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Hello, in this lecture for PhD

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students, we will focus on the

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possibilities of assessing genetic

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diversity and population structure using

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mitochondrial DNA and nuclear

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microsatellite markers applied to the 

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honeybee. The lecture is part of module

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1, Animal Genetics. The creation

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of this presentation was supported by the

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Erasmus+ KA2 grant within

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ISAGREED project, innovation of the

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content and structure of study programs

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in the field of animal genetics and food

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resource management using

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digitalization.

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This lecture will cover topics such as

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genetic data acquisition, assessment of

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genetic variability using mitochondrial

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DNA sequences, and assessment of genetic

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variability using nuclear STRs

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or microsatellite markers. Why

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is genetic diversity important? Genetic

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diversity is important for the yield of

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populations. It is a key source of

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the ability to build tolerance or

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resistance to current and future

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diseases, pathogens and predators.

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The current state of bee populations can

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be attributed in part to a reduction in

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diversity. Bee diversity

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has been assessed using morphometrics

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traits such as wing parameters,

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pigmentation, etc. The

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honey bee, Apis mellifera, is

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now known to comprise 31

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subspecies, breeds or races.

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DNA analyses, particularly of

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mitochondrial origin, has

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facilitated the description of

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evolutionary lineages including the

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Western Mediterranean type M, the

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Northern Mediterranean type C,

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the African lineage A,and the

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oriental lineage O. The

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honeybee genome has been completely

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sequenced on multiple occasions. The

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individual chromosomes are visible and

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the penultimate column illustrates the

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size of each chromosome in terms of the

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number of base pairs. A total

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of 12,398

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genes have been described or are

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estimated to exist in the honeybee genome.

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Of these,

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9935

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genes coded for some kind of

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protein, while

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2421 genes don't

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code for proteins, but rather code for

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other RNAs, such as transfer

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RNAs or other small nuclear

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RNAs.

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The mitochondrial DNA of most species is

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estimatedto be within the range of

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approximately 16 to 20 kilobases.

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The mitochondrial genome of the Western

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honeybee is estimated to comprise

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approximately

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16,500

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base pairs. In the reference genome

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NC001566,

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the mitochondrial DNA is

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16,343

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base pairs in size. In the

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complete genome of the carpathian

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mitochondrial DNA, the size of the

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mitochondrial DNA is

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16,358

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base pairs. The

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mitochondrial DNA of the honey bee

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contains 13 genes that encode

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proteins, as well as 22 genes

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that encode tRNA and two genes that

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encode ribosomal RNA. In

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particular, the barcoding sequence, which

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is the cytochrome oxidase 1 site

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sequence, and the so-called intergenic

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regime, which includes parts of the

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tRNA genes for leucin and

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cytochrome oxidase 2, are employed

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for phylogenetic and

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phylogeographic analysis.

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What degree of variability can be

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observed at the DNA level and which

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molecular genetic markers exist? At

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present, the most frequently utilized

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are biallelic single nucleotide

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polymorphisms which can

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be identified in both coding and

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non-coding regions.

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Additionally, data on insertions and

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deletions, defined as the presence or

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absence of base, can be employed.

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These are commonly referred to as indels.

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The image on the right illustrated this.

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The top row depicts SNP markers,

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while the bottom row displays deletionsof

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cytosine in the ACA sequence

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region.

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Following the isolation of the DNA from

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the B sample and the amplification of

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specific small section, for

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example, one of the two genes mentioned

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above, sequencing is conducted

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using a capillary electrophoresis-based

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sequencer. The resulting

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identification of the individual bases

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The sequence is illustrated in the

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feature. The

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individual peaks are represented by the

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colors used to identify the individual

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bases.

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2 mitochondrial DNA sequences

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were employed for the purpose of

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identifying subtypes, mitotypes or

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haplotypes within the lineage. These are

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the aforementioned. Intergenic region

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tRNA Leucine-Cox2

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and cytochrome oxidase 1 region.

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This section comprise 2 mitochondrial

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genes, the transfer RNA for

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Leucine and the cytochrome oxidase 2.

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This sequence is distinguished by a

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high mutation content in the table

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variations in nucleotide length and

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compositions across honey bee

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populations. This amplicon

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is cleaved by the restriction endonuclease

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Dra1, which

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specifically recognize the

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TTTAAA sequence

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to identify each lineage.

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The second sequence is the sequence for

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the barcoding region, and this is part

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of cytochrome oxidase 1, cox1 gene.

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This sequence is compared to

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sequence stored in databases such as the

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BOLD system or GeneBank.

