a genome-wide search for quantitative trait loci influencing substance dependence vulnerability in...

A genome-wide search for quantitative trait loci influencing substancedependence vulnerability in adolescence

Michael C. Stallings a,*, Robin P. Corley a, John K. Hewitt a, Kenneth S. Krauter b,Jeffrey M. Lessem a, Susan K. Mikulich c, Soo Hyun Rhee a, Andrew Smolen a,

Susan E. Young a, Thomas J. Crowley c

a Institute for Behavioral Genetics, University of Colorado, Campus Box 447, Boulder, CO 80309-0447, USAb Molecular, Cellular and Developmental Biology, University of Colorado, Boulder, CO 80309, USA

c Division of Substance Dependence, University of Colorado School of Medicine, Denver, CO 80262, USA

Received 16 September 2002; received in revised form 6 January 2003; accepted 7 January 2003

Abstract

This study describes results from a genome-wide search for quantitative trait loci (QTL) influencing substance dependence

vulnerability in adolescence. We utilized regression-based multipoint (and single-point) QTL mapping procedures designed for

selected sibpair samples. Selected sibling pairs included 250 proband-sibling pairs from 192 families. Clinical probands (13�/19 years

of age) were drawn from consecutive admissions to substance abuse treatment facilities in the Denver metropolitan area; siblings of

probands ranged in age from 12 to 25 years. In addition to the selected sample, a community-based sample of 3676 adolescents and

young adults were utilized to define a clinically-significant, heritable, age- and sex-normed index of substance dependence

vulnerability*/a priori and independent of our linkage results. Siblings and their parents were genotyped for 374 STR micro-

satellite markers distributed across the 22 autosomes (average inter-marker distance�/9.2 cM). Non-parametric single-point linkage

results indicated 17 markers on 11 chromosomes with nominally significant tests of linkage; six markers with LOD scores greater

than 1.0 and one marker (D3S1614) with a LOD score of 2.2. Multipoint mapping corroborated two locations and provided

preliminary evidence for linkage to regions on chromosome 3q24-25 (near markers D3S1279 and D3S1614) and chromosome 9q34

(near markers D9S1826 and D9S1838).

# 2003 Elsevier Science Ireland Ltd. All rights reserved.

Keywords: Genetics; Substance dependence vulnerability; Adolescence; Linkage study

1. Introduction

We present findings from the first genome-wide scan

for quantitative trait loci (QTL) influencing substance

dependence vulnerability in adolescence. Psychoactive

substance dependence is the most highly familial psy-

chiatric disorder (DSM-IV; American Psychiatric Asso-

ciation, 1994). The essential features of the disorder

include a cluster of cognitive, behavioral, and physiolo-

gical symptoms indicating tolerance and withdrawal,

impaired control of substance use, and continued use

despite adverse consequences.

Evidence suggests that substance dependence is a

multifactorial, complex disorder, likely to be influenced

by multiple genetic and environmental factors. Findings

from family, twin, and adoption studies strongly suggest

that genetic determinants play an important role in

patterns of drinking behavior and in the etiology of

alcoholism (Cotton, 1979; Goodwin, 1979, 1985; Heath

et al., 1997, 2001, 1991; Hrubec and Omenn, 1981;

McGue et al., 2001; Prescott et al., 1994; Prescott and

Kendler, 1999; Prescott et al., 1997; Robins, 1996; Rose

et al., 2001; Schuckit, 1987, 1994, 1999), with children of

alcoholics tending to show a 3- to 5-fold increased risk

for developing the disorder (Cloninger et al., 1981;

Cotton, 1979; Schuckit, 1999). Heritability estimates

from twin studies for smoking behaviors are also

substantial (in the range of 40�/50%; for a review see

* Corresponding author. Tel.: �/1-303-492-2826; fax: �/1-303-492-

8063.

E-mail address: [email protected] (M.C. Stallings).

Drug and Alcohol Dependence 70 (2003) 295�/307

www.elsevier.com/locate/drugalcdep

03765-8716/03/$ - see front matter # 2003 Elsevier Science Ireland Ltd. All rights reserved.

doi:10.1016/S0376-8716(03)00031-0

mailto:[email protected]

(Heath and Madden, 1995), and recent studies have

confirmed substantial heritable influences on smoking

initiation (Rhee et al., in review), quantity of cigarettes

smoked (Koopmans et al., 1999), persistent smoking(True et al., 1997) and nicotine dependence (McGue et

al., 2000; True et al., 1999).

Studies specifically investigating the etiology of illicit

drug abuse and dependence are less abundant than for

smoking and problem drinking, but the findings, in

general, suggest important genetic and familial contri-

butions to illicit drug use and the development of illicit

substance use disorders as well (Annis, 1974; Gfroerer,1987; Han et al., 1999; Kendler et al., 2000; Kendler and

Prescott, 1998; Luthar and Rounsaville, 1993; Maddux

and Desmond, 1989; Maes et al., 1999; McGue et al.,

2000; Merikangas and Avenevoli, 2000; Moss et al.,

1994; Newlin et al., 2000; Rounsaville et al., 1991;

Tsuang et al., 1996, 1998).

