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Hello,
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I would like to welcome you
on a presentation with topic
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"Fine-scale analysis of population
structure based on genomic data
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and quantification of selection effect
on livestock genome", which was prepared
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for the third degree of education.
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This presentation is a part of the ISEGREED
project, which is supported
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by the European Union.
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This presentation belonging to the module
number 2: Conservation and sustainable
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use of animal genetic resources.
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My name is Nina Moravčíková.
This presentation was also
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prepared by Professor Kasarda.
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We are working on the Slovak University
of Agriculture in Nitra,
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the Faculty of Agrobiology
and Food Resources and Institute
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of Nutrition and Genomics.
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This presentation is divided to four
parts: quality control of genomic data,
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approaches and tools for population
structure analysis,
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approaches and tools for evaluating
the impact of selection on the livestock
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genome, and the last part is functional annotation
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of region significantly
affected by selection pressure.
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Quality control of genomic data is really important step
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before any type of analysis.
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I would like to speak mainly about
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the quality control of data which is
related to the genomic data
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obtained by using SNP chips.
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If we have incorrect or low quality data,
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this can usually lead to errors
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in analysis and mainly errors related
to the interpretation of results.
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Data quality indicators which are usually
used are call rate of SNP markers overall
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in the meta-population,
and then also call rate of SNP markers
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within individuals in the population,
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then frequency of the minor allele
frequency and also deviation
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from the Hardy-Weinberg equilibrium.
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Sometimes it's also good to apply quality
control for linkage disequilibrium.
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Type of quality control which is used
before the analysis depends mainly
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on the type of the analysis and also
the main objective of the analysis.
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On this slide, you can see
standard quality control which is used if
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we would like to analyze
population genetic structure.
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Usually, this quality control of genomic
data covers call rate across SNPs
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and across animals,
which minimum value is usually set to 90%,
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and then also minor allele frequency.
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The minimum value for minor allele
frequency is based on the
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Mendelian Inheritance Law.
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Then we also applied
Hardy-Weinberg equilibrium test.
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Sometimes it's also good to control
level of linkage disequilibrium across
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SNPs because if we would like to analyze population
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structure, it will be good to have only
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information about neutral genetic markers.
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For this purpose, we can use several
types of programs and web-based tools.
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For example, we can use program PLING.
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On the right side of this slide,
you can see a graphical visualization
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of quality control of SNP chip
data in case of horses.
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Which type of analysis we can perform if
we are speaking about population
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structure and utilization of SNP data.
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We can analyze genetic differentiation
within and between populations,
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then we can also evaluate or estimate
the degree of genetic admixture
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within or between them,
as well as changes in their gene pool
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which have arisen, for example, due
to selection, migration, or genetic drift.
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But we can also estimate other parameters, for example,
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genomic relationship matrix and based
on the results optimize mating plans.
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The most common type of method which can
be use for the analysis of population
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structure are calculation of Wright's FST index,
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calculation of genetic distance
and relationship matrices,
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principal component analysis,
discriminant analysis of principal
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components, and Bayesian analysis
of genetic admixture and gene flow
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between populations,
and also construction of phylogenetic
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trees and genetic networks.
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Wright's fixation index FST
is one of the most commonly used
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parameters for evaluation of the degree
of genetic differentiation
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between and within populations.
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Its value range from zero to one.
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If the value is equal to zero, then
the populations are genetically identical.
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But if the value is equal to one,
we can say that the populations
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are genetically totally different.
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The interpretation of Wright's FST index is
relative easy, and also the time
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for the computation is relatively short.
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But the FST index cannot be use
for the quantification of genetic
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relationship between individuals
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that means on individual level.
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Also, if the level of diversity
in the population is low, then also
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the reliability of the results
is relatively low.
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For the calculation of FST index,
we can use many tools, for example,
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Arlequin, Genepop, and Genalex,
but these three programs are limited
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mainly in a connection to the number
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of SNPs for which we have genetic data.
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But we can use also many R packages,
for example, StAMPP.
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On the figure on the left side,
you can see dendrogram, which were
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made based on the FST matrix
for the 16 cattle breeds.
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This visualization is relatively nice
because we see that we have
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two genetic clusters composed of breed
which are somehow connected
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from historical point of view or
from phylogenetical point of view.
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Relationship matrices
express genetic similarities and also
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kinship between individuals
within a population.
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That means these matrices can be used
for the quantification of level of genetic
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relationship between individuals.
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Each element of the matrix represents
a measure of genetic similarity
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between a pair of individuals.
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Relationship matrices are most often
calculated based on the frequency
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of alleles in the population,
while the calculation itself can be based
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on various approaches, for example,
calculation of the IBD matrix
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or Nei's genetic distances.
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The calculation of relationship matrices
is also relatively easy,
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and after calculation,
we have relatively accurate estimates
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of relationship between
animals in the population.
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But sometimes, if we have information
about high number of individuals or
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animals in the population,
this type of analysis is time consuming.
