Lecture 11 Flashcards
RFLPs (Restriction length polymorphisms) can be used to calculate genetic distance between two alleles. They are good because they are.
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- co-dominant
- the genetic map can be directly related to DNA-sequence
- Can be found new ANY gene
- Many markers per cross (they can even be anonymous)
- Can crease a genetic map with high marker density spanning the two genome
- Useful for many species
Menedelizing Quantitative Traits
- Different segments of the genome can be marked with molecular variants that can be analysed individually with respect to the phenotype of interest.
Monkey Flowers:
- A type of flower with two species (of interest), with a region of sympatry, but no hybrids. One (lewisii) is designed for bees, the other (cardinalis) for hummingbirds.
Mimulus lewisii
- Designed for bees
- Pink
- Yellow nectar guides
- Vide corolla
- Small volume of concentrated nectar
- Short anther and stigmas
Mimulus cardinalis:
- Designed for humming birds
- Red
- No nectar guides
- Tubular corolla
- High volume of nectar
- Long anther and stigmas
Are the hybrids viable and fertile when artificially bred?!
Yes!
Step 1 in QTL mapping
- Start with two lines that differ for the trait of interest
- Best if the genetic variation between the lines is maximised
- Best if the genegtic variation within lines is minimized
Step 2 in QTL mapping
- Cross the two lines
- Allow recombination
- The more progeny, the more recombination, the greater the mapping resolution, can use F2 (3 progeny types, 2 homozygotes and 1 heterozygotes) or backcross (2 progeny types, 2 homozygotes)
Step 3 in QTL mapping:
- Choose molecular markers (RFLPs, AFLPs, SNPs)
- Marker density must e informative to the cross
- Consider cost, labour, marker density and co-dominance
Step 4 in QTL mapping:
- Score the F2 or backcross progeny and parents for molecular markers and create a linkage map
Step 5 in QTL mapping:
- Score the F2 or backcross progeny and parents for trait of interest
Step 6 in QTL mapping:
- For each marker (or interval) perform a statistical test for association with phenotype
Input (dataset) for a QTL experiment:
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- Trait values of individuals related by a known cross (eg. backcross of F2)
- Marker states for each individual
- Markers arranged in a map
T-tests:
- Compares the mean between two populations, high and low, given their variance
- Generally the L marker will have a lower trait value than the H marker
When the P value approaches 1:
- The means are about the same
- There is no significant difference
- There is no gene of interest there contributing to the effect
P < 0.05:
There is a significant difference
When there is a QTL above the threshold line:
- There is a significant association between the marker and the trait of interest, and they are correlated with our trait of interest
- They are our QTLs
Flaws with QTL mapping:
- The markers are unlikely to be the causal variants, but they will be linked to the causal variants
Thoday’s method for mapping QTLs within a chromosome:
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- Use mapping stock with two closely linked phenotypic markers (a and b) on a chromosome containing the QTLs.
- Cross this to a stock that is WT at the two phenotypic markers (A and B) and differs from marker stock in the trait of interest (eg. has a higher number of bristles).
- The QTLS can be outside the phenotypic markers to between them
- We want to know which it is
If the QTL is outside the markers:
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- Progeny with the parental marker types will exhibit intermediate trait means with high variance (ignoring double cross overs)
- Recombinant type will be either high or low with little variance
We can quantify where the QTL is in the interval:
- How many individuals are in the Ab low class? (5)
- How many individuals are in the aB high class? (3)
- There are 8 individuals that result from a recombination in region 1.
- Do the same for region two.. 12 from recombination in region two.
- The distance between our gene of interest and marker A is closer than that of our gene and marker B.
We can quantify the effect size is as well..
- From a graph comparing trait score and marker class calculate the mean of each class and average it.