Although it is generally well accepted that genetic

influences play a significant role in substance depen-dence vulnerability, the specific mechanisms by which

genetic factors exert their influence remain unknown.

Two important approaches to investigating potential

mechanisms have included: (1) understanding individual

differences in physiological and pharmacogenetic vul-

nerabilities to specific classes of substances (e.g. Chen et

al., 2002; Higuchi, 1994; Kreek, 2000; Reich et al., 1998;

Schuckit et al., 2001; Stitzel et al., 2000; Straub et al.,1999); and (2) investigating individual differences in

non-specific substance dependence vulnerability*/po-

tentially as part of a spectrum of risk behaviors related

to the inability to inhibit inappropriate behavior in

general (Babor et al., 1992; Cadoret et al., 1995;

Cloninger, 1987; Cloninger et al., 1981, 1988; Hessel-

brock et al., 1992; Iacono et al., 1999; Jessor et al., 1991;

Krueger et al., 2002; Moffitt, 1993; Sher and Trull, 1994;Young et al., 2000).

1.1. The present study

In this report we focus on non-specific substance

dependence vulnerability for several reasons. First, most

individuals with a substance use disorder (SUD) use

multiple substances (Glantz and Leshner, 2000), and

polysubstance use is particularly characteristic of ado-lescents (Johnston et al., 2001; Young et al., 2002).

Second, although the conclusions drawn from family

and twin studies examining the causes of comorbidity

among substance use disorders have been conflicting,

there is considerable support for the existence of

important common genetic or common familial influ-

ences across multiple substances (Bierut et al., 1998;

Cadoret et al., 1986, 1995; Kendler et al., 1997; True etal., 1999; Tsuang et al., 1998). Other studies have

stressed the importance of substance-specific familial

influences (Meller et al., 1988; Merikangas et al., 1998),

however, there is accumulating evidence to suggest that,

especially in adolescence, underlying genetic risk factors

are likely to have general influences on vulnerability to

substance dependence, and possibly externalizing beha-vior as well (Krueger et al., 2002; Slutske et al., 1998;

Young et al., 2000). Consequently, composite pheno-

types that assess non-specific substance dependence

vulnerability may index the effects of genetic influences

better than measures of substance-specific problem use

in adolescence.

2. Methods

2.1. Study samples

All subjects are participants in the family, twin, and

adoption studies of our Colorado Center on Antisocial

Drug Dependence (CADD; PI: Thomas J. Crowley)

funded by the National Institute on Drug Abuse.

2.1.1. Ascertainment of selected sibpair sample

Adolescent treatment probands were recruited from

three treatment facilities (one residential and two out-

patient facilities) in the Denver metropolitan area

operated by the Division of Substance Dependence of

the University of Colorado School of Medicine. The

probands were 13�/19 years of age (M�/15.9, S.D.�/

1.3) at time of assessment*/with most referred intotreatment by social service and/or juvenile justice

agencies for serious substance involvement and delin-

quency (ca. 1% are referred by parents or other sources).

Proband participants were drawn from consecutive

admissions to the treatment facilities between February

1993 and June 2001. Exclusionary criteria included

imminent danger to self or others, current psychotic

symptoms, or IQ scores B/80. Only probands with abiological full-sibling between the ages of 12 and 25 (ca.

60% of admissions) were utilized in the current study.

The resulting selected sibpair sample included 250

proband-sibling pairs from 192 families (after omission

of 24 individuals where paternity and/or full-sibling

status was questioned based on genetic analysis). The

self-reported ethnicity distribution of the 192 families

was 7.8% African-American, 36.5% Hispanic, 52.1%Caucasian, and 3.6% other.

2.1.2. Ascertainment of community samples

In addition to the selected proband-sibling sample, we

used data from unselected adolescent and young adult

participants in the CADD family, twin, and adoption

samples (N�/3676 individuals between the ages of 12

and 25) to assist with phenotype definition. Monozygo-tic (N�/322 pairs) and dizygotic (N�/294 pairs) twins

and their non-twin siblings were drawn from the Color-

ado Twin Registry, a community-based sample of twin

M.C. Stallings et al. / Drug and Alcohol Dependence 70 (2003) 295�/307296

families residing in Colorado. Adolescents and young

adults were also drawn from two other Colorado

community-based family samples: the Colorado Adop-

tion Project (CAP) and the Colorado Adolescent Sub-stance Abuse (ASA) family study. The CAP is an

ongoing, longitudinal study of the genetic and environ-

mental influences on behavioral, cognitive, and emo-

tional development (DeFries et al., 1994). Our

community-based sample included 143 CAP adoptive

sibling pairs and 162 full sibling pairs from the matched

community families in the CAP. The ASA project is a

study of the familial transmission of substance abuseand associated psychopathology. The study design