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For the calculation of relationship
matrices, we can use, for for example,
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PLINK, if you would like to calculate IBD
matrix,
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or we can also use different R packages,
for example, StAMPP, if you would like
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to calculate Nei's genetic distance matrix.
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On the left side,
you can see example of visualization
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of genetic distance matrix,
which is valid for the
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five breeds of dogs.
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Based on obtained result, we can say that
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animals which belong to the same breeds
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are connected together
and created one genetic cluster.
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Another type of method which can be use
for the evaluation of population
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structure is principal component analysis.
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PCA is a multivariate statistical method
that decomposes a covariance matrix
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of genetic data and extract the principal
component that reflect the variability
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of the data in the the dataset.
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For the visualization of the result,
usually first two principal
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components are used because
these two first principal components
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explain the highest proportion
of variability in the dataset.
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PCA provides basic information about
the genetic structure,
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which is useful when testing databases
with a large number of individuals.
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PCA is a time-saving method for assessing the state
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of genetic differentiation.
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Visualization of PCA components is
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really simple and good interpretable.
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But what are disadvantage
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of PCA analysis?
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It's mainly low sensitivity if
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we would like to estimate the degree
of genetic admixture
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within and between populations.
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For the calculation of principal component
analysis can be use also many tools,
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for example, PLINK or R package Adegenet.
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On the left side,
you can see example of visualization
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of principal component analysis
in case of 16 sheep breeds.
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On the figure, you can see that by using
this method, we really found three genetic
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groups, and deeper
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evaluation of the groups showed us that
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the obtained differentiation is connected
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mainly to the origin of each breed.
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Discriminant analysis of principal components is
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a method of discriminant analysis,
which is usually used for the evaluation
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of genetic structure between
predefined groups or clusters.
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It uses PCA to reduce the dimension
of the data and then discriminant analysis
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to maximize the resolution
between populations.
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Discriminant analysis of principal
components provides a more accurate
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representation of the genetic structure
between predefined clusters,
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compared to, for example, classical PCA.
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But sometimes, is this analysis sensitive
to low level of diversity
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in the population.
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If we use discriminant analysis
of principal components, we can
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expect relatively high accuracy
in detecting differences between
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populations, and also results which are
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relatively simply and easy interpretable.
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On this slide, on the left side,
you can see representative results
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from the discriminant analysis
of principal components.
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In this case, was used genomic data
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for red deer populations, seven farmed,
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and two wild red deer populations.
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By applying this method,
we found three clusters.
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First two clusters were composed from wild
populations, Slovak and Spain,
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and the third clusters was composed
from the populations of farmed animals.
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For the calculation of discriminant
analysis of principal components, we can
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use, for example, R package Adegenet.
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If you would like to estimate
the proportion of genetic admixture within
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the gene pool of population,
we can use Bayesian approach.
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Bayesian approach allows
the identification of genetic groups
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and the degree of admixture within
individuals without the need
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to predefine groups or clusters.
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Bayesian approach provides relatively
accurate identification of genetic
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clusters, and this method is flexible
if we are speaking about
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the complex structures.
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But mainly if we have information for high
number of animals, this method is
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time consuming compared to others.
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For analysis or for testing of degree
of genetic admixture, based on the
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Bayesian approach, we can use many tools.
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We can use, for example, program
Structure, Admixture, or Faststructure.
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On the left side, you can see representative results
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from the estimation of genetic admixture
between seven farmed and two
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wild populations of red deer.
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Similarly to discriminant analysis
of principal components, we found that two
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wild populations from Slovakia and Spain
were totally differentiated
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from farmed populations.
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As you can see on the figure,
farmed populations were relatively admixed.
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That means we found relatively high degree
of admixture between
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farmed populations of red deer,
mainly due to the migration of animals
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and also artificial insemination.
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We can use Bayesian approach also
for estimation of gene
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flow between populations.
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We can use, for example, program TreeMix.
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Program TreeMix is based on the allele
frequencies, and it creates phylogenetic
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trees with the possibility of testing
the intensity of migration
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between populations.
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This method is based on the maximum
probability and allows estimation
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of phylogenetic relationships
and migration between population.
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Program TreeMix allow the detection
of the itensity of migration and gene
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flow in the past,
but sometimes the reliability
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of the results depends on the amount of
available genomic data as well as
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on the reliability of the allele
frequency estimation.
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On this slide, on the right side,
you can see results from the analysis
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of gene flow intensity between red deer
populations based
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on the Bayesian approach.
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In this case, we used program Bayesass,
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which allows to determine the intensity
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of gene flow between
and also within populations.
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This program, compared to the TreeMix,
provides us information about the recent
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migration rate, not migration rate in the past.
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Population structure can be also evaluate
by constructing genetic networks,
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for example, by using package Netview.
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This package is a visualization tool
that uses genetic networks to show
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relationships between
individuals or populations.
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It creates genetic networks that show
genetic relationships
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and gene flow between populations.
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This package or Netview is really
suitable for assessing complex
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relationships as well as
the impact of migration.
