Genetic and Environmental Factors in Relative Body Weight ...

Behavior Genetics, Vol. 27. No. 4, 1997

Genetic and Environmental Factors in Relative BodyWeight and Human Adiposity

Hermine H. M. Maes,1,2 Michael C. Neale,1 and Lindon J. Eaves1

We review the literature on the familial resemblance of body mass index (BMI) andother adiposity measures and find strikingly convergent results for a variety of relation-ships. Results from twin studies suggest that genetic factors explain 50 to 90% of thevariance in BMI. Family studies generally report estimates of parent-offspring and siblingcorrelations in agreement with heritabilities of 20 to 80%. Data from adoption studiesare consistent with genetic factors accounting for 20 to 60% of the variation in BMI.Based on data from more than 25,000 twin pairs and 50,000 biological and adoptivefamily members, the weighted mean correlations are .74 for MZ twins, .32 for DZ twins,.25 for siblings, .19 for parent-offspring pairs, .06 for adoptive relatives, and .12 forspouses. Advantages and disadvantages of twin, family, and adoption studies are re-viewed. Data from the Virginia 30,000, including twins and their parents, siblings,spouses, and children, were analyzed using a structural equation model (Stealth) whichestimates additive and dominance genetic variance, cultural transmission, assortative mat-ing, nonparental shared environment, and special twin and MZ twin environmental var-iance. Genetic factors explained 67% of the variance in males and females, of whichhalf is due to dominance. A small proportion of the genetic variance was attributed tothe consequences of assortative mating. Ths remainder of the variance is accounted forby unique environmental factors, of which 7% is correlated across twins. No evidencewas found for a special MZ twin environment, thereby supporting the equal environmentassumption. These results are consistent with other studies in suggesting that geneticfactors play a significant role in the causes of individual differences in relative bodyweight and human adiposity.

INTRODUCTIONThe genetics of obesity has been the topic of atleast eleven reviews in the last decade (Price, 1987;Bouchard and Perusse, 1988, 1993, 1994; Stun-kard, 1991; Grilo and Pogue-Geile, 1991; Meyerand Stunkard, 1993, 1994; Sorensen and Stunkard,1994; Sorensen, 1995; Meyer, 1995). The focus ofthese reviews was usually on a particular type of

1 Virginia Institute for Psychiatric and Behavioral Genetics,Box 980003, Richmond, Virginia 23298.

2 To whom correspondence should be addressed. Fax: (804)828 8801. E-mail: [email protected].

study, be it adoption, twin, or family study. In re-cent years the growing body of work on the searchfor obesity genes has lead to reviewing findingsfrom linkage and association studies (Bouchard,1995). The shift of the focus of research in obesityis based in part on the increasingly widespread un-derstanding that genetic factors play a significantrole in the variation of body fatness. However, theagreement on "just how much of the variation isexplained by genetic factors" is not great. Theagreement within type of study is larger than be-tween study type, and, results from twin studies

3250001-8244/97/0700-0325$12.50 © 1997 Plenum Publishing Corporation

KEY WORDS: Human adiposity; body mass index; body weight; genetic factors; environmentalfactors; heritability; twin studies; family studies; adoption studies; review.

326 Maes, Neale, and Eaves

tend to indicate a higher heritability for body fat-ness than do adoption and family studies. Few re-ports have incorporated relatives of different typesin an attempt to integrate results from differentstudies. In this paper we

(i) summarize findings from different typesof studies on the genetics of obesity,

(ii) evaluate these findings in light of the as-sumptions of various designs,

(iii) test with results from an extended pedi-gree design, and

(iv) highlight special issues.The most widely used measure for obesity is

the body mass index (BMI), measured as weight(kg) divided by height2 (m). Skinfold measure-ments have been used in selected studies. Only onestudy has obtained more direct estimates of bodyfatness with underwater weighing on a large sam-ple of family members (Bouchard and Perusse,1988). The focus of this review is on BMI.

REVIEW OF STUDIESTwin Studies

The twin design has been extensively used tostudy the genetics of obesity. Most twin studieshave found evidence for the genetic factors on obe-sity. Reported heritabilities vary mostly between.50 and .90 (see Table I for review). Estimates fromtwin samples from adolescents tend to be higherthan those from adults.

BMI in Adolescent Twin Samples

Relatively few studies have focused on ado-lescent twins (Allison et al, 1994; Bodmtha et al,1990; Wang et al., 1990; Maes, 1992; Beunen etal, 1996; Meyer et al., 1996, 1997). The largeststudy is the MCV Twin Study (Bodurtha et al,1990; Meyer et al, 1996). In this longitudinal pro-ject, approximately 500 white and black twin pairswere ascertained through the schools in Virginiaand invited to participate in a range of cardiovas-cular evaluations, including an anthropometric as-sessment. Twins were first measured either at 11,and followed up every 18 months through the ageof 17, or at 9.5, and followed up through age 14.Genetic model fitting of BMI at age 11 (Bodurthaet al, 1990) and later ages (Meyer et al., 1997)showed high heritabilities (.87) for Caucasian pairs.

Similar estimates for heritability of BMI were re-ported by Allison et al. (1994) for 112 white and126 black twin pairs between 12 and 18 years ofage from Georgia, Kentucky, and Indiana, althoughboth genetic and environmental influences exerteda greater influence on BMI of black than white ad-ollescents. The Leuven Longitudinal Twin Study(LLTS) (Maes, 1992; Beunen et al., 1996) is a sim-ilar project in which 105 twin pairs were ascer-tained from the East Flanders Prospective TwinSitudy. Twins first participated at age 10 and werefollowed up every 6 months through the age of 16.The estimated heritability was between .89 and ,92at age 10 (Maes, 1992) up to age 14 (Beunen etal., 1996). A study (Wang et al, 1990) on 7- to12-year-old Chinese twins reported correlationsconsistent with a heritability of .90. Height andweight were also measured for 1412 twin pairsfrom the Virginia Twin Study of Adolescent Be-havioral Development (VTSABD) (Meyer et al.,1996). Heritability estimates increased from .67 for8- to 10-year-old twins, to .88 for 11- to 13-year-old twins, to .93 for 14- to 16-year-old twins.

Skinfolds in Adolescent Twin Samples

Two adolescent twin studies have obtainedskinfold measurements as adiposity indicators(Brook et al, 1975; Borjeson, 1976). Triceps andsubscapular skinfolds were obtained from 222 like-sex twins aged 3 to 15 (Brook et al, 1975). Clas-sical heritability estimates [2(rMZ — rDZ)] obtainedfrom MZ and DZ correlations were .98 for trunkfat and .46 for limb fat. Borjeson (1976) selected101 7-year-old Swedish twin pairs on the basis oftheir height and weight and calculated intrapairmean differences for triceps, subscapular, and ab-dominal skinfolds. For all measures, greater simi-larity was found for MZ than for DZ twins,supporting a genetic variability in adiposity. Skin-fold measures were also obtained in the MCV andLLTS twin samples (Bodurtha et al, 1990; Maes,1992; Beunen et al, 1996). Results from thesestudies provided more evidence for a strong geneticcomponent to fatness in adolescence.

BMI in Adult Twin Samples

Data from adult samples are abundant. Heightand weight data have been collected on over 20,000twin pairs, aged 18 or older. Regardless of sample,age and sex of the twins MZ twin pairs are consis-

Relative Body Weight and Adiposity 327

Table I. Review of Twin Studies on Obesity

Author Year Sample Size* Method Age (yr) mz dz h2

Feinlieb et at.Fabsitz et al.

Stunkard et al.

Bouchard et al.Austin et al.

Hunt et al.

Bodurtha et al.Wang et al.Stunkard et al.

Macdonald &Stunkard

Turula et al.

Price & Gottesman

Hewitt et al.Selby et al.

Tambs et al.

Korkeila et al.

Fabsitz et al.

Martin; In Neale& Cardon

77 NAS-NRC80 NAS-NRC

86 NAS-NRC

87 Quebec87 Kaiser Permanente

California89 Utah

90 Virginia90 China90 SATSA

90 Britain

90 Finnish TR

91 Britain

91 Birmingham, UK91 NAS-NRC

9 1 Norway

9 1 Finland

92 NAS-NRC

92 AustraliaNH&MRC

5141,028

4,071

156434

154

259110311

362

45

39

7,730

343880

513

267169 +rel

7,245

243

3,569

FalconerFalconer

Falconer

Beta modelFalconer

Falconer

LISRELChristianLISREL

Corr

LISREL

MZA

LISRELChristian

Falconer

SAS NLIN

LISREL

LISREL

LISREL

TT

T

T

T

TTA

T

A

T

T

ATTT

T

T

T

InductionInduction2540-5042-56Induction250 +Ad FEnvir vars26 +Envir vars117-12MFMFFMFMAd, -55MF

16-24Induction2540-5042-5619 +

18-24MF25-34MF35-44MF45-54MF

TotalMF

Induction2540-5042-5618-30MFMF31 +MFMF

.71

.83

.62

.66

.71

.81

.66

.88

.68

.68

.84

.90

.70

.66

.74

.66

.39

.64

.76

.68

.85

.91

.87

.83

.80

.58

.77

.72

.70

.70

.64

.73

.59

.56

.70

.68

.80

.73

.72

.69

.77

.78

.70

.69

.39

.43

.29

.41

.42

.42

.24

.34

.41

.43

.44

.51

.15

.25

.33

.27

.25

.54

.52

.47

.50

.47

.20

.41

.33

.39

.39

.33

.30

.29

.20

.38

.31

.42

.44

.35

.35

.32

.30

.23

.37

.32

.24

.64

.80

.67

.51

.57

.77

.84

.10

.89

.63

.54

.51

.87

.70'

.66'

.74

.69

.72

.66

.61

.75

.87

.68

.58

.81

.63

.39s2 = .08<? = .02r2 = .03

.72

.71

.74

.69

.67

.68

.67

.53

.72

.68

.82

.78

.73

.73

.80

.78

.70

.69

rg = .60rg = -71

rg = -69

c2 = .14c2 = .18

d2 = .57d2 = .37

d2

2G's

d2 = .44d2 = .38


Table I. Continued

Author Year Sample Size* Method Age (yr) mz dz h2

Meyer; in Neale &Cardofl

Maes

Allison et al.