includes families of adolescents in treatment for sub-

stance abuse and delinquency (described above) and the

families of unselected control adolescents age- and

ethnically-matched to the treatment probands. The

matched ASA control adolescents and their siblings

included 475 sibling pairs in the adolescent and young

adult age range.The complete community sample ranged in age from

12 to 25 years (M�/15.9; S.D.�/2.1) and was 54%

female (46% male). The ethnicity distribution of the

community sample was 81.6% Caucasian, 12.1% His-

panic, 2.3% African-American, with 4% of mixed or

unknown ethnicity. A recent report on this sample by

Young et al. (2002) has demonstrated that substance use

patterns and prevalence rates in our combined commu-nity sample are comparable to those reported in

national studies such as the Monitoring the Future

Study (Johnston et al., 2000; Johnston et al., 2001) and

the National Household Survey of Drug Abuse

(SAMHSA, 2001).

2.2. Assessment

All study participants were given an extensive batteryof cognitive, psychiatric, and socio-demographic assess-

ments that included both structured diagnostic inter-

views as well as self-report pencil-and-paper

questionnaires. Treatment probands were assessed in

the treatment facilities at time of admission. Siblings of

the treatment probands and all community adolescents

were assessed privately in their homes by trained lay

interviewers. All participants who were at least 18 yearsof age gave informed written consent to participate. For

juveniles, a parent or legal guardian gave written

consent for participation and written assent was ob-

tained from all juveniles. All research protocols and

consent forms were approved by institutional review

boards of the University of Colorado. All subjects were

paid for participation in the study.

Substance use data were obtained using the Compo-site International Diagnostic Interview-Substance

Abuse Module (CIDI-SAM; Cottler and Keating,

1990), a face-to-face structured diagnostic interview

designed to be administered by lay interviewers. The

CIDI-SAM has been shown to be valid and reliable in

both clinical and epidemiological samples (Cottler and

Keating, 1990; Horton et al., 2000), and in adolescentpopulations (Crowley et al., 2001). The structured

interview assesses substance use patterns and DSM-IV

(American Psychiatric Association, 1994) symptoms and

diagnoses of substance abuse and dependence for

tobacco, alcohol, and eight classes of illicit drugs. In

order to be asked follow-up questions regarding abuse

and dependence symptoms, subjects must report having

‘repeated use’, which was defined as using tobacco‘almost every day for at least a month’, consuming six

or more alcoholic drinks (lifetime), or using an illicit

drug (e.g. marijuana) six or more times (lifetime).

2.3. Phenotype definition

Ten candidate phenotypes were examined in our

community samples to identify (a priori and indepen-

dent of our linkage results) a phenotype to index non-specific dependence vulnerability. We desired a pheno-

type that: (1) was highly heritable in our community-

based samples; (2) showed a pattern of mean differences

in our clinical families that was consistent with strong

genetic transmission; (3) was not substantially influ-

enced by shared environmental factors; and (4) signifi-

cantly discriminated patients with substance use

disorders from community controls.Given our adolescent samples it was necessary to

consider age trends in substance dependence in our

community samples. Our data indicate a marked, almost

linear, increase in the number of substance dependence

symptoms (DSM-IV criterion counts) across the ado-

lescent age range 12�/18 (Young et al., 2002). Sex

differences in these trends were generally modest. To

adjust for age and sex effects, dependence symptomcounts were age- and sex-corrected using standard

regression procedures (i.e. residual scores were obtained)

and then standardized within sex groups to express

dependence vulnerability in relation to the population

averages and variability for adolescent males and

females.

Since there is no standard method for assessing

substance dependence vulnerability in adolescents weconsidered ten candidate phenotypes for this study. The

twin and non-twin sibling correlations for each index

from our community adolescent samples are shown in

Table 1. Comparisons of the sibling correlations and

biometrical model fitting (Corley et al., 2001) suggested

that the average number of dependence symptoms (i.e.

total symptom count across all classes of substances,

divided by the number of substances used more than fivetimes*/as defined by the CIDI-SAM) would provide the

best phenotype for use in our sibpair linkage analyses.

The sibling correlations for this phenotype show: (1) the

M.C. Stallings et al. / Drug and Alcohol Dependence 70 (2003) 295�/307 297

greatest discrepancy between MZ and DZ twins*/

indicating that it was the most highly heritable pheno-

type; (2) the least variation between DZ twins and full-

siblings from the ASA and CAP samples*/which can

differ only because of non-genetic factors; and (3) the

lowest correlation among adoptive siblings*/indicating

that it was one of the candidate phenotypes least

influenced by shared environmental effects. No other

candidate phenotype met all three of these criteria.

