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Hello.

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The topic of this lecture is genomic selection.

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The lecture is part of Module 3, Animal Breeding.

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The creation of this presentation was supported
by the ERASMUS+ KA2 grant within the project

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ISAGREED, Innovation of content and structure
of study programs in the field of management

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of animal genetic and food resources using
digitization.

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To begin, let's define a genome.

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A genome is the complete set of genetic information
of an organism.

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In eukaryotic organisms, the genome is contained
in the haploid set of chromosomes.

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After 2000, the development of next-generation
sequencing technologies allowed for faster

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and cheaper sequencing of whole genomes.

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The first sequencing of the whole genomes
of major livestock species took place around

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2009.

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Cattle have a genome size of 2.7 Gbp (30 chromosomes
/ 1n), pigs 2.5 Gbp (19 chromosomes / 1n),

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and chickens 1.05 Gbp (40 chromosomes / 1n).

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Generally, birds have about half the genome
size of mammals.

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Thanks to the sequencing of whole genomes,
tremendous variability has been found in short

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variants (mainly SNP, indel - insertion-deletion).

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Cattle have 97 million, pigs have approximately
71 million, sheep have 58 million, and chickens

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have 22 million of these polymorphisms.

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There are several genomic databases available,
and one of them is ENSEMBL www.ensembl.org/,

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or on the NCBI server.

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This made it possible to perform whole-genome
association analyses (GWAS) to identify QTL

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regions using thousands of SNP markers that
evenly cover the entire genome.

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Results from GWAS in livestock species and
humans lead to the conclusion that the individual

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QTL effect on complex traits is very small,
and therefore a large number of QTL is needed

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to explain the genetic variance in these traits.

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The gains from MAS programs using a small
number of DNA markers to detect a limited

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number of QTL are small, and alternative technologies
needed to be developed to use denser information

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through genomic SNPs, called genomic selection.

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Genomic selection uses a panel of whole-genome
markers where QTL are in linkage disequilibrium

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with one or more SNP.

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Estimated genomic breeding values are predicted
as the sum of the effects of these SNPs across

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the entire genome.

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Genomic selection is a higher version of MAS.

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This is possible thanks to the use of many
SNPs discovered during genome sequencing and

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new methods of genotyping a large number of
SNPs (DNA microarrays, SNP chips containing

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60,000 or 700,000 SNPs).

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The ideal method for estimating BVs from genomic
data is to calculate the conditional mean

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of the breeding value of a given genotype
of the animal at each QTL (~ marker).

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In practice, markers (SNPs) are used instead
of QTL genotypes, but the more we approach

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larger sequences and SNP data, the more ideal
the method will be.

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The idea of genomic selection is quite old.

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In 2001, Meuwissen et al. proposed the concept
of genomic selection as the use of a large

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number of genetic markers covering the entire
genome to predict the genetic value of individuals.

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The application of genomic selection is currently
used in dairy cattle, beef cattle, and pigs.

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However, this varies between different countries.

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In the Czech Republic, it is currently only
used in dairy cattle.

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The basic principle of genomic selection involves
genotyping of whole-genome SNP markers and

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utilizing this information to estimate the
true genetic variability.

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Subsequently, based on genomic SNP markers,
a kinship matrix is calculated and incorporated

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into the equations for Best Linear Unbiased
Prediction (BLUP), which have various variants

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and are constantly evolving.

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Further, it is necessary to estimate the genomic
heritability of the trait, resulting in an

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estimation of genomic estimated breeding value
(GEBV).

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The reason why genomic selection is expanding
to other groups of animals is that it enables

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more efficient breeding by reducing costs,
improving accuracy of predicted breeding values,

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shortening generation intervals, and reducing
inbreeding.

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Is there any difference in estimating breeding
values between traditional and genomic methods?

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Estimating breeding values using traditional
methods requires calculation of the additive

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genetic relationship matrix (A) based on the
expected proportion of shared genes from parents,

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so knowledge of pedigree data is necessary.

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Estimating genomic breeding values (~genomic
selection), on the other hand, also requires

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knowledge of relatedness, but this time based
on genotypes of genetic markers (SNPs), from

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which a genomic relationship matrix is calculated.

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Its elements express the estimates of the
realized proportion of genome that two individuals

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share from their parents.

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Basically, there is no difference between
traditional and genomic estimation of breeding

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values.

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However, the latter is more advantageous.

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The genomic selection system is depicted here.

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It is always necessary to create a prediction
equation for estimating genomic breeding values

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on a reference population and test it on reference
populations with known SNP genotypes and phenotypes

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(performance traits).

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Subsequently, the resulting prediction equation
is used to evaluate candidate parents (often

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very young, theoretically even embryos) for
which we do not need to know phenotypes, only

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SNP genotypes.

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The result is a very accurate estimation of
genomic breeding values for candidate parents.

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In the following diagram, we can see its application
in cattle.

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As you can see, the principle of breeding
has not really changed.

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We start with the reality of the population
from which we want to select individuals,

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we must know phenotypes, genetic parameters,
relatedness, and in the end, from a lot of

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data, we have only one number, which is the
value of genomic breeding value.

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The advantages of genomic selection include
improved accuracy of statistical estimates,

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shortened generation interval, increased genetic
and therefore economic gain, and the ability

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to effectively manage inbreeding.

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It is particularly advantageous for traits
with low heritability, sex-linked traits,

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or traits determined post mortem.

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What about the future?

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Due to constant technological advancement
in NGS and TGS, it will be possible to obtain

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more and more information from more individuals
- longer sequence reads in a shorter time,

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which will improve SNP identification - increasing
the number of detectable SNPs and reducing

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their false detection, leading to more informative
markers.

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It will also be economically advantageous
due to decreasing costs of whole genome resequencing

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to a few hundred USD.

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Furthermore, the estimation of whole-genome
heritability will be accurate down to the

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nucleotide level.

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In conclusion, genomic selection is very successful
in cattle as it provides greater genetic gain

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at similar or lower costs.

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Genomic selection is a very recent innovation,
yet it is quickly being implemented.

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Genomic selection is rapidly evolving, including
reducing the cost of genotyping, strategies

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for phenotyping new traits, approaches for
creating or replacing reference populations,

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and increasing the robustness and stability
of genomic predictions through the identification

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of causal mutations from genome sequences
or genomic predictions of genetics × environment

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interactions.

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And thank you for your attention.