Korkeila et al.

Harris et al.

Canmichael &McGue

Herskind et al.

Allison et al.

Beunen et al.

Meyer et al.

Meyer er al.

92 VA 30,000Virginia TRAARP

92 Belgium

5.588 LISREL

105 MX

94 Georgia, Kentucky, 496 LISREL,Indiana

95 Finland

95 Norway

95 MinneostaMTRMTSADA

96 Denmark

96 FinlandJapanArchival

96 Belgium LLTS

96 Virginia MCVTwin Study

96 Virginia ABD

MX

5,967 LiSREL

2,570 LISREL

610 LISREL282141

1,233 MX

17 Regression1026

105 MX

311-w MX81b

1,412 MX

T AdMFMF

T 10MFMF

T bMFMFwMFMF

T 18-54MF24-60MF

T 8-25

T 18-3839-5960-81

T 46-59MF60-76MF

A AdA 47-87A AdT 10

11121314

T 1112.51415.517

T 8-1011-1314-16

.70

.74

.87

.90

.85

.93

.89

.87

.65

.65

.62

.68

.7079

.83

.60

.63

.53

.78

.51

.74

.90

.90

.88

.91

.88

.71

.88

.93

.31

.35

.25

.60

.26

.49

.79

.54-.19

.67

.31

.11

.34

.27

.31

.28

.36

.36

.23

.29

.33

.22

.20

.34

.33

.41rp

r*

.52

.55

.52

.55

.51M.89.91.88.93.90

.36

.51

.46

.72

.75

.89

.93

.91

.67

.64

.61

.67

.71

.79

.82

.70

.63

.46

.77

.61

.75= .50= .70

.89

.92

.91

.92

.90F

.89

.89

.91

.90

.91

.67

.88

.93

d2 = .37

rg = .97rg = .98

d2

rg = .62

M rg = .83-.97F r, = .85-.97

rg = -91rg = .89

• mz, MZ correlation; dz, DZ correlation; h2, herilability; rt, genetic correlation; cP, dominance; T, reared together; A, reared apart;Ad, adult; w. white; b, black; M, male; F, female; Envir vars, environmental variables; Rel, relatives; s2, sibling environment; c2,cultural transmission; r2. GE covariance; rp, partial correlation; r.,, semipanial correlation.

b Number of twin pairs.' Direct estimate from MZAs.


tently more similar in BMI than are DZ twin pairs.One of the largest and most published data sets isthe National Academy of Sciences-National Re-search Council (NAS-NRC) sample. Twin pairs forthis registry were ascertained through the VeteransAdministration and all had prior military service.Height and weight measurements were availablefrom induction (mean age, 20) to the military ser-vice (N = 13,376) and from three follow-up ex-aminations with mean ages of 48 (N = 514), 59(N = 362), and 65 (N = 267). BMI was also cal-culated from 25-year follow-up questionnaire dataon 4071 pairs. Heritability estimates of BMI at in-duction vary between .64 and .82 based on varioussubsets of the sample (Feinlieb et al., 1977; Fabsitzet al., 1980, 1992; Stunkard et al., 1986; Selby etal., 1991). MZ twin correlations gradually decreasewith age; the trend is less clear for the DZ corre-lations, leading to more variable heritability esti-mates (.51 to .84).

Scandinavian SamplesAmong the larger twin studies with data on

BMI are the Scandinavian. Results from the Finn-ish Twin Registry on approximately 7000 twinpairs showed that 53-74% of the total variance isexplained by genetic factors (Turula et al., 1990;Korkeila et al, 1991, 1995). Heritability decreasedwith age in both genders and was significantlyhigher in males versus females. Higher heritabili-ties for males (.74) than females (.69) were alsoreported on 362 Swedish twins reared togetherfrom SATS A (Stunkard et al., 1990). Different her-itability estimates were obtained for males (.46-.61) and females (.7S-.77) with data on 1233 twinsfrom the Danish Twin Registry (Herskind et al.,1996). Lower estimates of the genetic variance inmales (.71) versus females (.79) were also foundfor 2570 18- to 25-year-old twins from the Nor-wegian Twin Panel, with a male-female correlationof .62 (Harris et al., 1995). The study on BMI infirst-degree relatives in Norway, which included196 twin pairs (Tambs et al., 1991), estimated thebroad heritability at .39, the lower limit of esti-mates from twin studies.

The Australian and Virginian SamplesData from two other large twin samples are

discussed by Neale and Cardon (1992). The first isthe Australian NH and Medical Research Council

(NH&MRC) twin sample, which contains 3569adult twin pairs. Heritability was estimated sepa-rately for a younger (age 18-30) and an older(30+) cohort. Consistent with results from theNAS-NRC sample, heritability was higher for theyounger (.78-. 80) than the older (.69-.70) twins.The second consists of 5580 twin pairs from the"Virginia 30,000" (Eaves et al., 1996), whichcombines data from the Virginia Twin Registry andtwins from the American Association for RetiredPersons (AARP). According to the best-fittingmodel, genetic factors explained 72 and 75% of thevariation in BMI in mates and females, respec-tively.

Other Twin Samples

Several other twin studies have reported sim-ilar heritabilities. Consistent with other results ondifferent age groups, estimates from 1033 Minne-sota twin pairs (from the Minnesota Twin Studyand MTSADA) decreased with increasing age from.82 to .63 (Carmichael and McGue, 1995). Datafrom Utah pedigrees including 154 twin pairsshowed evidence for genetic factors contributing 51to 54% of the variance (Hunt et al., 1989). Over-correction of the heritability estimate for environ-mental variables reduced the heritability from .89to .73 for data on 434 adult female twins from theKaiser Permanente Twin Registry in California(Austin et al., 1987). Heritability was estimated at.87 from 80 British 16- to 24-year-old twins (Hew-itt et al., 1991).

Nonadditive Genetic FactorsBased on most large-scale Scandinavian twin

studies (Stunkard et al., 1990; Tambs et al., 1991;Korkeila et al., 1995) and the Australian and Vir-ginian twin studies (Neale and Cardon, 1992), asignificant proportion of the genetic variance is ex-plained by nonadditive factors. Given that samplesizes of at least 1000 twin pairs are needed to detectdominance genetic variance accounting for 20% ofthe total variance in many circumstances (Martinet al., 1978; Maes, unpublished data), the fact thatonly samples of this size or more showed evidencefor dominance is expected. Shared environmentalfactors do not appear to contribute to the variationin BMI, except in one study in which they explain14-18% of the variance (Hunt et al., 1989). Sincethe sample sizes of several studies were large


enough to detect such effects, it is unlikely thatenvironmental factors shared with family memberscontribute substantially to variance in BMI.

Twins Reared ApartThese findings of significant evidence for ge-

netic factors on BMI were corroborated by the re-sults of studies of twins reared apart (Stunkard elal., 1990; Price and Gottesman, 1991; Allison etal., 1996a) which report heritability estimates in thesame range as those of twins reared together. TheMZA correlation serves as a direct estimate of theheritability. MZA pairs (N = 311) from theSATSA sample correlate .66-.70 (Stunkard et al.,1990). The MZA correlation for females (.39) wasmuch lower than that for males (.64) for a smallsample of 45 British twins (Macdonald and Stun-kard, 1990). Another sample of 34 British twinsreared apart showed a correlation of .61 (Price andGottesman, 1991). The partial correlation—takinginto account environmental covariates—between53 MZAs combined from Finnish, Japanese, andarchival data was .50 (Allison et al., 1996a).

Longitudinal StudiesAll but five studies to date have been cross

sectional in nature and therefore do not allow us totest whether the same genetic factors account forheritability at different ages. Two longitudinal stud-ies on adults have included estimates of the geneticcorrelation between different ages. In the NAS-NRC twin panel the estimates between young (ageat induction) and middle adulthood (25-year fol-low-up) ranged from .60 to .71 (Fabsitz et al.,1980; Stunkard et al., 1986), and those betweenmiddle and late adulthood, from .93 to .96 (Fabsitzet al., 1992). From a developmental analysis of thesame sample, Fabsitz et al. (1992) found two in-dependent genetic contributions to the variability inBMI: one at induction and one between age 20 andage 48. A much higher genetic correlation (.97-.9S)was reported for a shorter time interval (6 yearsbetween two measurements) for the Finnish TwinCohort (Korkeila et al., 1995). Repeated measureswere collected on three juvenile samples, the MCVTwin Study, the VTSABD, and the LLTS. Meyeret al. (1996) reported genetic correlations (rg be-tween .83 and .97 for males and between .79 and.97 for females in the MCV Twin Study, indicatingthat, for the most part, the same genetic factors in-

fluence BMI from age 11 to age 17. Similar resultswere found for the VTSABD (rg, between .86 and.88) (Meyer et al., 1996) and the LLTS (rg, between.78 and .98) (Maes, unpublished data).

Family StudiesFamily studies have been very popular in the

fidd of obesity. Any type of relationship betweenfamily members fall under this category. The mostcommonly studied are parent-offspring and siblingrelationships (see Table II). Most studies reportcorrelations; few estimate heritability. The majorityof the family studies concluded that genetic factorscontribute to variation in BMI.

The Quebec Family StudyOne of the larger, and certainly most pub-

lished, bodies of results of family studies are thoseof" the Quebec Family Study (Bouchard et al.,1987; Bouchard and Perusse, 1994). Data were ob-tained from 1698 members of 409 families ofFrench descent in Quebec. They comprised biolog-ical and adoptive relationships as well as twins.BMI and other fatness indicators were available for1239 parent-offspring pairs, 370 full sibs, 348spouse pairs, 322 parent-adopted child pairs, 120siblings by adoption, 183 second-degree relatives,69 DZ twins, and 87 MZ twins. The estimated cor-relations were .23 for parent-offspring, .26 for sib-ling, and .10 for spouse correlations. The betamodel (Rice et al., 1978) was used to analyze thedata, which allows a common environment param-eter that may differ for sibs, DZ, and MZ twins.The total transmissibility for BMI was .40; biolog-ical inheritance accounted for 10% of the variationin BMI. According to Table 3 of Bouchard et al.(1987), the nontransmissible variance was corre-lated .0 in siblings, . 16 in DZ twins, and .96 in MZtwins.