The heritability of this index of dependence vulner-

ability, also similarly age- and sex-corrected, was

estimated at 48% while shared environmental influences

explained only approximately 10% of the variance. The

average number of dependence symptoms per substance

used repeatedly also significantly discriminated (i.e.

demonstrated substantial mean differences) between

clinical probands (M�/2.19, S.D.�/1.41) and commu-

nity adolescents (M�/0.0 and S.D.�/1.0 due to stan-

dardization on our community adolescent sample). That

is, on average, clinical probands scored more than two

S.D. above the mean of unselected community adoles-

cents. This dependence vulnerability index also showed

a characteristic decreasing pattern of mean differences

across the clinical probands, full-siblings (M�/0.52,

S.D.�/1.35), and half-siblings (M�/0.32, S.D.�/1.34)

in our treatment families*/consistent with substantial

genetic influence.

2.4. DNA collection, preamplification, and genotyping

Genomic DNA was isolated from buccal cells using a

modification of published procedures (Freeman et al.,

1997; Lench et al., 1988; Meulenbelt et al., 1995).

Briefly, our method involved collecting buccal cells by

rubbing the cheeks with cotton swabs followed by a

rinse with Scope† mouthwash. The DNA was isolated

by solvent extraction, quantified with PicoGreen†

(Molecular Probes, Eugene, OR), and a working stock

of 20 ng/ul was prepared in TE buffer. The average yield

of DNA was 409/5 ug. Primer Extension Preamplifica-

tion (PEP) was performed on 1 ml aliqouts of the

genomic DNA using a modification (Krauter et al.,

2001) of the method of Zhang et al. (1992), which

resulted in an approximately 100-fold amplification of

the DNA. The PEP procedure was originally to be used

only on those few samples that had poor yields.

However, after confirming that allele calls for all

markers were identical when comparing PEP DNA

with DNA purified from cell lines in two CEPH

individuals (Krauter et al., 2001), PEP was routinely

used for all of the DNA samples.

Available parents and all sibling pairs from our

selected families (families of clinical probands) were

genotyped for 374 STR microsatellite markers (ABI

PRISM LMS2-MD10 panels, PE-Biosystems, Foster

City, CA) spanning all 22 autosomes. For 47% of the

Table 1

Sibling resemblance for ten candidate phenotypes indexing substance dependence vulnerability

Candidate phenotypes MZ twins

(N a�/322)

DZ twins

(N�/294)

ASA full-sibs

(N�/475)

CAP full-sibs

(N�/162)

CAP adop-sibs

(N�/143)

Total number of symptomsb, all substances 0.78 0.60 0.33 0.37 0.26

Total number of symptoms, excluding tob 0.74 0.64 0.34 0.27 0.19

Total number of symptoms, excluding alc and tob 0.69 0.80 0.38 0.28 0.23

Averagec number of symptoms, all substances 0.67 0.43 0.26 0.33 0.16

Average number of symptoms, excluding tob 0.65 0.48 0.26 0.26 0.19

Average number of symptoms, excluding

alc and tob

0.63 0.73 0.33 0.28 0.24

Marijuana symptoms only 0.62 0.73 0.34 0.29 0.25

Number of substances with at least one symptom 0.66 0.51 0.34 0.33 0.16

Number of substance dependence diagnoses 0.76 0.81 0.28 0.16 0.25

Number of symptoms for at least one substanced 0.67 0.56 0.32 0.30 0.18

a N , number of pairs.b Total number of symptoms�/the total number of DSM-IV substance dependence criteria endorsed combined across all ten substance categories

of the CIDI-SAM.c Average�/total number of symptoms divided by the number of substances used repeatedly*/as defined by the CIDI-SAM.d Number of symptoms for at least one substance�/the total number of DSM-IV criteria endorsed, but the same criterion endorsed across

multiple substances was counted only once (i.e. the total range was zero to seven symptoms).


families genotypes were available from both parents;

49% included genotypes from one parent and for 4% of

the sibships no parental genotypes were available.

Sibships included 147 pairs, 35 trios, seven sibships offour, and three sibships of five. Genotypes were

determined by PCR amplification of polymorphic loci

using primers labeled with fluorescent probes. DNA

fragments were analyzed using an ABI 377 DNA

sequencing instrument and GENESCAN and GENOTYPER

software. Allele calling was performed by technicians

blind to subject identity and phenotype. Sex-averaged

marker maps were obtained from the Marshfield Centerfor Medical Genetics database (http://research.marsh-

fieldclinic.org/genetics), and allele frequencies were esti-

mated from the full sample. The average inter-marker

distance across the 22 autosomes was approximately

9.24 cM, with an average marker heterozygosity of

78.29% and average polymorphism information content

(PIC) of 0.55.

