Download IBD Estimation in Pedigrees - Institute for Behavioral Genetics

19th International Workshop on
Methodology of Twin and Family
Studies: Introductory course
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Mike Neale (director)
Hermine Maes
Nathan Gillespie
Ben Neale
Fruhling Rijsdijk
Dorret Boomsma
Danielle Posthuma
Danielle Dick
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John Hewitt (host)
Jeff Lessem
Stacey Cherny
Nick Martin
Sarah Medland
Manuel Ferreira
Kate Morley
History of International
Methodology Workshops
TC1
TC2
TC3
TC4
Year
1987
1989
1990
1991
Location
Leuven
Leuven
Boulder
Leuven
TC5
TC6
TC7
TC8
TC9
TC10
TC11
1993
1994
1995
1996
1997
1998
1998
Boulder
Boulder
Helsinki
Boulder
Boulder
Boulder
Leuven
TC12
TC13
TC14
TC15
TC16
TC17
TC18
1999
2000
2001
2002
2003
2004
2005
Boulder
Boulder
Boulder
Boulder
Boulder
Boulder
Boulder
Type
Introductory
Introductory
Introductory
Introductory
Advanced
Introductory
Introductory
Introductory
Introductory
Introductory
Introductory
Introductory
Advanced
Advanced
Introductory
Advanced
Introductory
Advanced
Introductory
Advanced
#Faculty
10
11
11
14
12
13
16
10
10
10
12
10
13
12
12
18
18
15
16
18
# Students
24
41
28
49
55
49
43
29
49
55
57
55
62
37
63
65
95
82
93
64
Attendance at International
Methodology Workshops
Frequency
Faculty
Student
1
2 3 4 5 6
8
4 3 2 5 2
507 171 32 14 5 4
# of 'Unique' Students
Introductory Workshop # of Students
Advanced
Workshop # of Students
Total
7
3
8
1
1
9 10 16 17 19 20
2 3 1 1 3 3
41
734
730
365
1095
Causes of Human Variation
Nick Martin
Queensland Institute of Medical Research
Boulder workshop: March 6, 2006
It’s all about genetic variation ...
Stature in adolescent twins
Women
700
600
500
400
300
200
Std. Dev = 6.40
100
Mean = 169.1
N = 1785.00
0
145.0
155.0
150.0
Stature
165.0
160.0
175.0
170.0
185.0
180.0
190.0
[Galton, 1889]
The height vs.
pea debate
(early 1900s)
Biometricians
Mendelians
Do quantitative traits have the same
hereditary and evolutionary properties
as discrete characters?
Trait
Qq
qq
QQ
m-a
m+d
m+a
RA Fisher (1918).
Transactions of
the Royal Society
of Edinburgh
52: 399-433.
var(A)=2p(1-p)a2
Kenneth Mather 1911-1990
John Jinks 1929-1987
People and Ideas
Galton (1865-ish)
Mendel
Correlation
Family Resemblance
Twins
Ancestral Heredity
Fisher
Darwin (1858,1871)
(1865)
Natural Selection
Sexual Selection
Evolution
Particulate Inheritance
Genes: single in gamete
double in zygote
Segregation ratios
Spearman
(1918)
(1904)
Common Factor Analysis
Correlation & Mendel
Maximum Likelihood
ANOVA: partition of variance
Wright
(1921)
Path Analysis
Mather (1949) &
Thurstone (1930's)
Jinks (1971)
Multiple Factor Analysis
Biometrical Genetics
Model Fitting (plants)
Joreskog (1960)
Jinks & Fulker (1970)
Model Fitting applied to humans
Segregation
Linkage
Morton (1974)
Population
Genetics
Rao, Rice, Reich,
Cloninger (1970's)
Martin & Eaves (1977)
Neale (1990) Mx
Covariance
Structure Analysis
LISREL
Path Analysis &
Family Resemblance
Elston etc (19..)
Genetic Analysis of
Covariance Structure
Watson &
Crick (1953)
2000
Assortment
Cultural Inheritance
Molecular
Genetics
Polygenic Traits
1 Gene
2 Genes
3 Genes
4 Genes
 3 Genotypes
 3 Phenotypes
 9 Genotypes
 5 Phenotypes
 27 Genotypes
 7 Phenotypes
 81 Genotypes
 9 Phenotypes
3
3
2
2
1
1
0
0
7
6
5
4
3
2
1
0
20
15
10
5
0
Central Limit Theorem
The normal distribution is
to be expected
whenever variation is
produced by the
addition of a large
number of effects.
