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Motivation Methods Results Family-based analysis of genome-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

  1. Motivation Methods Results Family-based analysis of genome-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

  2. Motivation Methods Results Motivation Methods Family-based Association Test External Data Results Example Discussion Marit Ackermann Biotec TU Dresden Family-based analysis of genome-wide gene × gene interactions

  3. Motivation Methods Results Outline Motivation Methods Family-based Association Test External Data Results Example Discussion Marit Ackermann Biotec TU Dresden Family-based analysis of genome-wide gene × gene interactions

  4. Motivation Methods Results Epistasis ◮ Epistasis: interaction between two or more genes ◮ known to be fundamental for the function of regulatory pathways in mammals ◮ implies its importance for the development of complex diseases such as cancer, Alzheimer‘s disease, diabetes Marit Ackermann Biotec TU Dresden Family-based analysis of genome-wide gene × gene interactions

  5. Motivation Methods Results Traditional Approaches ◮ for yeast and worms large scale double knock-outs and knock-downs exist ◮ linkage and association studies in mammals concentrate on either single locus associations or interactions between few preselected loci ◮ major reasons: non-availability of large and suitable data for analysis of interaction effects, low power of the studies Marit Ackermann Biotec TU Dresden Family-based analysis of genome-wide gene × gene interactions

  6. Motivation Methods Results Genome-Wide Screen in Mammals ◮ recent advances in biotechnology allow genome-wide genotyping of thousands of individuals → can be used to study epistatic effects over whole genome ◮ genotyped individuals possibly related → take population structure into account; even make use of known relationships Marit Ackermann Biotec TU Dresden Family-based analysis of genome-wide gene × gene interactions

  7. Motivation Methods Results Outline Motivation Methods Family-based Association Test External Data Results Example Discussion Marit Ackermann Biotec TU Dresden Family-based analysis of genome-wide gene × gene interactions

  8. Motivation Methods Results Family-based Association Test Outline Motivation Methods Family-based Association Test External Data Results Example Discussion Marit Ackermann Biotec TU Dresden Family-based analysis of genome-wide gene × gene interactions

  9. Motivation Methods Results Family-based Association Test Method ◮ idea: two markers whose BB Bb bb genotypes are correlated are AA n AABB n AABb n AAbb likely to interact Aa n AaBB n AaBb n Aabb aa n aaBB n aaBb n aabb ◮ measure association via χ 2 -test for contingency table Marit Ackermann Biotec TU Dresden Family-based analysis of genome-wide gene × gene interactions

  10. Motivation Methods Results Family-based Association Test Method ◮ idea: two markers whose BB Bb bb genotypes are correlated are AA n AABB n AABb n AAbb likely to interact Aa n AaBB n AaBb n Aabb aa n aaBB n aaBb n aabb ◮ measure association via χ 2 -test for contingency table ◮ make use of family information to avoid spurious findings: compare observed allele combination with what could have been inherited from parents ◮ additional correction for allelic drift Marit Ackermann Biotec TU Dresden Family-based analysis of genome-wide gene × gene interactions

  11. Motivation Methods Results Family-based Association Test Problem ◮ extremely large number of interactions (example: 10,000 markers: ∼ 10 8 interactions) ◮ leads to underpowered analysis, many false positive findings Marit Ackermann Biotec TU Dresden Family-based analysis of genome-wide gene × gene interactions

  12. Motivation Methods Results Family-based Association Test Problem ◮ extremely large number of interactions (example: 10,000 markers: ∼ 10 8 interactions) ◮ leads to underpowered analysis, many false positive findings ◮ need to complement with additional, external information Marit Ackermann Biotec TU Dresden Family-based analysis of genome-wide gene × gene interactions

  13. Motivation Methods Results External Data Outline Motivation Methods Family-based Association Test External Data Results Example Discussion Marit Ackermann Biotec TU Dresden Family-based analysis of genome-wide gene × gene interactions

  14. Motivation Methods Results External Data Databases ◮ use public knowledge about gene × gene interactions to confirm results; e.g. STRING: database of known and predicted physical and functional interactions ◮ include information from regulatory pathways Marit Ackermann Biotec TU Dresden Family-based analysis of genome-wide gene × gene interactions