2.5. Validation of genotypes and relationship status

The Discovery Manager† (Genomica; Boulder, CO)

database system was used for storage of genetic (raw

allele sizes) and phenotypic data, and for initial error

checking. Additional relationship and genotype valida-

tion was performed using the Graphical Relationship

Representation (GRR) program (Abecasis et al., 2001),

PEDSTATS (Abecasis et al., 2000), MERLIN (Abecasis etal., 2002), and GENEHUNTER version 2.0 (Kruglyak et

al., 1996). Identity-by-descent (IBD) sharing for all

sibpairs was estimated at 1 cM intervals (and also

single-point at each marker) across the genome (auto-

somes) using MERLIN. GRR was used to confirm the

putative family pedigree relationships in the final

sample. This software provides a graphical plot of the

average identity-by-state (IBS) by the S.D. of IBS for allavailable markers for all pairs of individuals. Analysis

indicated 24 individuals where paternity and/or full-

sibling status was questioned and two pairs of mono-

zygotic twins. These individuals were removed from the

final sample prior to linkage analysis. Comparison of

allele calls in the two pairs of identical twins indicated

that our undetectable genotyping error rate is less than

1%.

2.6. Data analysis

The regression-based sibpair linkage analysis that we

utilized was proposed by Fulker and colleagues (Fulker

and Cardon, 1994; Fulker et al., 1991) as an extension of

the methods originally developed by DeFries and Fulker

(1985, 1988) for the analysis of selected twin data. Therationale of the DeFries�/Fulker (DF) linkage analysis

for selected sibling samples is that the siblings of selected

(clinical) probands should demonstrate differential re-

gression toward the unselected (population) mean on a

quantitative trait (e.g. our index of dependence vulner-

ability), commensurate with the proportion of marker

alleles they share IBD with the proband*/given that aquantitative trait locus (QTL) is linked to a given

chromosomal marker.

The following regression model was fit to data from

250 proband-sibling pairs:

C�a�b1P�b2(pi)�o

where C is the quantitative score of the co-sibling of a

selected (clinical) proband, P is the proband’s score, pi

is the estimated average proportion of alleles sharedIBD for the sibling pair at location (i ), and a and o are

the regression constant and disturbance term, respec-

tively. A t-test of the significance of the b2 regression

coefficient (t�/b2/Seb2) provides a test of linkage, which

can be expressed as an approximate LOD score (t2/

4.6:/LOD score). We used the DF regression-based

QTL linkage analysis as operationalized in QMS2

(Lessem and Cherny, 2001), a series of integratedSAS# macros (SAS, 2000) available at: http://ibgwww.-

colorado.edu/�/lessem/software/qms2.html.

3. Results

3.1. Descriptive results

The percentage of subjects in our clinical and com-munity samples meeting DSM-IV criteria for a diagnosis

of substance dependence are presented in Table 2.

Community control probands are adolescents matched

to the clinical probands by age, sex, ethnicity and

zipcode of residence. Note the substantially greater

prevalence of substance dependence in the clinical

probands and their siblings compared with community

adolescents. Prevalence rates also highlight the substan-

Table 2

Prevalence of substance dependence in our clinical and community

samples

Dependence diagnosisa Clinical Community

Probands Siblings Probandsb Siblings

Tobacco 56.3 26.5 8.3 9.0

Alcohol 36.0 9.4 5.7 4.0

Marijuana 59.9 14.4 5.1 6.7

Otherc 26.8 7.4 1.9 2.3

One or more diagnosis 78.6 33.5 14.0 13.0

Two or more diagnoses 56.7 14.4 4.4 6.2

Three or more diagnoses 34.8 8.2 1.9 1.7

a DSM-IV substance dependence diagnosis.b Community probands, adolescents matched to the clinical pro-

bands by age (9/1 year), sex, ethnicity and zipcode of residence.c Other, illicit substance other than marijuana.


http://research.marshfieldclinic.org/genetics

http://research.marshfieldclinic.org/genetics

http://ibgwww.colorado.edu/~lessem/software/qms2.html







tial polysubstance use among adolescents in treatment

for substance use disorders, with over half of the clinical

probands meeting criteria for two or more substance

dependence diagnoses and about one third meetingcriteria for three or more diagnoses. Siblings of the

clinical probands also show substantially higher rates of

substance dependence and dependence on multiple

substances than community adolescents. These data

underscore the necessity of utilizing a composite index

of dependence vulnerability in our adolescent samples.

3.2. Single-point linkage results

Table 3 shows chromosomal markers where single-

point DF regression analyses resulted in a significant t-

value (P B/0.05). Reported P -values are actual values

obtained without adjustment for multiple testing. Re-

sults indicated 17 nominally significant single-point

results on 11 chromosomes: 2, 3q, 4q, 6q, 7q, 9q, 11p,13q, 18q, 19q and 20. For six marker locations (located

on chromosomes 3q24-25, 9q34, 11p15, 20p11 and

20q11) LOD scores exceeded 1.0. At marker D3S1614

on chromosome 3q25, a single-point LOD score of 2.24

was obtained.

