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Twin Research and Human Genetics
page 1 of 13
C
The Author(s) 2015 doi:10.1017/thg.2015.29
The CODATwins Project: The Cohort Description
of Collaborative Project of Development of
Anthropometrical Measures in Twins to Study
Macro-Env ironmental Variation in Genetic and
Environmental Effects on Anthropometric Traits
Karri Silventoinen,
1,2
Aline Jelenkovic,
1,3
Reijo Sund,
1
Chika Honda,
2
Sari Aaltonen,
1,4
Yoshie Yokoyama,
5
Adam D. Tarnoki,
6
David L. Tarnoki,
6
Feng Ning,
7
Fuling Ji,
7
Zengchang Pang,
7
Juan R. Ordo
˜
nana,
8,9
Juan F. S
´
anchez-Romera,
9,10
Lucia Colodro-Conde,
8,11
S. Alexandra Burt,
12
Kelly L. Klump,
12
Sarah E. Medland,
11
Grant W. Montgomery,
11
Christian Kandler,
13
Tom A. McAdams,
14
Thalia C. Eley,
14
Alice M. Gregory,
15
Kimberly J. Saudino,
16
Lise Dubois,
17
Michel Boivin,
18
Claire M. A. Haworth,
19
Robert Plomin,
14
Sevgi Y.
¨
Oncel,
20
Fazil Aliev,
21,22
Maria A. Stazi,
23
Corrado Fagnani,
23
Cristina D’Ippolito,
23
Jeffrey M. Craig,
24,25
Richard Saffery,
24,25
Sisira H. Siribaddana,
26,27
Matthew Hotopf,
28
Athula Sumathipala,
26,29
Timothy Spector,
30
Massimo Mangino,
30
Genevieve Lachance,
30
Margaret Gatz,
31
David A. Butler,
32
Gombojav Bayasgalan,
33
Danshiitsoodol Narandalai,
33,34
Duarte L. Freitas,
35
Jos
´
e Antonio Maia,
36
K. Paige Harden,
37
Elliot M. Tucker-Drob,
37
Kaare Christensen,
38,39
Axel Skytthe,
38
Kirsten O. Kyvik,
40,41
Changhee Hong,
42
Youngsook Chong,
42
Catherine A. Derom,
43
Robert F. Vlietinck,
43
Ruth J. F. Loos,
44
Wendy Cozen,
45,46
Amie E. Hwang,
45
Thomas M. Mack,
45,46
Mingguang He,
47,48
Xiaohu Ding,
47
Billy Chang,
47
Judy L. Silberg,
49
Lindon J. Eaves,
49
Hermine H. Maes,
50
Tessa L. Cutler,
51
John L. Hopper,
51,52
Kelly Aujard,
53
Patrik K. E. Magnusson,
54
Nancy L. Pedersen,
54
Anna K. Dahl Aslan,
54,55
Yun-Mi Song,
56
Sarah Yang,
52,57
Kayoung Lee,
58
Laura A. Baker,
31
Catherine Tuvblad,
31,59
Morten Bjerregaard-Andersen,
60,61,62
Henning Beck-Nielsen,
62
Morten Sodemann,
63
Kauko Heikkil
¨
a,
4
Qihua Tan,
64
Dongfeng Zhang,
65
Gary E. Swan,
66
Ruth Krasnow,
67
Kerry L. Jang,
68
Ariel Knafo-Noam,
69
David Mankuta,
70
Lior Abramson,
69
Paul Lichtenstein,
54
Robert F. Krueger,
71
Matt McGue,
71
Shandell Pahlen,
71
Per Tynelius,
72
Glen E. Duncan,
73
Dedra Buchwald,
73
Robin P. Corley,
74
Brooke M. Huibregtse,
74
Tracy L. Nelson,
75
Keith E. Whitfield,
76
Carol E. Franz,
77
William S. Kremen,
77,78
Michael J. Lyons,
79
Syuichi Ooki,
80
Ingunn Brandt,
81
Thomas Sevenius Nilsen,
81
Fujio Inui,
2,82
Mikio Watanabe,
2
Meike Bartels,
83
Toos C. E. M. van Beijsterveldt,
83
Jane Wardle,
84
Clare H. Llewellyn,
84
Abigail Fisher,
84
Esther Rebato,
3
Nicholas G. Martin,
11
Yoshinori Iwatani,
2
Kazuo Hayakawa,
2
Finn Rasmussen,
72
Joohon Sung,
52,57
Jennifer R. Harris,
81
Gonneke Willemsen,
83
Andreas Busjahn,
85
Jack H. Goldberg,
86
Dorret I. Boomsma,
83
Yoon-Mi Hur,
87
Thorkild I. A. Sørensen,
88,89,90
and Jaakko Kaprio
4,91,92
1
Department of Social Research, University of Helsinki, Helsinki, Finland
2
Osaka University Graduate School of Medicine, Osaka University, Osaka, Japan
3
Department of Genetics, Physical Anthropology and Animal Physiology, University of the Basque Country UPV/EHU,
Leioa, Spain
4
Department of Public Health, University of Helsinki, Helsinki, Finland
5
Department of Public Health Nursing, Osaka City University, Osaka, Japan
6
Department of Radiology and Oncotherapy, Semmelweis University, Budapest, Hungary
7
Department of Noncommunicable Diseases Prevention, Qingdao Centers for Disease Control and Prevention, Qingdao,
China
8
Department of Human Anatomy and Psychobiology, University of Murcia, Murcia, Spain
9
IMIB-Arrixaca, Murcia, Spain
10
Department of Developmental and Educational Psychology, University of Murcia, Murcia, Spain
11
QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
12
Michigan State University, East Lansing, MI, USA
13
Department of Psychology, Bielefeld University, Bielefeld, Germany
1
Karri Silventoinen et al.
