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![Page 1: The future of twin studies in the post-genomic era? Jaakko Kaprio Odense Twin Methods Course 27 May 2013.](https://reader036.fdocuments.in/reader036/viewer/2022062714/56649d125503460f949e5d26/html5/thumbnails/1.jpg)
The future of twin studies in the post-genomic era?
Jaakko Kaprio
Odense
Twin Methods
Course
27 May 2013
![Page 2: The future of twin studies in the post-genomic era? Jaakko Kaprio Odense Twin Methods Course 27 May 2013.](https://reader036.fdocuments.in/reader036/viewer/2022062714/56649d125503460f949e5d26/html5/thumbnails/2.jpg)
![Page 3: The future of twin studies in the post-genomic era? Jaakko Kaprio Odense Twin Methods Course 27 May 2013.](https://reader036.fdocuments.in/reader036/viewer/2022062714/56649d125503460f949e5d26/html5/thumbnails/3.jpg)
Outline
Causality and observational epidemiological studies Value of heritability estimates Accounting for missing heritability? Early consequences of weight gain and mechanisms in
obesity
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Finnish Twin Cohort study
established 37 years ago a unique longitudinal study resource with a richness of
phenotypic data collected in repeated surveys, interviews, and by register linkage
Cancer registry, hospitalizations, medications, disability pensions and causes of death
The older cohort of twins (c. 16500 pairs known zygosity) born before 1958 and studied since 1975 has contributed to longitudinal genetic epidemiological studies.
Cancer data contributes to NorTwinCan, a collaboration of all Nordic Twin Cohorts and Cancer Registries
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Suicide among smokers – evaluation of epidemiological associations observed in unrelated individuals
Smoking status available for 26020 subjects with follow-
up data 226 suicides Causal relationship or confounded by psychiatric illness
and/or family factors?
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Men
0.00
0.01
0.02
0.03
0.04
0.05
Cu
mul
ativ
e h
azar
d
20 40 60 80 Age (years)
smokstat = neversmokstat = Formersmokstat = Current
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The discordant pair design
Suicide caseNon-smoker Smoker
Cotwin (alive or death from other causes)
Non-smoker A B
Smoker C D
Pairs from cells A and D are non-informative
The ratio B/C is an estimate of the risk associated with smoking controlling for family/genes. Statistical significance tested by McNemar’s test, and conditional logistic regression
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Suicides and smoking in twin pairs
In the discordant pairs, there were 23 pairs in which the
suicide case was a current smoker in 1975 and the cotwin
(non-suicide) was a never smoker, vs. 2 pairs in which the
suicide case was a never smoker and the cotwin was a
current smoker. The OR is 11.5, 95% CI 2.84 – 100). Chi-
sq(1)=17.4, p<0.0001 The numbers for MZ pairs were 5 vs. 0, exact McNemar
p=0.06 The association between current smoking and suicide is
independent of family factors, probably causal.
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Outline
Causality and observational epidemiological studies Value of heritability estimates Accounting for missing heritability? Early consequences of weight gain and mechanisms in
obesity
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Photo: Elina Ketola, Helsinki
Linkage to hospital discharge registry and National Insurance Institute medication registry to identify diabetes cases to end of 2004
16430 twin pairs baseline cohort (MZ, DZ, XZ)
Total of 2336 diabetes cases, of which 2077 type 2 diabetes(rest are T1DM, gestational DM and secondary cases)
Lehtovirta et al, Diabetologia 2010
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(Lehtovirta et al, Diabetologia 2010)
Risk for T2D in the twins with an affected cotwin
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Heritability >70% using a frailty model, Strong phenotypic association with BMI, but less than
20% was due to shared genetic effects
Development of novel methods to correctly take into
account censorship in survival analyses (Hjelmborg et al) Competing causes of death/co-morbidity
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A Genome Wide Association Study Identifies 31 Genetic Loci Associated with Human Serum Metabolites
• Aim was to estimate the heritabilities and catalogue genetic variants modifying circulating levels of 216 metabolic traits identified using thorough 1H NMR metabonomic screening techniques
• We test for associations between 7.7 million genetic markers derived from 1000 genomes European imputation reference and metabonomic measurements in 8322 randomly ascertained Finnish individuals
• Gwas reveals thirty-three independent genomic markers associated with one or more metabolites
• Ft12 and FT16 data used to estimate heritability and as a independent replication data set
• Together, these loci explain up to 40% of the genetic variance in an individual metabolic measure.