3.3. Multipoint results

Fig. 1 shows LOD score plots from multipoint DF

regression analyses performed at 1 cM intervals across

the genome (22 autosomes). Cumulative distance in

Haldane cM is shown along the bottom of the figure and

the corresponding chromosome numbers are shown at

the top of Fig. 1. Multipoint mapping results corrobo-

rated our single-point linkage results for two locations,

and suggested potential QTL on chromosome 3q (in the

vicinity of markers D3S1279 and D3S1614), and near

the telomere of chromosome 9q (in the vicinity of

makers D9S1826 and D9S1838). The two peak LOD

score locations (LOD�/1.5) on chromosome 3q24-25

(LOD�/1.60) and chromosome 9q34 (LOD�/1.92) are

shown in greater detail in Figs. 2 and 3. Descriptive

statistics from our DF regression analyses at these two

locations are presented in Table 4.

An important assumption of the regression-based

methods that we employed is that the residuals (i.e.

differences between observed and predicted scores) are

normally and equally distributed across levels of the

independent variable (estimated multipoint IBD). Plots

of residuals revealed no obvious violations of this

assumption and evaluation of regression influence

statistics indicated no particularly influential cases or

outliers. Co-sibling means were plotted as a function of

the estimated average number of alleles shared IBD with

the clinical probands for the peak locations on chromo-

some 3q24-25 and chromosome 9q34. To compute the

co-sibling means we collapsed the estimated IBD

distribution into three categories based on the following

intervals: 0�/25; �/25�/75; and �/75% alleles shared IBD

at each location. The co-sibling means for dependence

vulnerability showed an approximate linear increase in

relation to the proportion of alleles shared IBD with the

proband at both peak locations. These data suggested a

mean difference of approximately one-half a S.D. on

our dependence vulnerability index between sibpairs

with an estimated IBD of less than 0.25 at the peak

Table 3

Single-point linkage results

Chromosome Marker Positiona t b P Infoc LOD

2 D2S319 7.60 1.87 0.03 0.42 0.76

2 D2S347 131.51 1.74 0.04 0.31 0.66

2 D2S338 250.54 1.89 0.03 0.36 0.78

3 D3S1279 169.60 2.50 0.01 0.50 1.36

3 D3S1614 177.75 3.21 0.01 0.42 2.24

4 D4S424 144.56 1.71 0.04 0.46 0.64

6 D6S446 189.00 1.66 0.05 0.37 0.60

7 D7S661 155.10 1.76 0.04 0.44 0.67

9 D9S1826 159.61 1.85 0.03 0.32 0.74

9 D9S1838 163.84 2.68 0.01 0.57 1.56

11 D11S902 21.47 2.32 0.01 0.30 1.17

13 D13S171 25.08 2.06 0.02 0.28 0.92

18 D18S478 52.86 1.82 0.04 0.37 0.72

19 D19S220 62.03 1.96 0.03 0.53 0.84

19 D19S420 66.30 1.71 0.04 0.51 0.64

20 D20S112 39.25 2.46 0.01 0.50 1.32

20 D20S107 55.74 2.26 0.01 0.42 1.11

a Position, distance in cM from the pterm.b t , test statistic from single-point DF regression analysis.c Info, single-point information at each marker; markers with single-point LOD scores greater than 1.0 are shown in bold face.


locations, compared with sibling pairs with an estimated

IBD of greater than 0.75.

3.4. Qualitative analyses

Employing a variant of the affected sibpair method,

we defined affected status as scoring above a z -score

cutoff of 1.0 (and 1.96) on our dependence vulnerability

index. That is, sibling pairs were considered ‘affected’

sibpairs (i.e. extreme-scoring pairs) if they both scored

above the respective z-score cutoffs. When only the

proband exceeded the threshold and the co-sibling did

not, the pair was considered a discordant pair. Because

affection status is somewhat arbitrarily defined on the

quantitative trait, we used two definitions to define

affected sibpairs: a z -score greater than 1.0 would be

expected to include about 16% of the general popula-

tion; and a z-score greater than 1.96 would include

Fig. 1. Multipoint LOD scores Across the Genome. x -axis�/cumulative Haldane centimorgans (cM); y -axis�/multipoint LOD score estimated

from DF regression analyses; chromosome number is shown along the top of the figure.

Fig. 2. Multipoint results for chromosome 3. Chromosome 3 marker locations are shown along the x -axis; y -axis�/multipoint LOD score estimated

from DF regression analyses.


approximately 2.5% of the general population. Note,

however, that by defining affection status this way we

are not suggesting that many of our sibling pairs are

‘unaffected’ pairs. This was used simply to determine

whether sibling pairs concordant for extreme scores on

our quantitative index also showed increased allele

sharing IBD at the locations suggested by our regression

analyses.