Multifactorial Threshold Model
of Disease
Single threshold
unaffected
Disease liability
affected
Multiple
thresholds
normal
mild mod
Disease liability
severe
Complex Trait Model
Linkage
Marker
Gene1
Linkage
disequilibrium
Linkage
Association
Mode of
inheritance
Gene2
Disease
Phenotype
Individual
environment
Common
environment
Gene3
Polygenic
background
3 Stages of Genetic Mapping
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Are there genes influencing this trait?
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Where are those genes?
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Genetic epidemiological studies
Linkage analysis
What are those genes?
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Association analysis
Variance components
Unique
Environment
Shared
Environment
Additive
Genetic
Effects
C
A
E
c
Dominance
Genetic
Effects
D
a
e
d
Phenotype
P = eE + aA + cC + dD
Controversy: nature vs nurture
Designs to disentangle G + E
Resemblance between relatives caused by:
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shared Genes (G = A + D)
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environment Common to family
members (C)
Differences between relatives caused by:
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nonshared Genes
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Unique environment (U or E)
http://genepi.qimr.edu.au/staff/classicpapers.html
Designs to disentangle G + E
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Family studies – G + C confounded
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MZ twins alone – G + C confounded
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MZ twins reared apart – rare, atypical,
selective placement ?
Adoptions – increasingly rare, atypical,
selective placement ?
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MZ and DZ twins reared together
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Extended twin design
MZ twins reared apart - note the same way of
supporting their cans of beer
Body postures of MZ twins reared apart
Body postures of DZ twins reared apart
Designs to disentangle G + E
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Family studies – G + C confounded
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MZ twins alone – G + C confounded
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MZ twins reared apart – rare, atypical,
selective placement ?
Adoptions – increasingly rare, atypical,
selective placement ?
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MZ and DZ twins reared together
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Extended twin design
Percentage of adoptees convicted of violent and
property offenses by biological parents’ convictions
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Denmark
14,427 nonfamilial
adoptions 1927-47
Court convictions
available for
biological and
adoptive parents
Mednick et al (1984)
Science 224:891-4
Designs to disentangle G + E
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Family studies – G + C confounded
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MZ twins alone – G + C confounded
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MZ twins reared apart – rare, atypical,
selective placement ?
Adoptions – increasingly rare, atypical,
selective placement ?
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MZ and DZ twins reared together
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Extended twin design
Placentation and zygosity
Dichorionic
Two placentas
Dichorionic
Fused placentas
Monochorionic
Diamniotic
Monochorionic
Monoamniotic
MZ 19%
DZ 58%
MZ 14%
DZ 42%
MZ 63%
DZ 0%
MZ 4%
DZ 0%
Identity at marker loci except for rare mutation
MZ and DZ twins:
determining zygosity using
ABI Profiler™ genotyping
(9 STR markers + sex)
MZ
DZ
DZ
Total mole count for MZ and DZ twins
DZ twins - 199 pairs, r = 0.60
400
400
300
300
Twin 1
Twin 1
MZ twins - 153 pairs, r = 0.94
200
200
100
100
0
0
0
100
200
300
Twin 2
400
0
100
200
300
Twin 2
400
Twin Research 6: 399-408
Genetic covariance between relatives
covG(yi,yj) = aijsA2 + dijsD2
a = additive coefficient of relationship
= 2 * coefficient of kinship (= E(p))
d = coefficient of fraternity
= Prob(2 alleles are IBD)
Examples
Relatives
a
d
Parent-offspring
MZ twins
Fullsibs
Double first cousins
½
1
½
¼
0
1
¼
1/
16
[Lynch & Walsh 1998]
ACE Model for twin data
1
MZ=1.0 / DZ=0.5
E
C
e
c
PT1
A
a
A
C
a
c
PT2
E
e
Structural equation modeling
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Both continuous and categorical variables
Systematic approach to hypothesis testing
Tests of significance
Can be extended to:
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More complex questions
Multiple variables
Other relatives
E
G
VAR 1
G
VAR 2
E
G
VAR 3
E
G
E
Sources of variation in male sexual orientation
EC
AC
Homosexuality
Orientation of
sexual
feelings
AF
EF
Attitude to
sex with a
man
Number of
same-sex
partners
AA
AP
EA
EP
Direction of causation modeling
with cross-sectional twin data
Model
Full Bivariate
Reciprocal
Distress Parenting
Parenting Distress
No causation
Final
c2
145.66
146.00
161.74
146.71
376.29
151.26
A
AIC
-69.34
.34
-70.00
16.08 -56.26
1.05
-71.29
230.63 156.29
5.60
-80.74
A
E
.45
.38
DISTRESS
.63
C
.55
.56
ANX
E
.20
.25
PARENTING
+ .18
.49
.67
DEP
C
Dc2
df
107
108
109
109
110
116
SOM
COLD
E
A
E
A
E
.36
.13
.21
.11
.40
C
.52
.16
OVERP
E
.17 .26
A
C
E
.21 .14 .49
AUTON
C
E
.11 .37
Designs to disentangle G + E
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Family studies – G + C confounded
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MZ twins alone – G + C confounded
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MZ twins reared apart – rare, atypical,
selective placement ?