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Its visualization is intuitive
and suitable for displaying
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admixture and differentiation.
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But if we have information about the large
number of individuals,
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its utilization is relatively limited.
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On this slide, you can see graphical
visualization of the results of testing
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three different scenarios
of development of intra-population
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and inter-population genetic relationships
within 16 cattle breeds using Netview.
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Compared to the results from, for example,
PCA or discriminant analysis
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of principal components,
we found that animals are clustered
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together if they have common historical background.
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That means if there is really high
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intensity of gene flow between them.
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Another type of graphical visualization
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of genetic relationships between animals
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or between populations is
a construction of phylogenetic trees.
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Phylogenetic trees are graphical
representations
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of evolutionary relationships between
populations or species
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derived from the genetic data.
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They are usually used to visualize
genealogical or genetic relationships,
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model evolutionary processes,
and also track population
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differentiation and migration.
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They can be created using a variety
of algorithm and models,
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but most commonly used models are based
on the genetic distances, for example,
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Nei's genetic distance,
or probabilistic models like maximum
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likelihood and Bayesian methods.
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00:19:04,560 --> 00:19:09,250
For the preparation of phylogenetic tree,
we can use different tools,
245
00:19:09,320 --> 00:19:13,250
for example, SplitsTree or various R packages.
246
00:19:13,320 --> 00:19:20,770
On this slide, you can see an example
of phylogenetic tree, and this tree was
247
00:19:20,840 --> 00:19:27,610
derived from the Nei's genetic distance
matrix calculated for eight horse breeds.
248
00:19:27,680 --> 00:19:34,160
This study was mainly oriented to the analysis
of genetic relationship of Slovak
249
00:19:34,226 --> 00:19:39,720
warmblood horse to another historically connected horse
250
00:19:39,786 --> 00:19:44,210
breeds which can be found in the Europe.
251
00:19:44,280 --> 00:19:48,400
Now, we are going to the next part of this
presentation,
252
00:19:48,466 --> 00:19:52,650
which is related to the approaches
and tools that can be use
253
00:19:52,720 --> 00:19:58,450
for the evaluation of the impact
of selection on the livestock genome.
254
00:19:58,520 --> 00:20:02,600
Genomic regions under strong selection
pressure are usually
255
00:20:02,666 --> 00:20:05,330
called selection signals.
256
00:20:05,400 --> 00:20:10,130
Analysis of selection signals
distribution in the genome allows
257
00:20:10,200 --> 00:20:15,560
for a better understanding of evolutionary
processes and also the impact
258
00:20:15,626 --> 00:20:19,960
of domestication,
and then also the impact of natural
259
00:20:20,026 --> 00:20:25,610
and intensive artificial selection
of specific genomic regions which control
260
00:20:25,680 --> 00:20:31,600
preferred phenotypic traits
in terms of adaptability, resilience,
261
00:20:31,666 --> 00:20:38,410
or performance of individuals,
populations, and also livestock species.
262
00:20:38,480 --> 00:20:41,170
Analysis of selection signals or selection
263
00:20:41,240 --> 00:20:46,440
signatures also allows us to identify
264
00:20:46,506 --> 00:20:51,200
genomic regions showing a decrease or
increase in genetic variability
265
00:20:51,266 --> 00:20:53,250
or genetic diversity.
266
00:20:53,320 --> 00:20:58,760
In this type of analysis,
we don't need to have information
267
00:20:58,826 --> 00:21:02,050
about the phenotype of animals.
268
00:21:02,120 --> 00:21:06,930
Approaches and methods for evaluation
of the selection signals distribution
269
00:21:07,000 --> 00:21:11,690
in the livestock genome can
be divided to two groups.
270
00:21:11,760 --> 00:21:18,360
First group of methods is group which is
based on the evaluation of inter-population
271
00:21:18,426 --> 00:21:21,210
or inter-breeds differences.
272
00:21:21,280 --> 00:21:26,720
The second one is group of method
for evaluation of variability
273
00:21:26,786 --> 00:21:29,250
at the intra-population level.
274
00:21:29,320 --> 00:21:34,520
In the case of first group,
we can speak about the calculation
275
00:21:34,586 --> 00:21:38,810
of Wright's FST index at the genome-wide level,
276
00:21:38,880 --> 00:21:43,570
quantification of differences in linkage disequilibrium,
277
00:21:43,640 --> 00:21:47,720
which is method based on the analysis of
278
00:21:47,786 --> 00:21:52,770
haplotype structure and also PCA analysis.
279
00:21:52,840 --> 00:21:58,760
In the case of second group of method,
we can speak about the distribution
280
00:21:58,826 --> 00:22:05,130
of runs of homozygosity or
heterozygosity-rich regions in the genome,
281
00:22:05,200 --> 00:22:10,570
and also level of linkage disequilibrium,
282
00:22:10,640 --> 00:22:16,080
RDA analysis, or Tajima's D statistics.
