1 / 16

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

kyria
Télécharger la présentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related