  15. Motivation Methods Results Outline Motivation Methods Family-based Association Test External Data Results Example Discussion Marit Ackermann Biotec TU Dresden Family-based analysis of genome-wide gene × gene interactions

  16. Motivation Methods Results Example Outline Motivation Methods Family-based Association Test External Data Results Example Discussion Marit Ackermann Biotec TU Dresden Family-based analysis of genome-wide gene × gene interactions

  17. Motivation Methods Results Example Data ◮ Solberg, L.C. et al. (2006). A protocol for high-throughput phenotyping, suitable for quantitative trait analysis in mice. Mammalian Genome , 17 , 129-146. ◮ genotype data from more than 2000 outbred mice consisting of ∼ 12 , 000 markers ◮ only consider interactions on two different chromosomes Marit Ackermann Biotec TU Dresden Family-based analysis of genome-wide gene × gene interactions

  18. Motivation Methods Results Example Modified χ 2 -Test Marit Ackermann Biotec TU Dresden Family-based analysis of genome-wide gene × gene interactions

  19. Motivation Methods Results Example Modified χ 2 -Test Marit Ackermann Biotec TU Dresden Family-based analysis of genome-wide gene × gene interactions

  20. Motivation Methods Results Example Confirmation with STRING ◮ fraction of SNP pairs with a low χ 2 p-value that lie close to interacting genes ◮ proportion of confirmed interactions should increase with increasing χ 2 score Marit Ackermann Biotec TU Dresden Family-based analysis of genome-wide gene × gene interactions

  21. Motivation Methods Results Example Confirmation with STRING ◮ fraction of SNP pairs with a low χ 2 p-value that lie close to interacting genes ◮ proportion of confirmed interactions should increase with increasing χ 2 score Marit Ackermann Biotec TU Dresden Family-based analysis of genome-wide gene × gene interactions

  22. Motivation Methods Results Example Confirmation with STRING ◮ fraction of SNP pairs with a low χ 2 p-value that lie close to interacting genes ◮ proportion of confirmed interactions should increase with increasing χ 2 score Marit Ackermann Biotec TU Dresden Family-based analysis of genome-wide gene × gene interactions

  23. Motivation Methods Results Example Incorporating Pathway Information ◮ interactions in one pathway can be crucial, e.g. when signal weakened by two consecutive dysfunctional pathway members Marit Ackermann Biotec TU Dresden Family-based analysis of genome-wide gene × gene interactions

  24. Motivation Methods Results Example Incorporating Pathway Information ◮ interactions in one pathway can be crucial, e.g. when signal weakened by two consecutive dysfunctional pathway members ◮ interactions between pathways indicate common endpoint Marit Ackermann Biotec TU Dresden Family-based analysis of genome-wide gene × gene interactions

  25. Motivation Methods Results Example Example: KEGG Pathway KEGG: database of signaling and metabolic pathways Marit Ackermann Biotec TU Dresden Family-based analysis of genome-wide gene × gene interactions

  26. Motivation Methods Results Example Example: KEGG Pathway histogram of KEGG interaction p−values 3.0 2.5 KEGG: database of signaling and 2.0 Density metabolic pathways 1.5 1.0 0.5 0.0 0 1 2 3 4 −log10 p−value indicates importance of olfactory receptors in em- bryonic development and interplay with notch Marit Ackermann Biotec TU Dresden Family-based analysis of genome-wide gene × gene interactions

  27. Motivation Methods Results Discussion Outline Motivation Methods Family-based Association Test External Data Results Example Discussion Marit Ackermann Biotec TU Dresden Family-based analysis of genome-wide gene × gene interactions

  28. Motivation Methods Results Discussion ◮ we propose a new approach to infer epistatic interactions in mammals Marit Ackermann Biotec TU Dresden Family-based analysis of genome-wide gene × gene interactions

  29. Motivation Methods Results Discussion ◮ we propose a new approach to infer epistatic interactions in mammals ◮ works on a genome-wide scale Marit Ackermann Biotec TU Dresden Family-based analysis of genome-wide gene × gene interactions

  30. Motivation Methods Results Discussion ◮ we propose a new approach to infer epistatic interactions in mammals ◮ works on a genome-wide scale ◮ population structure explicitly taken into account Marit Ackermann Biotec TU Dresden Family-based analysis of genome-wide gene × gene interactions

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