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The DNA fragment is highly conserved

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within taxa and is often used to

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distinguish taxa and species.

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The tRNA-Leucin-cox2

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sequence structure allows for the

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identification of distinct evolutionary

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lineages within the honeybee,

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the C lineage, which

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encompasses the honeybee,

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Apis mellifera, as well as the ligustica,

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macedonica and other related

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subspecies is characterized

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by the presence of a single copy of the Q

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sequence. The aforementioned

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lines contain one to two copies

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of the aforementioned Q sequence, in

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addition to the so-called

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P0 segment, the

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M lineage, which is the original black

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bee, Apis mellifera mellifera, which is no

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longer found in the Czech Republic, may

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contain one, two or three repeats of the

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aforementioned Q sequence,

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in addition to the so-called P

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sequence. By sequencing and

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comparing individual sequences, it is

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possible to identify an evolutionary 

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lineage in each bee.

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The identification of particular lineage

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is possible through cleavage with the

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restriction enzyme Dra1,

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which recognize the altered

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TTTAAA sequence.

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Once this change occurs as a result of

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mutation, this enzyme is unable to

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recognize this sequence. Instead, it is

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unable to cleave it. Using a

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classical PCR reaction

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based on the length of the individual

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fragments, it is possible to distinguish

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between variants such as C and

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A1 or A4.

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The second option is to obtain the entire

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sequence of a given segment by sequencing

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and subsequently analyzing it. The

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following example illustrates the

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sequencing and subsequent analysis of

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tRNA-leucin - Cox2 sequences from

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several individuals.

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Some software, such as UniPro

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Ugene, enables the user to

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perform the cleavage with Dra1

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restriction enzyme, in silico, that is

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on a computer. The following

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example illustrates the cleavage of a

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sequence belonging to C lineage,

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which contains 3 cleavage sites,

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resulting in three fragments of specific

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length.

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In another sample, only two

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cleavage sites were identified and the

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length of the fragment suggests that this

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is a bee belonging to the A lineage or

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African lineage.

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Subsequently, the sequences

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obtained from the larger population are

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compared using the method of multiple

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sequential alignment, MSA.

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This process could be completed

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manually; However, software has been

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developed with algorithms that facilitate

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this task. Some of these programs are

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accessible online, for example on the

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European Bioinformatics Institute

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servers. For our purposes,

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Kalign was the most suitable.

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However, there are other tools such as

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Clustal Omega, MAFT

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and so on. Additionally, there are

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programs that can be downloaded and

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installed to perform this analysis, such

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as MEGA or the Unipro Ugene.

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The DnaSP program was

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employed to identify DNA polymorphisms

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and haplotypes in both mitochondrial DNA

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regions, utilizing all sequences from

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multiple sequence alignments in FASTA

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format.

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Moreover, nucleotide substitutions and

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insertion deletions for each haplotype

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were compared with the reference genome.

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To identify specific haplotypes in the

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tRNA leucin - cox2, lineage

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C and A, reference sequences

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with 100% identity were

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further searched using BLAST

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local pairwise alignment tools. Again,

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sequences found in the National Center

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for Biotechnology Information (NCBI)

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database at the US GenBank.

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BLAST was also employed to verify the

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cox1 haplotypes, with the sequences

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subsequently validated using the

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BOLD database based on the multiple

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alignments using Kalign, necessitating

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additional manual refinement.

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A total of 13 haplotypes were

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identified, three of which belonged to

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the A lineage and the rest to the

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C lineage. The most prevalent

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haplotype was C1a, which is

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typical for Apis meliffera linguistica, the

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Italian bee. The table illustrates the

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classification of individual haplotypes

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into C and A lineages based

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on Dra1 spectrum cleavage and

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sequencing. The individual haplotypes

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and their sequences have been uploaded to

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the Genebank databases on the NCBI

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server. In the third column, the

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reference sequences are

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displayed. Additionally, the numbers

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and lengths of the fragments produced by

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the cleavage are shown, which also

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demonstrate the considerable variability.

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In the last two columns, the

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identification of or comparison with

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other sequences in the GenBank database

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is presented, where sequences

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with 100% identity to our

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sequences have been selected.

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It is notable that all haplotypes

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belonging to C lineage have been

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previously described in Apis meliffera

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carnica. Additionally, 3

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distinct African haplotypes are

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identified as Apis meliffera

286
00:13:20,168 --> 00:13:23,155
iberica. However, a single

287
00:13:23,398 --> 00:13:26,022
sequences exhibiting complete

288
00:13:26,022 --> 00:13:28,566
identity wasn't assigned to any

289
00:13:28,566 --> 00:13:30,262
particular subspecies.

