Journal of Internal Medicine 2002; 252: 184–205
REVIEW
The Swedish Twin Registry: a unique resource for clinical,
epidemiological and genetic studies
P. LICHTENSTEIN1, U. DE FAIRE2,3, B. FLODERUS4,5, M. SVARTENGREN6, P. SVEDBERG1
& N. L. PEDERSEN1,7
From the 1Department of Medical Epidemiology, Karolinska Institutet, Stockholm; 2Divison of Cardiovascular Epidemiology, Institute of
Environmental Medicine, Karolinska Institutet, Stockholm; 3Department of Cardiology, Karolinska Hospital, Stockholm; 4Division of Environmental
Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm; 5National Institute for Working Life, Stockholm; 6Department
of Public Health Science, Karolinska Institutet, Stockholm, Sweden; and 7Department of Psychology, University of Southern California, CA, USA
Abstract. Lichtenstein P, de Faire U, Floderus B,
Svartengren M, Svedberg P, Pedersen NL (Karolinska Institutet; Karolinska Hospital; Institute of
Environmental Medicine, Karolinska Institutet,
National Institute for Working life, Stockholm,
Sweden; and University of Southern California, CA,
USA) The Swedish Twin Registry: a unique resource
for clinical, epidemiological and genetic studies
(Review). J Intern Med 2002; 252: 184–205.
The Swedish Twin Registry (STR), which today
has developed into a unique resource, was first
Compiling the Registry
There are three cohorts in the registry, each of which
differs in method of ascertainment and extent of data
collected. The compilation of the first two cohorts has
been described previously [1, 2] and in the following,
we describe the current structure of the registry.
The cohort born in 1886–1925
When the twin registry was initiated, all parishes in
Sweden were contacted to obtain information concerning multiple births between 1886 and 1925.
Each potential pair of twins was manually followed
until status in 1959 was established. In 1960–61, a
184
established in the late 1950s to study the importance of smoking and alcohol consumption on cancer
and cardiovascular diseases whilst controlling for
genetic propensity to disease. Since that time, the
Registry has been expanded and updated on several
occasions, and the focus has similarly broadened to
most common complex diseases. In the following,
we will summarize the content of the database,
describe for the first time recent data collection
efforts and review some of the principal findings that
have come from the Registry.
questionnaire was sent to all like-sexed twins where
both were alive and living in Sweden. Since then
questionnaires have been sent out in 1963 and 1967
(Table 1). Only the information from pairs in which
both responded was kept from the initial questionnaire and the 1963 questionnaire. Depending on
nonresponse to the 1967 questionnaire or some
items in that questionnaire, selected pairs were
requested to respond to a questionnaire in 1970.
The information was mainly of demographic, medical and life-style character, with special attention to
cardiovascular and respiratory disease, general
health, legal drug use, diet and psychosocial
conditions (Fig. 1). Recently information from
1699 unlike-sexed pairs born in 1906–25 has been
Ó 2002 Blackwell Science Ltd
REVIEW: THE SWEDISH TWIN REGISTRY
185
Table 1 Participation in questionnaires by zygosity and birth cohort
MZ
Cohorts
Not
eligible
DZ
Non
responder
Responder
Unknown zygosity
Not
eligible
Q61: Birth years 1886–1925
Cohorts
1886–1895
0
1896–1905
0
1906–15
0
1916–25
0
Total
0
804
1470
2338
2696
7308
0
0
0
0
0
Q62: Birth years 1886–1925
Cohorts
1886–1895
0
136
1896–1905
0
188
1906–15
0
302
1916–25
0
316
Total
0
942
668
1282
2036
2380
6366
3
2
1
1
7
Q67: Birth years
Cohorts
1886–1895
1896–1905
1906–15
1916–25
Total
151
260
397
427
1235
458
1079
1868
2250
5655
Q70: Birth years 1886–1925
Cohorts
1886–1895
477
78
1896–1905
773
100
1906–15
1176
185
1916–25
1487
206
Total
3913
569
Non
responder
Responder
Not
eligible
Non
responder
Responder
1210
2678
4464
5242
13 684
0
0
0
0
0
100
222
242
314
878
245
458
671
747
2121
962
2308
3792
4494
11 556
0
0
0
0
0
20
52
52
60
184
80
170
190
254
694
303
261
188
76
828
220
462
868
833
2383
687
2045
3408
4333
10 473
9
23
11
5
48
32
47
45
55
179
59
152
186
254
651
249
597
977
1003
2826
729
1355
2018
2834
6936
126
235
428
410
1199
355
1178
2018
1998
5549
42
108
105
142
397
15
20
25
33
93
43
94
112
139
388
2348
3359
3625
1097
10 429
38
46
13
2
99
258
409
446
74
1187
4029
5220
5695
1696
16 640
214
207
123
12
556
1119
1270
1596
258
4243
808
962
1199
241
3210
1886–1925
195
131
73
19
418
Q73: Birth years 1926–58
Cohorts
1926–35
22
1936–45
27
1946–55
2
1956–58
2
Total
53
122
286
267
32
707
Note: Not eligible means not alive at time of sending out questionnaire, or later discovered to be nontwin.
For Q61 and Q62, responses were only kept for pairs in which both responded.
computerized and matched on name, date and
county of birth [3]. The registry is regularly matched
to national health care registries to obtain information on cancer diagnoses, inpatient discharges,
causes of death, and current address and vital status.
The cohort born in 1926–58
In 1970 a new cohort of twins born in 1926–67
was compiled, this time by use of nationalized birth
registrations. A birth register consisting of all
50 000 twin births was established [2]. Members
of like-sexed pairs from the cohort born in 1926–58
were sent a mail out questionnaire in 1972–73.
Responses were received from about 36 000 individuals including 14 000 twin pairs. This questionnaire was very similar to those sent to the older
cohort. In addition, a short version of the Eysenck
Personality Inventory was developed [4] as well as a
series of questions about environmental agents such
as air pollution and noise. Information is maintained
concerning both the initial birth cohort as well as
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186
P . L I C H T E N S T E I N et al.
Fig. 1 Domains of the questionnaires for the cohorts born 1886–1925 and 1926–58.
the subsample of like-sexed pairs from which the
questionnaire information was obtained.
The cohort born in 1959–90
Twins born in 1959–68 had already been identified
in the birth register when the middle cohort was
compiled, but were not contacted. In 1993, twins
born in 1969–90 were included by record linkage to
the Medical Birth Registry. From this cohort, only
the parents of twin pairs born between 1985 and
1986 have been contacted [5], as well as a sub
sample of pairs included in the pilot screening
described below. Because no contact has been made,
we have no information regarding zygosity of the
majority of the like-sexed pairs.
Current data collection: screening across
the lifespan twin study
The Swedish Twin Registry (STR) is currently in the
final phase of a complete telephone interview
screening of all twins born in 1958 or earlier
regardless of gender composition or vital status of
the pair. This effort is known as Screening Across
the Lifespan Twin study (SALT).
SALT-pilot study. During the fall of 1996 and spring
1997 a random sample of 1000 pairs aged 5–
85 years was selected for a pilot study to evaluate
the feasibility of screening all twins in the registry.
For the 150 pairs under the age of 18, parents of the
twins were interviewed; 95% responded. Approximately equal numbers of pairs from each birth year
between ages 18–85 were contacted for a telephone
interview; 1321 of 1700 individuals responded
(78%), including both members of 572 pairs (pairwise response rate 67%). Mean age for the adult
sample was 46.5 years (SD ¼ 16.4). Forty-six per
cent of the respondents were male.
The telephone interview contained mostly the
same questions as those sent to the larger SALT
sample (see below and Fig. 2). The twins were also
asked to go to their local health care centre and
provide blood for analyses of clinical chemistries and
zygosity determination. Sixty-one per cent of the
adult participants (n ¼ 811 in 294 pairs) provided a
49-mL blood sample (see Table 2 for description), of
which 30 mL per person were stored in a )80 °C
freezer for future analyses. Fifty-one per cent
(n ¼ 73 pairs) of the pairs under the age of 18
provided 17 mL of blood of which 10 mL were
frozen for future analyses. Weight and height were
measured for both children and adults at the same
occasion as blood collection. We also measured
blood pressure for adult twins.
