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RFLPs (Restriction length polymorphisms) can be used to calculate genetic distance between two alleles. They are good because they are.

- 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?!



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:

- Trait values of individuals related by a known cross (eg. backcross of F2)
- Marker states for each individual
- Markers arranged in a map



- 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:

- 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:

- 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.