Genetic variation in pleiotropy: differential epistasis in the allometric relationship between long bone lengths and bod

1. Genetic variation in pleiotropy: differential epistasis in the allometric relationship between long bone lengths and body weight Presented by Wen-Hua Wei QGJC Autumn Session 2008 Why chose this paper: (how many familiar with evolution theories) Working on methods of mapping epistatic QTL for years but still puzzled about how to interpret epistatic effects Epistasis has been a hot topic in evolutionary studies so bound to have different views and methods Attractive keywords in the title: pleiotropy, differential epistasis - A good opportunity to learn something from it I have to say that this is a very hard paper to me because of the writing style and presenting. I certainly did not enjoy reading it. However It is guaranteed that you are going to hear plenty new things (jargons in particular) today and I hope to entertain you with the stories in evolution.Why chose this paper: (how many familiar with evolution theories) Working on methods of mapping epistatic QTL for years but still puzzled about how to interpret epistatic effects Epistasis has been a hot topic in evolutionary studies so bound to have different views and methods Attractive keywords in the title: pleiotropy, differential epistasis - A good opportunity to learn something from it I have to say that this is a very hard paper to me because of the writing style and presenting. I certainly did not enjoy reading it. However It is guaranteed that you are going to hear plenty new things (jargons in particular) today and I hope to entertain you with the stories in evolution.

2. Other key papers Cheverud et. al. 2004 J. Exp. Zool. 302B:424-435 [differential epistasis, relationship QTL] Wolf et.al. 2005 Genetics 171:683-694 [epistatic pleiotropy] Gibson & Wagner 2000 BioEssays 22:372-380 [canalization] Phillips 2008 Nat Rev Gen 9:855 [epistasis in structure and evolution]

3. Morphological integration Related (functional and developmental) traits evolve as an integrated unit Higher genetic correlations hence evolve “co-ordinately” as modules e.g. floral morphology; butterfly wings Modular pleiotropy is the key Skip those don’t understand.Skip those don’t understand.

4. Modular vs. Ubiquitous pleiotropy Modular: traits are affected by different sets of loci resulting in genetic correlation among phenotypes belonging to a trait set but no correlation between the sets Ubiquitous: all loci affect all traits but some have opposite effects on traits belonging to different sets and resulting in genetic correlation within trait sets and no correlation betweenModular: traits are affected by different sets of loci resulting in genetic correlation among phenotypes belonging to a trait set but no correlation between the sets Ubiquitous: all loci affect all traits but some have opposite effects on traits belonging to different sets and resulting in genetic correlation within trait sets and no correlation between

5. Epistasis Modules have been formed – so to evolve need to have variation – epistasis contribution Dominant epistasis 12:3:1 ? discrepancy between the prediction of segregation ratios based on individual genes Epistasis in three distinct defs: functional relationship between genes; genetic ordering of regulatory pathways; statistical differences of allele-specific effects.Modules have been formed – so to evolve need to have variation – epistasis contribution Dominant epistasis 12:3:1 ? discrepancy between the prediction of segregation ratios based on individual genes Epistasis in three distinct defs: functional relationship between genes; genetic ordering of regulatory pathways; statistical differences of allele-specific effects.

6. Differential epistasis (pleiotropy) Loci X and Y affecting both traits A and B The two loci both have additive only effect on trait A without epistasis There is epistasis in trait B where the X locus affects phenotypes only when the small y allele is present ?same pairwise interaction but with different patterns in the two traits so called differential epistasis ?since the loci involved in epistasis have pleiotropic effects also called epistatic pleiotropy In evolution context, ? from the locus level, evolution at Y locus causes variation of pleiotropy expressed at the X locus and vice versa; ? from the trait level, the relationship between traits A and B varies among genotypes at the alternate loci – pleiotropic variationLoci X and Y affecting both traits A and B The two loci both have additive only effect on trait A without epistasis There is epistasis in trait B where the X locus affects phenotypes only when the small y allele is present ?same pairwise interaction but with different patterns in the two traits so called differential epistasis ?since the loci involved in epistasis have pleiotropic effects also called epistatic pleiotropy In evolution context, ? from the locus level, evolution at Y locus causes variation of pleiotropy expressed at the X locus and vice versa; ? from the trait level, the relationship between traits A and B varies among genotypes at the alternate loci – pleiotropic variation

7. Relationship QTL & Allometry QTL based on direct effects via linkage Single trait and/or multi-trait rQTL based on relationship with (conditional on) another trait (covariate) Identify pleiotropic QTL May/may not be direct QTL Discover ‘hidden’ direct QTL Best to use allometry (part to whole relationship)

8. Regressions at rQTL genotypes So what we end up with are the regressions of trait Y on trait X based on different genotypes at an rQTL; Pool data together, we have simplified graph to interpret the relationship between the two traits at the rQTL: independent at LL; highly correlated at SS. Not sure though what role epistasis is playing in such relationships for pair-wise traits.So what we end up with are the regressions of trait Y on trait X based on different genotypes at an rQTL; Pool data together, we have simplified graph to interpret the relationship between the two traits at the rQTL: independent at LL; highly correlated at SS. Not sure though what role epistasis is playing in such relationships for pair-wise traits.