14
MRC Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s
College London, London, UK
15
Department of Psychology, Goldsmiths, University of London, London, UK
16
Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
17
School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa, Ottawa, Ontario, Canada
18
´
Ecole de psychologie, Universit
´
e Laval, Qu
´
ebec, Canada
19
Department of Psychology, University of Warwick Coventry, Coventry, UK
20
Department of Statistics, Faculty of Arts and Sciences, Kırıkkale University, Kırıkkale, Turkey
21
Departments of Psychiatry, Psychology, and Human and Molecular Genetics, Virginia Institute for Psychiatric and
Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
22
Department of Actuaria and Risk Management, Karabuk University, Karabuk, Turkey
23
Istituto Superiore di Sanit
`
a –– National Center for Epidemiology, Surveillance and Health Promotion, Rome, Italy
24
Murdoch Childrens Research Institute, Royal Children’s Hospital, Melbourne, Victoria, Australia
25
Department of Paediatrics, University of Melbourne, Melbourne, Victoria, Australia
26
Institute of Research & Development, Battaramulla, Sri Lanka
27
Faculty of Medicine & Allied Sciences, Rajarata University of Sri Lanka, Saliyapura, Sri Lanka
28
NIHR Mental Health Biomedical Research Centre, South London and Maudsley NHS Foundation Trust and, Institute of
Psychiatry Psychology and Neuroscience, King’s College London, London, UK
29
Research Institute for Primary Care and Health Sciences, School for Primary Care Research (SPCR), Faculty of Health,
Keele University, Staffordshire, UK
30
Department of Twin Research and Genetic epidemiology, King’s College, London, UK
31
Department of Psychology, University of Southern California, Los Angeles, CA, USA
32
Institute of Medicine, National Academy of Sciences, Washington, DC, USA
33
Healthy Twin Association of Mongolia, Ulaanbaatar, Mongolia
34
Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
35
Department of Physical Education and Sport, University of Madeira, Funchal, Portugal
36
CIFI2D, Faculty of Sport, University of Porto, Porto, Portugal
37
Department of Psychology, University of Texas at Austin, Austin, TX, USA
38
The Danish Twin Registry, Institute of Public Health, Epidemiology, Biostatistics & Biodemography, University of
Southern Denmark, Odense, Denmark
39
Department of Clinical Biochemistry and Pharmacology and Department of Clinical Genetics, Odense University
Hospital, Odense, Denmark
40
Department of Clinical Research, University of Southern Denmark, Odense, Denmark
41
Odense Patient data Explorative Network (OPEN), Odense University Hospital, Odense, Denmark
42
Department of Psychology, Pusan National University, Busan, South Korea
43
Centre of Human Genetics, University Hospitals Leuven, Leuven, Belgium
44
The Charles Bronfman Institute for Personalized Medicine, The Mindich Child Health and Development Institute, Icahn
School of Medicine at Mount Sinai, New York, NY, USA
45
Department of Preventive Medicine, Keck School of Medicine of USC, University of Southern California, Los Angeles,
CA, USA
46
USC Norris Comprehensive Cancer Center, Los Angeles, CA, USA
47
State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
48
Centre for Eye Research Australia, University of Melbourne, Melbourne, Victoria, Australia
49
Department of Human and Molecular Genetics, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia
Commonwealth University, Richmond, VA, USA
50
Department of Human and Molecular Genetics, Psychiatry & Massey Cancer Center, Virginia Commonwealth University,
Richmond, VA, USA
51
The Australian Twin Registry, Centre for Epidemiology and Biostatistics, The University of Melbourne, Melbourne,
Victoria, Australia
52
Department of Epidemiology, School of Public Health, Seoul National University, Seoul, South Korea
53
Centre for Epidemiology and Biostatistics, The University of Melbourne, Melbourne, Victoria, Australia
54
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
55
Institute of Gerontology, School of Health Sciences, J
¨
onk
¨
oping University, J
¨
onk
¨
oping, Sweden
56
Department of Family Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South
Korea
57
Institute of Health and Environment, Seoul National University, Seoul, South Korea
58
Department of Family Medicine, Busan Paik Hospital, Inje University College of Medicine, Busan, South Korea
59
¨
Orebro University, School of Law, Psychology and Social Work,
¨
Orebro, Sweden
60
Bandim Health Project, INDEPTH Network, Apartado, Bissau Codex, Guinea-Bissau
61
Research Center for Vitamins and Vaccines, Statens Serum Institute, Copenhagen, Denmark
62
Department of Endocrinology, Odense University Hospital, Odense, Denmark
63
Department of Infectious Diseases, Odense University Hospital, Odense, Denmark
64
Epidemiology, Biostatistics and Biodemography, Institute of Public Health, University of Southern Denmark, Odense,
Denmark
65
Department of Public Health, Qingdao University Medical College, Qingdao, China
66
Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine, Stanford, CA,
USA
67
Center for Health Sciences, SRI International, Menlo Park, CA, USA
68
Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
69
The Hebrew University of Jerusalem, Jerusalem, Israel
2 TWIN RESEARCH AND HUMAN GENETICS
The CODATwins Project
70
Hadassah Hospital Obstetrics and Gynecology Department, Hebrew University Medical School, Jerusalem, Israel
71
Department of Psychology, University of Minnesota, Minneapolis, MN, USA
72
Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden
73
