Epistatic qtl for gene expression in mice potential for bxd expression data
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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

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
Introduction expression data

  • 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


Data expression 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


Research questions
Research questions expression data

  • 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


Data and analysis issues
Data and analysis issues expression data

  • 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?


Repeatability
Repeatability expression data

  • Upper limit of heritability

  • Mixed linear model in Genstat

  • No consistent effect of sex and age


Outliers
Outliers expression data

  • Outliers identified as individual expression measures + or – 3 s.d. from mean

  • 3 treatments of outliers:

    • Ignore

    • Remove

    • Shrink to 3 s.d.


Weighted analysis of means
(Weighted) analysis of means expression data

  • 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)]


Qtl analysis
QTL analysis* expression data

  • 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


Training data
“Training” data expression 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


Computational considerations
Computational considerations expression data

  • 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)


A flavour of the results
A flavour of the results expression data


A flavour of the results1
A flavour of the results expression data


A flavour of the results2
A flavour of the results expression data


Acknowledgements
Acknowledgements expression data


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