The Canada Fitness SurveyA second large Canadian study including sev-

eral family relationships is the Canada Fitness Sur-vey. Perusse et al. (1988) used the tau model (Riceet al., 1978) to estimate transmissibility for BMIfrom 4825 spouse pairs, 8881 parent-offspringpairs, 3929 sibling pairs, and 128 second-degreerelatives, totaling 18,073 subjects. The correlationsfor parent-offspring, siblings, and spouses were


Table II. Review of Family Studies on Obesity

Author

Bayley

Tanner &Israelsohn

Mueller

Byard et al.

Heller et al.

KJioury et al.

Longini et al.

Province & Rao

Zonta et al.

Friedlander et al.

Bouchard et al.

Perussc et al.

Byard et al.

Hunt et al.

Bums jt al.

Year Sample

54 Berkeley

63 London

78 Columbia

83 Ohio

83 Framingham

83 Cincinnati

84 Tecumseh, MI

85 Brazil

87 Italy

87 Jerusalem,ethnicallydiverse

87 Quebec

88 Canada

89 Ohio

89 Utah

89 Iowa

Size'

43f

117f

111

523

7,948

877125wp52bp

5,174

6,750l,076f

179f

4,203f

1,698409f

18,073

500

1,102

284f

Method

Corr

Corr

Corr

Con-

Con-

Con-

Con-model

Tau model

Con-

BetamodelTau model

Con-

Con-

Con-

Age (yr)

0-21

0-7

20-60<7A>7A0-18

30

20-49

w<20>20

b<20>206-19

20-740-30

bmic

17 +

0-

7-69

2-18

17 +

5-14

po

fs, -.54-23fd, -.16-32

ms, -.32-51md, .07-.81

fs, .00-. 15fd, .30-.44

ms, -.09-. 10md, .03-. 16

fs, .27fd, .23

ms, .23md, .21

pc.23-.29.Ol-.ll

.07-.08

.03-.37fo, .27

mo, .25

.25fo, .25

mo, .25fo, .31

mo, .37.22

fs, .27fd, .21

ms, .21md, .17

.23

.20

fs, -.10-.40fd, .07-.70

ms, .15-.64md, -.12-34

fo, .2S-.32mo, .21 -.36

sib sp

.17

.51

.21bb, .20-.50ss, .30-.55bs, .15-.40bb, .27-.29 .19ss, .24-.2Sbs, .09-.29

.35 .14

.16

.39 .20

.48

.36 .12

.17ss, .30 .15

.36 .13

.33 .08bb, .38ss, .43bs, .25

.26 .10

.31 .12

bb. .31 -.49bs, .24-.44ss, .43-.61

bb, .18 .10ss, .15

.51 .16

av, .05-. 14

hz = .31-37co, -.04

av, .14

av, -.11gpc. .05

h2 = .24h'- = .21h- = .36-.S2


Table II. Continued

Author

Price et at.

Gam et at.

Moll et al.Ness et al.

Tambs et al.

Donahue et al.

Ayatoilahi

Nirmala et al.

Ramirez

Esposito-delPuente et al.

Sellers et al.

Guillaume et al.Rotimi & Cooper

SkinfoldsTwin studies

Brook et al.Borjeson

Year

90

89

9191

91

92

92

93

93

94

94

9595

7576

Sample

New York

PhiladelphiaBostonBuenos AiresTecumseh, MI

IowaNew York?

Norway

Minnesota

Iran

India

Utah

Italy

Iowa

BelgiumIllinois

LondonSweden

Sizeb

566o

l,743r

I,4l9o

1,58060bf

961bf

74,994

1,767712f

1,207

1,691486f

529

110

1,166

1,028534b

222p!27p

Method

Relativerisk

Relativerisk

PAPCon-

Pointer

SASNUN

Corrquimiles

Corrquintilcs

Con-

Con-

Corr

Corr

p valuesCon-

FalconerH index

Age (yr)

Ad

12-249-74

bw

19+h2 = .39s2 = .08c2 = .02r2 = .03

13-17

6-12 bmi'

5 +

11-20

10

Ad F

6-1210-40

3-157

po

fo,fs.fd.

mo,ms,md.ms,md,

fs,fo.PS.pd.fo,

mo.

ms.md,

fs.fd.fo.

mo.md.

md.ms.

mz.

.22

.16

.18

.19

.21

.17

.20

.20

.21

.32

.38

.37

.39

.20

.20

.15

.17

.24

.18

.03

.29

.12

.38

.27

.21

.33

.26

.77

sib

bb.ss,bs,

bb,ss,bs.bb,ss,

sp

RR, 1.84ch/ad

RR, 1.5

.35 .17 av .08-.09

.36 .44

.22 .09

.21 .12 2nd rel.

.26 -.22-.072>

.23

.55 .37

.28

.36

.46 - .05

.21

ss, .17-.21 2nd rel.

ss,bb,sb,

dz.

.07-. 10

.28 .12

.30

.27

.40 If = .74


.20, .31 and .12, respectively. The model fitted thedata well and the transmissibility was estimated at.36. Hypotheses of no assortative mating, no pa-rental transmission, and no shared sibling environ-mental effect were rejected. This model does notallow the separation of biological and culturaltransmission.

The Tr0ndelag Study in Norway

The study on BMI in a Norwegian sample offirst- and second-degree relatives is without a doubtthe largest of its type (Tambs et al., 1991). Thissample includes 23,936 spouse pairs, 43,586 par-ent-offspring pairs, 19,157 sibling pairs, and more

than 2400 second-degree relatives. The estimatedcorrelation for spouses was .12, which was ascribedto assortative mating. The average parent-offspringcorrelation was .20 and varied little by the age ofthe parents or the offspring. Similarly the siblingcorrelations showed a narrow range by sex, rangingfrom .21 for brothers to .26 for sisters. Second-degree relatives did not correlate significantly. Us-ing a path model that includes genetic and culturaltransmission, assortative mating, and genotype-en-vironment covariance, Tambs estimated the heri-tability at .39, of which a large proportion was dueto dominance. Cultural transmission (.02) and sib-ling environment (.08) were both significant, intro-ducing genotype-environment covariance (.03).

Table 11. Continued

Author Year Sample Size* Method Age (yr) po sib sp

Family studiesHawk & 79 London 1,083 Corr Ad fs, .19 bb, .30

Brook fd,.10 ss, .05ms, .21md, .20

Gam et al. 79 Tecumseh, MI 6,405p Corr 5-18 po, .19-.21 si, .2J-.29apo, .09-. 11 asi. .12-.29

Mueller & 80 Philadelphia 114b Corr 6-12 b bb, .19-.23Malina 101w ss, .58-.S9

bs, .33-.44w bb, .26-36

ss, .19-.37bs, .33-.46

Kaur& Singh 81 India 321 Coir 18-39 .15-27 .20-24 .05-.24Kapooreia/. 85 India 120 Corr Newborn md, .01-.33 ss, .47-.6S

300 ms, .0-.2818-26 md, .27-.3S

Bouchard 85 Quebec 971 Corr 8-26 asi, -.06-. 15 .18-.49et al. unr, .05-17 dz, .30-.49

co, .06-.30 mz, .76-.S7Pcrusse et al. 87 Quebec 1,258 Corr 0- .22 .26 .06

304f CorrBouchard 87 Quebec 1,698 Corr .18-.34 .18-.42 -.01 av, .00-30

409f apo, .19-28 asi, .06-. 18 -.19dz, .30-.49mz, .76-.87

Commuzzie 94 Texas 408 16-84 h2 = .26-.5Set al. rt = .37-.9S

" po, parent-offspring correlation; sib, sibling correlation; sp, spouse correlation; av, avuncular correlation; co, cousin correlation;gpc, grandparent-child correlation; 2nd rel, second-degree relatives correlations; A2, heritability; .s2, sibling environment; c2, culturaltransmission; r2, GE covariance; o, obese; RR, relative risk; ch/ad, child/adult; ad, adult; M, male; F, female; f, families; p, pairs;r, relatives; w, white; b, black; pc, parent-child; fo, father-offspring; mo, mother-offspring; fs, father-son; fd, father-daughter;ms, mother-son; md. mother-daughter; bb, brother-brother; ss, sister-sister; sb, brother-sister; a, adoptive; unr, unrelated.

b Number of individuals.• BMI-like measure.


lability at .39, of which a large proportion was dueto dominance. Cultural transmission (.02) and sib-ling environment (.08) were both significant, intro-ducing genotype-environment covariance (.03).According to this study, the largest proportion ofvariance is explained by the unique environment.

Parent-Adult Offspring BMI Data

Three American and one ethnically diversestudy in Jerusalem presented parent-offspring cor-relations with offspring aged 18 or older. One ofthe early large family studies was the FraminghamHeart Study, in which 7948 individuals were stud-ied (Heller et al., 1983). In Jerusalem, 4203 fami-lies from different ethnic backgrounds participatedin a family study (Friedlander et al., 1987). Some1166 adult women were measured in Iowa (Sellerset al., 1994). The correlations between parents andtheir adult offspring in these three studies variedbetween .17 and .27. In the Princeton School Dis-trict Family Study in Cincinnati (Khoury et al.,1983), separate parent-offspring correlations werereported for black and white probands and their rel-atives (N = 1928). Correlations varied between .01and .11 for white and between .03 and .37 for blackpairs with offspring aged 20 years or older.