Center for Clinical and Epidemiological Research, University of Washington, Seattle, WA, USA
74
Institute for Behavioral Genetics, Boulder, CO, USA
75
Department of Health and Exercise Sciences and Colorado School of Public Health, Colorado State University, Fort
Collins, CO, USA
76
Psychology and Neuroscience, Duke University, Durham, NC, USA
77
Department of Psychiatry, University of California, San Diego, CA, USA
78
VA San Diego Center of Excellence for Stress and Mental Health, La Jolla, CA, USA
79
Boston University, Department of Psychology, Boston, MA, USA
80
Department of Health Science, Ishikawa Prefectural Nursing University, Kahoku, Ishikawa, Japan
81
Norwegian Institute of Public Health, Division of Epidemiology, Department of Genes and Environment, Oslo, Norway
82
Faculty of Health Science, Kio University, Nara, Japan
83
Department of Biological Psychology, VU University Amsterdam, Amsterdam, Netherlands
84
Health Behaviour Research Centre, Department of Epidemiology and Public Health, Institute of Epidemiology and
Health Care, University College London, London, UK
85
HealthTwiSt GmbH, Berlin, Germany
86
Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA
87
Department of Education, Mokpo National University, Jeonnam, South Korea
88
Novo Nordisk Foundation Centre for Basic Metabolic Research, Section on Metabolic Genetics, Faculty of Health and
Medical Sciences, University of Copenhagen, Copenhagen, Denmark
89
Institute of Preventive Medicine, Bispebjerg and Frederiksberg Hospitals, Copenhagen, The Capital Region, Denmark
90
MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
91
National Institute for Health and Welfare, Helsinki, Finland
92
Institute for Molecular Medicine FIMM, Helsinki, Finland
For over 100 years, the genetics of human anthropometric traits has attracted scientific interest. In particular,
height and body mass index (BMI, calculated as kg/m
2
) have been under intensive genetic research.
However, it is still largely unknown whether and how heritability estimates vary between human populations.
Opportunities to address this question have increased recently because of the establishment of many new
twin cohorts and the increasing accumulation of data in established twin cohorts. We started a new
research project to analyze systematically (1) the variation of heritability estimates of height, BMI and
their trajectories over the life course between birth cohorts, ethnicities and countries, and (2) to study
the effects of birth-related factors, education and smoking on these anthropometric traits and whether
these effects vary between twin cohorts. We identified 67 twin projects, including both monozygotic (MZ)
and dizygotic (DZ) twins, using various sources. We asked for individual level data on height and weight
including repeated measurements, birth related traits, background variables, education and smoking. By
the end of 2014, 48 projects participated. Together, we have 893,458 height and weight measures (52%
females) from 434,723 twin individuals, including 201,192 complete twin pairs (40% monozygotic, 40%
same-sex dizygotic and 20% opposite-sex dizygotic) representing 22 countries. This project demonstrates
that large-scale international twin studies are feasible and can promote the use of existing data for novel
research purposes.
Keywords: twins, height, BMI, heritability, international comparisons
The genetics of human anthropometric traits has long at-
tracted scientific interest. Height is a prototypical anthro-
pometric phenotype because it is approximately normally
distributed and does not change in adulthood except for
slight shrinking in old age. By the late 19th century, Galton
(1886) analyzed height of parents and offspring and inferred
that ‘when dealing with the transmission of stature from
parents to children, the average height of the two parents is
all we need care to know about them’. Later, Pearson and Lee
(1903) presented correlations of height between relatives,
also suggesting genetic influence. The first heritabilit y esti-
mate of height was calculated by Fisher (1918) in his semi-
nal paper presenting the statistical principles of quantitative
genetics. Interest in the genetic influences on height was re-
newed when genetic linkage studies enabled research into
genetic effects over the whole genome on quantitative traits
(Perola et al., 2007). Later genome-wide association (GWA)
studies allowed for the genome-wide identification of can-
didate genes. In 2010, a large scale GWA study identified
180 loci associated for height (Lango Allen et al., 2010), and
since then several large GWA studies have been published
RECEIVED 30 March 2015; ACCEPTED 20 April 2015.
ADDRESS FOR CORRESPONDENCE: Karri Silventoinen, Population
Research Unit, Department of Social Research, University of
Helsinki, P.O. Box 18, FIN-00014 University of Helsinki, Finland.
E-mail: karri.silventoinen@helsinki.fi
TWIN RESEARCH AND HUMAN GENETICS 3
Karri Silventoinen et al.
focusing on height on populations of European (Weedon
et al., 2008), Asian (Cho et al., 2009, Hao et al., 2013; Okada
et al., 2010), and African ancestry (N’Diaye et al., 2011).
The latest GWA study for height published in 2014 found
697 genetic p olymorphisms associated with height in pop-
ulations of European ancestry ( Wood et al., 2014). As a
polygenic and normally distributed trait, height serves also
to explore new methodological approaches to human genet-
ics, such as assumption-free estimation of heritability from
genome-wide identity-by-descent sharing between full sib-
lings (Hemani et al., 2013; Visscher et al., 2006).