Table 5 shows the estimated alleles shared IBD for

siblings defined as ‘affected’ and ‘discordant’ sibling

pairs for our peak locations, and for the flanking

markers proximal to each of the peak locations. We

found significantly greater allele sharing (63% vs. the

null expectation of 50%) only at marker D3S1279 on

chromosome 3q24 when affection status was defined as

a z -score greater than 1.96. However, the affected

sibpair sample sizes are relatively small, particularly

for our z-score cutoff of 1.96. In general, there is a trend

for increased allele sharing among extreme scoring

sibling pairs at both peak locations. Significantly

decreased allele sharing among discordant sibpairs was

found for all chromosome 9 locations for the more

extreme 1.96 threshold.

4. Discussion

The current study describes findings from our initial

autosome-wide search for QTL influencing substance

dependence vulnerability in adolescence. We utilized

non-parametric QTL mapping procedures designed for

selected sibling-pair samples (Fulker et al., 1991; Fulker

and Cardon, 1994). The sample included 250 proband-

sibling pairs from 192 families. Patient probands were

adolescents (13�/19 years of age) recruited from con-

secutive admissions to treatment facilities for severe

substance abuse and delinquency. Parents, and siblings

of the probands between the ages of 12 and 25, were also

recruited for participation. A quantitative phenotype,

indexing non-specific substance dependence vulnerabil-

ity was determined*/a priori and independent of our

linkage results*/utilizing structured interview data from

our clinical sample and a sample of 3676 unselected

adolescents and young adults. This quantitative pheno-

type (average number of dependence symptoms�/the

total number of dependence symptoms divided by the

number of substances used repeatedly), age- and sex-

normed on our community samples, was substantially

Fig. 3. Multipoint Results for Chromosome 9. Chromosome 9 marker locations are shown along the x -axis; y -axis�/multipoint LOD score

estimated from DF regression analyses.

Table 4

Multipoint results: peak chromosomal regions yielding LOD scores greater than 1.50

Chromosome Positiona (cM) Informationb t c P LOD

3q24-25 173 0.62 2.72 B/0.004 1.60

9q34 161 0.88 2.97 B/0.002 1.92

a Position, distance in cM from the pterm.b Information, multipoint information content at the position.c t , test statistic from multipoint DF regression analysis.


heritable (h2�/0.48) and significantly discriminated

adolescent patient probands from unselected commu-

nity adolescents.

Multipoint mapping analyses suggested that the

strongest evidence for QTL influencing substance de-

pendence vulnerability was detected on chromosome

3q24-25 (in the vicinity of markers D3S1279 and

D3S1614) and chromosome 9q34 (in the vicinity of

markers D9S1826 and D9S1838). Although these loca-

tions do not meet criteria for significant linkage (see

Lander and Kruglyak, 1995), they are of sufficient

magnitude (LOD scores�/1.50) to provide a focus for

further study.

Both of these regions have been suggested in other

studies. Uhl et al. (2001), also investigating QTL for

polysubstance abuse vulnerability*/but utilizing adult

samples in a genome-wide association scan using a

pooled DNA strategy*/reported a significant associa-

tion to a region on chromosome 3 within 10 cM of our

peak location (173 cM from the pter). Long et al. (1998)

also reported suggestive evidence for linkage to three

markers on chromosome 3 spanning the region 165.3�/

181.1 cM in a genome scan for genetic linkage to alcohol

dependence. Bergen et al. (1999) report a similar region

associated with smoking status (ever vs. never smoked)

on chromosome 3 (171.7 cM), and also a region close to

our peak location on chromosome 9 (161 cM from the

pter).

A systematic search for genes within 15 cM of our

peak location on chromosome 3 (i.e. a 30 cM total

region) using the Golden Path Genome Browser (Kent

et al., 2002) from UCSC Human Genome Resources

(http://genome.ucsc.edu) yielded a total of 88 known or

suspected genes in the region. Although no candidate

genes previously associated with substance-related phe-

notypes are in this region, a number of potential

candidates (i.e. genes abundantly expressed in the brain

that may play important roles in neural developmentand/or signal transduction) include: the phospholipase

D1 gene (PLD1), a serine proteinase inhibitor gene

(SERPINI1), claudin 11 an oligodendrocyte transmem-

brane protein gene (CLDN11), and neuroligin 1

(NLGN1). Putative gene function was ascertained

from the GeneCards (Rebhan et al., 1997) database

(http://bioinformatics.weizman.ac.il/cards/).

A similar search of the region near our chromosome 9peak location yielded 91 known or suspected genes

within 15 cM of the 9q telomere. Again, putative gene

function was ascertained from GeneCards and candi-

dates were ranked in terms of their potential functional

significance. A potentially important candidate within

this region is the dopamine beta-hydroxylase gene

(DBH). However, when we added a SNP marker within

the DBH gene (DBH-1021C0/T; Zabetian et al., 2001)to our chromosome 9 map there was no evidence of

linkage to this marker suggesting our putative QTL is

distal to the DBH locus*/closer to the telomere. Other

potential candidates in the region include a neuroblas-

toma protein gene (AMY), a chloride intracellular

channel gene (CLIC3) and a calcium channel gene

(CACNA1B).