The pilot study demonstrated that it was feasible
to collect data through telephone interviews from
twins in all age groups.
SALT data collection. In March 1998, full scale
screening of the twins born in 1958 or earlier was
initiated. Data collection was performed with a computer assisted telephone interview including a number of items asked to all twins regarding different
diseases and symptoms. Efforts were made to interview members of a pair within a month of each other
to minimize the risk of biasing the results by differential age effects. Most recent information on last
name and address were linked to the telephone company files to obtain telephone numbers. Introductory
letters describing the study were sent to a random
Ó 2002 Blackwell Science Ltd Journal of Internal Medicine 252: 184–205
REVIEW: THE SWEDISH TWIN REGISTRY
187
Fig. 2 Flowchart of the computer assisted telephone interview in SALT.
sample of approximately 1000 twin pairs each
month. All screening data were collected over the
Table 2 Description of standard chemical blood analysis for all
twins that part
Twin children (5–17 years old):
S-Glucose
S-IgE, S-IgG, S-IgA
S-Phadiatop
Adult twins (18–85 years old)
B-Hb
S-Triglycerides
S-Cholesterol, S-HDL, S-LDL, S-Apo A1, S-Apo B, S-Lp (a)
S-Glucose
S-ASAT, S-ALAT, S-gGT
S-Iron, S-Calcium, S-Potassium, S-Sodium
S-IgE
S-Phadiatop
S-SHBG
telephone by trained interviewers (with adequate
medical background) using a computer-based data
collection system. A number of introductory items
were asked concerning birth order and weight, zygosity (similarity), contact with twin partner and
family constellation (age and gender of all first-degree
relatives) (Fig. 2). Thereafter, a checklist of common
illnesses, prescription and nonprescription medication use, and permission to collect medical records
were asked for. All twins were also interviewed
regarding their occupation, education and consumption of alcohol, tobacco and caffeine, whereas
presentation of some items was gender- and age-specific (for example, women were asked about hormone
replacement therapy and only twins aged 65 years
and above were screened for memory problems). The
twins’ interest in participating in further studies
involving a health examination and interview was
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188
P . L I C H T E N S T E I N et al.
also solicited. If the twin was unable to be interviewed,
the interview was conducted with an informant.
Disease identification. With this telephone interview
instrument as much information as necessary was
collected to screen for most common complex diseases. Special emphasis was put on diagnostic items
that could determine whether a twin was likely to
have a disease (rather than simply asking the twin
whether they have a disease). Items were presented
in a ÔbranchingÕ format such that individuals were
asked follow-up items (within item domain) if they
responded positively to key introductory items.
Areas that were covered in the interview with all
adult twins are described in Fig. 2. The items were
compiled by contacting experts in each of the disease
domains. If there were standardized instruments
available, these were used, e.g. the short Computerized International Diagnostic Interview (CIDI) for
psychiatric disorders [6]. For each disease we have
developed algorithms as a preliminary mechanism
for using combinations of symptom responses to
identify individuals who may be considered suspect
for a disease. These algorithms map on to diagnostic
criteria for each disorder. The specificity and
sensitivity of the diagnostic algorithms will be evaluated and subsequently revised. Table 3 describes
the response frequencies to some of the major
diagnoses and exposures by age group and gender
for the sample of respondents 55 years of age or
older at time of interview.
Twin Study Support System (TWISST). All administrative data as well as all data collected through the
telephone screening were entered into an administrative database known as TWISST. A series of webbased pages (with secure access) were designed to
facilitate online administration of the registry as a
whole as well as the status of the ongoing screening.
TWISST is updated daily, so that those with access
to the system can see the exact progress of data
collection. We had a limit to the number of attempts
made to call a twin or a next of kin (Ômax triesÕ). For
twins 65 years and older, this was a total of eight,
and for the younger participants it was six. By May
2001 all twins in the age groups 55–65 and
65 years or older were contacted. Individual as well
as pairwise response patterns to the interview contact for all twins above 55 years are described in
Tables 4 and 5 by age group, gender and zygosity.
Data from more than 39 000 twins have been
collected between March 1998 and March 2002
with the same screening instrument as in the pilot
with one exception only; no blood samples were
collected from these twins. The response rate was
74%. By the end of the summer 2002 all 52 080
twins born in 1958 or earlier will have been
contacted. For the like-sexed twin pairs, this new
contact represents a 27–32 year longitudinal
follow-up.
Zygosity diagnoses
Twins older than 18 years. In the previous studies of
the Twin Registry, zygosity assignments were based
on responses to the question, ÔDuring childhood,
were you and your twin partner as like as ‘‘two peas
in a pod’’ or not more alike than siblings in general?Õ
that was included in the 1961 and 1973 questionnaires (the Swedish version actually asks whether
twins Ôare similar as two berriesÕ). If both individuals
of a pair responded Ôalike as two peas in a podÕ they
were classified as monozygotic (MZ); if both
responded Ônot alikeÕ they were classified as dizygotic
(DZ). If the twins did not agree, or if only one
member of the pair responded to the question, the
zygosity was considered Ônot determinedÕ (XZ). This
method of zygosity determination has been proven
to correctly diagnose more than 95% of the twins in
Sweden [7, 8] as well as in other countries [9].
Although many of the pairs had a zygosity
assignment from the earlier questionnaires, many
did not. Thus, we set about using the SALT-pilot data
(including blood) to develop an algorithm that could
be used for augmenting previously collected information. We added to the items concerning twin
similarity the question ÔHow often did strangers have
difficulty in distinguishing between you and your
twin partner when you were children?Õ If the
zygosity was undetermined after the Ôpeas in a podÕ
question and both individuals of a pair responded
Ôalmost always or alwaysÕ or ÔoftenÕ being confused as
children, they were classified as MZ. If both responded ÔseldomÕ or Ôalmost never or neverÕ, they were
classified as DZ. The zygosity classification was
validated using 13 DNA markers in the SALT-pilot
(n ¼ 199 adult pairs), and proved correct in 99% of
the pairs. Only one pair classified as MZ by the
algorithm was misclassified (i.e. differed on five micro
satellite markers) and only one pair initially consid-
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REVIEW: THE SWEDISH TWIN REGISTRY
189
Table 3 Frequencies of common complex diseases based on self-reports during SALT, by age group and sex
55–64 years
65+ years
Women
Men
Women
Men
Cancer
Angina pectoris
Blood pressure (high)
Type 2 diabetes
Parkinsonism
Allergy/hay-fever
Asthma
Chronic bronchitis
Reflux
FBD
Obesity (BMI > 30)
Recurrent headache
Osteoarthritis
Osteoporosis
Rheumatoid arthritis
Major depression
Alcohol abuse
Alcohol dependence
670
334
1725
172
394
2016
566
368
1048
1053
573
1922
421
443
774
1620
94
124
(10.4%)
(5.2%)
