1. Which of these phenotypes share the same genetic influences?

2. Pleiotropy: Textbook Example Cystic Fibrosis, simple autosomal recessive One gene influences multiple organs: lungs, liver, pancreas, small intestine, reproductive tract, skin (sweat glands)

3. Trait 1 V A Trait 2 V A No Pleiotropy

4. Trait 1 V A Trait 2 V A Partial Pleiotropy

5. Trait 1 V A Trait 2 V A Complete Pleiotropy

6. Phenotypic correlations can be broken down into genetic and environmental components  P  h 1 2 h 2 2  G  1  h 1 2 1  h 2 2  E h 1 2 and h 2 2 = heritabilities for traits 1 and 2  P = phenotypic correlation  G = additive genetic correlation  E = environmental correlation

7. Extension to multivariate analysis   G  2  +E  I  =Kronecker product operator E=residual environment covariance matrix G=additive genetic covariance matrix

8. Nested models for bivariate analyses Parameters estimated Model  e1 &  e2  a1 &  a2  e  g ---------------------------------------------------------------------------- General additive++++ No pleiotropy+++0 Complete pleiotropy+++1

9. Examples from the GAIT Project.

10. Phenotypic correlations can be broken down into genetic and environmental components  P  h 1 2 h 2 2  G  1  h 1 2 1  h 2 2  E h 1 2 and h 2 2 = heritabilities for traits 1 and 2  P = phenotypic correlation  G = additive genetic correlation  E = environmental correlation

11. Phenotypic correlations among vitamin K dependent proteins. FVIIFIXFXPCtPSfPS FII0.390.460.540.450.290.41 FVII0.350.480.460.200.29 FIX0.430.420.300.29 FX0.410.450.45 Protein C0.310.35 Total Protein S0.63

12. Genetic and environmental correlations among vitamin K dependent proteins. FIIFVIIFIXFXPCtPSfPS FII0.230.530.350.310.460.86 FVII0.550.080.140.420.220.57 FIX0.410.550.230.330.350.51 FX0.820.710.530.150.650.86 Protein C0.590.510.480.610.330.67 Total PS0.300.240.230.370.330.80 Free PS0.190.210.300.300.250.55

13. t Discrete (disease) Continuous (liability) 01

14. Trait  p  g  e APCR-0.23*-0.65*0.67* FVII0.03-0.350.57* FVIII0.29*0.69*-0.13 FIX0.150.60*-0.20 FXI0.21*0.56*0.07 FXII0.17*0.35*-0.15 Homocysteine0.23*0.65*-0.28 t-PA0.18*0.75*-0.10 vWF0.26*0.73*-0.18 Correlations with liability to thrombosis

15. Genetic correlations Liability toAPCR Thrombosis Factor VIII levels -0.54 ±0.12 p = 0.009 -0.65 ± 0.14 p = 0.000001 0.69 ± 0.15 p = 0.0005

16. Inference A single gene or set of genes influences variation in risk for thrombosis, factor VIII levels, von Willebrand factor levels, and activated protein C resistance. However, each of these traits is also affected by additional genes not shared with the others.

17. Genetic correlations Liability to Thrombosis HomocysteineAPCR Factor VIII 0.65 ± 0.20 p = 0.002 -0.20 ± 0.19 -0.16 ± 0.23

18. Inference Evidence exits for a single gene or set of genes which influences both homocysteine levels and risk for thrombosis. This is a different gene or set of genes than the one with common influences on factor VIII, vWF, APCR, and thrombosis.

19. Another way of looking at  G FXII - liability  G = 0.35, h2 = 0.68 for FXII, 0.61 for liability 12.25% of genetic variance shared in common (i.e.  G squared) FXII: 8.3% of variance genes shared w/ liability, 59.7% of variance genes not shared w/liability Liability: 7.5% of variance genes shared w/FXII, 53.5% of variance genes not shared w/FXII

20. Extension to multivariate analysis  Q  ˆ  G  2  +E  I  =Kronecker product operator Q=additive genetic covariance matrix for QTL G=residual additive genetic covariance matrix E=environmental covariance matrix

21. Nested models for bivariate analyses Parameters estimated Model  e1 &  e2  a1 &  a2  q1 &  q2  e  g  q --------------------------------------------------------------------------------- Sporadic+00+ Additive++0++ Linkage++++++ Pleiotropy+++++1 Coincident+++++0

22. Pleiotropy: a single gene influencing two or more phenotypes,  q = 1 or -1 Coincident Linkage: two closely placed genes, each influencing different phenotypes,  q = 0

23. Why do multivariate linkage analysis? Exploits pleiotropy to improve power to detect linkage Allows a formal test of pleiotropy versus coincident linkage Improves estimate of QTL location and effect size

24. Trait  p  g  e APCR-0.23*-0.65*0.67* FVII0.03-0.350.57* FVIII0.29*0.69*-0.13 FIX0.150.60*-0.20 FXI0.21*0.56*0.07 FXII0.17*0.35*-0.15 Homocysteine0.23*0.65*-0.28 t-PA0.18*0.75*-0.10 vWF0.26*0.73*-0.18 Correlations with liability to thrombosis

25. Results of FXII genome screen (LODs > 1) LocationLOD Ch 5, 193 cM4.73 Ch 10, 38 cM3.53 Ch 2, 9 cM2.26 Ch 11, 10 cM1.30 Ch 14, 63 cM1.16 Ch 15, 79 cM1.03

26. FXII – Chromosome 10 QTL

27. FXII – Chromosome 5 QTL

28. FXII levels are genetically correlated with risk of thrombosis. Do either of the QTLs detected through FXII levels influence liability to thrombosis?

29. Does the QTL identified through trait1 also influence trait2? Parameters estimated Model  e1 &  e2  a1 &  a2  q1 &  q2  e  g  q -------------------------------------------------------------------------------------- No pleiotropy+++/0++ Pleiotropy+++/+++1

30. Bivariate analysis with liability to thrombosis Chromosome 10 – no improvement in likelihood Chromosome 5 – h2q for liability > 0, p = 0.004 Inference: The FXII QTL on chromosome 5 also influences susceptibility to thrombosis.

31. Summary Two QTLs on chromosomes 5 and 10 influence FXII levels. The QTL on chromosome 5 also influences liability to thrombosis and is likely to be the FXII structural gene. FXII 46C/T appears to functionally influence FXII levels, but our results suggest additional functional variants exist in or near FXII.