Genetic studies of obesity and BMI (calculated as kg/m
2
)
also have a long history. In an article published in 1923, Dav-
enport showed that the tendency for obesity varies between
families, and he interpreted this finding to suggest genetic
effects on obesity (Davenport, 1923). After this initial paper,
the evi dence on the genetic effects on obesity accumulated,
and in 1966 a review paper on previous family studies con-
cluded that genetic factors played an important role in obe-
sity (Seltzer & Mayer, 1966). After this review, interest in the
genetics of BMI has rapidly increased because of the health
consequences and related impact on public health of in-
creased mean BMI over the world. The studies by Stunkard
and colleagues demonstrating the importance of genetic
factors underlying variation in BMI in studies based on twin
(Stunkard et al., 1986a) and adoption data (Stunkard et al.,
1986b; 1990) were a major achievement in this area. These
findings corroborated earlier results reported on Finnish
twins reared apart (Langinvainio e t al., 1984). In 2007, the
FTO gene was found to be associated with obesity in a case-
control study of type 2 diabetes (Frayling et al., 2007), and
it is now recognized to be the most promising candidate
gene of obesity. The latest GWA study on BMI identified 97
loci explaining 2.7% of the variation of BMI, while all mea-
surable variants accounted for around 20% of the variance
(Locke et al., 2015).
After over a hundred years of research, we might assume
that the heritability of height and BMI is a lready well known.
However, surprisingly little research is available on the vari-
ation of heritability estimates of height and BMI between
populations. Changes in mean height (Eveleth & Tanner,
2003) and BMI (Finucane et al., 2011)overtimeandchanges
in BMI across the human life span (Dahl et al., 2014)have
been reported. According to basic principles of quantitative
genetics, heritability estimates are not constant but rather
are statistics describing the magnitude of genetic variation
in a particular population and dependent on the underlying
genetic make-up of the population under study and the en-
vironmental variation at play. Accordingly, these estimates
maychangeoverthelifecourseandvarybetweenstudypop-
ulations. A meta-analysis of nine twin studies found that the
heritability of BMI increased over childhood and the effect
of common environmental factors disappeared after mid-
childhood (Silventoinen et al., 2010). Increasing heritability
of height and BMI after early childhood was also found in a
study of four twin cohorts (Dubois et a l., 2012). However,
these two studies did not reveal systematic variation in the
heritability estimates between populations. A meta-analysis
based on 88 independent heritability estimates of BMI re-
ported inter-study variation in the heritability estimates,
but meta-regression did not reveal any systematic patterns
behind these differences (Elks et al., 2012). It is possible that
this negative result was due to methodological limitations
since many of the heritability estimates were based on data
covering large age ranges, birth cohorts and social classes,
and the authors did not have access to the original data.
Twin studies for adult height (Silventoinen et al., 2003)and
BMI (Schousboe et al., 2003) in seven European popula-
tions and Australia also found some variation in heritabil-
ity estimates but were not able to find systematic patterns
in these estimates. A study based on eight populations of
adolescent twins found higher genetic variance of height
and weight in Caucasian as compared to East Asian popu-
lations; however, because total variance for height and BMI
was also higher in Caucasian populations, the heritability
estimates were approximately equivalent (Hur et al., 2008).
Thus, the previous meta-analyses have demonstrated the
variation in the genetic components of height and BMI but
have largely failed to identify factors behind the variation
between populations.
The scant evidence on the variation of genetic and envi-
ronmental contributions on height and BMI between pop-
ulations may, however, reflect methodological limitations
of previous studies rather than the lack of this type of vari-
ation. Previous studies conducted in Denmark (Rokholm
et al., 2011a) and Sweden (Rokholm et al., 2011b)have
demonstrated that genetic variation of BMI has increased
over time in birth cohorts as the mean BMI increased; how-
ever, heritability estimates did not change. A Finnish study
reported that environmental variation of height decreased
especially in women from cohorts born at the beginning of
the 20th century compared to those born after the World
War II, leading to higher heritability estimates of height (Sil-
ventoinen et al., 2000). There is also evidence that parental
social position may modify the genetic architecture of BMI
in childhood (Lajunen et al., 2012). International compar-
isons addressing the methodological limitations of previous
studies may be able to demonstrate comparable v ariation
in genetic and environmental effects between populations.
During the recent decade, possibilities for international
comparisons in twin studies have improved because of the
establishment of new twin cohorts and the increasing ac-
cumulation of data in established twin cohorts. Thus, the
number of twins available internationally for research has
greatly increased, expanding the ability to examine eth-
nic, economic, and cultural var iation between twin co-
horts. These new opportunities to answer research ques-
tions not possible to address before led to the start of a
new international research project: COllaborative project
of Development of Anthropometrical measures in Twi ns
4 TWIN RESEARCH AND HUMAN GENETICS
The CODATwins Project
(CODATwins). The aims of this project are to analyze sys-
tematically: (1) the variation of heritability estimates of
height, BMI and their trajectories over the life course be-
tween birth cohor ts, ethnicities, and countries; and (2) to
study the effects of birth-related factors, education, and
smoking on these anthropometric traits and whether these
effects vary between twin cohorts. Additionally, this project
aims to gain practical knowledge on the feasibility and op-
portunities offered by pooling a large number of twin co-
horts as suggested by the International Network of Twin
Registries (INTR) consortium (Buchwald et al., 2014).
Collection of a Collaborative Database
We started the CODATwins project in May 2013 by identi-
fying all twin projects in the world. The only criterion was
the availability of data from both MZ and DZ twin pairs.
The main sources used to identify the projects were a special
issue of Twin Research and Human Genetics (Hur & Craig,
2013) and the participants of the INTR consortium (Buch-
wald et al., 2014, van Dongen et al., 2012); these sources
werecomplemented by personal communications. Together
we identified 67 eligible twin projects. We sent e-mail in-
vitations to principle investigators of all these projects in
September 2013 along with the study protocol. We asked
the investigators to send us individual level data on height
and weight including repeated measurements, birth-related
traits (birth weight, birth length, birth order, and gestational
age), background variables (twin identifier, sex, zygosity,
ethnicity, birth year, and age at the time of measurements),
education (own education for adults and mother’s and fa-
ther’s education for children) and smoking for adults to the
CODATwins data management center at the University of
Helsinki. To those who did not respond, we sent reminders
in October 2013, January 2014 and September 2014; with
the final reminder, we sent the first year progress report
including the list of all twin projects already collaborating
with this project.