(26.6%)
(2.6%)
(6.1%)
(31.4%)
(8.7%)
(5.7%)
(16.3%)
(16.3%)
(9.0%)
(29.8%)
(6.5%)
(6.8%)
(12.0%)
(25.1%)
(1.5%)
(1.9%)
253
509
1439
235
232
1242
337
191
849
401
515
890
346
150
282
691
356
390
Smoking status
Current
Former
Nonsmoker
Use of prescribed medicine
1416
1794
3177
4339
(22.2%)
(28.1%)
(49.7%)
(65.3%)
Civil status
Married/cohabitating
Divorced/separated
Widow/widower
Single
4722
706
419
624
Educational level
Primary school
Secondary/high school
University/college
Other type of education
Single person household
Multiple person household
2376
2443
1439
183
1554
4916
Total
(4.4%)
(8.9%)
(25.3%)
(4.1%)
(4.1%)
(21.8%)
(5.9%)
(3.3%)
(14.9%)
(7.1%)
(9.2%)
(15.8%)
(6.1%)
(2.6%)
(5.0%)
(12.2%)
(6.2%)
(6.9%)
806
1053
2632
440
649
1710
579
457
1052
814
603
–
835
1509
945
998
33
32
(11.5%)
(15.1%)
(37.7%)
(6.3%)
(9.6%)
(24.5%)
(8.3%)
(6.5%)
(15.5%)
(12.0%)
(9.6%)
(8.3%)
(19.6%)
(32.1%)
(7.5%)
(9.0%)
(18.5%)
(7.4%)
(5.7%)
(14.4%)
(6.2%)
(6.9%)
(12.4%)
(22.2%)
(13.5%)
(14.7%)
(0.5%)
(0.5%)
454
1079
1761
413
482
1009
403
312
780
335
366
–
447
325
356
398
178
201
1121
2364
2158
2544
(19.8%)
(41.9%)
(38.3%)
(43.9%)
653
1404
4686
5116
(9.7%)
(20.8%)
(69.4%)
(73.6%)
688
2515
2162
3579
(12.8%)
(46.9%)
(40.3%)
(62.4%)
3878/24 138 (16.0%)
8077/24 138 (33.5%)
12 183/24 138 (50.5%)
15 578/24 940 (62.5%)
(73.0%)
(10.9%)
(6.4%)
(9.6%)
4572
416
101
605
(80.3%)
(7.3%)
(1.7%)
(10.6%)
3451
487
2445
649
(49.1%)
(6.9%)
(34.7%)
(9.2%)
4139
267
572
552
(74.8%)
(4.8%)
(10.3%)
(9.9%)
16 884/24 727 (68.3%)
1876/24 727 (7.6%)
35 37/24 727 (14.3%)
2430/24 727 (9.8%)
(36.9%)
(37.9%)
(22.3%)
(2.8%)
(24.0%)
(76.0%)
2230
2107
1225
113
977
4713
(39.3%)
(37.1%)
(21.6%)
(2.0%)
(17.2%)
(82.9%)
3976
1843
693
256
3371
3641
(58.7%)
(27.3%)
(10.2%)
(3.8%)
(48.0%)
(52.0%)
2775
1643
767
193
1278
4242
(51.6%)
(30.6%)
(14.3%)
(3.6%)
(23.1%)
(76.8%)
11 357/24 262 (46.85%)
80 36/24 262 (33.1%)
4124/24 262 (17.0%)
745/24 262 (3.1%)
7180/24 692 (29.0%)
17 512/24 692 (71.0%)
(8.3%)
(6.0%)
(6.5%)
(7.4%)
(3.3%)
(3.8%)
2183/24 635 (8.9%)
2975/24 635 (12.1%)
7557/24 635 (30.7%)
1260/24 635 (5.1%)
1757/24 243 (7.2%)
5977/24 635 (24.3%)
1885/24 636 (7.6%)
1328/24 635 (5.4%)
3729/24 318 (15.3%)
2603/24 321 (10.7%)
2057/23 541 (8.7%)
2812/12 062 (23.4%)
2049/24 205 (8.5%)
2427/24 286 (10.0%)
2357/24 635 (9.6%)
3707/24 271 (15.3%)
661/24 234 (2.7%)
747/24 248 (3.1%)
Type 2 diabetes, Noninsulin-dependent diabetes mellitus.
ered DZ by the algorithm was considered as MZ based
on similarity for all 13 markers. Of the 13 pairs not
classifiable from the questionnaire, seven were found
to be identical on all 13 markers. This finding has
recently been replicated in the ÔTwin MomsÕ study
using 10 DNA micro satellite markers on 287 female
pairs (98% correct diagnoses). Of the four pairs not
classifiable from the questionnaire, none were found
to be identical on all 10 markers [10].
For the singleton responders to SALT for whom
there is no previous zygosity assignment, we are
now using both the peas in a pod and the similarity
questions. If a singleton respondent of a pair
responds Ôalike as two peas in a podÕ and Ôalmost
always or alwaysÕ or ÔoftenÕ being confused on the
similarity question, she/he was classified as MZ. If
she/he responded Ônot alikeÕ on the peas in a pod
question and ÔseldomÕ or Ôalmost never or neverÕ on
the similarity question, she/he was classified as DZ.
We currently use the Ôbest availableÕ information
from different sources to determine zygosities; with
the following priorities (low numbers have priority).
(i) DNA based analyses;
(ii) Pair wise responses to questionnaires during
registry compilation in 1961 or 1973 that give
a an assignment of MZ or DZ;
(iii) Pair wise response to the SALT screening
interview (if previously categorized as XZ);
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190
P . L I C H T E N S T E I N et al.
Table 4 Distribution of events, for in total 36 075 individuals in SALT by May 2001
55–64 years
65 years and older
Women
Event*
Finished
Eligible
Ineligible
Refused
Total
Men
Women
Men
MZ
DZ
OS
XZ
MZ
DZ
OS
XZ
MZ
DZ
OS
XZ
MZ
DZ
OS
XZ
1556
76
8
159
1799
2257
109
9
250
2625
2333
194
64
395
2986
347
181
51
348
927
1271
84
5
115
1475
1953
151
8
179
2291
2121
322
54
372
2869
397
259
67
346
1069
1810
121
169
532
2632
3128
198
294
978
4598
2313
165
153
832
3463
270
120
69
417
876
1302
56
84
222
1664
2233
139
175
389
2936
2119
214
163
571
3067
284
126
62
326
798
*Finished ¼ Completed interview; Eligible ¼ Not yet completed, Get back later, Max-tries, Move forward (scheduled for interview), Need
attention (not completed due to different reasons such as twins have questions about something in interview), No answer, Not reachable,
Wrong phone number, Not yet contacted, sent letter; Ineligible ¼ Non-existent (dead), Not in selection (e.g. emigrated), Not interviewable
(e.g. deaf); Refused ¼ Contacted, do not want to participate.
(iv) Individual response to the SALT screening
interview (if previously categorized as XZ).
Twins 5–17 years of age. Zygosity for the children in
the SALT-pilot study was determined by a series of
five questions concerning parental responses on
physical similarity. The five questions used were
Ôalike as two peas in podÕ, Ôstrangers had difficulty
distinguishingÕ, Ôsimilarity in eye colourÕ, Ôsimilarity
in hair colourÕ, and Ôsimilarity in hair curlinessÕ. An
algorithm was derived from a discriminant analysis
on 54 twin pairs with known zygosity determined by
analyses of the same 13 polymorphic micro satellite
DNA markers used for the adult assignments. The
algorithm classified only pairs that had a 95%
probability of being correctly classified as MZ or DZ.
Applied to the 54 pairs with DNA, the algorithm
correctly classified 50 and the remaining four pairs
were classified as undetermined zygosity. Of the four
pairs not classifiable from the questionnaire, three
were identical on all 13 markers.
Methods used in twin research
Data from the STR can be used for several purposes.
Quantitative genetic methods, including comparisons of concordances or intraclass correlations and
structural equation modelling, can be used to investigate the relative importance of genetic and environmental influences on a phenotype. Another method
is the co-twin control method, which is applied in
situations where one wants to investigate the
importance of a putative risk factor after controlling
for genetic and early environmental effects. Finally,
the STR contains longitudinal data on large samples,
and can therefore be used for conventional epidemiological analyses disregarding twinship status.
Quantitative genetic analyses and heritability estimation
Analyses of the relative importance of genes and
environments for a phenotype can be carried out
with traditional quantitative genetic methods that
are well developed for twin studies [11]. The classical
twin method is based on the fact that MZ twins share
100% of their genome, whereas DZ twins share on
average 50% of their segregating genes. Historically,
concordances were computed separately for MZ and
DZ pairs. When MZ concordances are greater than
DZ concordances, genetic influences are indicated.
Tables can be used to estimate heritability from
concordances, however, these estimates are hampered by a number of limitations [12].
Information concerning sharing genetic and environmental influences allows one to use a set of
linear structural equations and fit models over all
types of twins to best describe the causes of variation
in a phenotype. The total variance in the trait can be
partitioned into genetic variance (A), common
environmental variance [including shared (familial)
environmental variance, C] and unique environmental variance (E).
