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Composite interval mapping Significance thresholds Confidence intervals Experimental design

Composite interval mapping Significance thresholds Confidence intervals Experimental design. Association between genotype and phenotype. Interval mapping vs. Composite interval mapping. Interval mapping

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Composite interval mapping Significance thresholds Confidence intervals Experimental design

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  1. Composite interval mappingSignificance thresholdsConfidence intervalsExperimental design

  2. Association between genotype and phenotype

  3. Interval mapping vs. Composite interval mapping 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

  4. Composite interval mapping 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.

  5. Composite interval mapping Zeng 1994; Genetics 136:1457-1468 • There is a trade-off between estimation of QTL location (esp. if linked QTL) and power to detect QTL with small effects. • QTL cartographer

  6. 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

  7. Permutation test • Permute genotypes/phenotypes (removes any real association)

  8. Permutation test • Permute genotypes/phenotypes (removes any real association)

  9. Permutation test • Permute genotypes/phenotypes (removes any real association)

  10. 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

  11. Example

  12. Permuted data

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

  14. 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

  15. 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

  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 • 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

  17. Experimental design • Phenotyping – what phenotype to measure? • Endophenotypes Schmidt et al. 2003 JOURNAL OF BONE AND MINERAL RESEARCH 18: 1486-1496

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

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

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