Adoptions – increasingly rare, atypical,
selective placement ?

MZ and DZ twins reared together
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Extended twin design
Extended Twin Design
Truett, et al (1994) Behavior Genetics, 24: 35-49
acm
acm
cf
Gendercommon
Additive
Genes
mf
mm
Malespecific
Additive
Genes
Female
Unique
Environment
Malespecific
Additive
Genes
cm
0.5
0.5
ef
Female Twin
Environment
0.5
hfc
em
sf
tm
Male parent
Environment
wmm
dm
wff
Female
Dominant
Genes
Gendercommon
Additive
Genes
hfc
Male
Dominant
Genes
Female
Unique
Environment
Malespecific
Additive
Genes
Malespecific
Additive
Genes
Male Unique
Environment
hmm
ef
rt
tf Female Twin
Male Twin
Environment
Environment
Female twin
hmc
tm
Male twin
sf
df
em
Gendercommon
Additive
Genes
sm
Female
Sibling
Environment
rs
Female
Dominant
Genes
rd
Male Sibling
Environment
Male
Dominant
Genes
dm
Extended
kinship
model
• twins
sm Male Sibling
wfm
wmf
df
Male Twin
Environment
hmc

Female parent
0.5
0.5
0.5
tf
Female
Sibling
Environment
0.5
hmm
0.5
Gendercommon
Additive
Genes
Male Unique
Environment
• siblings
• parents
• children
• grandparents
• aunts, uncles
• cousins
Finding QTLs
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Linkage
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Association
We also run a journal
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Editor: Nick Martin
Editorial assistant +
subscriptions:
Marisa Grimmer
Publisher: Australian
Academic Press
Fully online
http://www.ists.qimr
.edu.au/journal.html
Rationale for QTL analysis
QTL = quantitative trait locus
Biology: Understanding genetic variation
by dissecting complex traits
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basic biology
applications in agriculture
applications in medicine
Women
700
600
500
400
300
200
Std. Dev = 6.40
100
Mean = 169.1
N = 1785.00
0
145.0
155.0
150.0
Stature
165.0
160.0
175.0
170.0
185.0
180.0
190.0
Linkage analysis
Thomas Hunt Morgan – discoverer of linkage
Linkage = Co-segregation
A3A4
A1A2
A1A3
A1A2
A1A4
A2A4
A3A4
A2A3
A3A2
Marker allele A1
cosegregates with
dominant disease
Linkage Markers…
x
1/4
1/4
1/4
1/4
IDENTITY BY DESCENT
Sib 1
Sib 2
4/16 = 1/4 sibs share BOTH parental alleles IBD
= 2
8/16 = 1/2 sibs share ONE parental allele IBD
= 1
4/16 = 1/4 sibs share NO parental alleles IBD
= 0
For disease traits (affected/unaffected)
Affected sib pairs selected
1000
750
500
250
Expected
1
2
3
Markers
127
310
IBD =
2IBD =
1
IBD =
0
For continuous measures
Unselected sib pairs
Correlation between sibs
1.00
0.75
0.50
0.25
0.00
IBD = 0
IBD = 1
IBD = 2
rMZ = rDZ = 1
rMZ = 1, rDZ = 0.5
E
e
E
^
rMZ = 1, rDZ = p
C
c
C
A
a
Twin 1
mole
count
A
Q
Q
q
q
a
Twin 2
mole
count
c
e