Parent-Adolescent Offspring BMI Data

Twelve studies reported parent-offspring cor-relations in which children or adolescents betweenage 5 and age 22 were included. Longini et al.(1984) modeled familial correlations based on asample of 5174 individuals from Tecumseh, Mich-igan. Province and Rao (1985) used a tau model(Rice et al., 1978) to derive transmissibility esti-mates from 6750 individuals comprising 1076 fam-ilies in Brazil. Both studies reported parent-offspring correlations between .25 and .27 for fa-ther-offspring and mother-offspring pairs. A obe-sity score, comparable to BMI, was calculated for179 Italian families and parent-offspring correla-tions varied between .31 and .37 (Zonta et al.,1987). A small Italian study (N = 110) presenteda correlation of .38 for fathers and .27 for mothersof 10-year-old children (Esposito-del Puente et al.,1994). The correlations between 13- to 17-year-oldadolescents and their parents from 712 families inMinnesota ranged from .32 to .39 (Donahue et al.,1992). A BMI-like measure was also obtained from1207 parents and 6- to 12-year-old offspring in

Iran, which correlated between .15 and .20 (Aya-tollahi, 1992). The parent-offspring correlation cal-culated from 1691 individuals from 486 families inIndia was .24 (Nirmala et al., 1993). The widestrange of parent-offspring correlations (.03-.29)was found for 529 individuals from Utah pedigrees(Ramirez, 1993). Bums et al. (1989) and Moll etal. (1991) reported on 1607 individuals from 284families in Iowa. From father-offspring (.2S-.32),mother-offspring (.21-.36), sibling (.35-.51), andspouse (.16-. 17) correlations, heritability was esti-mated between .36 and .52. Significant associationswere found in 1028 6- to 12-year-old Belgian chil-dren and their parents (Guillaume et al, 1995).Correlations between 105 Belgian twin pairs andtheir parents (N = 181) were .21 for father-off-spring and .36 for mother-offspring pairs (Maes,1992). All the previous studies were based on whitesamples. Only two studies to date have reportedcorrelations from black (N = 60) and white (N =961) families (Khoury et al., 1983; Ness et al.,1991). Parent-offspring correlations were higherfor whites (.23-.29) than blacks (.07) in the firstreport but similar for blacks (. 16) and whites (. 18)in the later report. Rotimi and Cooper (1995) foundcorrelations of between .26 and .33 for 534 AfricanAmerican mothers and their 10- to 40-year-old off-spring in Illinois. Two early studies reported cor-relations between parents and their offspringmeasured annually of from 0 to 21 (Bayley, 1954)and from 0 to 7 (Tanner and Israelsohn, 1963). Be-cause of the small samples, a large variability wasobserved in the correlations.

No clear trends were observed in the parent-offspring correlations by gender of the parents orthe offspring. The range of maternal correlationswas .03 to .38, compared to .12-.39 for the paternalcorrelations. Correlations between parents and theirdaughters ranged from .01 to .39; those with par-ents and their sons, between .0 and .37.

Adult Sibling BMI Data

Most studies that include parent-offspringcorrelations also report sibling correlations. Adultsibling correlations for BMI vary between .09 and.29 for siblings from the Framingham Heart Study(Heller et al., 1983) and from Tecumseh (Longiniet al., 1984), between .25 and .43 for ethnicallydiverse siblings in Jerusalem (Friedlander et al.,1987), between .15 and .18 for sister and brother


pairs from Utah pedigrees (Hunt et al, 1989), andbetween .17 and .21 for women in Iowa (Sellers etai, 1994). Mueller (1978) estimated sibling corre-lations separately for siblings who were less than7 years different in age versus siblings more than7 years apart. Based on a sample of 111 individualsin Columbia, he found higher correlations betweensiblings close in age (.51) than between siblingsfarther apart (.21).

Adolescent Sibling BMI DataIn the Pels Longitudinal Study, approximately

500 participants were measured annually from 0 to18 years and, again, at age 30 (Byard et al., 1983,1989). This sample consisted of 523 sibling pairsand correlations were calculated between brothersand sisters at the same age. Brother correlationsranged from .31 to .49, correlations between sistersvaried between .43 and .61, and brother-sister cor-relations varied from .24 to .44. In most other sib-ling studies pairs were not measured at the sameage, but in most cases age-regressed BMI scoreswere used to calculate correlations. In three studiescorrelations between brothers (.30-.55) were higherthan correlations between sisters (.21-.28) andbrother-sister correlations (.27-.36) (Nirmala et al.,1993; Ramirez, 1993; Rotimi and Cooper, 1995).Three other studies reported a sibling correlation ofbetween .30 and .36 (Longini et al., 1984; Provinceand Rao, 1985; Zonta et al., 1987). Khoury et al.(1983) and Ness et al. (1991) reported a highersibling correlation for blacks (.36-.48) than forwhites (.16-.35).

The majority of the parent-offspring correla-tions were between .10 and .40, and most siblingcorrelations ranged from .15 to .55. These corre-lations are consistent with heritability estimates ofbetween .20 and .80.

BMI Data from SpousesTwenty studies reported spouse correlations

on BMI. Most of these vary between .10 and .19(Allison et al., 1996b; Heller et al, 1983; Longiniet al., 1984; Province and Rao, 1985; Zonta et al.,1987; Bouchard et al., 1987; Perusse et al., 1988;Hunt et al., 1989; Bums et al., 1989; Moll et al.,1991; Tambs et al, 1991; Rotimi and Cooper,1995; Annest et al, 1983). Lower estimates (-.05,.05, .07, and .08, respectively) were obtained byRamirez (1993), Rice et al (1995), Maes (1992),

and Friedlander et al. (1987). Higher estimates (.23and .37, respectively) were found for spouses inIran (Ayatollahi, 1992) and in India (Nirmala et al,1993). A higher correlation was observed for black(.20-.44) versus white (.09-. 14) spouses (Khouryet al, 1983; Ness et al, 1991).

Skinfold Family Data

Not all family studies on the genetics of obe-sity included BMI. Four studies reported parent-offspring correlations for several skinfolds. Par-ent-offspring correlations ranged from .20 to .21for 1083 British individuals (Hawk and Brook,1979), from .15 to .27 for 321 individuals fromIndia (Kaur and Singh, 1981), and between .27 and.35 for 300 mother-daughter pairs in India (Kapooret al, 1985). The latter study also included corre-lations between 120 newborn children and theirmothers in India, which ranged from .0 to .28 forsons and .01 and .33 for daughters (Kapoor et al,1985). Adult sibling correlations for skinfolds varybetween .05 and .30 for British siblings (Hawk andBrook, 1979) and between .20 and .24 for Indiansiblings (Kaur and Singh, 1981). Mueller and Mal-ina (1980) reported correlations separately for 114black and 101 white adolescent sib pairs from Phil-adelphia. They found higher sister-sister similari-ties compared to brother-brother similarities inblacks, but not in whites, and conclude that thesecould arise from expression of sex-limited genesunder less favorable environmental conditions.Newborn sister correlations in India ranged from.47 to .68 (Kapoor et al, 1985). Spouse correla-tions of between .05 and .25 were reported by Kaurand Singh (1981). Commuzie et al. (1994) per-formed a multivariate genetic analysis on eightskinfolds, measured in 408 subjects in Texas, andreported heritabilities of between .26 and .58. Thegenetic correlations showed the highest values be-tween skinfolds from the same region.

Reports from the Quebec Family Study alsoinclude other measures of obesity besides BMI.Skinfolds were measured on 971 8- to 26-year-oldadoptive and biological siblings (Bouchard et al,1985). Correlations were reported for full siblings(.18-49), DZ twins (.30-.49), MZ twins (.76-.87),cousins (.06-.30), adoptive siblings (-.06-. 15),and unrelated siblings (.05-. 17). Two later reportspresented parent-offspring, sibling, and spouse cor-relations of .22, .26, and .06 (Perusse et al, 1987)


and .18-.34, .18-42, and -.01-.19 (Bouchard,1987), respectively, for six skinfolds. The latter re-port also included adoptive relatives; correlationsvaried between .19 and .28 for adoptive parent-offspring and between .06 and .18 for adoptive sib-lings.

Adoption Studies

The earliest studies on the genetics of obesitywere adoption studies. Comparing the correlationsbetween adoptees and their adoptive parents tothose with their biological parents provides a directestimate of cultural versus genetic transmission ofobesity (see Table III). Relatively small samplesizes for which both adoptive and biological par-ents are available (complete adoption design) havelimited the use of adoption studies.

The Danish Adoption Register

One exception is the Danish Adoption Regis-ter. This register contains data on every completelynonfamilial adoption granted in Denmark between1924 and 1947. Height and weight data were avail-able on 3580 adoptees. Four weight groups—namely, thin, medium weight, overweight, andobese, each constituting 4% of the sample—wereselected for further study (N = 540). Informationon BMI of biological and adoptive parents and sib-lings was obtained only for the selected sample.Several reports on this sample have concluded diatthere were strong relationships between the weightclass of the adoptees and the BMI of their biolog-ical parents (Stunkard et al,, 1986) and siblings andhalf-siblings (Sorensen et al., 1989) but no relationwith the BMI of their adoptive parents and siblings.BMI was further calculated from school examina-tions at ages 7-13 years on 269 adoptees of a se-lected sample of 840 (Sorensen et al, 1992a, b).Correlations of the BMI of adoptees with that oftheir adoptive parents (.03-. 10) were lower thanthose with their biological parents (.16-. 17) andlower than correlations between biological parentsand their non-adopted-away offspring (. 16-.36). Asimilar trend was noted for the sibling correlations(.14 for adoptees with adoptive siblings, .59 withbiological siblings, and .77 for biological siblingsreared with biological parents) and for half-siblingcorrelations. Vogler et al. (1995) assessed the in-fluences of genes and shared family environment

on BMI using a comprehensive path model on asubsample of 660 Danish adoptees and 1486 rela-tives. Heritability was estimated at .34; neithershared familial environment nor assortative matingwas significant.

