Download Results from the GAIT project: Genetic analysis of

Which of these phenotypes share the
same genetic influences?
Pleiotropy: Textbook Example
Cystic Fibrosis, simple autosomal recessive
One gene influences multiple organs:
lungs, liver, pancreas, small intestine,
reproductive tract, skin (sweat glands)
Trait 1
VA
Trait 2
VA
No Pleiotropy
Trait 1
VA
Trait 2
VA
Partial
Pleiotropy
Trait 2
VA
Trait 1
VA
Complete
Pleiotropy
Phenotypic correlations can be broken down
into genetic and environmental components
P 
2
1
h and h
h
2
2
2
1
h G  1  h
2
2
2
1
1  h E
2
2
= heritabilities for traits 1 and 2
 P = phenotypic correlation
 G = additive genetic correlation
 E = environmental correlation
Extension to multivariate analysis
  G  2 + E  I
 = Kronecker product operator
E = residual environment covariance matrix
G = additive genetic covariance matrix
Nested models for bivariate analyses
Parameters estimated
Model
e1 & e2 a1 & a2
e
g
---------------------------------------------------------------------------General additive
+
+
+
+
No pleiotropy
+
+
+
0
Complete pleiotropy
+
+
+
1
Examples from the GAIT Project.
Phenotypic correlations can be broken down
into genetic and environmental components
P 
2
1
h and h
h
2
2
2
1
h G  1  h
2
2
2
1
1  h E
2
2
= heritabilities for traits 1 and 2
 P = phenotypic correlation
 G = additive genetic correlation
 E = environmental correlation
Phenotypic correlations among vitamin K
dependent proteins.
FVII FIX FX
0.39 0.46 0.54
0.35 0.48
0.43
FII
FVII
FIX
FX
Protein C
Total Protein S
PC
0.45
0.46
0.42
0.41
tPS
0.29
0.20
0.30
0.45
0.31
fPS
0.41
0.29
0.29
0.45
0.35
0.63
Genetic and environmental correlations among
vitamin K dependent proteins.
FII
FII
FVII
FIX
FX
Protein C
Total PS
Free PS
0.55
0.41
0.82
0.59
0.30
0.19
FVII FIX FX
0.23 0.53 0.35
0.08 0.14
0.55
0.23
0.71 0.53
0.51 0.48 0.61
0.24 0.23 0.37
0.21 0.30 0.30
PC
0.31
0.42
0.33
0.15
tPS
0.46
0.22
0.35
0.65
0.33
0.33
0.25 0.55
fPS
0.86
0.57
0.51
0.86
0.67
0.80
Discrete
Continuous
(disease)
(liability)
0
1
t
Correlations with liability to thrombosis
Trait
p
APCR
FVII
FVIII
FIX
FXI
FXII
Homocysteine
t-PA
vWF
-0.23*
0.03
0.29*
0.15
0.21*
0.17*
0.23*
0.18*
0.26*
g
-0.65*
-0.35
0.69*
0.60*
0.56*
0.35*
0.65*
0.75*
0.73*
e
0.67*
0.57*
-0.13
-0.20
0.07
-0.15
-0.28
-0.10
-0.18
Genetic correlations
Liability to
Thrombosis
-0.65 ± 0.14
p = 0.000001
APCR
-0.54 ±0.12
p = 0.009
0.69 ± 0.15
p = 0.0005
Factor VIII levels
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.
Genetic correlations
0.65 ± 0.20
p = 0.002
Homocysteine
-0.20 ± 0.19
-0.16 ± 0.23
Liability to
Thrombosis
APCR
Factor VIII
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.
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
Extension to multivariate analysis
ˆ  G  2 + E  I
Q P
 = Kronecker product operator
Q = additive genetic covariance matrix for QTL
G = residual additive genetic covariance matrix
E = environmental covariance matrix
Nested models for bivariate analyses
Parameters estimated
Model
e1 & e2 a1 & a2 q1 & q2
e
g
q
--------------------------------------------------------------------------------Sporadic
+
0
0
+
Additive
+
+
0
+
+
Linkage
+
+
+
+
+
+
Pleiotropy
+
+
+
+
+
1
Coincident
+
+
+
+
+
0
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
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
Correlations with liability to thrombosis
Trait
p
APCR
FVII
FVIII
FIX
FXI
FXII
Homocysteine
t-PA
vWF
-0.23*
0.03
0.29*
0.15
0.21*
0.17*
0.23*
0.18*
0.26*
g
-0.65*
-0.35
0.69*
0.60*
0.56*
0.35*
0.65*
0.75*
0.73*
e
0.67*
0.57*
-0.13
-0.20
0.07
-0.15
-0.28
-0.10
-0.18
Results of FXII genome screen (LODs > 1)
Location
LOD
Ch 5, 193 cM
Ch 10, 38 cM
Ch 2, 9 cM
Ch 11, 10 cM
Ch 14, 63 cM
Ch 15, 79 cM
4.73
3.53
2.26
1.30
1.16
1.03
FXII – Chromosome 10 QTL
FXII – Chromosome 5 QTL
FXII levels are genetically correlated
with risk of thrombosis. Do either of the
QTLs detected through FXII levels
influence liability to thrombosis?
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
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.
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.