In order to estimate the parameters of interest, the
equation for one of the twins can be written as:
Vp1 ¼ a A1 þ c C1 þ e E1
ð1Þ
where Vp1, A1, C1, and E1, are the total phenotypic
variance, additive genes, shared environments and
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REVIEW: THE SWEDISH TWIN REGISTRY
191
Table 5 Distribution of events for 14 938 pairs of twins (age 55 and older) in SALT
55–64 years old
MZ male
MZ female
DZ male
DZ female
OS
XZ male
XZ female
65 years and older
MZ male
MZ female
DZ male
DZ female
OS
XZ male
XZ female
Event*
Finished
Finished
Eligible
Ineligible
Refused
Finished
Eligible
Ineligible
Refused
Finished
Eligible
Ineligible
Refused
Finished
Eligible
Ineligible
Refused
Finished
Eligible
Ineligible
Refused
Finished
Eligible
Ineligible
Refused
Finished
Eligible
Ineligible
Refused
541
37
4
50
695
28
5
55
786
88
5
113
961
52
7
124
1706
243
63
382
59
92
26
141
58
55
29
126
Finished
Eligible
Ineligible
Refused
Finished
Eligible
Ineligible
Refused
Finished
Eligible
Ineligible
Refused
Finished
Eligible
Ineligible
Refused
Finished
Eligible
Ineligible
Refused
Finished
Eligible
Ineligible
Refused
Finished
Eligible
Ineligible
Refused
408
13
20
79
619
24
40
118
657
35
32
125
969
52
87
293
1445
149
109
548
37
44
23
97
37
35
31
93
Eligible
Ineligible
Refused
15
0
9
0
0
21
9
1
27
0
2
32
12
1
18
0
1
16
8
1
28
0
1
40
51
11
97
2
21
94
32
7
60
7
8
46
26
4
51
1
6
71
7
1
7
3
8
26
16
6
29
11
22
128
7
11
23
6
14
32
15
8
48
10
35
166
25
20
79
17
63
219
11
7
27
3
7
63
10
6
38
3
9
105
*Finished ¼ Completed interview; Eligible ¼ Not yet completed, Get back later, Max-tries, Move forward (scheduled for interview), Need
attention (not completed due to different reasons such as twins have questions about something in interview), No answer, Not reachable,
Wrong phone number, Not yet contacted, sent letter; Ineligible ¼ Non-existent (dead), Not in selection (e.g. emigrated), Not interviewable
(e.g. deaf); Refused ¼ Contacted, do not want to participate.
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P . L I C H T E N S T E I N et al.
unique environments, respectively, for the first twin
in the pair. A similar equation can be written for the
second twin. The theoretical expectations for variance and covariance within twin pairs can be
described with the following equations:
Vp ¼ a2 þ c2 þ e2
ð2Þ
Cov ðMZÞ ¼ a2 þ c2
ð3Þ
Cov ðDZÞ ¼ 0:5 a2 þ c2
ð4Þ
The parameters a, c and e can then be estimated
with maximum likelihood methods e.g. Mx [13],
and the relative importance of genes and environments can be evaluated. Heritability, the relative importance of genetic influences for variation
in a trait, is defined as genetic variance (a2)
divided by the total phenotypic variance. The twin
model is commonly illustrated with a path model
(Fig. 3).
Extensions of the basic model. There are several
extensions of the classical twin method [13].
Non-additive (dominant) genetic variance can be
modelled and if twins reared apart are included,
rearing environmental effects can be separated from
other types of shared environments [e.g. 14, 15]. It
is possible to use the model on dichotomous phenotypes (i.e. diseased versus not diseased), when
assuming an underlying normally distributed
susceptibility for the disease [e.g. 16, 17]. Genotype–
environment interactions can be estimated by
stratifying heritability estimates into categories based
on a specific exposure. Sex differences in the magnitude of genetic and environmental influences can
also be evaluated, and by including opposite-sexed
twins it is possible to evaluate if the same or different
genetic effects are important for the sexes [e.g. 18].
It is also possible to extend the twin method to
evaluate multivariate relationships. For example,
several analyses of the STR have evaluated the extent
Fig. 3 Path model describing
parameters of interest. MZ, monozygotic; DZ, dizygotic; TRA, twins
reared apart; TRT, twins reared
together.
REVIEW: THE SWEDISH TWIN REGISTRY
to which genetic effects explain the associations
amongst traits [19–22]. During recent years, there
has been an explosion in methodological advancements for analysing longitudinal data, including
cohort sequential analyses [23], random effects
modelling [24] and application of latent growth
models [25]. Models are also being developed which
take into account age at onset [26–28] and time of
follow-up by application of frailty models to survival
data [29–32]. Finally, measured environmental factors can be incorporated into the model, much the
same way as measured genotypes can be evaluated.
Epidemiological analyses
Because the STR is a large sample with longitudinal
data, several studies have been performed on the
association between exposure and outcomes using
the registry as a population based cohort [e.g. 33–
36]. or as the basis for nested case–control studies
[37, 38]. When using twin data for these types of
studies, the dependency between the twins in a pair
should be taken into account by using generalized
linear models or other techniques [e.g. 39].
Co-twin control analyses
Similar to quantitative genetic methods, the co-twin
control method takes advantage of the fact that MZ
and DZ twins share different degrees of genetic
relatedness. These methods are used when the
relationship between a putative risk factor and a
disease is studied with control for genetic background and unmeasured early environment shared
by twins. In fact, the STR was originally established
in order to test associations between environmental
exposures (primarily smoking) and chronic diseases
[40]. Both disease discordant twins and exposure
discordant twins approaches can be used in such
studies. It should be noted that the co-twin-control
method may entail control of factors in the biological pathway between exposure and disease, which
may cause an underestimation of the exposure
studied.
Co-twin control analyses: disease discordant twins. In
studies of disease discordant twins, two control
groups usually are used: external (not related) controls and internal (co-twin) controls. The analyses
can be performed in three steps.
193
Step 1: Association between exposure and outcome
(comparison with external controls). The first step,
which is essentially a classic case–control study, is to
compare twins diagnosed as cases with external
controls (other twins not related to the index probands), and to evaluate the risk for disease given an
exposure. This approach facilitates comparisons
with results from ordinary case–control studies on
singletons.
Step 2: Controlling for confounding from unmeasured
early environment (healthy co-twin as control). In
the second step, the healthy co-twin (in both MZ
and DZ twin pairs) can be used as a control for the
diseased twin. Because twins share the same intrauterine environment and typically are reared
together, the co-twin control method provides a
very effective tool to minimize confounding by differences in (unmeasured) childhood or adolescent
environment. If analyses with external controls
show associations between exposure and disease
and the relative risk remains similarly high in the
within pair (co-twin) analyses, it speaks in favour
of a causal effect of the exposure on the disease. On
the other hand, if the relative risk is not increased
in the within pair comparisons (but only in the first
step analyses with external comparisons), this indicates that environmental factors early in life (for
example foetal environment, maternal smoking or
childhood SES) are responsible for the initially observed findings. If the relative risks from steps 1
and 2 differ, a direct test of significance of difference in risks can be performed by regressing the
exposure on control status (external versus internal
control).
Step 3: Controlling for unmeasured genetic background
(healthy monozygotic co-twin as control). In the
third step, analyses are applied only to disease discordant MZ pairs. This design is ideal in controlling
for potential confounding from genetic factors as the
cases and controls are genetically identical. Thus,
one is confident that an observed effect is not confounded by genetic predisposition. If the twin with
the exposure in MZ pairs more often has a specific
chronic disease, this will be a strong support for the
likelihood that the exposure contributes to the
causation of the disease. On the other hand, if an
association exists in analyses of external controls
amongst disease discordant DZ pairs, but not
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P . L I C H T E N S T E I N et al.
amongst MZ pairs, genetic effects probably have
confounded the results.