290
00:13:32,523 --> 00:13:34,420
This table illustrates the

291
00:13:34,420 --> 00:13:36,843
identification of the most significant

292
00:13:36,843 --> 00:13:39,548
polymorphic sites indicating the bases

293
00:13:39,548 --> 00:13:42,294
present in each haplotype. The

294
00:13:42,294 --> 00:13:44,878
positions were found to exhibit mainly

295
00:13:44,878 --> 00:13:47,502
single nucleotide polymorphisms and

296
00:13:47,502 --> 00:13:50,046
deletions. It is notable that

297
00:13:50,126 --> 00:13:52,064
position 50 displays

298
00:13:52,387 --> 00:13:55,052
polymorphisms with the standard

299
00:13:55,052 --> 00:13:58,040
allele identified as C. The remaining

300
00:13:58,040 --> 00:14:00,624
haplotypes at this position exhibited a

301
00:14:00,624 --> 00:14:01,432
deletion.

302
00:14:03,450 --> 00:14:05,873
Similarly, the cox1 sequence

303
00:14:06,115 --> 00:14:08,942
was analyzed, whereby 13

304
00:14:08,942 --> 00:14:11,687
different haplotypes for barcoding

305
00:14:12,252 --> 00:14:15,159
were identified. As with the

306
00:14:15,159 --> 00:14:17,340
previous analysis, individual SNP

307
00:14:17,340 --> 00:14:20,085
mutations were observed. No insertion or

308
00:14:20,085 --> 00:14:22,508
deletion were identified with the

309
00:14:22,508 --> 00:14:25,011
barcoding sequence. Only SNP

310
00:14:25,011 --> 00:14:27,191
substitutions were presented.

311
00:14:30,341 --> 00:14:32,925
The tables below present the results of

312
00:14:32,925 --> 00:14:35,186
the haplotype frequencies in the

313
00:14:35,186 --> 00:14:37,851
tRNA-Leucin - cox2 gene

314
00:14:38,254 --> 00:14:40,354
and in the cox1 gene in the Czech

315
00:14:40,354 --> 00:14:43,261
Republic. It can be observed that there

316
00:14:43,261 --> 00:14:46,007
are a number of haplotypes that are

317
00:14:46,087 --> 00:14:48,348
relatively well represented, such as

318
00:14:48,348 --> 00:14:50,690
C1A, C2L,

319
00:14:50,932 --> 00:14:52,951
C2E, and C2C.

320
00:14:53,759 --> 00:14:56,423
In contrast, there are haplotypes that

321
00:14:56,423 --> 00:14:59,169
have been identified in only one or a

322
00:14:59,169 --> 00:15:02,157
few individuals. A similar

323
00:15:02,238 --> 00:15:05,064
situation was observed in the cox1

324
00:15:05,064 --> 00:15:07,244
gene where the first four

325
00:15:07,244 --> 00:15:09,747
haplotypes were the most frequently

326
00:15:09,747 --> 00:15:11,928
represented, while the others were

327
00:15:12,009 --> 00:15:14,431
present in minority or individual

328
00:15:14,431 --> 00:15:15,239
samples.

329
00:15:18,469 --> 00:15:20,487
From a population of bees in the Czech

330
00:15:20,487 --> 00:15:23,071
Republic comprising over 300

331
00:15:23,071 --> 00:15:26,059
samples, certain characteristics were

332
00:15:26,059 --> 00:15:28,724
calculated, including haplotype diversity

333
00:15:28,724 --> 00:15:31,389
parameters in the tRNA leucine -

334
00:15:31,389 --> 00:15:34,054
cox2 and cox1 sequences.

335
00:15:34,700 --> 00:15:37,284
The genetic diversity indices, namely

336
00:15:37,284 --> 00:15:40,272
haplotype diversity Hd, molecular

337
00:15:40,272 --> 00:15:43,098
diversity pí and Tajima´s D

338
00:15:43,421 --> 00:15:46,409
were estimated. These indices

339
00:15:47,135 --> 00:15:49,154
were evaluated using PEGAS

340
00:15:50,043 --> 00:15:52,304
in program R, although

341
00:15:52,465 --> 00:15:55,211
alternative software such as DnaSP,

342
00:15:55,614 --> 00:15:58,521
MEGA or Arlequine can be also employed.

343
00:16:01,509 --> 00:16:03,851
Additionally, the so-called

344
00:16:04,093 --> 00:16:06,677
haplotype networks were determined.