283
00:22:16,280 --> 00:22:20,520
Similarly, as in case of analysis
of population structure,
284
00:22:20,586 --> 00:22:25,440
also in this case, Wright's
FST is one of the most commonly used
285
00:22:25,506 --> 00:22:32,450
approach for analysis of selection
signals distribution in the genome.
286
00:22:32,520 --> 00:22:37,400
In this case, selection signals are
identified based on the differences
287
00:22:37,466 --> 00:22:40,730
in allelic frequencies between
populations,
288
00:22:40,800 --> 00:22:45,080
which arose as a result of, for example,
different breeding goals
289
00:22:45,146 --> 00:22:47,170
or breed standards.
290
00:22:47,240 --> 00:22:54,490
Two basic types of signals we can obtain
if we use this approach
291
00:22:54,560 --> 00:23:00,080
in which the different type of selection
correspond to the regions represented
292
00:23:00,146 --> 00:23:05,210
by several loci or SNP markers with a high
value of FST index,
293
00:23:05,280 --> 00:23:09,810
and on other hand,
by the regions with a low value
294
00:23:09,880 --> 00:23:13,760
represent genomic regions that were
subject to the same type
295
00:23:13,826 --> 00:23:16,800
of selection in a given breeds.
296
00:23:17,400 --> 00:23:22,320
Threshold value, defining the signal,
is usually set up as 1%
297
00:23:22,386 --> 00:23:25,250
of the highest FST values.
298
00:23:25,320 --> 00:23:31,330
This method is relatively simply method
for calculation and is widely
299
00:23:31,400 --> 00:23:34,730
used in population genetics.
300
00:23:34,800 --> 00:23:39,570
But this method cannot be used if you
would like to analyze selection
301
00:23:39,640 --> 00:23:43,810
signals at the intra-population level.
302
00:23:43,880 --> 00:23:49,610
For the calculation of Wright's FST index
on the genome-wide level,
303
00:23:49,680 --> 00:23:54,770
we can use, for example, PLINK
and for the visualization program R.
304
00:23:54,840 --> 00:23:58,520
On the left side,
you can see example of the visualization
305
00:23:58,586 --> 00:24:04,330
of Wright's FST distribution
in the autosomal genome.
306
00:24:04,400 --> 00:24:10,450
This study was based on the genomic
data for beef cattle breeds.
307
00:24:10,520 --> 00:24:16,960
What is typical for this type of study
is also description of selection signals.
308
00:24:17,026 --> 00:24:22,570
That means we usually analyze start
and end position of the selection signals,
309
00:24:22,640 --> 00:24:29,480
protein coding genes which are located
directly or very close to the selection
310
00:24:29,546 --> 00:24:36,890
signals, and also QTLs, which are located
in the region of selection signal.
311
00:24:36,960 --> 00:24:42,330
In the table on the right side,
you can really see that we found
312
00:24:42,400 --> 00:24:47,400
many QTLs, which were previously
associated with important
313
00:24:47,466 --> 00:24:51,010
phenotypic traits in cattle.
314
00:24:51,080 --> 00:24:56,560
Another approach for estimation
of selection signals distribution
315
00:24:56,626 --> 00:25:02,210
in the livestock genome is approach
which is based on the variability in
316
00:25:02,280 --> 00:25:09,370
linkage disequilibrium, or we can say,
differences in linkage disequilibrium
317
00:25:09,440 --> 00:25:11,330
between breeds.
318
00:25:11,400 --> 00:25:15,720
In this case, I would like to speak about
integrated haplotype score,
319
00:25:15,786 --> 00:25:19,640
which is very frequently used
for the analysis of selection
320
00:25:19,706 --> 00:25:23,330
signals distribution in the genome.
321
00:25:23,400 --> 00:25:28,360
In this case, selection signals are
derived from a change in the linkage
322
00:25:28,426 --> 00:25:32,210
disequilibrium in the genome of the evaluated breeds
323
00:25:32,280 --> 00:25:39,050
and the emergence of specific haplotypes
due to the linkage disequilibrium.
324
00:25:39,120 --> 00:25:45,690
Integrated haplotype score value can be
defined simply as a measure of how
325
00:25:45,760 --> 00:25:51,010
unusual a haplotype consisting
of a specific SNP marker is
326
00:25:51,080 --> 00:25:53,200
compared to the rest of the genome.
327
00:25:53,266 --> 00:25:58,240
Integrated haplotype score is a particularly
328
00:25:58,306 --> 00:26:02,810
sensitive method for detecting the effect
of recent selection that led
329
00:26:02,880 --> 00:26:06,050
to an increase in the frequency of a certain
330
00:26:06,120 --> 00:26:10,960
allelic variant in a population,
but has not yet eliminate
331
00:26:11,026 --> 00:26:14,080
other variants at a given locus.