Co-twin control analyses: exposure discordant twins. As
mentioned above, one can also focus on exposure
discordant pairs that are followed longitudinally for a
disease outcome. In this case t-tests or proportional
hazard regressions can be utilized for estimating the
relative risk between exposed and unexposed individuals, whereas matched analyses should be used in
within pair analyses similar to the disease discordant
pairs [e.g. 41].
Summary of recent research
Coronary heart disease
As mentioned earlier, one of the primary purposes of
the older cohort of the STR was to study environmental and genetic factors of importance for the
development of cardiovascular diseases. The methods used have moved from simple comparisons of
concordance rates between MZ and DZ pairs and
analyses of intra- and interpair differences towards
more complex quantitative genetic models.
Early data on concordance for coronary heart disease. The possible impact of genetic factors on the
occurrence of angina pectoris and coronary heart
disease (CHD) was demonstrated in several early
clinical studies in the registry summarized by
deFaire and Pedersen [42]. The experiences from
these early studies indicated that the STR is an
outstanding epidemiological resource for the study
of genetic influences on cardiovascular diseases.
The influence of age on the genetic risk for coronary
heart disease. In an extended mortality follow-up
based upon 26 years of follow-up (1962–87), 2810
deaths from CHD and 949 deaths from stroke
occurred [43]. The cumulative probability of survival given the index twin already had died of CHD
was less in MZ than in DZ twins. The relative hazard
of death from CHD when the index twin died from
CHD before the age of 55 years as compared with
the hazard when the index twin had not died before
55 was 8.1 for male MZ twins and 3.8 for DZ male
twins. Amongst females, the relative hazard was
15.0 when the index twin had died of CHD before
the age of 65 years for MZ twins and 2.6 for DZ
twins. With increasing age of the index twin at the
time of death from CHD, the relative hazards
decreased continuously for both MZ and DZ twins,
and the ratio between MZ and DZ twins converged
towards 1. The relative hazards were only marginally influenced by other risk factors for CHD. These
data clearly show the impact of genetic factors on
death from CHD. Another interesting finding was
that in both MZ and DZ pairs, death from CHD at
early ages in the twin partner had a greater impact
on the risk to die from CHD than any of the other
cardiovascular risk factors.
In further analyses of the older cohort of the STR,
a correlated gamma-frailty model has been used to
assess heritability for CHD death. Based upon
35 years of follow-up (from 1961 to 1997) it was
found that the genetic influences on CHD death were
moderately large, with heritability estimates of 0.57
(95% CI: 0.45–0.69) amongst males, and 0.38 (95%
CI: 0.26–0.50) amongst females. The correlated
gamma-frailty model is a bivariate life-time model
that deals with both censured and truncated observations, and allows one to combine survival analyses with the methods of quantitative genetics [30].
Genetic influences on phenotypic variation in cardiovascular risk factors. Twins are particularly useful for
the study of genetic influences on continuous traits
such as cardiovascular risk factors. Early clinical
studies suggested a closer resemblance both between
blood pressure levels and serum levels of cholesterol
and triglycerides in MZ than in DZ pairs, with significantly lower intrapair variances for MZ twins
than DZ twins. These results have been described
extensively [42] and will not be further commented
upon here.
Twins reared apart and reared together. The adoptedtwin design of The Swedish Adoption/Twin Study of
Ageing (SATSA) [82] has been of particular value in
assessing the relative importance of genetic and
nongenetic influences on several metabolic cardiovascular risk factors. Structural modelling techniques
have been used to compute the heritabilities of various
cardiovascular risk factors in the SATSA material, as
summarized in Table 6. The influence of genetic factors tended to decrease across age groups for lipids and
lipoproteins, as well as for systolic blood pressure,
whereas the genetic influences did not vary with age
for insulin- and insulin-related factors.
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REVIEW: THE SWEDISH TWIN REGISTRY
Table 6 Heritability estimates for cardiovascular traits, based
upon Swedish twins reared apart and together
Heritability (%)
Trait
Lipids/lipoproteins
Total cholesterol
HDL cholesterol
Triglycerides
Apolipoprotein B
Apolipoprotein A1
Lp(a)
Blood pressure (BP)
Systolic BP
Diastolic BP
Age-group
52–65 years
Age-group
66–86 years
63
76
72
78
69
38
55
28
51
52
90
65
26
12
28
Insulin/insulin-related factors
Insulin
Insulin resistance
IGF-1
IGFBP-1
48
39
63
36
Coagulation/fibrinolysis
PAI-1
Factor VII
42
52
Anthropometric data
BMI
52
Heritability estimates are given separately by age groups only if
analyses were performed to evaluate differences across age-groups.
Comparisons of younger (52–65 years) and older
twins (66–86 years) disclose significant age differences in heritabilities for various serum lipid levels
(total cholesterol, HDL cholesterol, apolipoprotein
A1 and B, triglycerides) with heritabilities in the
younger age group ranging from 0.63 to 0.78, and
in the older age group from 0.28 to 0.55 [45].
Furthermore, in analyses of the associations
amongst lipid measures, it appeared that the genetic
correlations between various lipids seemed to be
more important in the younger age groups, as
compared with the older age groups [46]. Comparisons of twins reared together with those reared
apart indicated that the rearing environment substantially contributed to variation in the level of
total cholesterol, perhaps reflecting dietary practices
learned at home. Sharing the same environment
affected the other lipid measures much less than
genetic factors and unique environmental factors
not shared by twins.
An investigation of 725 like- and unlike-sex twin
pairs, ages 17–85, using data from SATSA, the
SALT-pilot, and the GENDER study [3] showed that
195
cross-sectional differences in phenotypic variation
for cholesterol and apolipoprotein B depended
almost entirely on the accumulation of environmental experience throughout life, whereas there
were no distinct differences in phenotypic variance
for apolipoprotein A1 and triglycerides [47].
Further studies of the SATSA sample suggested
that the influence of genetic factors on variation in
blood pressures is lower than that for serum lipid
levels (Table 6) [48]. The influence of genetic factors
decreased with increasing age for systolic blood
pressure, but not for diastolic pressure. In longitudinal analyses of repeated blood pressure measurements, the genetic influences on systolic and
diastolic blood pressures were stable over time.
Furthermore, the polymorphisms of genes for the
angiotensin-I converting and the angiotensin-II
receptor type 1 enzymes explained less than 1% of
the total phenotypic variance in blood pressures
[49].
Estimates of heritabilities for other components of
the insulin resistance syndrome such as serum
insulin, insulin growth factor-1 (IGF-1), and its
binding protein IGFBP-1 are also summarized in
Table 6 [50]. Moderate genetic influences were
noted for haemostatic factors such as plasminogen
activator inhibitor-1 (PAI-1). It also appeared that
all genetic influences for PAI-1 were more or less
shared with those for triglycerides and body mass
index (BMI). Likewise, genetic factors in common to
Factor VII and triglycerides explained about 7% of
the total variance for Factor VII [51]. As in
conventional twin samples, analyses on Swedish
twins reared apart and together also disclosed high
heritability estimates for lipoprotein(a) [Lp(a)] [52].
Co-twin differences in Lp(a) levels indicated that sex
hormone use might be of importance for Lp(a)
variation in women.
Studies on the complex genetic and environmental architecture of five of the principal components
contained in the insulin resistance syndrome (insulin resistance, BMI, triglycerides, HDL cholesterol
and systolic blood pressure), revealed that they were
more or less influenced by a genetic factor in
common to the measures, whereas three of the
components (triglycerides, insulin resistance and
HDL cholesterol) were associated with each other
because of individual-specific environmental factors
[53]. A strong genetic association was noted in
particular amongst BMI and insulin resistance, and
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to a lesser degree, triglycerides. It is thus likely that a
set of genes in common exists for the five principal
components of the insulin resistance syndrome, and
that this set might initiate the occurrence of insulin
resistance. Information from quantitative genetics
might be valuable as a first step before starting
exploration of molecular mechanisms. Reasonably,
it should be more rewarding to search for candidate
genes and chromosomal regions once the relative
importance of genetic influences is proven.