• Fifteen of the loci are novel
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Heatmap of associations with the novel loci across all metabolites. LIPO, LIPID and LMWM refer to the spectral windows utilized in the quantification.
Significant associations (p<2.31*10-10) are presented with black outline in the figure.
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The heritabilities (blue) and proportion of variance explained by significant SNPs in metabonomic traits
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Summary of main findings
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Outline
Causality and observational epidemiological studies Value of heritability estimates Accounting for missing heritability? Early consequences of weight gain and mechanisms in
obesity
![Page 20: The future of twin studies in the post-genomic era? Jaakko Kaprio Odense Twin Methods Course 27 May 2013.](https://reader036.fdocuments.in/reader036/viewer/2022062714/56649d125503460f949e5d26/html5/thumbnails/20.jpg)
Heritability of BMI is considerable
Twin Research 2003; 6(5): 409-421
Based on comparison of MZ to DZ twin similarity, using standard twinmodelling approaches to estimate heritability
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Heritability estimates for BMI in men and women aged 20-29
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
Men Women
Australia Denmark Italy Finland Netherlands Sweden UK
Notable lack of shared environmental effects – i.e. non-genetic factors common to family members
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Coming together of many events
Slide based on Mark McCarthy, 2009
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Mark McCarthy, modified from Nat Reviews Genetics 2008
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Peter M. Visscher, Matthew A. Brown, Mark I. McCarthy, and Jian Yang. Five Years of GWAS Discovery. Am J Hum Genet 2012 (online)
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Published Genome-Wide Associations through 03/2011, 1,319 published GWA at p≤5x10-8 for 221 traits
NHGRI GWA Catalogwww.genome.gov/GWAStudies
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Three large lung cancer GWAs in 2008 identified chr 15q25 region with nicotinic receptors alpha5, alpha3, beta 4 as linked to smoking/nicotinic dependence
Pooled GWAs increase sample size and power to detect contributing genes (CGASP, TAG, OX-GSK, ENGAGE)
Three coordinated papers published in May 2010 issue of Nature Genetics on 143 000 subjects
Over past few years hundreds of new genes
in tens of common diseases and traits have
been identified http://www.genome.gov/gwastudies
Genome-wide Case-Control Analyses
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Figure 2
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The CHRNA5 variant is functional
The CHRNA5 SNP Accounts for 1% of the
variance in cigarettes per day but 4.3% of the variance in serum cotinine levels (Keskitalo et al, 2009, Munafo et al, 2012)
Results in an amino acid change in α5 , D398N
The high risk α5 Asn 398 is associated with reduced permeability of Ca2+ et desensitises faster than the wildtype variant Asp 398(α4β2)2 of α5 (Kuryatov 2011)
Genotype of rs1051730
GG GT TTCigarettes per day 10.1 11.2 12.2
N 5956 6287 1702
Kuryatov, A., Berrettini, W. & Lindstrom, J. Acetylcholine receptor (AChR) a5 subunit variant associated with risk for nicotine dependence and lung cancer reduces (a4b2)2a5 AChR function. Mol. Pharmacol. 79, 119–125 (2011).