332
00:26:14,160 --> 00:26:20,000
The analysis begins with the calculation
of extended haplotype homozygosity,
333
00:26:20,066 --> 00:26:24,250
which quantifies the decrease
in homozygosity of the haplotype
334
00:26:24,320 --> 00:26:29,800
from a certain SNP marker,
and then continues with the calculation
335
00:26:29,866 --> 00:26:34,880
of the integrated haplotype score value,
which is based on the logarithm
336
00:26:34,946 --> 00:26:39,850
of the ratio of integrated extended
haplotype homozygosity values
337
00:26:39,920 --> 00:26:43,250
for two allelic variants.
338
00:26:43,320 --> 00:26:48,960
Integrated haplotype score can reach
positive values when haplotype carrying
339
00:26:49,026 --> 00:26:54,080
a single allele is longer
and has a higher extended haplotype
340
00:26:54,146 --> 00:26:59,490
homozygosity, indicated a significant
effect of positive selection
341
00:26:59,560 --> 00:27:04,320
or negative values
when an alternative allele has a higher
342
00:27:04,386 --> 00:27:08,440
extended haplotype homozygosity which can
also reflect selection
343
00:27:08,506 --> 00:27:11,010
but in opposite direction.
344
00:27:11,080 --> 00:27:15,210
Threshold value defining the signal is set
345
00:27:15,280 --> 00:27:18,250
similar to previous approach, for example,
346
00:27:18,320 --> 00:27:24,850
as 1% of the highest positive
values of integrated haplotype score.
347
00:27:24,920 --> 00:27:30,040
This approach is suitable for detecting
the effect of recent selection
348
00:27:30,106 --> 00:27:35,730
and identification of signals which can
arise as a result of adaptation,
349
00:27:35,800 --> 00:27:42,760
but is also sensitive for the data
quality, and if you would like to obtain
350
00:27:42,826 --> 00:27:48,330
reliable estimates, you need high
quality and robust genomic data.
351
00:27:48,400 --> 00:27:52,880
For the calculation of integrated
haplotype score, we can use, for example,
352
00:27:52,946 --> 00:27:57,250
program Haploview or other R packages.
353
00:27:57,320 --> 00:28:00,200
On the left side,
you can see example
354
00:28:00,266 --> 00:28:04,800
from the analysis of variability
in linkages equilibrium in the genome
355
00:28:04,866 --> 00:28:09,760
of milk and beef cattle breeds,
and on the right side,
356
00:28:09,826 --> 00:28:12,650
you can see description of identified
357
00:28:12,720 --> 00:28:17,880
selection signals and also genes and QTLs,
358
00:28:17,946 --> 00:28:23,760
which were located directly
in the region of the signals.
359
00:28:24,640 --> 00:28:30,320
Evaluation of the inter-population or
interbreed differences and the following
360
00:28:30,386 --> 00:28:38,090
analysis of selection signatures can be
also performed by using PCA analysis.
361
00:28:38,160 --> 00:28:43,040
In this case, this analysis assumes
that the signals in the genome arose as
362
00:28:43,106 --> 00:28:46,360
a result of the local adaptation
of individuals to the
363
00:28:46,426 --> 00:28:49,490
environmental conditions.
364
00:28:49,560 --> 00:28:55,800
PCA analysis is in this context
an alternative method for identifying
365
00:28:55,866 --> 00:28:59,570
selection signals to the Wright's FST index.
366
00:28:59,640 --> 00:29:04,880
Detection of selection signals is based
on the assumption of the existence
367
00:29:04,946 --> 00:29:11,080
of a correlation between genetic variants
and principal components which reflects
368
00:29:11,146 --> 00:29:15,810
the local adaptation of population
to the production environment.
369
00:29:15,880 --> 00:29:20,690
To identify selection signal,
different tests can be used,
370
00:29:20,760 --> 00:29:23,690
for example, Mahalanobis distance test.
371
00:29:23,760 --> 00:29:29,370
In this case, the identification of SNP
markers showing association with positive
372
00:29:29,440 --> 00:29:34,370
selection is based on the construction
of a Z-score vector
373
00:29:34,440 --> 00:29:39,530
obtained by regression analysis
of the relationship between SNP markers
374
00:29:39,600 --> 00:29:42,770
and the principal components of K.
375
00:29:42,840 --> 00:29:48,050
The threshold value which defined
the signal of selection can be,
376
00:29:48,120 --> 00:29:54,250
in this case, determined, for example,
based on the false discovery rate test.
377
00:29:54,320 --> 00:29:59,730
This method is really efficient
in case of visualization.
378
00:29:59,800 --> 00:30:06,130
But because this method is alternative,
it's not so often used for the
379
00:30:06,200 --> 00:30:11,530
quantification of selection
signals in the genome.
380
00:30:11,600 --> 00:30:16,480
For the analysis of distribution
of selection signals in the genome
381
00:30:16,546 --> 00:30:22,850
by using PCA analysis, can be use,
for example, R package PCAdapt.