Allergy and experimental studies of respiratory
symptoms
Another key purpose of the register was to study the
importance of smoking on health, including pulmonary function [54]. The importance of environmental exposures on questionnaire-based diagnoses
of respiratory symptoms and diseases such as
bronchitis were featured in early studies of the older
cohort [55, 56]. In 1971, Edfors-Lubs published a
classical study focused on genetic factors for allergy
[57]. Diagnoses of allergy were obtained from a new
questionnaire sent to 7000 twin pairs in the old
cohort, and sensitivity and specificity were verified
by clinical examination of a subsample. This study
showed the substantial influence of genetic factors
on allergic symptoms.
Throughout the years the twin registry has been
the basis of a number of co-twin control studies with
an experimental design to prove whether different
functional aspects of the respiratory system are
influenced by genetic factors. For example, one very
basic protective mechanism for the lung, namely
tracheobronchial clearance, is highly similar in MZ
twins. Smokers in MZ smoking-discordant twin pairs
had worse tracheobronchial clearance compared
with their twin partner. These results have important consequences, because tracheobronchial clearance is closely related to development of symptoms
of chronic bronchitis [58, 59].
The disease or symptom-discordant twin design
has also been used in a number of studies of
pulmonary function and asthma. There are no
within pair differences in tracheobronchial clearance in asthma discordant MZ pairs. Thus tracheobronchial clearance is not an important factor for
the development of symptoms amongst asthmatics
[60]. Experimental studies on MZ twins discordant
for asthma were also helpful to demonstrate that
bronchial hyper reactivity is not a genetically
influenced trait amongst those developing asthma.
On the other hand, bronchial reactivity was rather
closely related to symptoms of asthma in a subsample of asthma discordant MZ twins [61]. A similar
study of subjects with allergic rhinitis showed
increased bronchial reactivity in the affected twin
compared with the twin partner, even when no
asthma symptom had developed [61]. Bronchial
reactivity also increased during pollen season.
Cross-sectional data on pulmonary function and
on hearing indicate that environmental factors may
become gradually more important with age [62].
Pulmonary function has also been measured in
SATSA. These studies clearly showed that genetic
factors are important for pulmonary function in
elderly individuals (50 years and older). This is
especially interesting as pulmonary measures are
associated with clinical disease and mortality [63].
Furthermore, longitudinal data indicate that pulmonary function predicts cognitive function 6 years
later, and genetic factors are important for this
association [64].
The Young Twin study of 7–9-year-old twins has
evaluated genetic, environmental and gender specific factors of importance for atopic disease [5]. There
were interesting gender differences with boys presenting more symptoms from airways (asthma and
allergic rhinitis) and girls more symptoms from the
skin including eczema. Genetic effects accounted for
approximately 70% of the variation in liability for
asthma and somewhat less for the other atopic
diseases (hay fever, eczema and urticaria). The
clustering of atopic disease in families was nearly
entirely because of a common set of genes, but each
disease manifestation also seems to have specific
genes of importance [65].
Cancer
The association between smoking and cancer was
yet another prime motivation for establishing the
STR in the early 1960s. Large epidemiological
studies showed that smokers were at higher risk
for lung cancer, but the causal nature of the relation
was questioned, the main argument being the lack
of comparability of smokers and nonsmokers. Fisher
argued that the propensity to become a smoker and
susceptibility to lung cancer was because of a certain
genotype [66]. In theory, the twin method was well
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suited to resolve this question, but not in practice.
The long follow-up time required to obtain the
needed precision was not anticipated. After 21 years
of follow-up, only 23 lung-cancer deaths had
occurred amongst the smoking discordant pairs
available from the older cohort of the STR. Two
deaths occurred amongst nonsmoker twins, whilst
21 were found amongst the smoker twins. Despite
small numbers, the results clearly contradicted the
hypothesis that the increased incidence of lung
cancer amongst smokers could be explained by
confounding from genetic factors [67]. Even with
the expansion of the register to include the cohort
born in 1926–58, it was not until recently that
sufficient lung cancer cases have been identified in
order to be able to address this issue with any power.
A burgeoning number of studies have capitalized on
the growing number of cancer cases in the register,
as summarized in the following.
Risk factors for cancer. Data from the STR have been
used to show that low physical activity increases the
risk for colon [68, 69] and endometrial cancers [35],
but not for renal cancer [33]. Overweight has been
associated with an increased risk for endometrial
[35] and prostate cancers [70]. Total food consumption was a risk factor for prostate cancer [70],
whereas fruit and vegetables seemed to have a protective effect for stomach cancer [71]. A protective
effect was also found for fish consumption and
prostate cancer [36]. Cigarette smoking, mild obesity
and a sedentary lifestyle seem to be related to
increased risk for pancreatic cancer, whereas consumption of coffee, moderate amounts of alcohol,
and pork decreased the risk for pancreatic cancer
when evaluated in a 30-year prospective design
[72]. Further, long-term heavy smoking has been
associated with threefold increase in colorectal
cancer after a 30-year follow-up [73].
Foetal factors and risk for cancer. The association
between X-ray exposure in utero and risk of childhood cancer may be confounded by prenatal medical
problems that lead to X-ray examination. Twins
were exposed to X-ray examinations during foetal
life more often than singletons, and often without
any medical incentive. To reduce the potential for
confounding, the relation between X-ray exposure
in utero and risk of childhood cancer was studied in
twins; the results were consistent with nontwin
197
studies, suggesting an increased sensitivity of the
developing foetus to the carcinogenic effects of ionizing radiation [74].
In 1990 Trichopoulos suggested that intrauterine
exposure to endogenous pregnancy oestrogens may
be of importance in breast cancer aetiology [75].
This issue has been investigated in the STR in a
series of studies. Twins have a higher oestrogen
exposure in utero than singleton foetuses, and Braun
et al. [76] found an elevated risk for breast cancer
amongst DZ twins 20–29 years of age when compared with the total population. They also found an
increased risk for testicular cancer amongst the DZ
twins. Both results are congruent with the hypothesis that pregnancy hormones are associated with
risk of testicular and breast cancer. On the other
hand, a study of male breast cancer based on data
from four twin registries showed no increased risk
amongst MZ or DZ twins [77].
Birth weight has been associated with intrauterine oestrogen exposure [78] and two recent case–
control studies on the registry have examined the
importance of foetal exposures on cancer. Hübinette
showed an association between foetal characteristics
(high birth weight and long gestational age) and
breast cancer when breast cancer cases were compared with external controls. When internal
(co-twin) controls were used, birth weight remained
a risk factor and thus the study indicated a causal
effect of birth characteristics on breast cancer [79].
Similar results were found when breast cancer
amongst unlike sexed twins was investigated. Kaijser et al. [37] found a more than 10-fold increase in
breast cancer risk for the highest birth weight
category (>3500 g) as compared with the lowest
(<2000 g). This might indicate that the presence of
androgens in unlike-sexed twin pregnancies modifies the effect of endogenous oestrogens.
Quantitative genetic research. Familial clustering has
been observed for many cancers, but the relative
importance of genetic and environmental effects has
only rarely been studied, mainly because of limited
power in existing databases. The Swedish twins
have been followed for cancer incidence since 1959,
and now quantitative studies start to be meaningful.
Grönberg et al. [80] showed a marked genetic effect
for susceptibility to prostate cancer. Another study
of the registry found a significantly greater risk of
cancer in co-twins of affected MZ twins than
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amongst co-twins of affected DZ twins for prostate
cancer and for cervix cancer in situ, indicating that
hereditary factors are of importance for these cancers. There were also suggestions of genetic effects
for cancer of the stomach, colorectum and breast.
Shared environmental influences were indicated for
stomach, colorectal, lung and breast cancer [81].
Recently, these analyses were extended by using
data from 44 788 twin pairs from Sweden, Denmark and Finland [16]. With this sample, significant genetic effects were identified for breast,
colorectal and prostate cancer. For these cancers,
as well as for leukaemia and cancers of the
stomach, pancreas, lung, ovary and bladder, heritable effects explained between 21 and 42% of the
variation in susceptibility (Fig. 4). The magnitude of
the genetic effects for the different cancers explains
much more of the susceptibility than the oncogenes
known today.