345
00:16:07,485 --> 00:16:09,907
We use the Randomized Minimum Spanning

346
00:16:09,988 --> 00:16:12,814
Tree method (RMSAT),

347
00:16:13,622 --> 00:16:16,085
which takes into account frequencies and

348
00:16:16,206 --> 00:16:18,306
relationships between haplotypes.

349
00:16:19,436 --> 00:16:22,101
These haplotype networks were processed

350
00:16:22,182 --> 00:16:24,443
using the PEGAS package in R.

351
00:16:25,169 --> 00:16:27,754
However, other programs such as PopArt

352
00:16:28,521 --> 00:16:29,853
and others can be used.

353
00:16:34,698 --> 00:16:37,524
Here we see the result of haplotype

354
00:16:37,524 --> 00:16:39,543
network analysis for

355
00:16:39,543 --> 00:16:42,006
308 individuals in the

356
00:16:42,006 --> 00:16:44,711
tRNA-Leucine - cox2 sequence.

357
00:16:45,438 --> 00:16:48,103
The most common haplotype is C1A,

358
00:16:48,507 --> 00:16:51,494
followed by C2E, which has two point

359
00:16:51,494 --> 00:16:54,402
mutations, and C2C, which is the

360
00:16:54,402 --> 00:16:57,228
third most common haplotype in the Czech

361
00:16:57,228 --> 00:17:00,054
Republic. Each color in the circle

362
00:17:00,054 --> 00:17:02,800
represents a specific region from which

363
00:17:03,607 --> 00:17:06,272
the bee was obtained. The aim was to

364
00:17:06,272 --> 00:17:09,139
cover the whole Czech Republic, with

365
00:17:09,179 --> 00:17:11,723
the sampling evenly distributed

366
00:17:11,723 --> 00:17:12,894
across all regions.

367
00:17:14,993 --> 00:17:17,900
This slide presents the analysis

368
00:17:17,941 --> 00:17:20,161
of the haplotype network for the cox1

369
00:17:20,161 --> 00:17:22,342
sequence. As observed

370
00:17:22,422 --> 00:17:24,199
previously, if a haplotype was

371
00:17:24,199 --> 00:17:26,864
sufficiently abundant, it occurred in

372
00:17:26,944 --> 00:17:29,448
almost all regions. This is

373
00:17:29,771 --> 00:17:32,637
exemplified by HpB02, 

374
00:17:34,616 --> 00:17:36,150
HpB03, 

375
00:17:36,473 --> 00:17:38,411
HpB01 and 

376
00:17:38,492 --> 00:17:40,349
HpB04.

377
00:17:41,722 --> 00:17:44,064
Conversely, the other haplotypes were

378
00:17:44,064 --> 00:17:46,971
present in a few individuals or in only

379
00:17:46,971 --> 00:17:48,021
one individual.

380
00:17:51,251 --> 00:17:53,350
Subsequently, further

381
00:17:53,512 --> 00:17:56,177
phylogenetic analysis was conducted on

382
00:17:56,177 --> 00:17:58,599
these sequences. Following the

383
00:17:58,599 --> 00:18:01,425
completion of the MSA multiple

384
00:18:01,425 --> 00:18:04,252
sequence alignment, phylogenetic tree

385
00:18:04,817 --> 00:18:07,482
generation was conducted using maximum

386
00:18:07,482 --> 00:18:10,268
likelihood method and the Tamura-Nei

387
00:18:10,268 --> 00:18:12,731
model in Mega X software.

388
00:18:13,619 --> 00:18:16,284
This method entitled the construction of

389
00:18:16,607 --> 00:18:19,352
a bootstrap consensus tree based on

390
00:18:19,433 --> 00:18:21,936
10,000 replicates. The

391
00:18:21,936 --> 00:18:24,520
individual branches correspond to

392
00:18:24,601 --> 00:18:27,104
partitions produced in less than 50% of

393
00:18:27,104 --> 00:18:30,092
bootstrap replicates, as well

394
00:18:30,092 --> 00:18:32,353
as the percentage of replication trees

395
00:18:32,353 --> 00:18:35,341
that clustered related haplotypes in

396
00:18:35,341 --> 00:18:37,925
the bootstrap test. The

397
00:18:37,925 --> 00:18:40,913
initial tree for the heuristic

398
00:18:40,994 --> 00:18:43,255
search was obtained automatically by

399
00:18:43,255 --> 00:18:46,081
applying the Neighbor-Joining and BioNJ

400
00:18:46,848 --> 00:18:49,473
algorithms to the pairwise distances

401
00:18:49,473 --> 00:18:52,460
matrix estimated by the Tamura-Nei model,

402
00:18:52,945 --> 00:18:55,448
and then selecting the topology with the

403
00:18:55,448 --> 00:18:57,467
highest log-likelihood value.