382
00:30:22,920 --> 00:30:28,200
This method also allows you to quantify
383
00:30:28,266 --> 00:30:31,330
genetic differentiation in the data set
384
00:30:31,400 --> 00:30:36,880
and then provide you information about the
selection signals distribution, as you
385
00:30:36,946 --> 00:30:40,730
can see on the slide on the figure 13.
386
00:30:40,800 --> 00:30:46,120
Then the last step of analysis is usually
description of the selection signals,
387
00:30:46,186 --> 00:30:50,530
that mean description of the start
and the end position of the signal,
388
00:30:50,600 --> 00:30:55,280
number of genes and number of QTLS, which are located
389
00:30:55,346 --> 00:30:58,890
directly or very close to the signal.
390
00:30:58,960 --> 00:31:03,520
If we would like to analyze
distribution of selection signals
391
00:31:03,586 --> 00:31:08,960
at the intra-population level,
we can use method which is based
392
00:31:09,026 --> 00:31:13,530
on the identification of runs
of homozygosity in the genome.
393
00:31:13,600 --> 00:31:18,440
This approach assumes that regions
in the genome showing strong selection
394
00:31:18,506 --> 00:31:24,250
signals are the results of an increase
in local homozygosity due to intensive
395
00:31:24,320 --> 00:31:28,240
breeding to traits defined
in the breed standard of each breed.
396
00:31:28,306 --> 00:31:31,640
Runs of homozygosity regions forming
397
00:31:31,706 --> 00:31:36,730
selection signals located in the genome
are formed by the alleles derived
398
00:31:36,800 --> 00:31:41,360
from common ancestors,
which can be inherited from generation
399
00:31:41,426 --> 00:31:46,370
to generation in unchanging form.
400
00:31:46,440 --> 00:31:52,680
Selection signals are then
identified based on the frequency of SNP
401
00:31:52,746 --> 00:31:57,960
markers in runs of homozygosity
in specific region across
402
00:31:58,026 --> 00:32:00,410
individuals in the population.
403
00:32:00,480 --> 00:32:07,450
Threshold value for defining the signal is
similarly to another approach set to
404
00:32:07,520 --> 00:32:10,570
as 1% of the highest value.
405
00:32:10,640 --> 00:32:16,970
This method allows to detect regions where
there has been a decrease in diversity.
406
00:32:17,040 --> 00:32:23,200
Because of this, this method also
can serve as a good indicator
407
00:32:23,266 --> 00:32:25,730
of the effect of positive selection.
408
00:32:25,800 --> 00:32:31,520
But if we would like to obtain reliable
estimates or reliable results,
409
00:32:31,586 --> 00:32:36,970
we need to also have high
quality and robust genomic data.
410
00:32:37,040 --> 00:32:40,810
On this slide, you can see results from the analysis
411
00:32:40,880 --> 00:32:46,130
of distribution of runs of homozygosity
segments in the genome
412
00:32:46,200 --> 00:32:49,090
of Slovak warmblood horse.
413
00:32:49,160 --> 00:32:53,610
Based on the threshold value, we found the
414
00:32:53,680 --> 00:32:57,170
selection signals on chromosome 1, 2, 6,
415
00:32:57,240 --> 00:33:03,050
9, 11, 15, and 16,
And we also identified many genes
416
00:33:03,120 --> 00:33:10,120
inside the regions of selection signals
which were included in the formation or
417
00:33:10,186 --> 00:33:15,050
in the genetic control of important
phenotypic traits for horses.
418
00:33:15,120 --> 00:33:19,640
On the other hand,
we can also analyze selection signals
419
00:33:19,706 --> 00:33:24,840
distribution in the genome based on the
regions showing high
420
00:33:24,906 --> 00:33:27,850
level of heterozygosity.
421
00:33:27,920 --> 00:33:34,440
This method is usually used to detect
regions which may be important,
422
00:33:34,506 --> 00:33:40,200
for example, in terms of adaptability or
response to environmental changes
423
00:33:40,266 --> 00:33:42,530
or the occurrence of pathogens.
424
00:33:42,600 --> 00:33:47,720
This method is based on the assumptions
that the heterozygous individuals have
425
00:33:47,786 --> 00:33:52,840
usually higher fitness than
homozygous ones.
426
00:33:52,960 --> 00:33:56,800
In this case, a high level of heterozygosity may be
427
00:33:56,866 --> 00:34:02,320
the result of balancing selection effect
that means the preservation of genetic
428
00:34:02,386 --> 00:34:05,280
diversity within a population.
429
00:34:05,346 --> 00:34:10,930
Similar to analysis of
430
00:34:11,000 --> 00:34:12,930
runs of homozygosity
431
00:34:13,000 --> 00:34:18,450
selection signals are
derived from the frequency of SNP markers
432
00:34:18,520 --> 00:34:24,000
in heterozygosity-rich regions
in a specific genomic region across
433
00:34:24,066 --> 00:34:26,370
individuals in the population.
434
00:34:26,440 --> 00:34:29,120
Threshold value is usually set based
435
00:34:29,186 --> 00:34:34,250
on the 1% of the highest values.