Other Adoption Studies on BMI

Five other studies included correlations onBMI in adopted children or adolescents and theiradoptive and biological parents and siblings. In anearly study, Withers (1964) collected data on over-weight in 142 adopted 8- to 13-year-old children.Correlations with adoptive parents (.11-.16) werelower than those with biological parents (.14-.24).As part of the Take Off Pounds Sensibly (TOPS)study, data were collected from 254 adoptive and10337 nonadoptive families with children between4 and 11 years of age. Based on correlations withadoptive parents (.03-. 11) and those with biologi-cal parents (.10-.11), the heritability of BMI wasestimated at .12 and the contribution of shared en-vironmental factors at .32 (Hartz et al., 1977).Three Canadian adoption studies reported correla-tions on 374 families and 407 families in Montrealand 409 families in Quebec, respectively (Biron etal., 1977; Annest et al., 1983; Bouchard et al.,1985). Correlations between adopted children andtheir adoptive parents ( — .03—.10) were lower thanthose with their biological parents (.02 to .18) forthe Montreal samples but similar for the Quebecsample (.22 for adoptive and .23 for biological par-ents). Adoptive children correlated less with adop-tive siblings (.00-.08) than with biological siblings(.13-.40) in the three studies.

BMI Data on Adult Adoptees

Only one study reported data on adult adop-tees (18-38 years) (Price et al, 1987). Consistentwith reports on adopted children, correlations of357 adoptees in Iowa with their adoptive parents(-.09-.09) were lower than those with their bio-logical parents (.08-.40). Most of the evidence thuspoints to genetic factors accounting for familialcovariation in BMI, given the significant correla-tions between biological relatives and nonsignifi-cant correlations between adoptive relatives. Onlythe Canadian studies concluded that the shared en-vironment explained part of the familial resem-blance.


Table III. Review of Adoption Studies on Obesity

Author Year Sample Size* Method Age (yr) Adop Biol sp

Withers

Hartz et al.

Biron et al.

Garn et al.

Annest et al.

Bouchardet al.

Stunkard et al.

Price et al.

Sorensen et at.

Sorensen et al.

Cardon

Vogler et al.

64

77

77

7976

83

85

86

87

89

92

92

95

London

TOPS

Montreal

Tecumseh,

Montreal

Quebec

Denmark

Iowa

Denmark

Denmark

Colorado

Denmark

142

254a10,337b

374

MI 160l,800b

407f

409

3,65Ia540s

357a

3,580a540s

3,580a840s

241a245na

660al,486r

Corr

Corr

Corr

Corr

Corr

Corr

4 weightclasses

Corr

4 weightclasses

4 weightclasses

Model fitting

Path modelNo c2, i

8-13 afo., 11amo, .16

4-11 ao, .03amo, .11

wt/ht, 1-21 apo, .00asi, .00afs, .22

ams, .09afd, .23

amd, .191-10 afo, -.03

amo, .10asi, .01

0- apo, .22asi, .08

amo, NSafo, NS

18-38 amd, .06ams, .04afd, .09

afs, -.09

7-13 amo, .10afo, .03asi, .14

0-9 h2

.09

.01

.09

.37

.52

.38

.55

.38

.57

fs, .14fd, .24

ms, .19md, .18bfo, .11

mfo, .10bpo, .06bsi, .13

fs, .31ms, .27fd, .25

md, .22bfo, .02 .11

bmo, .18bsi, .40

bpo, .23bsi, .26

mo, Smd, S

fo, Smd. .40 .05-.25ms, .15 sel pifd, .18 -.07-.18fs, .08fsi, S

mhsi, Sphsi, NSmo, .17 mbo, .36

mho, .16fo, .16 fbo, .28

fho, .17si, .59 fsi, .77

mhsi, .16 mhsi, .44phsi, .03 phsi, .34

h2

.88

.43

.53

.64

.52

.32

.39

.581.00

ss, .22bb. .29bs, .28

h2 = .12c2 = .32

c2

h2 = .34

a Adop, adoptive relatives; Biol, biological relatives; sp, spouse correlation; sel pl, selective placement; h2, heritability; c2, sharedenvironment; i, assortative mating; ad, adult; M, male; F, female; f, families; r, relatives; a, adoptive; na, nonadoptive; b, biological;s, selected; NS, not significant; S, significant; pc, parent-child; si, siblings; hsi, half-siblings; fo, father-offspring; mo, mother-oflspring; fs, father-son; fd, father-daughter; ms, mother-son; md, mother-daughter; bb, brother-brother; ss, sister-sister; sb,brother-sister.

* Number of individuals.


The Colorado Adoption ProjectWhile most adoptive samples date back a

number of years, the Colorado Adoption Project(CAP) is an ongoing longitudinal, prospectivestudy in which 241 adoptive and 245 nonadoptivefamilies have been evaluated at annual or semian-nual intervals from birth to age 9 (Cardon, 1995).Continuity of BMI, as assessed by correlations overtime, resulted entirely from generic factors, whichwere transmitted from previous measurements tosubsequent occasions. Age-specific environmentaleffects were substantial at nearly all ages, withouta lasting effect. After about age 2 heritability esti-mates from analyses of siblings (.S2-.64) were sim-ilar to those from analyses of parents and offspring(.37-.S7). Heritability in infancy was high for sib-ling analyses but low for parent-offspring analyses,indicating that the genetic determinants of BMI ininfancy relate to those in later childhood, but notin adulthood. However, the genetic effects thatarise later in childhood do continue to determinethe BMI in adulthood.

METHODOLOGICAL CONSIDERATIONSTwin Studies

The Classical Twin StudyThe classic twin study includes MZ and DZ

twins reared together. It is a powerful design forestimating genetic effects. One thousand twin pairs(500 MZ, 500 DZ) provide 80% power to reject apurely environmental model with a probability of.05 if additive genetic factors explain 10% (onlyunique environment) and 30% (unique and sharedenvironment) of the variance, respectively. Otheradvantages are that twins are matched for age.Therefore age-dependent influences of genes or en-vironmental factors are the same for both twins. Ifage accounts for a significant proportion of the var-iance, both the MZ and the DZ correlation will beinflated. It is thus important to analyze age-re-gressed scores or, better, to include age as a mea-sured variable in the model. Including male andfemale same-sex twin pairs allows testing whetherthe magnitudes of the effects of genes and envi-ronment are the same for both sexes. Adding op-posite-sex twins further enables testing ofsex-limited effects. A DZ opposite correlation sig-nificantly smaller than the DZ same-sex correla-

tions could indicate that different sets of genes ordifferent environmental factors influence males andfemales.

Twins, Singletons, and ZygosityTwin studies are often criticized for several

reasons. The first is that results from twin studiesmay not be generalizable because twins may not berepresentative of the population from which theyare drawn. For a large number of characteristics,including BMI, twins have been found not to besignificantly different from singletons (Wilson,1986; Luke et al., 1995). For some variables, dif-ferences between twins and singletons wereobserved at birth and during the first years of life,but twins tend to "catch up" by the age of 8 (Wil-son, 1986). Second, most twin studies reviewedhere used self-report zygosity measures, which of-ten include questions about difficulty being toldapart by parents or teachers. MZ twins who arediscrepant in height and weight (at birth or contin-ued later in life, e.g., because of different intrau-terine experiences) would be more likely viewedand classified as DZ twins. This bias would lead toincreasing heritability estimates.

Equal-Environment AssumptionA third major potential flaw of the twin design

is the "equal-environment" assumption (EEA). Itis assumed that MZ twins do not share a greaterproportion of salient environmental factors than DZtwins. In various reports where this assumption hasbeen tested, it was found to be valid (see, e.g.,Scarr and Carter-Saltzman, 1979; Kendler et al.,1993). Similarity between twins may lead to morecontact and thus more shared environmental expe-riences (Lykken et al.t 1990; Rose et al., 1990). Inthe same trend, parents may be responding to,rather than creating, similarities or differences be-tween the twins (Lytton, 1977). Although MZtwins may be closer in a variety of "environmen-tal" factors than DZ twins (Fabsitz et al., 1978;Austin et al., 1987; Heller et al., 1988), these mayresult from more regular contact. Genetic factorsmay even control the degree to which "environ-mental" factors are shared by twins. A related as-sumption is that there is no special twinenvironment, which requires that twins are nottreated more alike and do not share more salientenvironmental factors than regular siblings. Viola-


tion of this assumption would cause an increase inthe correlation of both MZ and DZ twins, whichcould lead to overestimation of the shared environ-mental variance. A special case of twin environ-ment is intrauterine effects on the growth of thefetus. Siblings would share some of this source ofvariance, being born from the same uterus at dif-ferent times, whereas both MZ and DZ twins sharethe same uterus at the same time and would likelybe exposed to similar levels of nutrition and terat-ogens. It is possible that monochorionic twins sharesuch factors to an even greater extent than dicho-rionic twins, so comparison of BMI correlationsfrom samples where chorionicity has been mea-sured would be one useful way to discern such ef-fects. One observational twin study correlatingintrapair differences in birth weight with intrapairdifferences in adult BMI suggested that the intra-uterine period is not a critical period for the de-velopment of adiposity (Allison el al., 1995).

GE Correlation and G X E Interaction

Two further potential disadvantages of thetwin design are the assumptions of no genotype-environment (GE) correlation and no genotype Xenvironment (G X E) interaction. GE correlationrefers to the situation in which generic and envi-ronmental factors are not independent. For exam-ple, GE correlation can occur if having a particulargenetic background limits ones choice of environ-ments. Another form of GE correlation occurswhen both genes and environment have a commonsource, such as the parent's genotype, if the par-ent's phenotype affects the offspring's environ-ment. This may result from the combined presenceof genetic and cultural transmission and can be es-timated in models of twins and their parents. It ispossible that different genotypes actively select dif-ferent environments, which in turn cause pheno-typic variation. This "eliciting" is attributed as agenetic effect—even though it has an environmen-tal pathway. G X E interaction may result if thesensitivity to the within-family environment is gen-otype dependent. If the critical aspect of the envi-ronment can be measured, models including twinpairs concordant and discordant for environmentalfactors allow testing the significance of the G X Einteraction. If not explicitly modeled, G X E inter-action forms part of the specific environmental var-iation. One study supporting the concept of G X E

interaction for BMI is the overfeeding of identicaltwins study of Bouchard et al, (1990). They con-cluded that genetic factors were involved in the in-trapair similarity in weight gain.

