20
Composite interval mapping Significance thresholds Confidence intervals Experimental design

Composite interval mapping Significance thresholds Confidence intervals Experimental design

Embed Size (px)

Citation preview

Page 1: Composite interval mapping Significance thresholds Confidence intervals Experimental design

Composite interval mappingSignificance thresholds

Confidence intervalsExperimental design

Page 2: Composite interval mapping Significance thresholds Confidence intervals Experimental design

Association between genotype and phenotype

Individual Marker 1 Marker 2 Marker 3 Marker 4 Marker 5 Marker 6 Phenotype

A 1 1 1 1 1 1 1 1 1 1 1 1 0.07

B 1 1 1 1 1 1 1 1 1 1 1 1 0.35

C 2 2 2 2 2 2 2 2 2 2 2 2 0.46

D 2 2 2 2 2 2 2 2 2 2 2 2 0.67

E 1 2 1 2 1 2 1 2 1 2 1 2 0.41

F 1 2 1 2 1 2 1 2 1 2 1 2 0.30

Page 3: Composite interval mapping Significance thresholds Confidence intervals Experimental design
Page 4: Composite interval mapping Significance thresholds Confidence intervals Experimental design

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

Page 5: Composite interval mapping Significance thresholds Confidence intervals Experimental design

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.

Page 6: Composite interval mapping Significance thresholds Confidence intervals Experimental design

Zeng 1994; Genetics 136:1457-1468

Composite interval mapping

• There is a trade-off between estimation of QTL location (esp. if linked QTL) and power to detect QTL with small effects.

• QTL cartographer

Page 7: Composite interval mapping Significance thresholds Confidence intervals Experimental design

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

Page 8: Composite interval mapping Significance thresholds Confidence intervals Experimental design

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 1 1 1 1 1 1 1 1 1 1 1 0.07

B 1 1 1 1 1 1 1 1 1 1 1 1 0.35

C 2 2 2 2 2 2 2 2 2 2 2 2 0.46

D 2 2 2 2 2 2 2 2 2 2 2 2 0.67

E 1 2 1 2 1 2 1 2 1 2 1 2 0.41

F 1 2 1 2 1 2 1 2 1 2 1 2 0.30

Page 9: Composite interval mapping Significance thresholds Confidence intervals Experimental design

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 1 1 1 1 1 1 1 1 1 1 1 0.67

B 1 1 1 1 1 1 1 1 1 1 1 1 0.35

C 2 2 2 2 2 2 2 2 2 2 2 2 0.30

D 2 2 2 2 2 2 2 2 2 2 2 2 0.07

E 1 2 1 2 1 2 1 2 1 2 1 2 0.46

F 1 2 1 2 1 2 1 2 1 2 1 2 0.41

Page 10: Composite interval mapping Significance thresholds Confidence intervals Experimental design

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 1 1 1 1 1 1 1 1 1 1 1 0.41

B 1 1 1 1 1 1 1 1 1 1 1 1 0.67

C 2 2 2 2 2 2 2 2 2 2 2 2 0.46

D 2 2 2 2 2 2 2 2 2 2 2 2 0.35

E 1 2 1 2 1 2 1 2 1 2 1 2 0.07

F 1 2 1 2 1 2 1 2 1 2 1 2 0.30

Page 11: Composite interval mapping Significance thresholds Confidence intervals Experimental design

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

Page 12: Composite interval mapping Significance thresholds Confidence intervals Experimental design

Example

Page 13: Composite interval mapping Significance thresholds Confidence intervals Experimental design

Permuted data

Page 14: Composite interval mapping Significance thresholds Confidence intervals Experimental design

Distribution of test statistic by permutation

Permutation results

Traditional statistical analysis of real data

Page 15: Composite interval mapping Significance thresholds Confidence intervals Experimental design

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

Page 16: Composite interval mapping Significance thresholds Confidence intervals Experimental design

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 aa 5

Aa Aa 8

AA Aa 6

aa Aa 9

Marker 1 Marker 2 Pheno

AA Aa 4

AA Aa 4

aa Aa 9

aa Aa 9

AA Aa 6

Marker 1 Marker 2 Pheno

Aa Aa 8

Aa Aa 8

Aa Aa 8

AA Aa 4

aa Aa 9

Page 17: Composite interval mapping Significance thresholds Confidence intervals Experimental design

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

Page 18: Composite interval mapping Significance thresholds Confidence intervals Experimental design

Experimental design

• Phenotyping – what phenotype to measure?– Endophenotypes

Schmidt et al. 2003 JOURNAL OF BONE AND MINERAL RESEARCH 18: 1486-1496

Page 19: Composite interval mapping Significance thresholds Confidence intervals Experimental design

Experimental design

• Phenotyping – what phenotype to measure?• Type of cross

– Pedigree vs. cross– Inbred vs. outbred– F2 vs. backcross

Page 20: Composite interval mapping Significance thresholds Confidence intervals Experimental design

Experimental design

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