Fowler et al, Nature 2011
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GIANT study
Gwas meta-analysis associations between body mass
index (BMI) and ~2.8 million SNPs in 123,865
individuals Targeted follow-up of 42 SNPs in 125,931 additional
individuals. Confirmed 14 known obesity-
susceptibility loci and identified 18 new loci
associated with BMI (P<5x10-8)
Speliotes EK et al. Association analyses of 249,796
individuals reveal eighteen new loci associated with
body mass index. Nature Genetics, 2010 Accounts less than 5% of the variance
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Population impact is modest
Very few persons have very many risk allelesObesity is a multifactorial disorder influenced by multiple genesEach gene has a small impact on disease riskUniversally acting genes appear to play a minor role
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Lusis et al, Nat Rev Genetics 2008; 9: 819-30
Manolio TA et al, Nature, 461: 747-733 (October) 2009
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Peter M. Visscher, Matthew A. Brown, Mark I. McCarthy, and Jian Yang. Five Years of GWAS Discovery. Am J Hum Genet 2012 (online)
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EXPLANATIONS FOR
MISSING HERITABILITY OF
BMI?
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The effect of genes depends on age
01020
3040
5060
7080
90
11 y(Ft12)
14 y(FT12)
16 y(FT16)
17 y(FT12)
17 y(FT16)
25 y(FT16)
Genetic Common environment
Pietiläinen et al. 1999, Mustelin et al. Int J Obes 2009, Lajunen et al. Int J Obes 2009> 4000 pairs from Finnish FT12 and FT16 cohorts
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Gene-environmentinteractions
Conceptual model of individual’s phenotype:Y = μ + G + C + E, and where Environment = C+E
I.e. we assume independent effects
Hence, variance can be decomposed:σ2 = σ2G + σ2C + σ2E
Is the assumption of no gene-environment interaction realistic?
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G-E interaction model (Purcell S, Twin Research 2002)
T is phenotype in classic twin model, A additive genetic effect, C common
environmental and E uniquE environmental; M is moderator variable
M
A
T
C E
c+βYM e+βZMa+βXM
μ+βMM
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Physical activity modulates genetic effects on body composition – waist circumference
Mustelin L et al, Int J Obes 2009 (based on Finntwin16 adults)Now replicated in three other twin data sets (Vietnam Era vets , FinnTwin12, Geminakar – both papers in Am J Clin Nutr 2009)
020
4060
8010
0H
erita
bilit
y %
Low Medium HighLeisure time physical activity
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Heritability of different obesity indicators by physical activity in the pooled data of
Danish and Finnish men and women0
2040
6080
100
Her
itabi
lity
Low Medium HighPhysical activity
BMI
Waist circumference
Proportion of fat body mass
Silventoinen et al, Am J Clin Nutr, 2009
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Multiple genes and g by e interaction
Significant interactions of physical activity
and genetic BMI risk score on BMI (cross-
sectionally) and weight change
(prospectively)
SNPs representing the obesity susceptibility loci near or in
NEGR1, SEC16B, TMEM18, ETV5, GNPDA2, BDNF,
MTCH2, FAIM2, SH2B1, FTO, MC4R, and KCTD15 genes
summed to form a genetic risk score
Work and leisure physical activity index
Covariates and comorbidies did not affect interactions
Li S et al. Physical activity attenuates the genetic
predisposition to obesity in 20,000 men and women of
European descent. PLoS Med, 2010
Willer et al, Nat Genet 2009:41:25-34
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Cross-sectional analysis
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Parental Monitoring modifies the heritability of Smoking Quantityin Finnnish14 year olds
00,10,20,30,40,50,60,70,80,9
1
4 5 6 7 8 9 10 11 12
a2c2e2
Parental MonitoringLow High
Sta
ndar
dize
d V
aria
nce
Dick et al, J Abn Psychol, 2007
Additive genetics
Shared enviromentSpecific Enviroment
4 5 6 7 8 9 10 11 12
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
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Odds ratio for heavy versus light smoking and rs16969968 allele A across studies where subjects are stratified by age of onset of regular smoking (AOS) at or before age 16 versus after age 16..
