Topic 8: Assignment tests Flashcards

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1
Q

What are assignment tests?

A
  • tests used to assign individuals into a subpopulation, based on their genotypes. Will assign them where their genotype has a higher probability of occurring
  • can tell us if individuals migrated from one population to another
  • assumes that sample locations are genetic groups
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2
Q

Highly separated populations on an assignment scatterplot tell us….

A
  • that there is a high Fst (large genetic difference between the two groups)
  • that the two populations are very genetically differentiated, and we would have a good chance of assigning individuals correctly to their population of origin.
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3
Q

the number of cross-assigned individuals (migrants) is a measure of _____ ______ between populations in assignment tests

A

genetic divergence

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4
Q

What are some uses for assignment tests?

A
  • assigning individuals to natal populations
  • determining how many subpopulations there many be
  • measuring genetic differentiation
  • identifying immigrants/gene flow
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5
Q

What are alternative methods to assignment tests?

A

bayesian methods

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6
Q

What are bayesian methods?

A
  • more dynamic tests than assignment tests
  • dont assume that sampling locations are genetic groups, instead build genetic clusters based on the data/genetic profile
  • would use this if we suspect that populations are genetically structured differently than the geography would suggest
  • cluster individuals in a way to maximize the differences between groups (Fst) and minimize differences within groups (Fis)
  • bayesian methods also allow for admixture between genetic groups
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7
Q

What are admixture plots?

A
  • graph that visualizes each sampled individuals genetic assignment
  • every individual appears as a column
  • genetically distinct clusters are plotted in different colours
  • genetic composition of each individual (column) shows how much of their genetic background can be assigned to each of the clusters
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8
Q

How will migrants appear (visually) in admixture plots? admixed/hybrid individuals?

A
  • migrants will have different colours that the population in which they are sampled in.
  • hybrids will have their bar broken into more than one colour
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9
Q

When determining K for admixture plots ( how many genetic groups), underestimating the structure can lead to …

A
  • management actions at the wrong spatial scale
  • inflating population size estimates and preventing protection under legislations
  • incorrect grouping when conducting association studies
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10
Q

When determining K for admixture plots ( how many genetic groups), overestimating the structure can lead to …

A
  • costly conservation actions for ‘rare’ taxa, such as translocations or habitat restoration to unnecessarily promote gene flow
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11
Q

How do you determine the best ‘k’ number?

A
  • either the greatest likelihood value
  • or biggest rate of change in likelihood ( ex. if there is no increase after 5, choose 5)
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12
Q

what is the difference between local ancestry and global ancestry?

A
  • global ancestry is a single genome-wide estimate of admixture proportion
  • local ancestry is a population assignment of individual loci or regions of the genome
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13
Q

What can we determine from local ancestry?

A
  • migration processes
  • identify regions of selection
  • infer recombination rates
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14
Q

What is PCA?

A
  • Principle component analysis
  • takes complex data sets and simplifies it, transforms it to reduce complexity
  • similar to assignment tests, but can have more than 2 populations included
  • attempts to show as much genetic variability as possible in a few axes
  • individuals closer to one another on the axis are more genetically similar than those further away
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15
Q

What are the pros and cons of bayesian methods?

A

pros:
- can be used to find k
- can identify admixed individuals

cons:
- assumes HWE within populations
- assumes linkage equilibrium between loci
- computationally intensive

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16
Q

What are the pros and cons of PCA?

A

Pros:
- no assumptions made about the underlying genetic model
- computationally quick
- can handle large data sets

Cons:
- inherently exploratory
- generally cannot be used to find k
- cannot be used to identify admixed individuals