436
00:34:34,320 --> 00:34:39,480
This approach allows us to detect regions
in which there is an increased
437
00:34:39,546 --> 00:34:42,530
proportion of heterozygous genotypes.
438
00:34:42,600 --> 00:34:49,440
That means that also can serve us as
an indicator of genomic regions which can
439
00:34:49,506 --> 00:34:54,610
be important in terms of adaptation
or evolutionary potential.
440
00:34:54,680 --> 00:35:00,130
But if we would like to have reliable
result, we need to also analyze high
441
00:35:00,200 --> 00:35:04,130
quality and robust genomic data.
442
00:35:04,200 --> 00:35:08,440
On this slide,
you can see results from the analysis
443
00:35:08,506 --> 00:35:12,050
of distribution of heterozygosity-rich
444
00:35:12,120 --> 00:35:15,650
regions in the five horse breeds,
445
00:35:15,720 --> 00:35:20,760
and this study was based especially on the
analysis of distribution
446
00:35:20,826 --> 00:35:26,280
of heterozygosity-rich regions
in the genomic coordinates of major
447
00:35:26,346 --> 00:35:29,480
histocompatibility complex.
448
00:35:29,960 --> 00:35:36,640
Another interesting approach is
identification of selection signals
449
00:35:36,706 --> 00:35:40,130
in the genome based on the RDA analysis.
450
00:35:40,200 --> 00:35:44,680
RDA tests the relationship between genetic
variability and also
451
00:35:44,746 --> 00:35:46,690
environmental factors.
452
00:35:46,760 --> 00:35:53,650
That means it quantified the influence of
natural selection on the genome structure.
453
00:35:53,720 --> 00:35:59,810
This approach is basically a method
of evaluating genotype environment
454
00:35:59,880 --> 00:36:05,240
association that evaluates the percentage
of genomic variability explained
455
00:36:05,306 --> 00:36:10,280
by environmental variables and also
detects loci under a strong
456
00:36:10,346 --> 00:36:12,360
selection pressure.
457
00:36:12,480 --> 00:36:15,120
This method is two-step analysis
458
00:36:15,186 --> 00:36:19,810
in which genetic and environmental data
are evaluated using
459
00:36:19,880 --> 00:36:23,720
multivariate linear regression.
460
00:36:24,480 --> 00:36:30,290
From advantages of this method, we can
461
00:36:30,360 --> 00:36:33,170
Mention that this method is really
462
00:36:33,240 --> 00:36:36,920
good approach to evaluate
the relationships between genetic
463
00:36:36,986 --> 00:36:41,410
variability within a population
and environmental factors.
464
00:36:41,480 --> 00:36:47,050
But similarly to previous approaches,
if you would like to have
465
00:36:47,120 --> 00:36:51,530
good results or results with high
466
00:36:51,600 --> 00:36:54,600
reliability, you need to also have information
467
00:36:54,666 --> 00:36:59,410
about high number of SNP
markers and animals.
468
00:36:59,480 --> 00:37:04,170
For RDA analysis, we can use, for example,
469
00:37:04,240 --> 00:37:07,880
R Package vegan or DeepGenomeScan program.
470
00:37:08,960 --> 00:37:15,690
Last approach which I would like
to mention is Tajima's D statistic,
471
00:37:15,760 --> 00:37:21,130
which evaluates population diversity
and can be used
472
00:37:21,200 --> 00:37:25,920
as an indicator of balancing selection.
473
00:37:26,120 --> 00:37:30,210
Tajima's D can reach positive
or negative values.
474
00:37:30,280 --> 00:37:36,290
Positive values indicated significant
effect of balancing selection,
475
00:37:36,360 --> 00:37:41,880
and negative values, on the other hand,
can be associated with the effect
476
00:37:41,946 --> 00:37:47,890
of positive selection on the genome
of analyzed population or breed.
477
00:37:47,960 --> 00:37:53,890
Threshold value is defining similar
to other approach, for example,
478
00:37:53,960 --> 00:37:59,730
as the 1% of the highest positive values.
479
00:37:59,800 --> 00:38:04,400
This method allows us to detect
regions in which there is an increased
480
00:38:04,466 --> 00:38:06,610
proportion of heterozygous genotypes.
481
00:38:06,680 --> 00:38:12,640
That means it's relatively good
indicator of regions important,
482
00:38:12,706 --> 00:38:17,330
for example, in term of adaptation.
483
00:38:17,400 --> 00:38:21,930
But also, if we would like to have results
484
00:38:22,000 --> 00:38:25,200
with good quality,
485
00:38:25,266 --> 00:38:29,650
we also need to have information about
high number of markers
486
00:38:29,720 --> 00:38:33,720
and high number of animals.
487
00:38:34,320 --> 00:38:39,400
Here you can see the results
from the analysis of selection signals
488
00:38:39,466 --> 00:38:43,480
distribution derived from the Tajima's D statistic
489
00:38:43,546 --> 00:38:50,370
across the genome of five horse breeds
coming from Czech Republic and Slovakia.