Hartz S et al. Increased genetic vulnerability to smoking at CHRNA5 in early-onset smokers (Arch Gen Psychiatry, 2012)
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Meta-analysis of association between rs16969968 genotype and heavy (CPD > 20) vs. light (CPD ≤ 10) smoking, stratified by early-onset (onset ≤ 16) and late onset (onset > 16) smoking. Odds ratios are given relative to late-onset smokers with GG genotype (Hartz et al, Arch Gen Psychiatry in press)
Interaction between rs16969968 A allele and early-onset smoking on risk of heavy smoking, OR= 1.16, n=36,936, P=0.01
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Interaction of parental monitoring and chr 15 genetic variant on nicotine dependence
Chen et al, Addiction 2009
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Outline
Causality and observational epidemiological studies
Value of heritability estimates Accounting for missing heritability? Early consequences of weight gain
and mechanisms in obesity
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Weight discordant twin pairs as a
model to study the metabolic
disturbances due to obesity
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kg/m2
kg/m2
Kaprio J, FinnTwin16
15 20 25 30 35 40 45
15
20
25
30
35
40
45
Twin A, BMI
Tw
in B
, B
MI
kg/m2
kg/m2
15 20 25 30 35 40 45
15
20
25
30
35
40
45
Twin A, BMIT
win
B,
BM
I
DZ twinsMZ twins
FinnTwin16, 90% of all twins born in Finland 1975-79 n=2453 pairs at 25 y
r=0.79p<0.001
r=0.37p=0.03
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MZ twins discordant for obesity
FinnTwin16Birth cohorts 1975-1979
N=2500 twin pairs at 25 yN=658 MZ-pairs
14 obesity-discordant MZ-pairs, > 10 kg diff
BMI
0 5 10 15 20 y
NON-OBESEBMI 25
OBESEBMI 30
Obese co-twins15 kg, 20% heavier 70% more sc fat100% more ia fat
280% more liver fat
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Measures for examination of metabolic features,
behavioural characteristics, and physical fitness
Fasting blood samples for DNA, routine hematology, chemistry and lipids, cytokines, neuropeptides, lipidomics etc
body composition and anthropometrics by DXA, bioelectrical impedance, skinfolds, and circumferences
body fat accumulation (subcutaneous and visceral fat content by MRI, intrahepatic and intramyocellular fat content by proton spectroscopy)
adipocyte gene expression from subcutaneous fat biopsies, candidate genes and genome-wide microarray analyses
in MZ discordant twins, mitochondrial DNA sequencing & telomere length assays, epigenetics
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Detailed phenotypes intra-arterial endothelic function whole body insulin sensitivity under normoglycemic
hyperinsulinemic conditions (the clamp technique) Faeces samples and microbiome analyses test meal with ghrelin and leptin assays PROP- and fat-tasting procedures resting energy expenditure (indirect calorimetry) and a 14 day-
total energy expenditure in free living conditions (the doubly labeled water technique)
Accelerometers & physical fitness by bicycle spiroergometer questionnaires and interviews on past and current food intake,
food preferences, physical activity, use of alcohol and smoking, health-related attitudes, weight history, family history and quality of life
structural and functional brain MRI, SPET autonomic nervous system assays structured psychiatric interview questionnaires and interviews of the parents
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A physical activity discordant MZ male pair
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Non-obese
0
25
50
75
100%
0
25
50
75
100%
30% lower insulin sensitivity
30% lower endothelial function
Obese
Metabolic changes in obesity
Pietiläinen et al. Am J Phys Endo Met 2005Pietiläinen et al. Obesity 2006
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0
100
200
300%
2-3 –fold upregulation of inflammatory pathways
Non-obese Obese
Adipose tissue in obesity
Pietiläinen et al. PLoS Med 2008
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Serum lipidomics in obesity
Obese co-twins
Non-obese co-twins
Pietiläinen et al. PLoS One 2007
LV 2
LV 1
ProinflammatoryLysoPCs
AntioxidantPlasmalogens
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Fatty acid composition of adipose tissue in acquired obesity
Fatty acid profile was measured in adipose tissue biopsies in each of the 44 subjects. Selected fatty acid relative amounts in 13 twin pairs discordant for BMI. Lines connect the co-
twins.