4.1. Limitations

Several limitations of the current study should be

noted that suggest some caution in interpreting our

results. A strength of our design was the use of a sample

of 250 selected sibling pairs which provides roughly the

same power as 1000�/2000 unselected sibling pairs

(Fulker and Cardon, 1994) utilizing our analytical

methods. Even so, the power to detect QTL of loweffect sizes (e.g. that account for less then 15% of the

phenotypic trait variance) is limited even in a highly

selected sample. Thus, it is possible that our findings

Table 5

Average allele sharing IBD at peak locations on C3 and C9 for affected and discordant sibpairs

Location Z a�/1.00 Z a�/1.96

Affectedb pairs (N�/58) Discordantc pairs (N�/128) Affectedb pairs (N�/31) Discordantc pairs (N�/109)

Chromosome 3q

D3S1279 0.57 0.48 0.63 0.48

D3S1614 0.59 0.52 0.58 0.51

Peak location 0.58 0.49 0.60 0.49

Chromosome 9q

D9S1826 0.53 0.45 0.58 0.42

D9S1838 0.54 0.44 0.59 0.41

Peak location 0.54 0.45 0.58 0.42

a Z , standardized score on quantitative index of substance dependence vulnerability.b Affected pairs, sibling pairs where both the proband and co-sibling both score above the respective z -score cutoff.c Discordant pairs, proband score is above threshold but co-sibling score is not; cell entries are the average percentage of alleles shared identical-

by-descent (from multipoint IBD probability estimates) at the respective locations; bold entries indicate significant deviations from the null

expectancy of 50%.


http://genome.ucsc.edu

http://bioinformatics.weizman.ac.il/cards/

represent false positives, and replication in larger

adolescent samples is imperative. Substance use dis-

orders also develop over time. We used an age- and sex-

normed quantitative index of dependence vulnerabilityto address this. However, there remains some level of

uncertainty regarding the scores of adolescents and

young adults, particularly for our youngest subjects,

who have not fully passed through the age of risk. Our

measure of dependence vulnerability is most appropriate

for adolescents who have had experience with multiple

substances (i.e. it quantifies the extent to which indivi-

duals show dependence symptoms across a number ofsubstances that they have used repeatedly*/more than

five times as defined by the CIDI-SAM). Although

polysubstance use is characteristic of adolescent popula-

tions, and particularly our high risk probands and their

siblings, not all of our subjects have had experience with

multiple substances, and some have not used any

substances. Individuals who had never used any sub-

stances were scored zero on our vulnerability index,which may be a limitation of this phenotype for

adolescents in general. Further, the causes of adolescent

substance dependence are likely to include both general

and substance-specific risk factors. Our approach is very

unlikely to detect QTL that influence responses or

vulnerabilities to specific drugs. Instead, in this study

we have focused on attempting to identify QTL that

underlie general risk factors that may be shared acrossmultiple substances. A full understanding of the causes

of substance dependence will clearly require both

approaches. Finally, we should point out that our

clinical sample is selected for both problem substance

use and conduct disorder; thus, our findings may not

generalize to broader substance abusing populations.

In summary, we have performed the first genome scan

for QTL influencing substance dependence vulnerabilityin adolescence. Although our results do not indicate

significant evidence for linkage (see Lander and Kru-

glyak, 1995), few studies of comparable sample size will

individually have sufficient power to identify QTL at

this level of significance. An important strength of the

current study was our adherence to developing a

quantitative index of substance dependence vulnerabil-

ity in adolescence*/a priori and independent of ourlinkage results*/to minimize the inflation of Type-I

errors inherent in testing multiple phenotypes. Further,

although our results are preliminary, our strongest

evidence for linkage indicates two chromosomal regions

(3q24-25 and 9q34) also suggested by other recent

studies of substance-related phenotypes in adult popula-

tions (Bergen et al., 1999; Long et al., 1998; Uhl et al.,

2001).Ultimately, the identification of specific candidate

genes that contribute to substance dependence vulner-

ability in adolescence will improve our clinical under-

standing of substance use disorders in this population

and eventually lead to more accurate diagnosis and

effective treatments.

Acknowledgements

We want to especially thank the participants of the

clinical, family, twin, and adoption studies of the Center

for the Genetics and Treatment of Antisocial Drug

Dependence. Genotyping was conducted by Dr. Brianna

Dennehey, Liping Liu and Jane Hutchinson. We also

want to acknowledge Dr Stacey Cherny and Dr Marissa

Ehringer for statistical and bioinformatics consultation.This research was supported in part by NIDA grants

DA-05131, DA-11015, and DA-12845.

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a genome-wide search for quantitative trait loci influencing substance dependence vulnerability in...

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