404
00:18:58,396 --> 00:19:00,495
As a result, the following phylogenetic

405
00:19:00,495 --> 00:19:02,716
tree were obtained.

406
00:19:04,977 --> 00:19:07,319
The phylogenetic tree based on the

407
00:19:07,319 --> 00:19:10,064
analysis of tRNA leucine-cox2

408
00:19:10,064 --> 00:19:12,648
sequence is displayed on the left.

409
00:19:13,133 --> 00:19:15,798
The phylogenetic tree based on the cox1

410
00:19:15,798 --> 00:19:18,705
sequence is displayed on the right. It

411
00:19:18,705 --> 00:19:21,127
can be observed that the bees from lineage

412
00:19:21,127 --> 00:19:24,115
A cluster together, in

413
00:19:24,115 --> 00:19:26,376
contrast to the other bees,

414
00:19:26,699 --> 00:19:29,687
namely those from lineage C, which are

415
00:19:29,687 --> 00:19:30,979
marked in red.

416
00:19:35,662 --> 00:19:38,610
The second type of markers used

417
00:19:38,610 --> 00:19:40,508
to assess genetic variability are

418
00:19:40,508 --> 00:19:42,849
microsatellites. These are

419
00:19:42,849 --> 00:19:45,797
polymorphisms that occur exclusively in

420
00:19:45,797 --> 00:19:48,098
nuclear DNA. These

421
00:19:48,098 --> 00:19:50,521
polymorphisms are characterized by the

422
00:19:50,521 --> 00:19:53,105
repetition of a particular motif,

423
00:19:53,589 --> 00:19:56,456
such as GC, in a series of

424
00:19:56,456 --> 00:19:59,323
units called tandem repeats. Each

425
00:19:59,323 --> 00:20:01,665
allele is referred to be the length of

426
00:20:01,665 --> 00:20:04,572
this repeat. To illustrate, we

427
00:20:04,572 --> 00:20:06,752
have an allele with eight repeats,

428
00:20:07,236 --> 00:20:09,659
another with three repeats, and the last

429
00:20:09,659 --> 00:20:11,637
with ten repeats.

430
00:20:12,727 --> 00:20:15,312
The designation of allele is dependent

431
00:20:16,200 --> 00:20:18,784
on the region in which the microsatellite

432
00:20:18,784 --> 00:20:21,287
is located, which is bounded by

433
00:20:21,287 --> 00:20:23,669
primers. The length of the segment

434
00:20:23,669 --> 00:20:25,648
containing the microsatellite can be

435
00:20:25,648 --> 00:20:28,232
determined using a sequencer and

436
00:20:28,232 --> 00:20:31,179
fragmentation analysis. The figure

437
00:20:31,179 --> 00:20:33,804
below illustrates the genotyping for a

438
00:20:33,804 --> 00:20:36,711
particular microsatellite, which in this

439
00:20:36,711 --> 00:20:39,699
case is characterized by three alleles

440
00:20:39,941 --> 00:20:42,040
numbered 156,

441
00:20:42,484 --> 00:20:45,028
152, and

442
00:20:46,966 --> 00:20:48,016
142.

443
00:20:52,457 --> 00:20:54,718
We can see that the microsatellites are

444
00:20:54,718 --> 00:20:56,979
very polymorphic, they can have

445
00:20:57,868 --> 00:21:00,654
not just three alleles, but 20

446
00:21:00,654 --> 00:21:03,318
alleles, which in population can mean a

447
00:21:03,318 --> 00:21:05,943
large number of different combinations of

448
00:21:05,943 --> 00:21:08,688
genotypes. So they are useful

449
00:21:08,688 --> 00:21:11,192
for assessing diversity in populations.

450
00:21:11,757 --> 00:21:14,583
Here is an example of variability in

451
00:21:14,583 --> 00:21:17,571
two populations. The population on

452
00:21:17,571 --> 00:21:19,913
the left has little variability

453
00:21:20,397 --> 00:21:22,901
containing only three types of alleles

454
00:21:23,143 --> 00:21:25,162
and a large number of homozygous

455
00:21:25,242 --> 00:21:27,503
individuals. The second

456
00:21:27,503 --> 00:21:30,047
population on the right contains a

457
00:21:30,047 --> 00:21:32,591
large number of alleles and may often

458
00:21:32,591 --> 00:21:35,256
contain heterozygous genotypes.