Random MatingA final assumption often made in twin studies

is that of random mating. It is assumed that partnersdo not choose each other for the characteristic un-der study. If the marital correlation is significant,however, the heritability will be overestimatedwhen fitting an AE model (additive genetic factorsand unique environment) and underestimated whenfitting a ACE model (additive genetic factors,shared and unique environment) because shared en-vironmental factors may become significant. Sincespouse correlations were significant but low, andshared environmental factors were not significant,heritability for BMI estimated from classical twinstudies may have been slightly overestimated. Al-lison et al. (1996b) quantify the magnitude of thisbias and obtained a premarital correlation of .13 forBMI.

MZ and DZ Twins Reared ApartThe most powerful genetic epidemiologic de-

sign includes reared-apart MZ or DZ twins or bothbecause of the resulting separation of genetic andenvironmental effects. The correlation of MZ twinsreared apart (MZA) is a direct 'estimate of the her-itability. The only sources of familial resemblancebetween members of a twin pair reared in differentenvironments is their shared genetic backgroundand prenatal environment. One weakness of this de-sign lies in the age of separation of the twins. Iftwins are not separated close to birth, they mayshare postnatal environmental factors. Another isthat biological parents who put their children upfor adoption may not be representative of parentsin the population. Likewise, parents who adopt maynot be representative of parents generally. There-fore sampling of both genotypes and environmentsis suspect in an adoption study. Finally, childrenmay not be placed at random with adoptive parents,which may violate the assumption of independenceof generic and cultural transmission.

Advantages and AssumptionsThe classical twin study advantages of age

matching and sex limitation testing are true of the


MZA DZA study. Similarly, the assumptions are(i) generalizability of twin data, (ii) no G X E in-teraction, (iii) random mating, and (iv) randomplacement. Given random placement, there is noGE covariance. However, there is no way to esti-mate the effects of common environment in thisdesign; such factors would form part of theenvironmental variance. The equal environmentsassumption is no longer necessary, as twins are notraised by the same parents if twins are separatedearly in life.

Family Studies

The Nuclear Family Design

Family studies typically include parent-off-spring pairs, sibling pairs, and spouse pairs. Thepopularity of the design is due partly to the abun-dance of such relationships in the general popula-tion. Unfortunately parent-offspring and siblingcorrelations do not allow the separation of geneticand environmental transmission. When significantcorrelations are observed between children andtheir parents or between siblings, they may resultfrom shared genes or from shared environmentalfactors. In the case of siblings, the shared environ-mental factors may be aspects of the environmentshared with the parents (cultural transmission) ornonparental shared environment. Adding measuredindices of the environment may provide informa-tion to sort out the pathways. It is, however, almostimpossible to construct an environmental index thatis free from any genetic background. A better res-olution of genetic versus environmental transmis-sion may be obtained by comparing correlationsfrom biological relatives with relationships whichshare only genes or only environmental factors,such as adoptive relatives. This design is discussedbelow.

The Twin Parent Design

Another strategy that allows separation of ge-netic and environmental transmission is the twin-parent design. Basically this design augments theclassical twin design with data from the parents ofthe twins (Fulker, 1982). Ignoring sex differences,this design includes five unique statistics: MZ twin,DZ twin, parent-offspring and spouse covariances,and total variance. These allow estimating fiveparameters: genetic variance (heritability), cultural

transmission, nonparental shared environment, as-sortative mating, and unique environmental vari-ance. This model—including sex differences—wasapplied to data from the Leuven Longitudinal TwinStudy (Maes et al., 1996). The sum of skinfoldswas calculated for 105 twin pairs and their parents.Shared environmental factors (cultural transmissionand nonparental shared environment) did not con-tribute significantly to the observed variances andcovariances. Heritability was estimated at .79 formales and .90 for females. A small percentage ofvariation (2%) resulted from the genetic effects ofassortative mating.

The Extended Family DesignTo identify parameters for genetic and cultural

transmission, it is not necessary to collect data fromthe parents of twins, but this design is a relativelystraightforward extension of twin studies. Largesamples of first- and second-degree relationships(Tambs et al., 1991; Bouchard et al., 1985) tend toinclude relatively few twin pairs—consistent withthe population prevalence of twins—compared toparent-offspring and sibling pairs. The addition ofsecond-degree relatives allows the estimation of ad-ditional parameters, such as dominance. As dis-cussed above, Tambs et al. (1991) estimated theheritability of BMI at .39 based on a sample of74994 relatives including 169 twin pairs. Culturaltransmission explained 2% and sibling environment8% of the variance. Bouchard et al. (1985) reporteda transmissibility of .40 for data from 1698 rela-tives including 156 twin pairs. The transmissibilityis the sum of the genetic and cultural inheritance.This estimate is thus consistent with those ofTambs et al. (.39 + .02). The estimate for the bi-ological inheritance is .10 according to Bouchard.The tau model which was used in his analyses,however, allows for a separate parameter for theshared environment for sibs and DZ and MZ twins.This parameter is highly significant for MZ twins,less for DZ pairs, and even less for siblings. It doesnot appear realistic that the environment of MZpairs is so much more alike than that of DZ pairsand siblings. A genetic explanation for this differ-ence in the correlations seems more appropriate(Allison, 1995; Neale and Eaves, 1995).

Assortative MatingAmong the advantages of family studies are

the fact that an estimate of assortment is often ob-


tained and that separate maternal and paternal ef-fects may be distinguished by comparing thefather-offspring and the mother-offspringcorrelations. The marital correlation usually servedas an estimate for assortment. Assortative matingmay not be the only reason for spouses to be cor-related. It may also result from cohabitation or mar-ital interaction. However, two studies havesuggested that assortarive mating may be a morelikely explanation of the small but significant mar-ital correlations for BMI than cohabitation (Allisonet al,, 1996b; Knuiman et al., 1996).

Age X Genotype InteractionOne of the major disadvantages of family

studies is that children and parents or siblings aremeasured at different ages. If different geneticand/or environmental factors account for the vari-ation in BMI at different ages, parent-offspring andsibling correlations would be reduced and herita-bility underestimated. Only two studies to ourknowledge have tried to address this problem bycomparing correlations of sibs close in age to thoseof sibs farther apart. Mueller and Malina (1980)found higher correlations between siblings closerin age. Tambs et al. (1991) reported a significanteffect in the expected direction only for brothers.Differences in these correlations have often beeninterpreted as evidence for different environmentalfactors. It has, however, become increasingly clearthat genetic factors may also be responsible forsuch discrepancies. Longitudinal twin studies inwhich genetic correlations were estimated betweentwins measured at different ages have all indicatedthat mostly the same genetic factors account for thevariation in BMI in younger and older twins, butthe genetic correlations are not unity, suggestingthat new genetic factors may switch on at variousages. Some of the discrepancies between resultsfrom family and twin studies may be attributed tothese effects.

Adoption StudiesThe Complete Adoption Design

The adoption design has long been recognizedas a powerful tool to resolve to effects from genesand shared environment. In the complete adoptiondesign data are collected from adopted and naturalchildren and their adoptive and biological parents.

Adopted children share genes with their biologicalparents but not with their adoptive parents; theyshare the environment with their adoptive parentsbut not with their biological parents. The correla-tion of adopted children with their biological par-ents thus provides an estimate of the genetictransmission. The correlation of adopted childrenwith their adoptive parents provides an estimate ofthe cultural transmission.

Selective Placement

The advantage of an estimate of assortmentand the disadvantage of age effects apply equallyto adoption and family studies. Two other assump-tions are critical to the adoption design. The first isthat of no selective placement. This implies that theadopted parents are not selected based on any char-acteristic of the adopted child or the biological par-ents of the adopted child. This would induce acorrelation between the adoptive parents and thebiological parents so that the separation of geneticand environmental pathways no longer holds. If se-lective placement exists and can be quantified, itshould be included in the models (Plomin andDeFries, 1990; Phillips and Fulker, 1989). The sec-ond assumption is that of no significant effects ofthe prenatal environment. If the prenatal environ-ment contributes significantly to the observed var-iance, the assumption of no genotype-environmentcovariance is violated. The same problem couldarise with respect to early postnatal environmentalinfluences if adoptees were not separated from theirbiological parents close to birth.

The Partial Adoption Design

It is not always possible to obtain data fromthe biological parents of adopted children. Datafrom adopted children and their adoptive parents,the partial adoption design are still useful as theyprovide an estimate of cultural transmission whichis not confounded with genetic transmission. If theadoptive parent-offspring correlations are com-pared with regular parent-offspring correlations, anestimate of the genetic transmission may be ob-tained by subtracting and doubling the adoptiveparent-offspring correlation from the biologicalparent-offspring correlation. The other advantagesand disadvantages of the complete adoption designare also present for the partial adoption design.


TOWARD AN INTEGRATED APPROACH

From the evaluation of twin, family, and adop-tion studies, it has become increasingly clear thatapproaches which integrate the best aspects of thesedesigns will prove useful in better understandingthe nature and nurture of quantitative traits. Onesuch approach is the so-called "stealth" model, de-veloped by Eaves et al (1996) within the "Virginia30,000" study. This study exploits all the collateraltwo-generational relationships identified in the kin-ships of twins to estimate the sex-dependent con-tributions of genes and environment to complextraits in the presence of assortative mating. Thisdesign includes the relationships typically studiedin twin and family studies. Although adoptive re-lationships could easily be incorporated in the de-sign, they were not formally specified in theVirginia 30,000 because of the relatively smallnumbers of these relatives in this sample. Theavailable first- and second-degree relatives (avun-cular, cousin, and grandparent-grandchildren) pro-vide statistics to resolve genetic and culturaltransmission and both special MZ and special DZtwin environment effects.

Ascertainment and Structure of the Virginia30,000 Sample

The Virginia 30,000 contains data from14,763 twins, ascertained from two sources. Detailsof the ascertainment have been published elsewhere(Truett etal.y 1994). Questionnaires were mailed totwins from the Virginia Twin Registry (VTR; twinsborn in Virginia between 1915 and 1971), withcomplete returns from 5287 families. The remain-der of the twins (N = 9476 individuals) respondedto a letter published in the newsletter of the Amer-ican Association of Retired Persons (AARP).

