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Epistatic QTL for gene expression in mice; potential for BXD expression data

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. Introduction. Genetical genomics: exciting new tool

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Epistatic QTL for gene expression in mice; potential for BXD expression data

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  1. 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

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

  11. Computational considerations • Means (29) vs. ind. measurements (77) • Single marker vs. IM: • 587 vs. 2100 tests for 1D scan • 343,982 vs. 4,410,000 tests for 2D scan • 1,000 genome-wide randomisations for 12,442 traits…  100.000 CPU hours on 512 processor Origin 3800 at CSAR, Manchester (£50K)

  12. A flavour of the results

  13. A flavour of the results

  14. A flavour of the results

  15. Acknowledgements

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