459
00:21:42,533 --> 00:21:45,198
Why are microsatellite markers still

460
00:21:45,198 --> 00:21:47,621
used when we have whole genome

461
00:21:47,621 --> 00:21:50,285
sequences? Microsatellite

462
00:21:50,285 --> 00:21:52,708
markers are relatively inexpensive,

463
00:21:53,354 --> 00:21:55,534
and their identification provides

464
00:21:55,534 --> 00:21:57,795
multilocus genotype information.

465
00:21:58,997 --> 00:22:01,177
They can easily be used to estimate the

466
00:22:01,177 --> 00:22:03,882
genetic diversity of populations and

467
00:22:03,882 --> 00:22:06,587
structures, which is also important in

468
00:22:06,587 --> 00:22:08,768
conservation genetics and breeding.

469
00:22:11,836 --> 00:22:14,016
The only method used to determine

470
00:22:14,016 --> 00:22:16,358
genotypes is fragmentation analysis,

471
00:22:17,004 --> 00:22:19,750
which is performed in sequencer using

472
00:22:19,750 --> 00:22:22,657
capillary electrophoresis. The fragments

473
00:22:22,738 --> 00:22:25,322
are separated according to their size,

474
00:22:25,564 --> 00:22:27,421
and the sensor detects the passage of the

475
00:22:27,421 --> 00:22:29,844
molecules, their color and signal

476
00:22:29,844 --> 00:22:32,024
intensity over time, providing

477
00:22:32,024 --> 00:22:33,720
information about the length of the

478
00:22:33,720 --> 00:22:36,465
fragments. The instrument used

479
00:22:36,546 --> 00:22:39,049
is a genetic analyzer. In our

480
00:22:39,049 --> 00:22:41,512
case, we used the ABIPrism 

481
00:22:41,512 --> 00:22:43,168
3500

482
00:22:44,056 --> 00:22:47,044
genetic analyzer. Fragment

483
00:22:47,044 --> 00:22:49,305
size were actually determined using

484
00:22:49,305 --> 00:22:52,131
GeneScan software and genotypes were

485
00:22:52,131 --> 00:22:54,554
determined using GeneMapper software.

486
00:22:58,834 --> 00:23:01,095
As we were looking at 22

487
00:23:01,095 --> 00:23:03,759
microsatellite loci, we grouped

488
00:23:03,759 --> 00:23:05,778
certain microsatellites in a single

489
00:23:05,778 --> 00:23:08,685
multiplex reaction. We were able

490
00:23:08,685 --> 00:23:11,390
to identify several microsatellites under

491
00:23:11,390 --> 00:23:14,015
the same conditions, distinguished by

492
00:23:14,015 --> 00:23:15,226
different color.

493
00:23:18,698 --> 00:23:20,879
The result of the analysis is displayed

494
00:23:21,121 --> 00:23:23,988
here for a particular microsatellite

495
00:23:23,988 --> 00:23:26,935
locus, we see the color-coded peaks,

496
00:23:27,137 --> 00:23:29,559
which are identified fragments of a

497
00:23:29,559 --> 00:23:30,771
particular length.

498
00:23:35,575 --> 00:23:38,563
The figure shows the evaluation

499
00:23:38,604 --> 00:23:40,744
of the variability of genotyping of

500
00:23:40,744 --> 00:23:43,247
microsatellite loci in three

501
00:23:43,247 --> 00:23:46,235
individuals. We can see that different

502
00:23:46,235 --> 00:23:48,052
alleles can be present at certain

503
00:23:48,052 --> 00:23:50,757
positions of the region, which allows

504
00:23:50,757 --> 00:23:53,018
easy discrimination of individuals.

505
00:24:00,124 --> 00:24:02,183
After determining the genotypes at

506
00:24:02,304 --> 00:24:04,969
all loci and for all individuals in the

507
00:24:04,969 --> 00:24:07,795
population, the next step is to

508
00:24:07,795 --> 00:24:10,460
perform a diversity analysis. It is

509
00:24:10,783 --> 00:24:13,609
to determine diversity parameters such as

510
00:24:13,609 --> 00:24:15,386
the number of alleles , the

511
00:24:16,597 --> 00:24:19,424
effective number of alleles , the

512
00:24:19,424 --> 00:24:21,372
Shannon Information Index ,

513
00:24:22,492 --> 00:24:23,865
the observed and expected

514
00:24:24,430 --> 00:24:27,257
heterozygosity

515
00:24:27,257 --> 00:24:30,244
respectively, and the unbiased expected

516
00:24:30,244 --> 00:24:33,071
heterozygosity uHE,

517
00:24:33,717 --> 00:24:36,462
and the so-called fixation index F.