All pedigrees included a male or female MZor a male, female, or unlike-sex DZ twin pair andall available parents, siblings, spouses, and childrenof the twins. This provides a rich combination of80 sex-specific two-generation relationships (par-ent-offspring, spouse, twin, siblings, cousins, etc.),BMI data were available on 800 male MZ, 596male DZ, 1925 female MZ, 1230 female DZ, and1370 unlike-sex DZ twin pairs. Among the rela-tives, there were 731 fathers of twins, 1130 mothersof twins, 1031 male twin siblings, 1561 femaletwin siblings, 1495 wives of twins, 2200 husbands

Fig. 1. The extended twin family model for opposite-sex DZtwin pairs and their parents.

of twins, 1655 sons of twins, and 2495 daughtersof twins.

All subjects were asked to give their height infeet and inches and their weight in pounds. Twinpairs were asked how frequently they saw theirtwin on a 6-point scale ranging from (1) we livetogether, to (2) almost every day, (3) at least oncea week, (4) once or twice a month, (5) a few timesa year, and (6) once a year or less. Those pairs whoreported that they lived apart were asked how fre-quently they contacted their cotwin, on a 5-pointscale similar to the previous scale except for (1).Pairs who reported living together were assigned avalue of 0 on the (non-)contact scale.

The Stealth Model

The model for family resemblance applied totwin pedigrees is referred to as the "stealth" modeland is described in detail by Truett et al. (1994).We highlight the main features of the modelthrough the path diagram (Fig. 1). Instead of usinga numeric counter, we use the path coefficients toenumerate the various aspects of the model. Thestealth model includes (a, b) additive genetic ef-fects, (K) genetic dominance, (c) environmental ef-fects, (m, n, p, 6) parent-to-offspring verticalcultural transmission, (i) phenotypic correlation be-tween mates modeled as primary phenotypic as-sortment, (l) residual sibling shared environment


(nonparental shared environment), (t) additionaltwin shared environment, (x) additional MZ sharedenvironment, (r) the correlation between commonand male-specific genetic effects, (s, v) the corre-lation between genotype and environment, (q, u)residual genetic variance, (e) unique environmentalvariance, (d, y, w) correlations across sexes of dom-inance, sibling, and twin shared environmental ef-fects. The subscripts rn and f refer to male andfemale parameters.

Before model fitting.the entire data set was logtransformed and corrected for the linear and quad-ratic effects of age, sex, twin status, source of as-certainment (Virginia vs. AARP), and interactionsbetween these terms. Subsequent analyses werebased on the normalized residuals from this regres-sion analysis. Rather than fitting the model to thecorrelations, we fitted directly to the raw data, us-ing the VL approach in MX (Neale, 1995), therebyobtaining maximum-likelihood estimates of theparameters and x2 goodness-of-fit statistics (fromthe difference between the sum of the individualpedigree likelihoods of the stealth model and afully saturated model). The significance of theparameters was tested by likelihood-ratio x2 tests-Confidence intervals were calculated for the mostparsimonious model.

Sample sizes and summary statistics of the 12types of relative in the design are shown in TableIV. Disproportionately large MZ and female sam-ple sizes are commonly observed in studies requir-ing voluntary participation (Lykken et al, 1992)and this pattern is seen here, but it is less than the

"rule of two-thirds" reported in many studies.There is no obvious pattern to the means that re-lates to either the type of relative or the type oftwin pair. Variances in females are greater than inmales.

The full model includes 93 estimated param-eters (of which 60 are parameters for the means)and 12 constraints. Parameter estimates and good-ness-of-fit statistics for the full model and a sub-model with no special MZ twin environment areshown in Table V. Evidently, the special MZ twinenvironment parameter x. is nonsignificantly differ-ent from zero (x2 = 2.95, df = 2, p = -12). Whenpresent, this parameter substantially reduces the es-timate of genetic dominance in both males and fe-males, and slightly reduces additive geneticvariance. Several further parameters could bedropped from the full model without significantlyworsening the fit. These were four parameters re-lating to nonscalar sex limitation (no male-specificgenetic effects, correlations between male andfemale dominance, shared environment and twinenvironment equal to 1.0) and the parameters forcultural transmission and nonparental shared envi-ronment. Fixing these parameters at zero reducesthe number of active constraints to three. The re-maining parameters for additive genetic effects,dominance, special twin environment, and uniqueenvironment could not be equated across sexeswithout significantly deteriorating the fit of themodel. Assortative mating was also significant.

Parameter estimates, proportions of variance,and confidence intervals for the parsimonious re-

Table IV. Sample, Size, Mean, and Variance of Normalized Residuals of BMI for Relatives of Twins in the Virginia 30,000°

TlT2FaMoBrSiSplSp2SIDlS2D2

MZM

806800113168140203330283113188109157

Mean

.03

.00

.09

.07-.06-.16-.02-.17-.09-.18-.02-.16

Var

.69

.71

.601.60.771.181.021.11.71

1.16.71

1.19

DZM

6025981111471201S522218761754769

Mean Var MZF

.08 .75 1940

.07 .79 1921

.07 .80 180

.09 1.16 319

.12 .58 289-.01 1.47 479-.19 1.22 639-.13 1.19 556

.00 1 .03 349-.17 1.05 545

.19 .55 340-.04 1.05 523

Mean Var

-.06 1.13-.05 1.1 1-.13 .91-.02 1.12-.01 .68-.00 1.19

.04 .72

.09 .72

.01 .82-.04 1.21-.02 .71-.11 1.12

DZF

12341238123191196356353280205299149246

Mean

.02-.00

.08

.11.11.07.09.13.02

-.06.15.05

Var

1.161.12.67

1.28.71

1.26.70.74.64

1.38.66

1.21

DZMF

13731378205308286373474375123191162211

Mean

.03-.03

.04

.06

.04-.05-.05

.03

.08-.07-.09-.06

Var

.801.12.69

1.18.73

1.301.21.82.79.97

1.041.18

•Tl, twin 1; T2, twin 2; Fa, father; Mo, mother; Br, brother; Si, sister; Spl, spouse of twin 1; Sp2, spouse of twin 2; SI, son ofWin 1; Dl, daughter of twin 1; S2, son of twin 2; D2, daughter of twin 2.


duced model are presented in Table V. Additivegenetic factors explain 35% of the variance inmales and 39% in females, of which 2% result fromassortative mating. Dominance accounts for 31 and26% of the variance for males and females, re-spectively. These estimates add up to broad heri-tabilities of .66 and .65 for males and females.Seven to eight percent of the variance is explainedby special twin environment. These are aspects ofthe environment that twin pairs share, but not otherfamily members. The remaining 27% is accountedfor by unique environmental factors. Ninety-fivepercent confidence intervals on these estimates arequite narrow, due to the large sample sizes and therelative simplicity of this model, which accountsfor covariation in 88 different familial relationshipswith only 10 parameters. Correlations for 80 rela-tionships are given in Table VI for reference.

DISCUSSION

The review of the literature of familial resem-blance for BMI reveals strikingly convergent re-

sults for a wide variety of types of relationship. Asexpected, studies with smaller sample sizes showgreater variability in estimates of correlation be-tween relatives than do studies with larger samplesizes. Pooling across studies, including the newdata reported in this article, the weighted mean cor-relation for MZ twins is .74, for DZ twins .32, forsiblings .24, for parents and offspring .19, and forspouses .12 (Fig. 2). More distant and adoptive rel-atives (.06) typically show smaller correlations,consistent with a substantial role for genetic factorsin the etiology of individual differences in BMI.The data reported hi this article are in good agree-ment with those reported in other studies, with cor-relations close to the weighted means from otherstudies.

In most reports, data were analyzed usingstructural equation modeling (i.e., path analysis)and almost always yielded broad heritability esti-mates from .5 to .9. The "outlier" in this area arethe reports by Bouchard and colleagues (1987),who used a model that involved separate specialtwin environment parameters for MZ and for DZ

Table V. Statistics, Parameter Estimates, Proportion of Variance, and Confidence Intervals of the Best-Fitting Model for BMI inthe Virginia 30,000°

Full model withspecial MZ

twin environment

*m(asm)D2m£*„cr*m&„CJm

r-nTmz>m

A\(asm)&<E\CT-t

s><c\r,7mzJ,

.28+. 16.01.00.23.00

-.01.02.03.24.26.01.00.21.00.03.10.06.31

Parameter estimate ofFull model without

special MZ twinenvironment

#m(asm)^mE*m

crmS*mC'mpmrmz*mA2

A f(asm)j>.E\CT\s\cjfr»rTmz\

.19+.20.01.27.27.00.01.00.03

.28

.01

.32

.27

.01

.03

.01

.06

Best-fittingmodel

A,£„E(

An

ArmTVpmP<i

.521

.637

.474

.543

.501

.532-.236

.2921.094.819.159

Proportion ofvariance of best-

fitting model

A\(asm)jym

£*m

Fn

AJf(asm)&tE\T,

.351(.020).307.274.068

.394(.022).259.269.078

Confidence intervals

.290-.415

.2 11 -.395

.247-.304

.023-134

.350-.437

.192-.324

.252-.28S

.033-. 128

" Goodness-of-fit statistics of best-fitting model: observed statistics, 24,230; estimated parameters, 72; constraints, 12; active con-straints, 7; —2 times log-likelihood of data, 64,988.057; degrees of freedom, 24,158. A2, additive genetic factors; asm, assortment;£>*, dominance factors; &, unique environmental factors; CT2, cultural transmission; S2, genotype-environment covariance; C*,nonparental shared environment; P, special twin environment; Tmz2, special twin environment; f and m subscripts, males andfemales.


Table VI. Observed Correlations of BMI for Biological Relationships in the Virginia 30,000 (Eaves, Unpublished Data)a

Family N

S 4751

Sib<J<? 1493Sib 2 9 3524Sib<J ? 4255

DZiJcJ 573Dz29 1164Dz<J9 1307Mz<J<J 775Mz99 1847

Fa-So 2160Fa-Da 2971Mo-So 3035Mo-Da 4476

r

.144

.234

.317

.224

.292

.360

.264

.692

.730

.190

.194

.227

.257

Avuncular

PSibcJcJPSib<J 2NSibS <JNSibS 9PSib2cJPSib22NSibcJ &NSib<J 9

PDzcJtJPDzcJ2NDz2c5NDz?2PDz9tJPDz22NDz<J<JNDztf?