We did not receive a response from eight projects; inter-
net searches (PubMed and Google) indicated that these
projects had not been active in recent years and some
of them may not even have ever been established. Eight
projects declined: two because of lack of height and weight
data, one because of lack of information on zygosity, and
four because the delivery of the data was not possible to or-
ganize due to local regulations. One project informed that
they are currently publishing their own results, but the data
may become available later when the original articles have
been published. Three projects that initially accepted the
invitation have not sent data. Based on the correspondence,
the main reason was the lack of resources to prepare the
data file. By the end of 2014, 47 projects had sent data to the
data management center. Additionally, one cohort is avail-
able through the remote access system but is not part of the
67
contacted
8 not available
8 not
responded
51 agreed to
parcipate
47 delivered
data
3 not delivered
data
1by remote
access
FIGURE 1
Accumulation of the CODATwins database.
pooled database. Figure 1 describes the accumulation of the
CODATwins database.
Structure of Database
Table 1 presents the twin cohorts participating in the CO-
DATwins project. Because one twin project can include sev-
eral cohorts, there are 54 twin cohorts available represent-
ing 22 countries. From these cohorts, 35 are longitudinal.
Figure 2 presents the number of height and weight measures
by sex and age. Together there are 893,458 measures. Chil-
dren are well represented, and 41% of the measures were
conducted at 18 years of age or younger. Overall, about
half of the measures are for females (52%); however, the
cohorts vary considerably regarding the proportion of their
samples that are females and some cohorts include only
males while others mainly include females (Supplementary
Table 1). Most of the heig ht and weight measures were self-
reported (63%) or parentally reported (21%) and only a
minority was based on measured values (16%). The reason
is that data in the largest cohorts were collected by question-
naires, and the collection of clinical measures was generally
conducted in cohorts smaller in size. In 27 cohorts we had
additional information on birth weight and in most of these
cohorts also had data on birth length (Supplementary Ta-
ble 1). Together, we have 122,321 birth weight measures
in the database; 77% of these measures were parentally re-
ported, 17% self-reported and 6% clinically measured.
In total, data are available for 434,723 twin individuals
having at least one height and weight measure. Most of the
twins are from Europe (60%) and North-America (30%),
followed by Aust ralia (6%), East Asia (3%), South Asia, and
the Middle East (1%) and Africa (less than 0.1%); no twin
cohort is available from Latin Amer ica. Figure 3 presents the
number of complete twin pairs by birth year and zygosity.
Together there are 201,192 complete twin pairs. Among
these pairs, 40% are MZ twins, 40% same-sex DZ twins,
and 20% opposite-sex DZ twins. A quarter of the twin pairs
TWIN RESEARCH AND HUMAN GENETICS 5
Karri Silventoinen et al.
TABLE 1
Number of Height and Weight Measures in the Twin Cohorts Participating in the CODATwins Project
Number of Number of
height and longitudinal
Cohort name Main reference Region weight measures surveys
Africa
Guinea-Bissau Twin Study (Bjerregaard-Andersen et al., 2013) Guinea-Bissau 1,042 7
Australia
Australian Twin Registry (Hopper et al., 2013) Australia 2,536 1
Peri/Postnatal Epigenetic Twins Study (PETS) (Loke et al., 2013) Australia, Melbourne 571 2
Queensland Twin Register (Liu et al., 2010) Australia, Queensland
province
55,479 > 10
East-Asia
Guangzhou Twin Eye Study (Zheng et al., 2013) China, Guangzhou province 1,122 1
Japanese Twin C ohort (Ooki, 2013) Japan 34,405 >10
Korean Twin-Family Register (Gombojav et al., 2013b) South Korea 2,702 4
Mongolian Twin Registry (Gombojav et al., 2013a) Mongolia 166 1
Osaka University Aged Twin Registry (Hayakawa et al., 2013) Japan 1,289 4
South Korea Twin Registry (Hur et al., 2013) South Korea 2,278 1
Qingdao Twin Registry (adults) (Duan et al., 2013) China, Shandong province 986 1
Qingdao Twin Registry (children) (Duan et al., 2013) China, Shandong province 1,175 1
West Japan Twins and Higher Order
Multiple Births Registry
(Yokoyama, 2013) Japan 7,617 >10
Europe
Adult Netherlands Twin Registry (Willemsen et al., 2013) Netherlands 37,638 >10
Berlin Twin Register (Busjahn, 2013) German, Berlin city 722 5
Bielefeld Longitudinal Study of Adult Twins (Kandler et al., 2013) German 2,366 1
Danish Twin C ohort (Skytthe et al., 2013) Denmark 34,665 1
East Flanders Prospective Twin Survey (Derom et al., 2013) Belgium, East Flanders
Province
803 1
Finnish Older Twin Cohort (Kaprio, 2013) Finland 68,683 4
FinnTwin12 (Kaprio, 2013) Finland 16,211 4
FinnTwin16 (Kaprio, 2013) Finland 24,438 5
Gemini Study (van Jaarsveld e t al., 2010) UK 19,639 >10
Genesis 12–19 Study (McAdams et al., 2013) UK 2,131 3