518
00:24:37,916 --> 00:24:40,823
We use the GenAlEx program, which runs

519
00:24:40,984 --> 00:24:43,528
in Microsoft Excel, but the data and

520
00:24:43,528 --> 00:24:46,152
parameters can also be calculated using,

521
00:24:46,475 --> 00:24:49,382
for example, diversity package

522
00:24:49,382 --> 00:24:51,886
in R. We see

523
00:24:53,420 --> 00:24:56,004
that the expected heterozygosity averaged

524
00:24:56,166 --> 00:24:58,992
over all loci combined is

525
00:24:58,992 --> 00:25:01,818
0.579

526
00:25:02,182 --> 00:25:04,806
and the actual observed heterozygosity

527
00:25:05,129 --> 00:25:05,371
is

528
00:25:06,340 --> 00:25:09,328
0.556. This is a

529
00:25:09,328 --> 00:25:12,154
relatively high heterozygosity which

530
00:25:12,154 --> 00:25:15,061
characterized these bees populations

531
00:25:15,465 --> 00:25:17,524
in sufficiently divergent.

532
00:25:20,795 --> 00:25:23,540
Since we knew which area, district

533
00:25:23,904 --> 00:25:25,801
of the Czech Republic each individual

534
00:25:25,801 --> 00:25:28,466
came from, we divided the

535
00:25:28,466 --> 00:25:31,131
population of the Czech Republic into 77

536
00:25:31,131 --> 00:25:33,311
districts, which characterized the

537
00:25:33,311 --> 00:25:36,218
geographical areas. This allowed

538
00:25:36,299 --> 00:25:38,883
us to calculate the so-called Wright´s F

539
00:25:38,883 --> 00:25:41,790
statistics, Fst, Fis,

540
00:25:41,952 --> 00:25:44,778
and Fit, and the so-called

541
00:25:44,778 --> 00:25:47,160
analysis of molecular variance, which

542
00:25:47,160 --> 00:25:49,139
determine the proportion of

543
00:25:49,219 --> 00:25:51,965
variability between populations,

544
00:25:52,126 --> 00:25:54,307
between individuals within populations,

545
00:25:54,387 --> 00:25:57,214
and within individuals. On the

546
00:25:57,214 --> 00:25:59,717
table on the left, characterize

547
00:26:00,282 --> 00:26:03,189
the individuality and the mean values of

548
00:26:03,189 --> 00:26:06,016
the F statistic. The Fst is

549
00:26:06,016 --> 00:26:08,519
most interesting because it determines

550
00:26:08,761 --> 00:26:10,296
the degree of variation between

551
00:26:10,296 --> 00:26:12,718
subpopulations. It is districts.

552
00:26:13,364 --> 00:26:14,575
The value of

553
00:26:14,979 --> 00:26:17,805
0.086 is not very high,

554
00:26:17,805 --> 00:26:19,743
but it is shown some

555
00:26:19,743 --> 00:26:22,731
diversification in the table

556
00:26:22,731 --> 00:26:25,638
on the right. We see the result of the

557
00:26:25,638 --> 00:26:27,738
analysis of molecular variances,

558
00:26:28,949 --> 00:26:31,291
where in the last column the variation

559
00:26:31,291 --> 00:26:33,875
between regional populations districts

560
00:26:34,198 --> 00:26:36,782
account for only 1% of the total

561
00:26:36,782 --> 00:26:39,285
variation. But even

562
00:26:39,528 --> 00:26:42,273
1 to 3% of variability between

563
00:26:42,273 --> 00:26:45,180
geographical areas are common figures

564
00:26:45,180 --> 00:26:47,603
according to the other publications.

565
00:26:48,733 --> 00:26:51,075
Variability between individuals within

566
00:26:51,075 --> 00:26:53,901
populations is expressed as 6%.

567
00:26:54,628 --> 00:26:56,687
and within individual variability

568
00:26:56,687 --> 00:26:58,868
accounts for most of the variability

569
00:26:58,868 --> 00:27:00,039
within populations.

570
00:27:04,884 --> 00:27:07,548
Paired Nei´s and paired FST

571
00:27:07,791 --> 00:27:10,455
genetic distances were calculated

572
00:27:10,455 --> 00:27:12,313
using the GenAlEx program.

573
00:27:13,201 --> 00:27:15,462
Paired values of these distances were

574
00:27:15,462 --> 00:27:17,885
used for principal component analysis

575
00:27:17,885 --> 00:27:20,388
calculations. The top graph

576
00:27:20,388 --> 00:27:22,730
depicts the distances between the first

577
00:27:22,810 --> 00:27:24,991
and second components for each district.

