1. Composite interval mapping Significance thresholds Confidence intervals Experimental design

2. Association between genotype and phenotype
Individual
Marker 1
Marker 2
Marker 3
Marker 4
Marker 5
Marker 6
Phenotype A 1
0.07 B
0.35 C 2
0.46 D
0.67 E
0.41 F
0.30

4. Interval mapping vs. Composite interval mapping
Uses flanking marker genotypes to infer probability of genotype at intervals between the markers
Associates probability of genotype with phenotype
Composite interval mapping
Uses markers in addition to flanking markers to control for QTL located elsewhere

5. Composite interval mapping
Uses markers in addition to flanking markers to control for QTL located elsewhere
including linked markers accounts for linked QTL- improved localisation of QTL
including unlinked markers reduces variation (noise) due to other QTL, and so increases power.

6. Composite interval mapping
Zeng 1994; Genetics 136:
There is a trade-off between estimation of QTL location (esp. if linked QTL) and power to detect QTL with small effects.
QTL cartographer

7. Significance thresholds
How do you determine whether a QTL is statistically significant?
Problem with multiple tests
Arbitrary threshold OR
Obtain an empirical distribution for the test statistic under the null hypothesis
Permutation tests

8. Permutation test
Permute genotypes/phenotypes (removes any real association)
Individual
Marker 1
Marker 2
Marker 3
Marker 4
Marker 5
Marker 6
Phenotype A 1
0.07 B
0.35 C 2
0.46 D
0.67 E
0.41 F
0.30

9. Permutation test
Permute genotypes/phenotypes (removes any real association)
Individual
Marker 1
Marker 2
Marker 3
Marker 4
Marker 5
Marker 6
Phenotype A 1
0.67 B
0.35 C 2
0.30 D
0.07 E
0.46 F
0.41

10. Permutation test
Permute genotypes/phenotypes (removes any real association)
Individual
Marker 1
Marker 2
Marker 3
Marker 4
Marker 5
Marker 6
Phenotype A 1
0.41 B
0.67 C 2
0.46 D
0.35 E
0.07 F
0.30

11. Permutation test
Permute genotypes/phenotypes (removes any real association)
Rerun genome-wide scan analysis, and calculate the highest test statistic across the genome
Repeat many times

12. Example

13. Permuted data

14. Distribution of test statistic by permutation
Permutation results
Traditional statistical analysis of real data

15. Confidence intervals
How do you assess uncertainty in the location of a QTL?
1 LOD support interval
LOD-based intervals are often too narrow
Bootstrappig

16. Bootstrapping
want to know what would happen if you repeated the experiment many times
use existing data set, and use it to create new, bootstrap, datasets by random sampling with replacement
Marker 1
Marker 2
Pheno AA Aa 4 aa 5 8 6 9
Marker 1
Marker 2
Pheno AA Aa 4 aa 9 6
Marker 1
Marker 2
Pheno Aa 8 AA 4 aa 9

17. Bootstrapping
want to know what would happen if you repeated the experiment many times
use existing data set, and use it to create new, bootstrap, datasets by random sampling with replacement
a given observation may appear more than once
bootstrap datasets have the same sample size as the real data set
Repeat QTL analysis with each bootstrapped data set
Bootstrapping is more robust/ conservative

18. Experimental design Phenotyping – what phenotype to measure?
Endophenotypes
Schmidt et al JOURNAL OF BONE AND MINERAL RESEARCH 18:

19. Experimental design Phenotyping – what phenotype to measure?
Type of cross
Pedigree vs. cross
Inbred vs. outbred
F2 vs. backcross

20. Experimental design Phenotyping – what phenotype to measure?
Type of cross
Sample size and power
Beavis effect
Marker density