Environmental effects were most important for
cancer susceptibility and were primarily individual
specific, i.e. nonshared. If a DZ twin had a colorectal,
breast or prostate cancer, the risks for the co-twin to
get the same cancer before 75 years were 0.05, 0.09
and 0.03, respectively. Thus, the risk for relatives to
patients with cancer to themselves being affected is
relatively modest, even for close relatives. Environmental effects seem to have the principal role in
causing sporadic cancer, although the relatively
large heritability in some cancers suggests major
gaps in our knowledge of the genetics of cancer.
Cognitive decline and dementia
The STR has been the source for a number of
studies of ageing and dementia, including the
SATSA [82], Origins of Variance in the Oldest-Old;
OCTO-Twin [80], Men and Women’s Ageing GENDER [3], and The Study of Dementia in Swedish
Twins [17].
Cognitive abilities and decline. The SATSA provides
some of the first evidence for the relative importance
of genetic and environmental influences on cognitive abilities in adults over 50 years of age. Eighty
per cent of the differences seen for general cognitive
ability are caused by genetic differences [14]. These
results are based on data from the first measurement
occasion (IPT1), and treat the sample as one age
group, with an age range from 50 to 85 years.
However, a further exploration into age differences
in heritability estimates during the last half of the
lifespan found some differences across age groups
[83]. Older Swedish twins (over 65 years) demonstrated a significantly lower heritability for general
cognitive abilities (g), suggesting that heritability
decreases later in life. Similarly, environmental influences become increasingly important for individual differences in cognitive ability late in life. Indeed,
findings from the OCTO-Twin study [80] confirm the
trend to slightly lower heritabilities for general
cognitive abilities in the oldest old. Heritability for g
in this sample of twins 80 years and older was 62%.
Finkel et al. [23] applied cohort-sequential analyses
combining cross-sectional and longitudinal information from the SATSA study. The results clearly
demonstrate that there is a longitudinal decrease in
genetic variance for the oldest cohorts. Heritability is
relatively stable longitudinally at approximately
80% in the younger cohorts. In the older cohorts,
heritability decreases from approximately 80% at
time 1 to 60% at time 3 [23], that is, the same
Fig. 4 Summary of components of
variation for various cancer forms.
(Modified from [16]).
REVIEW: THE SWEDISH TWIN REGISTRY
heritability as that reported for 80+ year old twins
in OCTO-Twin.
Decreasing heritability may reflect terminal
decline, i.e. individuals who are close to death show
a noticeable accelerating trajectory with poorer
performance the closer they are to death. It may
be that twin similarity for cognitive abilities decreases as members of a twin pair begin to decline at
slightly different times or the rate of decline differs
for the members of the pair. Thus, it appears as if
environmental factors are important for the timing
of entry into or the trajectory of terminal decline.
Hassing et al. [84] could demonstrate that much of
the mortality-related cognitive decline in OCTOTwin was related to cardiovascular disease and
symptoms.
Dementia. As the registry is growing older, a substantial number of twins are experiencing not only
cognitive decline, but also developing dementia.
Despite positive linkages to three genes for Alzheimer’s disease (APP, PS-I and PS-II) [85] and the
association with the susceptibility gene APO-E [86]
little was known about how important, relatively
speaking, genetic effects are for Alzheimer’s disease
and other dementias, particularly for ÔsporadicÕ and
late-onset cases. The first analyses of heritability for
dementia were based on cases identified from SATSA. Genetic effects were substantial (75%) for Alzheimer’s disease, but very modest and not significant
for vascular dementia [87]. Despite the relatively
high heritability, there was a wide range in variation in age of onset, even within MZ pairs, indicating that environmental factors are important either
for accelerating or delaying disease onset in individuals with the same genetic propensity.
Analyses of late onset disorders are plagued by
selection effects due to mortality. Multiple threshold
models, which in part took into account mortality
and which examined age of onset, indicated that
classical analytic designs overestimate shared environmental effects. However, these models suggested
that the previous estimates of heritability reported
by Gatz et al. were not substantially biased by
mortality [27]. Further attempts to evaluate the
role of mortality on the frailty for dementia indicate
that little of the genetic variance for dementia is
shared with genetic variance for death [28].
Thus far, most of the analyses of Alzheimer’s
disease have focused on prevalent cases. Analyses of
199
incident cases from OCTO-Twin and SATSA provide
important new insights into the role of genetic
effects for this disease. First, genetic effects are less
important for late onset Alzheimer’s disease (after
80 years of age) than for onset before 80 years of
age. Secondly, heritability is lower when examining
incident rather than prevalent cases. These findings
point to the increasing importance of environmental
influences on the age of onset rather than the
occurrence of dementia.
Clearly, environmental influences are important
for developing dementia, although genetic effects
have been identified. The STR is also being used to
study risk and protective factors for dementia.
Recent psychiatric illness and depression is associated with elevated risk of Alzheimer’s disease,
supporting the prodromal role of depression in
dementia [88]. Exposure to magnetic fields in the
last occupation prior to onset showed an elevated
risk for Alzheimer’s disease [89]. Case–control analyses with prevalent cases showed low education to
be a risk for Alzheimer’s disease but not dementia in
general. Low education did not significantly predict
incident cases. Affected twins showed less intellectual involvement early in their lives than their twin
partners who remained nondemented [87]. Diabetes
is a significant risk factor for vascular dementia, but
not for Alzheimer’s disease.
Through the SALT screening described earlier,
1565 individuals were positive for suspicion of
dementia and were referred for complete clinical
evaluation by a physician and a nurse. Once a
preliminary in-person evaluation suggested that the
suspected case was demented, the twin partner was
also invited for a clinical work-up. Data from this
study, known as HARMONY, are being used to
continue our efforts in understanding the nature of
genetic and environmental influences on Alzheimer’s disease and other dementias.
Psychiatric disorders and substance use and abuse
Alcohol and tobacco consumption data were initially
included as exposures in early analyses of the registry.
Although substance consumption may be considered
as an ÔenvironmentalÕ effect, individual differences in
these behaviours may reflect genetic variation. Indeed, analyses of smoking behaviour indicate that
genetic effects are important for lifetime regular
smoking. Furthermore, there are important age and
Ó 2002 Blackwell Science Ltd Journal of Internal Medicine 252: 184–205
200
P . L I C H T E N S T E I N et al.
sex effects. Genetic influences are stronger for males
for ever smoking, but genetic influences are stronger
for females for smoking persistence (inability to quit)
[90]. Analyses of the SATSA data indicate that the
heritability of smoking behaviour is quite stable for
men from different birth cohorts. In contrast, genetic
effects are substantially greater in females born after
1940 than those born earlier in the century [91]. It
appears as if genetic propensities to smoke could be
expressed only after culturally imposed restrictions on
female smoking were eased.
Besides information on alcohol and tobacco consumption, the STR contains information about registration with the temperance board due to offences
such as drunken driving for the years 1929–74. The
heritability of alcohol registration in males (54%),
which may be considered a proxy for alcohol abuse, is
considerably greater than that for consumption in the
normal range. Shared environmental effects were
also important, explaining 14% of the variation [92].
Furthermore, genetic effects seemed to be stronger for
registrations due to criminal offences than driving or
public drunkenness [93].
A series of studies in the STR have also explored the
aetiology of depressive symptoms and major depression. Although genetic effects are of relatively little
importance for depressive symptoms in older adults,
they do appear to increase slightly in importance after
60 years of age [94]. In contrast, the heritability of
affective illness is substantial: 0.79 for bipolar illness
and 0.60 for major depression [95, 96]. Kendler et al.
also explored genetic influences for generalized anxiety disorder (GAD). The heritability of GAD is
somewhat lower than that for major depression, but
interestingly, the genetic influences for GAD are
entirely shared with those for major depression [21],
whilst the two disorders have different environmental
determinants. These findings support a common
liability model for GAD and depression.