PMz<?<5PMz<J9NMz2(JNMz99

AT

92155402536131196236284

105137345525118188150202

217337673

1040

r

-.0266.004.185.083

-.0072.065.105.059

.292

.016

.152

.176

.393-.001

.185

.098

.141

.264

.124

.255

Cousins

Mzm(Jc5Mzm22Mzm«52Mzf<J<JMzf22Mzf<J9

Dzm<J<JDzm92DzmcJ2DzfcJt?Dzf99Dzf<J9DzocJiJDzo92Dzo£2Dzo9<?

N

3992

107153340449

19415252

13815938715172

r

.094

.223

.185

.040

.191

.064

-.375.070

-.072.260.095

-.025.176.118

-.118.141

In-laws

SibI<J 2Sibl9cJSibI<J 6Sibl$ 9

Dzlrf2Dzl2<JDzIcSdDzI2 9Mzld2

N r

337 -.075728 -.007422 .077447 .075

387 .047603 .126353 .038458 -.028589 .048

MzI$cJ 1139 .109

Fa-DaiFa-SolMo-DaiMo-Sol

205 -.068188 .044293 .024338 .102

SPDz9(5SPDz2 2SNDztJ<JSND<?9SPDz<J cJSPDz<J2SNDz2£SNDz99

SPMz9<JSPMz92SNMzcJdSNMzc? 2

SDzmSDzfSDzmfSMzmSMzf

N r54 -.22380 -.177

126 .114169 -.04336 -.25568 .14664 .09095 .106

129 .014213 .062342 -.107502 .040

100 .126120 -.065167 -.057172 .025300 .132

« S, spouse; Sib, sibling; Dz, DZ twin; Mz, MZ twin; Fa, father; Mo, mother; So, son; Da, daughter; P, paternal; N, maternal; 1,in-laws; m, male; f, female; o, opposite sex; d <J , male— male pair; 2 2 , female— female pair; <J 2 , male-female pair; 2 <3 , female-male pair.

twins. Such an approach effectively discounts thedata from twins, and can markedly decrease esti-mates of heritability. In our study, the special MZenvironment parameter was nonsignificant, al-though when estimated it did substantially reducethe estimate of dominance genetic effects. This isa natural consequence as special MZ environmentis highly correlated with dominance in the twin-family design.

The significance of dominance variance, as in-dicated by the Virginia 30,000 and other large sam-ples, may partly account for the higher heritabilityestimates found in twin versus family studies.Dominance variance is correlated 1.0 in MZ twinsand .25 in DZ twins and siblings but is uncorrelatedbetween parents and offspring.

There are reasons to believe that heritabilityestimates based on twin data may be better thanthose based on other types of relative. Twins arewell controlled for age effects, whereas correlationsbetween siblings or more distan relatives may beattenuated by nonlinear changes in BMI over time.In our data, however, we did not find evidence fora significant relationship between absolute differ-

ence in age and absolute difference in BMI (r —.05, p = .04) among siblings.

One potential source of difference betweentwin correlations and those from other types of rel-ative is a maternal effect. It is conceivable thatthere are intrauterine effects on the growth of thefetus that lead to lasting differences in body massin later life. The data presented here that includethe offspring of twins provide an indirect measureof maternal effects. If the maternal effect is of ge-netic origin, so that the mother's genotype influ-ences the quality of the uterus and the nutrition andteratogens relevant to later growth of her offspring,we would expect to see higher correlations betweenthe offspring of an MZ female twin and the off-spring of her twin sister than among the offspringof an MZ male twin and the offspring of his twinbrother. To a lesser extent, a similar pattern shouldbe observed for the children of male and femaleDZ twin pairs. In the present study, no such effectwas observed, with MZ offspring correlations ap-proximately (.15, .11, .27) for males and (.10, .10,.20) for females. If no evidence of maternal effectsis found, the special MZ twin environment might

T92

T92


Fig. 2. Reported correlations by sample size for MZ twins, DZ twins, siblings, parent—offspring pairs, spouses, and adoptiverelationships, x axis, sample size; y axis, correlations.

most logically be interpreted as epistatic effectsfrom the interaction of genes at two or more loci.Such effects, known as "emergenesis" when thenumber of loci is large (Lykken et aL, 1992),would contribute substantially to the MZ twin cor-relation but would have negligible effects on cor-relations between other relatives. Given that BMIis a complex trait stemming from many possiblegenetic and environmental mechanisms, it seemsquite plausible that epistatic/emergenetic effects in-fluence it. A summary of the assumptions of dif-ferent types of studies and their effect on thecorrelations and the estimates of genetic and envi-ronmental variance is presented in Table VII.

The results presented in this article are subjectto at least six potential limitations. First, as withany study where participation is voluntary, the sam-ple used may not be representative of the popula-tion. Part of the Virginia 30,000 sample wasinitially drawn from population records in the stateof Virginia, but a substantial portion was strictly

volunteer-based, being recruited as a result of anarticle in the AARP newsletter. For nonrandomsampling to affect the results of the study, it wouldneed to be relevant to BMI. In a previous report,significant differences were found in the means orvariances of twins whose cotwin had responded vs.twins whose cotwin had not (Neale and Cardon,1992), suggesting that there may be some effectsof volunteering. Comprehensive modeling of vol-unteer bias, using methods like those of Neale andEaves (1993) is beyond the scope of this article.

Second, another possible limitation of thesedata also concerns sampling, in that even a repre-sentative sample of twins may not be representativeof the population, and therefore conclusions fromtwins may not generalize. To some extent thismight seem to be the case for previously reportedstudies of BMI, with heritability estimates fromclassical twin studies higher than those of non-twins. In the present analyses, both twins and non-twins were used to obtain parameter estimates, so


the impact of using twins would be attenuated com-pared to studies of twins alone.

Third, self-reported height and weight used tocalculate BMI is not an optimal index of obesity,and therefore caution should be exercised whengeneralizing the results to other related measures offatness. While self-report BMI is likely to be quitehighly correlated with such measures, the correla-tion is less than perfect. A related issue is that theanalyses presented concern population-based sam-ples that were not specifically ascertained becausethey met some clinical or other criteria for obesity.Our findings may not generalize to the study ofextreme pathological levels of obesity if these havea different cause than extreme deviations on thehypothesized normal continuum of variation.

Fourth, heritability estimates based on twinstudies can be biased if MZ twins have greater lev-els of contact than DZ twins and if contact affectsintrapair similarity. In our data, we correlated twomeasures of contact with the absolute intrapair dif-ference in BMI and found small but significant cor-relations (r = .I0,p < .001, and r = .14 p < .001)for both measures, indicating greater similarity be-tween twins in closer contact. Quite possibly, theseeffects are reflected in the MZ and DZ special twinenvironment parameters in the Stealth model. More

explicit modeling of contact effects such as thosedescribed by Kendler et at. (1993) could test thishypothesis if incorporated into the stealth model.

Fifth, the structural equation model we usedis a simple linear model, but the relationship be-tween genes and phenotype or between environ-ment and phenotype may be nonlinear. Therelationship between these variables may also bemoderated by a variety of other genetic, environ-mental, or phenotypic factors, which have not beentaken into account here. Nevertheless, a linearmodel is a good place to begin the analysis of com-plex phenotypes, being simple, easily communi-cated, and easily falsified, which are all desirableproperties of a scientific model (Popper, 1961).

Finally, we emphasize that the heritability ofa trait as estimated from twin, adoption, and familystudies does not exclude environmental pathways.For example, a genetic predisposition to select orrequest a large proportion of fatty foods from theavailable diet is clearly dependent on the availabil-ity of such foods. Therefore while predisposition toobesity might be due largely to individual differ-ences in metabolism, it is also possible that behav-ioral and environmental pathways are significant.Simultaneous measurement of plausible metabolicand nutritional factors in a genetically informative

Table VII. Assumptions of Twin, Family, and Adoption Studies and Their Effect on Familial Correlations and Estimates ofGenetic and Environmental Variancea

Effect on estimate (IVp)

Assumption Study Effect on correlations h2 c2 e2

Twins = singletons T ? ? ?Correct self-report zygosity: systematic T MZ -» DZ ? + - —

discrepant MZ as DZsEqual environment T MZ •* + - —Special twin environment T MZ * DZ * = + —QE correlation TF MZ 2 •» DZ •* + = —GC covariance < cultural transmission TF MZ * DZ * PO ? SI •* = + ~G X E interaction TF MZ 2 •*. DZ •* PO •* SI ** - = +Random mating T MZ •» DZ <* +AEY- =/+ACE =Intrauterine/prenatal environment TA MZ * DZ -» ABS1 <» AS1 «« + = -Chorionicity T MZ -* + = ~Epistatis/emergenesis F MZ + = ~AgexG/C/E interaction FA PO v SI - + =Random placement A APO •» ASI -" + = ~Separation age/postnatal environment A ABSI •* ASI v + = ~

a T, twin studies; F, family studies; A, adoption studies; MZ and DZ, twins; PO, parent-offspring; SI, sibling; APO, adoptive PO;ASI, adoptive sibling; ABSI, adoptee with biological sibling; <*, increase; •>«., decrease; /Vp, over the phenotypic variance; h\heritability; c2, shared environment; el, unique environment; ?, unknown; +, overestimation; — , underestimation; =, no effect;AE, AE model; ACE, ACE model.


design is needed to distinguish between these al-ternatives. It seems quite likely that there existsheterogeneity in the population, such that what isa primary cause of obesity in one individual maybe a minor cause in another. Commingling analysismay reveal some of this heterogeneity, but expe-rience in the relative power and robustness of seg-regation analysis vs linkage analysis implies thatheterogeneity would most effectively be discernedwhen associated with some external, measurablefactor.

ACKNOWLEDGMENTS

This research was supported by NIH GrantsMH-45268 and RR-08123 and grants from the J.M. Templeton Foundation and the Carman Trust.We are grateful to Dr. K. S. Kendler for helpfulcomments on an early draft of this paper.

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