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Genetik > 4 > Flashcards

4 Flashcards

1
Q

What are some of the factors that need to be taken into account when deciding which molecular marker(s) you would use in an ecological study?

A
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2
Q

table: overview some markers

AFLP’s
Microsats
DNA-sequencing
SNP’s

A
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3
Q

neutral loci

A

useful to understand genetic drift or mutation (neutral processes)

  • Definition: Neutral loci are regions of the genome that do not experience strong natural selection. Genetic variation at neutral loci is primarily shaped by processes such as mutation, genetic drift, and migration.
  • Common Markers: Microsatellites (simple sequence repeats or SSRs) and many single nucleotide polymorphisms (SNPs) are often considered neutral markers.
  • Use: Neutral markers are frequently used to study population structure, historical demographic events, and gene flow. Because they are not under strong selective pressure, changes in neutral markers are assumed to reflect the demographic history of populations rather than adaptive processes.
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4
Q

functional loci

A

for understanding effect of selection in the past and local adaptation

  • Definition: Functional loci are regions of the genome that code for proteins or have a regulatory role in gene expression. Genetic variation at functional loci can be subject to natural selection based on the functional consequences of the genetic changes.
  • Common Markers: Exonic regions (coding regions) of genes, particularly non-synonymous SNPs (nsSNPs) that result in amino acid changes, are examples of functional markers. Regulatory regions that control gene expression are also considered functional markers.
  • Use: Functional markers are often used to study adaptive evolution, investigate the genetic basis of phenotypic traits, and understand how natural selection acts on specific genes or genomic regions. They are valuable for exploring the relationship between genotype and phenotype.
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5
Q

which DNA is better, when we want to study genetic variability?

A
  • nuDNA and not mtDNA
  • for comparing individuals, pop. or species
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6
Q

when would I use a uniparental marker?

A
  • Useful for studying maternal or paternal lineages specifically
  • mtDNA
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7
Q

when would I use a biparental marker?

A
  • Capture a broader range of genetic variation, making them suitable for population-level studies and mapping traits influenced by multiple genes.
  • Can be informative for studying population genetics, genetic diversity, and complex traits.
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8
Q

when is a co-dominant marker the better choice?

A
  • for calculating allelic frequencies
  • and therefor for detecting homo- and heterozygotes
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9
Q

wich marker when studying the evolutionary history?

A
  • very often mtDNA
  • also autosomal DNA: SNP’s and Microsats
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10
Q

intra-pop.-scale

A
  • differences inside the same pop
  • F(is) = measure for estimating variability in the same population
  • used to estimate inbreeding
  • values between -1 (outbreeding) and 1 (inbreeding)
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11
Q

formula for intra-pop-variability

A

F(is) = measure to estimate variability (-1< x < 1)
- Hs = expected heterozygosity under HWE
- Hi = observed heterozygosity in my sample

F(is) = fixation index at intrapopulation scale)

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12
Q

for intra-pop. scales: what if Hs>Hi?

A
  • less heterozygosity in my sample than expected
  • the value will be positive
  • indicates a level of inbreeding
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13
Q

for intra-pop. scales: what if Hs<Hi?

A
  • negative value
  • indicated outbreeding
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14
Q

for intra-pop. scales: what if F(is) = 0?

A
  • pop in HWE
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15
Q

what do we study if we conduct the intra-pop. study (fixation index F(is)) on many loci?

A
  • we then observe the fixation index on a genome scale
  • we can study forces like genetic drift, inbreeding or outbreeding

why? –> these forces act on genome scale

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16
Q

what do we study if we conduct the intra-pop. study (fixation index F(is)) on a single locus

A
  • we then can make hypothesis on forces that act on local scale:
  • assortative/disassortative mating
  • selection
17
Q

what do I measure with F(ST)

A
  • inter-pop. scale
  • measures variability AMONG population (>1)
  • differences in allelic frequencies mean that there is divergence

–> F(ST) measures divergence
(fixation index of sample respect to total)

18
Q

what values can F(ST) have? what does it mean?

A

0 = no divergence
1 = max divergence

19
Q

how do I calculate F(ST)?

A
20
Q

what if H(t) > (H(s) ?

A
  • the bigger H(t) / the bigger the difference between them, the higher the value –> the higher the divergency between these populations
21
Q

what if H(t) = H(s) ?

A

= 0
–> the populations are the same

22
Q

when does F(st) not work?

A
  • when all allelic freq. are the opposite BUT heterozygosity is equal
23
Q

spoken statistically: what is an assignment test?

A
  • finds the most likely pop. of origin of a certain specimen
24
Q

what is clustering?

A
  • a model
  • assumptions:
    (1) all the markers present in the individuals of the samples are independent and are in HWE
    (2) these individuals can be divided in clusters
    (3) no LD (linkage disequilibrium) = no association of alleles, they are not linked
25
Q

what is that?
what do we see?

A
  • model of clustering
  • K = 10: means there is 10 populations
  • 9 clusters = 9 colors
  • wach vertical line = one individual

–> pop. 8 and 9 are very similar poulations, same cluster
- analysis based on microsattelite markers
- suggests that 8 and 9 are one population

26
Q

techniques to group individuals in different populations

A
  • Clustering
  • PCA
27
Q

PCA

A

= principle component analysis
- shows differences between INDIVIDUALS
- quick method for pop. clustering
- PC1 (principle component 1) on x-axis: responsible for most variations among individuals
PC2: y-axis: contains second largest variations

28
Q

when do we go for the Discriminant Analysis?

A
  • when we want additional information e.g. pop. origin
  • itshows maximized differences between GROUPS
  • variables discriminate the groups, not the individuals
29
Q

what does Discriminant Analysis do?

A
  • primary goal: to find the combination of variables that best discriminate between the predefined groups
  • maximize differences between groups (not between INDIVIDUALS as in PCA)
  • because individuals are assigned to a group before analysis starts
  • variables, that clearly discriminates between GROUPS
30
Q

questions for Discriminant Analysis (DA)

A
  1. do these variables differ in observed groups?
  2. can we discriminate between a priori defined groups using variables?
  3. which variables are the best to discriminate the groups?
31
Q

what do you see?

A
  • example for DA
  • x-axis: discriminant variable/ function 1
  • y-axis: discriminant variable/ function 2
  • group 2 and 3 clearly discriminated by these two chosen values
  • group 1,5,4,6 not so well discriminated
  • histogram represents ALL values and their discrimination power (the first 2 are x- and y-axix)
32
Q

what is DACP?

A
  • combination of PCA and DA
  • first step: data on individuals transformed using PCA
  • next step: cluster-identification using DA

–> for DA we use the groups obtained from PCA

33
Q

what do we see here?

A
  • DACP
34
Q

when are neutral markers like microsatellites and SNPs appropriate?

A

If you are interested in understanding historical demography, population structure, or connectivity

35
Q

when do functional markers within coding or regulatory regions of genes become more relevant?

A

If your focus is on adaptive evolution, local adaptation, or the genetic basis of specific traits

Genetik (11 decks)

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