected less or more by external
conditions. In the case of plant variety testing in one growing cycle,
the main inﬂuence
is often identified as a soil heterogeneity. To avoid this, we should
apply not only
correct agrotechnics, but also appropriate design of an experiment.
This presentation contains the basic ideas of experimental design.
Concepts of randomization, complete and incomplete blocks, etc. are
discussed. With
Alpha design as an example, the criteria of design efficiency are
presented.
Different designs are presented brieﬂy. Moreover, problems related to
experimental series
design are discussed as well.

Basic
Models in Plant Variety Testing

David
Hampel, Jiří Hartmann, Martin
Tláskal

The main purpose of a plant variety
experiment realization is a
comparison of particular plant varieties from different insights:
yield,
frost resistance,
length, content of chemical substances. To achieve this, it is
necessary to use
appropriate models and relevant techniques of the statistical
inference. In this presentation, a complete procedure of one-factorial
experiment
evaluation is presented, including assumptions testing (normality,
variance
homogeneity, independence), general hypothesis building, pairwise
comparison, links to
regression and analysis of variance. Moreover, extension for two- and
more factorial
models is given and the related term “interaction” is explained.
Further, mixed models are introduced brieﬂy; estimation using REML
method is described as well. Examples of problems leading to different
types of
models are shown, for example nonparametric approach, generalized
linear model for
binomial data, nonlinear joint-regression model.

Elements
of Selected Multivariate Methods

David
Hampel, Lenka Viskotová

When dealing with data, where objects are described by more than one
characteristics, researchers face serious problems at different levels:
data
visualization, multicollinearity problem, characteristics with weak
information ability, etc. There are methods constructed especially for
this type of data. Firstly
we will deal with hierarchical cluster analysis, where we can measure
similarities
between objects and establish clusters of similar ones. The type of
distance between
the clusters plays an important role here. Further we investigate
Principal components analysis, where the set of
artificial independent variables is constructed based on original
characteristics.
The percentage of explained variability is observed and real dimension
of the exercise
is visible. Based on these results, Factor analysis can be performed to
find several
factors describing the original dataset with a negligible loss of
information. These artificial
factors should be identified using correlation to the original variables
(so-called
factors rotation is often used to improve this ability). Graphs based
on interim results of
factor analysis can be used to visualize multidimensional data
relationships