Hungarian Twin Registry (Littvay et al., 2013) Hungary 825 1
Italian Twin Registry (Brescianini et al., 2013) Italy 18,834 5
Murcia Twin Registry (Ordo
˜
nana et al., 2013) Spain, Region of Murcia 4,392 3
Norwegian Twin Registry (Nilsen et al., 2013) Norway 20,188 2
Portugal Twin Cohort (Maia et al., 2013) Portugal, North of mainland,
Azores, and Madeira Islands
1,789 1
Swedish Twin C ohorts (Magnusson et al., 2013) Sweden 110,117 3
Swedish Young Male Twins Study (adults) (Rasmussen et al., 2006) Sweden 5,702 3
Swedish Young Male Twins Study (children) (Rasmussen et al., 2006) Sweden 10,440 > 10
TCHAD-study (Lichtenstein et al., 2007) Sweden 7,521 4
Twins Early Development Study (TEDS) (Haworth et al., 2013) UK 59,108 7
TwinsUK (Moayyeri et al., 2013) UK 31,321 7
Young Netherlands Twin Registry (van Beijsterveldt et al., 2013) Netherlands 119,649 10
South Asia and Middle East
Longitudinal Israeli Study of Twins (Avinun & Knafo, 2013) Israel 1,228 2
Sri Lanka Twin Registry (Sumathipala et al., 2013) Sri Lanka 2,485 1
Turkish Twin Study (
¨
Oncel & Aliev, 2013) Turkey 584 1
North America
Boston University Twin Project (Saudino & Asherson, 2013) USA, Massachusetts 1,228 2
California Twin Program (Cozen et al., 2013) USA, California 27,237 1
Carolina African American Twin Study of
Aging
(Whitfield, 2013) USA, North Carolina 532 1
Colorado Twin Registry (Rhea et al., 2013) USA, Colorado 8,671 5
Michigan State University Twin Registry (Burt & Klump, 2013) USA, Michigan 22,172 2
Mid Atlantic Twin Registry (Lilley & Silberg, 2013) USA, Virginia, North Carolina,
South Carolina
11,801 1
Minnesota Twin Family Study (Iacono & M cGue, 2002) USA, Minnesota 3,269 3
Minnesota Twin Registry (Krueger & Johnson, 2002) USA, Minnesota 10,122 1
NAS-NRC Twin Registry (Gatz et al., 2014) USA, WWII veterans 54,904 4
Quebec Newborn Twin Study (Boivin et al., 2013) Canada, Greater Montreal
area
5,991 9
SRI-international (Krasnow et al., 2013) USA, California 1,092 1
Texas Twin Project (Harden et al., 2013) USA, Texas 565 1
University of British Columbia Twin Project (Jang, 2013) Canada, Greater Vancouver
area
1,450 1
University of Southern California Twin Study (Baker et al., 2013) USA, Greater Los Angeles area 3,622 5
University of Washington Twin Registry (Strachan et al., 2013) USA, Washington State 27,452 3
Vietnam Era Twin Study of Aging (Kremen et al., 2013) USA, Vietnam era veterans 2,245 2
6 TWIN RESEARCH AND HUMAN GENETICS
The CODATwins Project
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
45,000
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Males Females
Number of measures
Age
FIGURE 2
Number of height and weight measures by sex and age.
0
1000
2000
3000
4000
5000
6000
7000
1886 1896 1906 1916 1926 1936 1946 1956 1966
1976 1986 1996 2006
MZ SSDZ
OSDZ
Number of complete
twin pairs
Year of birth
FIGURE 3
Number of complete twin pairs by birth year and zygosity.
TWIN RESEARCH AND HUMAN GENETICS 7
Karri Silventoinen et al.
(25%) were born in the 1980s and 1990s. The numbers of
twin individuals and complete twin pairs by cohorts are
presented in Supplementary Table 1.
Discussion
We have successfully launched a large international twin
collaboration, and our database now includes slightly over
200,000 complete twin pairs with height and weight mea-
sures from 22 countries. The vast majority of established
twin cohorts responded positively to our request for in-
dividual level data. For some of the cohorts who did not
participate, the reason was the lack of suitable data or that
the cohort was no longer active. The value of pooling either
summar y data in GWA studies for heig ht (Wood et al., 2014)
and BMI (Locke et al., 2015) or pooling individual data for
psychiatric conditions (Schizophrenia Working Group of
the Psychiatric Genomics Consortium, 2014) is well recog-
nized. This project demonstrates that the same strategy can
be used in classical twin research as well.
However, this project also revealed certain limitations
with respect to available twin data. While European coun-
tries, especially in the northern and western parts of Europe,
North America, and Australia are well represented, there is
much less data on twins from other parts of the world. Our
final database is heavily weighted toward European-origin
populations following the Westernized lifestyle. The excep-
tion is East Asia, with several twin cohorts available from
China, Japan, and South-Korea and one from Mongolia.
Even though many of these non-Western cohorts are not
very large, these cohorts do provide an invaluable resource
for studying the potential genetic variations in anthropo-
metric phenotypes. It was unfortunate that there are few
twin cohorts from Southern Asia, Africa and all of South
America. As pointed out earlier, there is a real need and
value to the creation of new twin cohorts in the developing
world (Sung et al., 2006). Increasing collaboration between
established twin projects can be helpful to stimulate new
research activity and starting new twin projects (Buchwald
et al., 2014).
