family-based analysis of genome-wide gene gene interactions… · i dr. andreas beyer i my...

Motivation Methods Results

Family-based analysis ofgenome-wide gene × gene interactions

Marit Ackermann

Biotec TU Dresden

July 9, 2009

Marit Ackermann Biotec TU Dresden

Family-based analysis of genome-wide gene × gene interactions

Motivation

MethodsFamily-based Association TestExternal Data

ResultsExampleDiscussion

Outline

Motivation

Epistasis

I Epistasis: interaction between two or more genes

I known to be fundamental for the function of regulatorypathways in mammals

I implies its importance for the development of complexdiseases such as cancer, Alzheimer‘s disease, diabetes

Traditional Approaches

I for yeast and worms large scale double knock-outs andknock-downs exist

I linkage and association studies in mammals concentrate oneither single locus associations or interactions between fewpreselected loci

I major reasons: non-availability of large and suitable data foranalysis of interaction effects, low power of the studies

Genome-Wide Screen in Mammals

I recent advances in biotechnology allow genome-widegenotyping of thousands of individuals→ can be used to study epistatic effects over wholegenome

I genotyped individuals possibly related→ take population structure into account; even make use ofknown relationships

Outline

Motivation

Family-based Association Test

Outline

Motivation

Method

I idea: two markers whosegenotypes are correlated arelikely to interact

I measure association via χ2-testfor contingency table

BB Bb bbAA nAABB nAABb nAAbbAa nAaBB nAaBb nAabbaa naaBB naaBb naabb

I make use of family information to avoidspurious findings:compare observed allele combination withwhat could have been inherited from parents

I additional correction for allelic drift

Method

I idea: two markers whosegenotypes are correlated arelikely to interact

I measure association via χ2-testfor contingency table

BB Bb bbAA nAABB nAABb nAAbbAa nAaBB nAaBb nAabbaa naaBB naaBb naabb

I make use of family information to avoidspurious findings:compare observed allele combination withwhat could have been inherited from parents

I additional correction for allelic drift

Problem

I extremely large number of interactions(example: 10,000 markers: ∼ 108 interactions)

I leads to underpowered analysis, many false positive findings

I need to complement with additional, external information

Problem

I extremely large number of interactions(example: 10,000 markers: ∼ 108 interactions)

I leads to underpowered analysis, many false positive findings

I need to complement with additional, external information

External Data

Outline

Motivation

External Data

Databases

I use public knowledge about gene × gene interactions toconfirm results;e.g. STRING: database of known and predicted physical andfunctional interactions

I include information from regulatory pathways

Outline

Motivation

Example

Outline

Motivation

Example

Data

I Solberg, L.C. et al. (2006). A protocol for high-throughputphenotyping, suitable for quantitative trait analysis in mice.Mammalian Genome, 17, 129-146.

I genotype data from more than 2000 outbred mice consistingof ∼ 12, 000 markers

I only consider interactions on two different chromosomes

Example

Modified χ2-Test

Example

Modified χ2-Test

Example

Confirmation with STRING

I fraction of SNP pairswith a low χ2 p-valuethat lie close tointeracting genes

I proportion ofconfirmed interactionsshould increase withincreasing χ2 score

Example

Incorporating Pathway Information

I interactions in one pathway can becrucial, e.g. when signal weakenedby two consecutive dysfunctionalpathway members

I interactions between pathwaysindicate common endpoint

Example

Incorporating Pathway Information

I interactions in one pathway can becrucial, e.g. when signal weakenedby two consecutive dysfunctionalpathway members

I interactions between pathwaysindicate common endpoint

Example

Example: KEGG Pathway

KEGG: database of signaling andmetabolic pathways

histogram of KEGG interaction p−values

−log10 p−value

Den

sity

0 1 2 3 4

0.0

0.5

1.0

1.5

2.0

2.5

3.0

indicates importance ofolfactory receptors in em-bryonic development andinterplay with notch

Example

Example: KEGG Pathway

KEGG: database of signaling andmetabolic pathways

histogram of KEGG interaction p−values

−log10 p−value

Den

sity

0 1 2 3 4

0.0

0.5

1.0

1.5

2.0

2.5

3.0

indicates importance ofolfactory receptors in em-bryonic development andinterplay with notch

Discussion

Outline

Motivation

Discussion

I we propose a new approach to infer epistatic interactions inmammals

I works on a genome-wide scale

I population structure explicitly taken into account

I other counfounding factors readily included

I data integration from different sources increases power andfacilitates biological interpretation of results

Discussion

Acknowledgements

I Dr. Andreas Beyer

I my colleagues in theCellular Networks andSystems Biology group

I Klaus Tschira Foundation for funding

MotivationMethodsFamily-based Association TestExternal Data

family-based analysis of genome-wide gene gene interactions… · i dr. andreas beyer i my...

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