New directions
Foetal environment as a risk factor for adult disease. Studies during recent years have indicated that
the foetal environment is important for risk factors
for adult chronic diseases. Above all, Barker and his
group have put forward the hypothesis of Ôfoetal
programmingÕ, asserting that foetal malnutrition
during the middle or last trimesters leads to disproportional foetal growth, which in turn programmes
for later disease [97]. Compared with singletons,
twins have lower birth weight because of slower
foetal growth as well as a shorter gestational age.
One study on the STR found no differences in risk of
ischaemic heart disease mortality in twins compared
with the general population [98]. Similarly, the
overall mortality amongst Danish twins and the
general population does not differ after 6 years of
age [99]. Based on these studies and because results
from different studies are negative and/or contradictory, much criticism has been directed towards
the programming hypothesis. The most serious criticism is that other factors have not been properly
taken into account.
There are at least two alternative ways – which
both have empirical evidence that make them
plausible – that can explain the association between
birth weight and CHD in adult age. These are
conditions during childhood and genetic factors.
Co-twin-control studies are ideal for disentangling
causal effects from confounding because of conditions during childhood or genetic effects. In one
study, birth characteristics and subsequent risk of
acute myocardial infarction (AMI) were investigated. In the first part of the study, twins who were
diagnosed with AMI (cases) were compared with
matched unrelated control twin pairs (external
controls) and cases were found to have significantly
lower mean birth weight, birth length and head
circumference than controls. However, in withinpair (co-twin control) comparisons between AMI
cases and healthy co-twins, no significant differences in birth measures were found. Thus, the results do
not support the hypothesis that restricted foetal
growth increases adult risk of CHD. Rather, the lack
of an association between birth characteristics and
AMI within twin pairs suggests previously reported
associations are confounded by genetic and early
environmental factors other than foetal growth
[100]. As already discussed, studies on the STR
have shown a causal effect of birth characteristics
on breast cancer [37, 79]. The registry is currently
collecting birth records on all twin births from 1926
to 1958, in order to augment information on birth
characteristics and subsequently address a number
of issues relating to the importance of foetal environments.
New phenotypes. There are a number of diseases and
syndromes that have not been previously studied in
Ó 2002 Blackwell Science Ltd Journal of Internal Medicine 252: 184–205
REVIEW: THE SWEDISH TWIN REGISTRY
the STR. In other cases, the STR has been used to
identify small subsamples of individuals affected
with a particular disease. SALT screening allows us
to begin explorations of the aetiology of a number of
common, complex disorders about which relatively
little is known. One such domain is gastrointestinal
disorders, including inflammatory bowel disorders,
reflux, irritable bowel syndrome and peptic ulcer.
Recent results from the cohort of twins 55 years of
age and older in SALT indicate that genetic influences account for approximately 30% of the variance in liability to reflux [101]. Analyses of data
from the SALT-pilot suggest that renal and urinarytract problems are associated with irritable bowel
syndrome (IBS) [102], and that eating disorders in
childhood represent a familial environmental influence on IBS.
Data collection efforts are currently underway to
augment diagnostic and exposure information for
Parkinson’s disease, chronic fatigue syndrome, and
periodontal disease. We are also engaged in
analyses of SALT data to evaluate the feasibility
of studies of osteoporosis, osteoarthrosis, rheumatoid arthritis and psoriasis. For all of these diseases
and disorders, we will be able to use the exposure
data collected in the 1960s and 1970s for
prospective analyses of protective and risk factors.
Finally, for many of the disorders and diseases on
which many studies have already been performed,
such as cardiovascular outcomes, asthma and
allergy, and migraine, the updated SALT data will
provide us with opportunities to ask new questions
with greater power.
Co-morbidity. Many of the analyses of STR data were
univariate, focusing on one trait at a time. Nevertheless, with advances in statistical techniques, STR
researchers have started to address issues of comorbidity and the role of genetic factors in mediating the
associations between traits. As mentioned above, the
nature of the association between GAD and depression [21], and between peptic ulcer and Heliobacter
pylori infection [20] have been elaborated using STR
data. Similarly, the role of genetic effects in mediating the association between personality and joint
pain [19] and the associations amongst the components of the metabolic syndrome have also been
explored [53]. STR researchers are also planning
analyses of the association between alcohol consumption and cardiovascular disease, between peri-
201
odontal disease and cardiovascular disease (CVD),
and amongst Parkinson’s disease, depression and
dementia. Numerous other questions concerning
comorbidity can certainly be asked of the data.
Genetic association and linkage. In recent years, twin
researchers have been at the forefront of developing
methodological advancements for linkage and
association studies. A review by Martin et al. [103]
provides a helpful introduction to the many advantages of using twins for exploring genetic aetiology.
Not only can DZ twins be considered in affected sib
pair analyses of linkage, but concordant affected MZ
pairs can be utilized for testing association with
increased efficiency. Furthermore, MZ pairs are ideal
for testing gene–environment interaction.
Thus far, blood has been collected and DNA
extracted on only selected subsamples of the STR.
There are currently samples stored from approximately 5000 individuals who are participants in the
SALT-Pilot study, SATSA, the OCTO-Twin study, the
GENDER study, and HARMONY. There is only one
collaborative effort in which DNA from Swedish
twins is being used for a genome scan study
exploring linkages to cardiovascular disease endpoints. Most other molecular genetic efforts are
focused on either testing for linkage to candidate
regions, or for testing association. STR researchers
are currently focusing their molecular efforts on
blood pressure, obesity, depressed mood, cognitive
decline and Alzheimer’s disease.
Expansion of the registry. As noted above, the STR
has, with a few exceptions, never contacted twins
born after 1958. The exceptions are the ÔYoung
TwinÕ-study [5, 104], the ÔTwin momsÕ study [10,
105], and the study of Phobias in Twins [106].
Clearly, one of the prime goals of the STR is to collect
information from all cohorts of twins. We are considering a number of strategies to reach this goal,
such as web-based contacts followed by telephone
follow-ups for some cohorts as well as cohort
sequential ascertainment of specific age cohorts. For
example, if all 9 and 19-year old were contacted
each year, we would be able to ascertain all twins
currently under the age of 20 in a 9-year period. In
order to contact those currently at age 9, we will
also have to expand the registry to include the
cohort born after 1990. This will be accomplished
by a record linkage to the National Medical Birth
Ó 2002 Blackwell Science Ltd Journal of Internal Medicine 252: 184–205
202
P . L I C H T E N S T E I N et al.
Registry. Finally, we hope to establish a biobank
with DNA from as many pairs of twins as possible.
One step towards this goal is the STR participation
in an EU supported collaborative effort amongst the
European twin registries.
Conclusions
The Swedish Twin Registry (containing approximately 70 000 twin pairs born 1886–1990) has
been developed into a unique resource for clinical,
epidemiological and genetic studies. So far more
than 370 papers have been published using the The
Swedish Twin Registry. Initially the Registry was
first established to study the importance of smoking
and alcohol consumption on cancer and cardiovascular diseases, whilst controlling for genetic propensity to disease. Since then focus has broadened to
most common complex diseases. Currently the
Swedish Twin Registry is being updated on exposure
information and symptoms from a large number of
diseases and syndromes. Furthermore, we are now
planning to establish a large biobank with DNA
from as many pairs of twins as possible.
Acknowledgements
The Swedish Twin Registry is funded by a grant
from the Department of Higher Education, the
Swedish Scientific Council, and ASTRA Zeneca.
SALT was funded by the Swedish Council for the
Planning and Coordination of Research (FRN) and
by a grant from the National Institutes of Health
(grant AG 08724).
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Comprehensive Summaries of Uppsala Dissertations from the
Faculty of Social Sciences 1997.
Received 3 April 2002; revision received 18 July 2002; accepted
19 July 2002.
Correspondence: Nancy L. Pedersen, Department of Medical
Epidemiology, Karolinska Institutet, Box281, SE 171 77 Stockholm, Sweden (e-mail: [email protected]).
Ó 2002 Blackwell Science Ltd Journal of Internal Medicine 252: 184–205