Pietiläinen et al. Association of Lipidome Remodeling in the Adipocyte Membrane with Acquired Obesity in Humans. Plos Biol 2011
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Schematic representation of fatty acid compositional changes when comparing heavy and lean obesity-discordant co-twins. Significant
changes (p < 0.05; pairwise t-test) are marked with colors. The activities of specific fatty acid elongation or desaturation steps are estimated by
appropriate fatty acid concentration ratios.
Plos Biol 2011
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Regulation of lipid remodeling in adipose tissue. A dependency network was constructed from the selected gene expression, clinical, and lipidomic data from twin pairs discordant for BMI. Node shapes represent different types of variables and platforms (L, UPLC-MS lipidomics; FA, GC fatty acids; GE, gene expression), node color corresponds to significance and direction of regulation , and line width is proportional to strength of dependency.
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Model of physiological regulation of lipid membrane composition in obesity
PL, phospholipid; PUFA, polyunsaturated fatty acid; SFA, saturated fatty acid; MUFA, monounsaturated fatty acid.
Pietiläinen et al, Plos Biol 2011
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Outline
Causality and observational epidemiological studies Value of heritability estimates Accounting for missing heritability? Early consequences of weight gain and mechanisms in
obesity
Same designs can be used to address questions about
the epigenome The microbiome Rare variants Need to master many methods and approaches/need to
collaborations
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A tour of possibilities made possible by twin studies
A jack of all trades,
master of none, Though oftentimes better master of one
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Kiitos!
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Masculinization of female twins with a male co-twin?
Due to prenatal exposure to testosterone from opposite-sex twin (organizational effects of testosterone). Phenotype with known sex difference and correlation with (prenatal) testosterone
Due to postnatal socialization as a result of growing up with same age brother (exposure to male typical toys and activities). Phenotype with known sex difference and effect of practice.
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Vandenberg and Kuse MRT (based on the stimuli by Shepard and Metzler, 1971)
Sex difference in Mental Rotation Test (MRT)
• Males score on average even 1 SD higher than females. Range of effect size (depending on task) d = .75 – 1.00 (Voyer et al., 1995)
• Internet study: data from 53 countries (Lippa et al., 2010)
MRT: males scored higher than females in all countries
d = .47 (150 second time limit, six trials of Vandenberg and Kuse MRT)
• Females with CAH perform better than non-CAH females in male favoring spatial tests, males with CAH perform worse than non-CAH males (possible organizational effects of testosterone on spatial abilities, meta-analysis: Puts et al., 2008)
• Sex difference in MRT evident in 5 month olds
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Fig. 1. Mean mental rotation test scores (with 95% confidence intervals) for females and males from same-sex and opposite-sex twin pairs.
Vuoksimaa E et al. Psychological Science 2010;21:1069-1071
Copyright © by Association for Psychological Science
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Mental rotation performance (MRT score) as a function of “relation” (fraternal twin vs. regular sibling) and “sibling's sex” (same vs. opposite). Error bars indicate standard errors.
Masculinization of mental rotation
ability (V-K MRT) replicated in Heil et
al. (2011) Biol Psychol.
Females from opposite-
sex pairs (n=100, black
fraternal twin bar)
performed better than
females from same-sex
dizygotic pairs (n=100,
white fraternal twin bar),
non-twin females with
slightly older sister (n=100,
white sibling bar) or non-
twin females with slightly
older brother (n=100,
black sibling bar)
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Mean testosterone levels (with 95% confidence intervals) of two saliva samples for same-sex female (SSF), opposite-sex female (OSF), same-sex male (SSM), and opposite-sex male (OSM) twins from FinnTwin12. Vuoksimaa et al. (2010) Psychoneuroendocrinology, 35, 1462-1472.
No difference in fluctuating levels of testosterone and estradiol at age 14
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