9. Population

10. Traits

11. QTL analyses - rQTL Haley-Knott regression methods Genotype scores: Additive: +1 for LL; 0 for LS; -1 for SS Dominance: 0 for LL and SS; +1 for LS

12. Pleiotropy tests Knott-Haley likelihood ratio test (2000) Model comparison (based on F ratio?) to find the best explanatory Approximate chi2 test Sum of chi2 difference to be tested Two implemented but unclear how they were used together?!

13. QTL analyses - epiQTL Haley-Knott regression as well One dimensional scan for epistasis Only identified rQTL and traits involved Partition: aa, ad, da, dd

14. Thresholds Log probability ratio (LPR) score LPR = -log10(Prob); Prob ? F ratio; Comparable to LOD Unclear models compared for F ratios!! Threshold deriving – confusing!! ‘Permutation’ (1000) with family structures Full scans per permutation Derive 5% empirical thresholds

15. Analysis of slope variation

16. rQTL results At most one locus per trait per chromosome 25 rQTL found; 11 test significant pleiotropy 2 for all traits, 4 for single trait (don’t understand about that pleitropy test – significant affecting both single and body sizeAt most one locus per trait per chromosome 25 rQTL found; 11 test significant pleiotropy 2 for all traits, 4 for single trait (don’t understand about that pleitropy test – significant affecting both single and body size

17. Cause of variation in slope In total 25 length traits: 2 by only var(BS); 14 by only cov; 9 by both For Levene’s tests: when P > 0.1 i.e. homogeneity variance of body size, so only cov is relatively important to cause variation in slope. When P < 0.05, heterogeneity exists in BS variance so become important. In total 25 length traits: 2 by only var(BS); 14 by only cov; 9 by both For Levene’s tests: when P > 0.1 i.e. homogeneity variance of body size, so only cov is relatively important to cause variation in slope. When P < 0.05, heterogeneity exists in BS variance so become important.

18. Patterns in slope variations

19. Patterns in slope variations

20. epiQTL results 40 significant rQTL epistasis across 6 traits with 33 unique epiQTL loci! 8 out of 11 rQTL involved epistasis in BS (what does that mean) ? 16 (rather than 15!) epiQTL affecting BS - affecting everybody 40 significant rQTL epistasis across 6 traits with 33 unique epiQTL loci! 8 out of 11 rQTL involved epistasis in BS (what does that mean) ? 16 (rather than 15!) epiQTL affecting BS - affecting everybody

21. rQTL and epiQTL together 9 epiQTL were detected via linkage analysis in F2 population 9 epiQTL (not fully overlap) were detected using combined F2 and F3 populations Some epiQTL locations overlapped with rQTL locations9 epiQTL were detected via linkage analysis in F2 population 9 epiQTL (not fully overlap) were detected using combined F2 and F3 populations Some epiQTL locations overlapped with rQTL locations

22. Patterns in epistatic pleiotropy

23. Patterns in epistatic pleiotropy

24. Discussion - rQTL 4 to 6 rQTL per trait – preserved within-module relationships 8/11 rQTL = QTL with direct effects 70% overlapping vs. 30% in mandible traits Inbred strains selected on body size 5 QTL out of the 8 are for body size 3/11 rQTL concealed for opposite effect

25. Discussion - epistasis Most epistasis affect one of two traits of interest Mostly epistasis significant and strong in one trait, but weaker and marginal or nonsignificant in the other Majority of epistatic effects restricted to single phenotypes

26. Discussion – evolution implication ?? But concluded Differential epistasis should be seen as a mechanism enabling evolvability of constraints imposed by pleiotropy Genetic variation in pleiotropy due to differential epistasis allows for the evolution of modularity

27. Caveats? Epistasis test and threshold deriving Unclear what pleiotropy test used Much discussion on details in supplementary! Epistasis results integration per pair – independent scan rQTL*epiQTL

28. What learnt from Relationship QTL ? allow to see more ‘hidden’ genetic variation Would allometry enough? better than pairwise trait comparison? >2 traits? Epistasis (pleiotropy) still difficult