In addition to the lack of representation of specific ethnic
groups among the registry populations included, another
limitation is that the populations represented are relatively
affluent populations. Of the four countries officially clas-
sified as non-industrialized countries represented in this
project, only Guinea-Bissau can be regarded as a real devel-
oping country. In contrast, China and Sri Lanka are mod-
erately affluent societies and enjoy life expectancy nearly
comparable to the United States, whereas Mongolia can be
regarded as a middle-income country with life expectancy
at the level of East European countries (Wang et al., 2012).
Anthropometric data from twin pairs in diverse populations
going through the demographic transition would be invalu-
able in understanding the influence of broad societal change
on many phenotypes. However, it is noteworthy that we
have substantial variation in birth cohorts; the oldest twins
were born at the end of 19th century and around one-fifth of
them before 1940. Major changes in the prevalence of obe-
sity and standard of living during the 20th century allow for
the testing of different hypotheses as demonstrated before
for BMI in Denmark (Rokholm et al., 2011a)andSweden
(Rokholm et al., 2011b) and height in Finland (Silventoinen
et al., 2003).
Wh en considering further collaborative twin research
projects, it is noteworthy that only 16% of the height and
weight measures were based on clinical measure, whereas
the majority was obtained by self- or parental report. Height
and weight are some of few anthropometric traits possible
to measure relatively reliably based on self-report. Data on
even the most basic metabolic t raits such as blood glucose,
blood pressure, and blood lipids would require clinical as-
sessments that are currently lacking in many twin samples.
This shows that even when there are many large twin co-
horts available, more data collection using clinical measures
is still needed. Height and weight are widely available in
twin cohorts, and there is also much less variation in the
measurement protocols of these traits compared to other
anthropometric traits, such as waist circumference, making
harmonization straightforward; the biggest difference we
found was the measurement units used for height (cm vs.
foot and inch) and weight (kg and g vs. pound and ounce).
However, it is noteworthy that even for height and weight
there can be differences in the precision of equipment used
for measuring weight (scale) and height (tape, anthropome-
ter or stadiometer). When examining other traits, availabil-
ity of the data and differences in measurement protocols
will increase challenges to data harmonization.
In addition to the anthropometric traits, we collected
information on own education, parental education, and
smoking. After reporting the main results for the anthro-
pometric indicators, we will move to study how they are
modified by education and smoking. Working with these
variablesismuchmorechallengingcomparedtotheanthro-
pometric traits because of different classifications, varying
educational s ystems, and large differences in mean levels of
education between countries and birth cohorts. However,
this variation also presents an opportunity because it allows
for the study of these associations in very different environ-
ments and, for example, to study the relevance of absolute
and relative e ducation. In these future analyses, we can rely
on work done to harmonize these variables in other con-
texts, such as the OECD classification of educational level
(oecd.org) and the P3G consortium (p3g.org). This effort
also demonstrates the potential of international collabora-
tions of twin pro jects beyond calculating heritability esti-
mates. For example, there are 10,410 adult MZ twin pairs
discordant for BMI (more than 3 kg/m
2
)atleastatonetime
point when measured at the same age in the database. Previ-
ous studies have demonstrated the high value of BMI discor-
dant pairs for epigenetic research (Pietil
¨
ainen et al., 2008).
8 TWIN RESEARCH AND HUMAN GENETICS
The CODATwins Project
In conclusion, the CODATwins project demonstrates
that large-scale international studies obtaining individual-
level data from twin cohorts are feasible. Using the data
from these twin cohorts creates novel opportunities for ex-
amining how genetic and environmental influences may
vary across countries and regions. Future efforts in the CO-
DATwins project will continue to extract from the substan-
tial data already collected in the various twin projects in
order to contribute to this objective.
Acknowledgments
This study was conducted within the CODATwins project
(Academy of Finland #266592). Support for participating
twin projects: the University of Southern California Twin
Study is funded by a gr ant from the National Institute
of Mental Health (R01 MH58354). The Carolina African
American Twin Study of Aging (CAATSA) was funded by
a grant from the National Institute on Aging (grant 1RO1-
AG13662-01A2) to K. E. Whitfield. The NAS-NRC Twin
Registry acknowledges financial support from the National
Institutes of Health grant number R21 AG 039572. Waves
1–3 of Genesis 12–19 were funded by the W T Grant Foun-
dation, the University of London Central Research fund and
a Medical Research Council Training Fellowship (G81/343)
and Career Development Award (G120/635) to Thalia C.
Eley. Wave 4 was supported by grants from the Economic
and Social Research Council (RES-000-22-2206) and the In-
stitute of Social Psychiatry (06/07-11) to Alice M. Gregory
who was also supported at that time by a Leverhulme Re-
search Fellowship (RF/2/RFG/2008/0145). Wave 5 was sup-
ported by funding to Alice M. Gregory from Goldsmiths,
University of London. Anthropometric measurements of
the Hungarian twins were supported by Medexpert Ltd., Bu-
dapest, Hungary. South Korea Twin Registry is supported by
National Research Foundation of Korea (NRF-371-2011-1
B00047). The Danish Twin Registry is supported by the Na-
tional Program for Research Infrastructure 2007 from the
Danish Agency for Science, Technology and Innovation,
The Research Council for Health and Disease, the Velux
Foundation and the US National Institute of Health (P01
AG08761). Since its origin, the East Flanders Prospective
Survey has been partly supported by grants from the Fund
of Scientific Research, Flanders and Twins, a non-profit
Association for Scientific Research in Multiple Births (Bel-
gium). Korean Twin-Family Register was supported by the
Global Research Network Program of the National Research
Foundation (NRF 2011-220-E00006). The Colorado Twin
Registry is funded by NIDA funded center g rant DA011015
and Longitudinal Twin Study HD10333; Author Huibregtse
is supported by 5T32DA017637-10. The Vietnam Era Twin
Study of Aging was supported by National Institute of
Health gr ants NIA R01 AG018384, R01 AG018386, R01
AG022381, and R01 AG022982, and, in part, with resources
of the VA San Diego Center of Excellence for Stress and
Mental Health. The Cooperative Studies Program of the
Office of Research & Development of the United States De-
partment of Veterans Affairs has provided financial support
for the development and maintenance of the Vietnam Era
Twin (VET) Registr y. The content of this manuscript is
solely the responsibility of the authors and does not neces-
sarily represent the official views of the NIA/NIH, or the VA.