490
00:38:50,440 --> 00:38:54,760
As you can see, we found that selection
491
00:38:54,826 --> 00:38:58,080
signals were distributed non-uniformly
492
00:38:58,146 --> 00:39:04,490
across the genome of tested horse breeds,
but we also found that in some
493
00:39:04,560 --> 00:39:10,840
genomic regions, selection
signals overlapped across breeds.
494
00:39:12,320 --> 00:39:16,640
Next step, after identification of selection signals
495
00:39:16,706 --> 00:39:22,530
in the genome is usually description
of the regions of selection signals.
496
00:39:22,600 --> 00:39:29,130
This description is usually based
on the searching for quantitative trait
497
00:39:29,200 --> 00:39:36,040
loci or protein coding genes located
directly or very close to the
498
00:39:36,106 --> 00:39:39,160
region of selection signals.
499
00:39:39,360 --> 00:39:44,330
Then it's also important to analyze
500
00:39:44,400 --> 00:39:49,120
biological function of QTLs or genes.
501
00:39:49,186 --> 00:39:51,770
For this purpose, we can use
502
00:39:51,840 --> 00:39:56,800
several databases or tools, for example,
503
00:39:56,866 --> 00:40:02,040
GO, which is gene ontology or
KEGG, which is Kyoto Encyclopedia
504
00:40:02,106 --> 00:40:05,560
of Genes and Genomes.
505
00:40:06,120 --> 00:40:13,810
Here you can see really good databases
for the identification of QTLs or genes.
506
00:40:13,880 --> 00:40:18,760
For the identification of QTLs,
you can use animal QTL database
507
00:40:18,826 --> 00:40:24,610
in which you can find information
about different livestock species.
508
00:40:24,680 --> 00:40:30,200
Really good and simple web-based tool
for the obtaining information
509
00:40:30,266 --> 00:40:34,640
about the genes in a certain region is
510
00:40:34,960 --> 00:40:37,520
a tool, Biomart, providing
511
00:40:37,586 --> 00:40:40,370
by the Ensemble database.
512
00:40:40,440 --> 00:40:43,560
If you would like to analyze
513
00:40:44,200 --> 00:40:48,210
biological function of genes or biological
514
00:40:48,280 --> 00:40:51,640
pathways in which are genes included
515
00:40:51,706 --> 00:40:56,080
You can use, for example,
the web-based tool David.
516
00:40:58,760 --> 00:41:03,360
What are advantages of functional
annotation of regions significantly
517
00:41:03,426 --> 00:41:05,850
affected by selection pressure?
518
00:41:05,920 --> 00:41:12,160
The main advantage is mainly the fact
that the detailed analysis of regions
519
00:41:12,226 --> 00:41:16,290
in the genome significantly affected
by selection pressure
520
00:41:16,360 --> 00:41:21,680
allows the identification of specific
genes and biological pathways
521
00:41:21,746 --> 00:41:24,530
responsible for phenotypic traits.
522
00:41:24,600 --> 00:41:29,360
The future research of identified genes or
523
00:41:29,426 --> 00:41:33,240
QTLs in regions under strong selection
524
00:41:33,306 --> 00:41:39,890
pressure can be in the future potentially
used in the breeding programs.
525
00:41:39,960 --> 00:41:44,640
But functional annotation has also
526
00:41:44,706 --> 00:41:46,090
disadvantages.
527
00:41:46,160 --> 00:41:50,760
The most important problem is the fact
528
00:41:50,826 --> 00:41:53,560
that the overlap between selection signals
529
00:41:53,626 --> 00:42:00,000
and functional regions does not
always imply a causal relationship
530
00:42:00,066 --> 00:42:06,530
and also the fact that the information
in the available databases is
531
00:42:06,600 --> 00:42:12,640
limited to the current knowledge and may
not always cover all
532
00:42:12,706 --> 00:42:16,840
relevant genes or QTL loci.
533
00:42:17,800 --> 00:42:23,400
On this slide,
you find the list of the papers which were
534
00:42:23,466 --> 00:42:27,610
used for the preparation of this presentation,
535
00:42:27,680 --> 00:42:31,520
and the full text of the papers are also
536
00:42:31,586 --> 00:42:36,040
available in the folder Study Materials.
537
00:42:36,920 --> 00:42:41,410
By this slide, I would like
to thank you for your attention.
538
00:42:41,480 --> 00:42:48,360
If you will have questions or if you would
like to continue with this topic
539
00:42:48,426 --> 00:42:53,680
in the future and need help,
please contact me on my email address,
540
00:42:53,746 --> 00:42:56,130
which you can see on the slide.
541
00:42:56,200 --> 00:42:59,930
On the slide is also QR code.
542
00:43:00,000 --> 00:43:03,120
By scanning of this QR code,
543
00:43:03,186 --> 00:43:07,280
you can obtain access to other modules
544
00:43:07,346 --> 00:43:10,960
which were prepared
within the project ISAGREED.