578
00:27:25,718 --> 00:27:28,221
The bottom graph represents a

579
00:27:28,221 --> 00:27:30,118
calculation comparing the first and

580
00:27:30,118 --> 00:27:32,339
second components using the paired FST

581
00:27:32,339 --> 00:27:35,327
distances. We can see that, for

582
00:27:35,327 --> 00:27:37,830
example, the district of Děčín,

583
00:27:38,153 --> 00:27:40,657
DC, or Fridek Mistek, FM,

584
00:27:41,222 --> 00:27:43,241
and similar other districts are a little

585
00:27:43,241 --> 00:27:45,421
more distant from the central cluster,

586
00:27:46,228 --> 00:27:48,328
and there are some distances between

587
00:27:48,328 --> 00:27:51,114
them, but they are not the significant.

588
00:27:51,639 --> 00:27:54,384
So there are areas that are more

589
00:27:54,384 --> 00:27:56,080
distinct from other areas.

590
00:27:59,795 --> 00:28:02,338
The Bayesian clustering method and

591
00:28:02,338 --> 00:28:05,205
program structure were used to

592
00:28:05,245 --> 00:28:07,547
analyze the genetic diversity

593
00:28:08,031 --> 00:28:09,808
and admixture rate of honeybee

594
00:28:09,808 --> 00:28:12,150
populations. Ten

595
00:28:12,150 --> 00:28:14,330
independent simulations were run,

596
00:28:14,976 --> 00:28:17,641
each involving 10,000 burn-in steps,

597
00:28:18,045 --> 00:28:20,911
followed by100,000

598
00:28:21,113 --> 00:28:23,778
iterations of Markov Chain Monte

599
00:28:23,778 --> 00:28:26,604
Carlo. We then used the

600
00:28:26,604 --> 00:28:29,269
Clampack and Structure Selector programs,

601
00:28:29,754 --> 00:28:32,741
which implement Evann's methods and

602
00:28:32,741 --> 00:28:35,164
Puechmaille's method, respectively,

603
00:28:35,891 --> 00:28:37,829
to determine the optimal number of

604
00:28:37,829 --> 00:28:40,817
clusters K. The best

605
00:28:40,817 --> 00:28:43,481
fit data Delta

606
00:28:43,481 --> 00:28:46,469
K, MetMed K, MaxMedK,

607
00:28:46,550 --> 00:28:48,932
MedMeanK and

608
00:28:48,932 --> 00:28:51,879
MaxMeanK. In

609
00:28:51,879 --> 00:28:54,544
the case of this population, both methods

610
00:28:54,625 --> 00:28:56,725
determined that there are

611
00:28:57,128 --> 00:29:00,116
three genetically distinct populations

612
00:29:00,439 --> 00:29:02,539
in the honeybee population in the Czech

613
00:29:02,539 --> 00:29:05,446
Republic based on these 22

614
00:29:05,446 --> 00:29:08,111
microsatellite markers, and that each

615
00:29:08,111 --> 00:29:10,856
individual can be assigned to one of

616
00:29:10,856 --> 00:29:13,440
these three groups with some high

617
00:29:13,440 --> 00:29:15,136
degree of probability.

618
00:29:20,466 --> 00:29:23,090
Other methods can also be used to

619
00:29:23,090 --> 00:29:25,714
determine the genetic structure, such as

620
00:29:25,714 --> 00:29:28,137
the discriminant analysis of the

621
00:29:28,137 --> 00:29:30,802
principal components, DAPC,

622
00:29:31,448 --> 00:29:33,790
programmed in the adgenet

623
00:29:34,032 --> 00:29:36,616
package in R. This

624
00:29:36,616 --> 00:29:39,523
method, in turn, can be used to determine

625
00:29:39,523 --> 00:29:42,309
the structure of the population and to

626
00:29:42,309 --> 00:29:45,256
speak of clusters, groups that are

627
00:29:45,256 --> 00:29:48,002
genetically distinct from each other

628
00:29:48,163 --> 00:29:51,070
and to which individuals can be

629
00:29:51,353 --> 00:29:53,331
unambiguously assigned.

630
00:29:54,220 --> 00:29:56,723
In this population of honey bees in the

631
00:29:56,723 --> 00:29:59,630
Czech Republic, five groups, five

632
00:29:59,630 --> 00:30:02,497
clusters were estimated to be different.

633
00:30:06,534 --> 00:30:08,755
And thank you for your attention.