The Australian Twin Registry is supported by a Centre of
Research Excellence (grant ID 1079102) from the National
Health and Medical Research Council administered by the
University of Melbourne. The Michigan State University
Twin Registry has been supported by Michigan State Uni-
versity, as well as grants R01-MH081813, R01-MH0820-54,
R01-MH092377-02, R21-MH070542-01, R03-MH63851-
01 from the National Institute of Mental Health (NIMH),
R01-HD066040 from the Eunice Kennedy Shriver Na-
tional Institute for Child Health and Human Development
(NICHD), and 11-SPG-2518 from the MSU Foundation.
The content of this manuscript is solely the responsibility
of the authors and does not necessarily represent the official
views of the NIMH, the NICHD, or the National Institutes
of Health. The California Twin Program was supported by
The California Tobacco-Related Disease Research Program
(7RT-0134H, 8RT-0107H, 6RT-0354H) and the National
Institutes of Health (1R01ESO15150-01). The Guangzhou
Twin Eye Study is supported by National Natural Sci-
ence Foundation of China (grant #81125007). PETS was
supported by grants from the Australian National Health
and Medical Research Council (grant numbers 437015
and 607358 to JC, and RS), the Bonnie Babes Founda-
tion (grant number BBF20704 to JMC), the Financial Mar-
kets Foundation for Children (grant no. 032-2007 to JMC),
and by the Victorian Government’s Operational Infras-
tructure Support Program. Data collection and analyses
in Finnish twin cohorts have been supported by ENGAGE
–– European Network for Genetic and Genomic Epidemi-
ology, FP7-HEALTH-F4–2007, grant agreement number
201413, NationalInstitute of Alcohol Abuse and Alcoholism
(grants AA-12502, AA-00145, and AA-09203 to R. J. Rose,
the Academy of Finland Center of Excellence in Complex
Disease Genetics (grant numbers: 213506, 129680), and
the Academy of Finland (grants 100499, 205585, 118555,
141054, 265240, 263278 and 264146 to J. Kaprio). K. Sil-
ventoinen is supported by Osaka University’s International
Joint Research Promotion Program. S. Y. Oncel and F. Aliev
are supported by Kirikkale University Research Grant: KKU,
2009/43 and TUBITAK grant 114C117. The Longitudinal
Israeli Study of Twins was funded by the Starting Grant
no. 240994 from the European Research Council (ERC) to
Ariel Knafo. Data collection and research stemming from
the Norwegian Twin Registry is supported, in part, from
the European Union’s Seventh Framework Programmes
ENGAGE Consortium (grant agreement HEALTH-F4-
2007-201413, and BioSHaRE EU (grant agreement
HEALTH-F4-2010-261433). The Murcia Twin Registry is
TWIN RESEARCH AND HUMAN GENETICS 9
Karri Silventoinen et al.
supported by the Seneca Foundation, Regional Agency for
Science and Technology, Murcia, Spain (08633/PHCS/08
& 15302/PHCS/10) and Ministry of Science and Innova-
tion, Spain (PSI11560-2009). The Twins Early Development
Study (TEDS) is supported by a program grant (G0901245)
from the UK Medical Research Council and the work on
obesity in TEDS is supported in part by a grant from the
UK Biotechnology and Biological Sciences Research Coun-
cil (31/D19086). The Madeira data comes from the follow-
ing project: genetic and environmental influences on phys-
ical activity, fitness, and health: the Madeira family study
Project reference: POCI/DES/56834/2004 founded by the
Portuguese agency for research (The Foundation for Sci-
ence and Technology). The Boston University Twin Project
is funded by grants (#R01 HD068435 #R01 MH062375)
from the National Institutes of Health to K. Saudino. Twi n-
sUKwasfundedbytheWellcomeTrust;EuropeanCommu-
nity’s Seventh Framework Programme (FP7/2007-2013).
The study also receives support from the National Insti-
tute for Health Research (NIHR) BioResource Clinical Re-
search Facility and Biomedical Research Centre based at
Guy’s and St Thomas’ NHS Foundation Trust and King’s
College London. The University of Washington Twin Reg-
istry is supported by the grant NIH RC2 HL103416 (D.
Buchwald, PI). The Netherlands Twin Register acknowl-
edges the Netherlands Organization for Scientific Research
(NWO) and MagW/ZonMW grants 904-61-090, 985-10-
002, 912-10-020, 904-61-193,480-04-004, 463-06-001, 451-
04-034, 400-05-717, Addiction-31160008, Middelgroot-
911-09-032, Spinozapremie 56-464-14192; VU University’s
Institute for Health and Care Research (EMGO+); the
European Research Council (ERC - 230374), the Av-
era Institute, Sioux Falls, South Dakota (USA). Gem-
ini was supported by a grant from Cancer Research UK
(C1418/A7974).
Supplementary Material
To view supplementary material for this article, please visit
http://dx.doi.org/10.1017/thg.2015.29.
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