1. Dirk-Jan de Koning*, Örjan Carlborg*, Robert Williams†, Lu Lu†,
Epistatic QTL for gene expression in mice; potential for BXD expression data
Dirk-Jan de Koning*, Örjan Carlborg*, Robert Williams†, Lu Lu†,
Chris Haley*
*Roslin Institute, UK
†University of Tennessee Health Science Center, USA

2. Introduction Genetical genomics: exciting new tool
Analysis tools for experimental crosses widely available
More complex models have been proposed
Scale-up from 10 to 10K traits NOT trivial

3. Data 29 BXD RI lines 587 markers spanning all chromosome
Array data for 12,242 genes
77 arrays
Normalized: µ=8, σ2=2
1 - 4 replicates/line

4. Research questions
Proportion of variation in gene expression due to epistasis?
Epistasis more prevalent for certain types of genes?
For epistatic pairs of genes: both trans or 1 cis?
Magnitude of epistasis in relation to differences between founder lines and deviation of F1

5. Data and analysis issues
What is the repeatability?
What to do with outliers?
Means or single observations?
If means: weighted or un-weighted?
If weighted: what weights?
Single marker mapping or interval mapping?

6. Repeatability Upper limit of heritability
Mixed linear model in Genstat
No consistent effect of sex and age

7. Outliers
Outliers identified as individual expression measures + or – 3 s.d. from mean
3 treatments of outliers:
Ignore
Remove
Shrink to 3 s.d.

8. (Weighted) analysis of means
Weighted analyses should reflect difference in number of replicates
3 types of weighting:
No weighting
Inverse of variance
Very crude estimate
Strong effect of small SE!
Use expected reduction in variance:
n/[1+r(n-1)]

9. QTL analysis* Single QTL genome scan using least squares
2-dimensional scan fitting all pair-wise combinations of interacting QTL:
exhaustive search
Only additive x additive interaction
Permutation test: analyses ‘approximated’ using GA
* Carlborg and Andersson, Genetical Research, 2002

11. “Training” data
96 trait pseudo-randomly selected: proportional representation of r
Individual phenotypes
3 treatments of outliers
mean phenotypes
3 type of weighting
IM vs. single marker
Many scenarios to be evaluated

12. Computational considerations
Means (29) vs. ind. measurements (77)
Single marker vs. IM:
587 vs tests for 1D scan
343,982 vs. 4,410,000 tests for 2D scan
1,000 genome-wide randomisations for 12,442 traits…
 CPU hours on 512 processor Origin 3800 at CSAR, Manchester (£50K)

13. A flavour of the results

14. A flavour of the results

15. A flavour of the results

16. Acknowledgements