ORIGINAL PAPER

A Study of how Socioeconomic Status Moderates theRelationship between SNPs Encompassing BDNF and ADHDSymptom Counts in ADHD Families

J. Lasky-Su Æ Stephen V. Faraone Æ C. Lange ÆM. T. Tsuang Æ A. E. Doyle Æ J. W. Smoller ÆN. M. Laird Æ J. Biederman

Received: 12 May 2006 / Accepted: 20 November 2006 / Published online: 10 January 2007� Springer Science+Business Media, LLC 2007

Abstract Recent animal research suggests that brain-

derived neurotrophic factor (BDNF), may mediate

response to different environmental stimuli. In this

paper, we evaluated the possible role of BDNF as a

moderator of attention deficit hyperactivity disorder

(ADHD) in the context of different socioeconomic

classes. We genotyped ten single nucleotide polymor-

phisms (SNPs) in and around BDNF in 229 families

and evaluate whether there are SNP-by-socioeco-

nomic status (SES) interactions for attention deficit

hyperactivity. We developed three quantitative phe-

notypes for ADHD from nine inattentive and nine

hyperactive-impulsive symptoms that were used in

SNP-by-SES interaction analyses using a new meth-

odology implemented in the computer program

PBAT. Findings were adjusted for multiple compar-

isons using the false discovery rate. We found

multiple significant SNP-by-SES interactions using

the inattentive symptom count. This study suggests

that different SES classes may modify the effect of

the functional variant(s) in and around BDNF to have

an impact on the number of ADHD symptom counts

that are observed. The two exons within BDNF

represent potential functional variants that may be

causing the observed associations.

Keywords Gene-by-environment interaction �BDNF � ADHD � SES � Family-based association test �PBAT

Background

Accumulating evidence suggests attention deficit

hyperactivity disorder (ADHD) is a complex disorder

with both genetic and environmental causes. Family,

twin, and adoption studies show the etiology of ADHD

is influenced by genes. Family studies show that

relatives of ADHD individuals have higher rates of

ADHD compared with relatives of controls (Bieder-

man et al. 1992; Cantwell 1972; Doyle et al. 2001;

Faraone et al. 1993, 1994; Morrison and Stewart 1971;

Welner et al. 1977). Additional lines of evidence from

twin and adoption studies suggest this familial trans-

mission is, in part, due to genes, with higher concor-

dance rates for ADHD found in monozygotic twins

Edited by Stacey Cherny

J. Lasky-Su � S. V. Faraone (&)Medical Genetics Research Program, Department ofPsychiatry and Behavioral Sciences, SUNY Upstate MedicalUniversity, 750 East Adams Street, Syracuse, NY 13210,USAe-mail: faraones@upstate.edu

C. Lange � N. M. LairdDepartment of Biostatistics, Harvard School of PublicHealth, Boston, MA, USA

M. T. TsuangDepartment of Psychiatry, Institute of BehavioralGenomics, University of California, San Diego, CA, USA

A. E. Doyle � J. BiedermanPediatric Psychopharmacology Unit, Massachusetts GeneralHospital, Boston, MA, USA

J. W. SmollerDepartment of Psychiatry, Psychiatric andNeurodevelopmental Genetics Unit, Massachusetts GeneralHospital, Boston, MA, USA

Behav Genet (2007) 37:487–497

DOI 10.1007/s10519-006-9136-x

123

compared with dizygotic twins (Sherman et al. 1997)

and higher rates of ADHD found in biological parents

of ADHD adoptees compared with the adoptive

parents (Cantwell 1975; Morrison and Stewart 1973).

Heritability estimates for ADHD suggest that 76% of

the phenotypic variance is attributable to genetic

factors (Faraone et al. 2005).

In addition to genetic influences, environmental

influences have also been implicated in the etiology of

ADHD. In Rutter’s classic studies examining psycho-

social factors as risk factors for child mental disorders,

children living in distinctly different geographic envi-

ronments, an inner London borough and the Isle of

Wight, were compared. These studies suggested that

six family environmental conditions were associated

with child mental disorders including low socioeco-

nomic status (SES), severe marital discord, large family

size, paternal criminality, maternal mental disorder,

and foster care placement (Rutter et al. 1975). Addi-

tional research by Biederman et al. (1995) found that

the Rutter’s index, generated from the six environ-

mental conditions, was associated with a diagnosis of

ADHD. This group also found that low social class

increased the risk for ADHD independently of gender

and other risk factors (Biederman et al. 2002). Other

groups have also found an association between low

SES and ADHD (Pineda et al. 1999), a relationship

that is well established for psychiatric disorders in

general (Dohrenwend et al. 1992; Kessler et al. 1994).

Bor et al. (1997) identified an association between low

family income and general psychological disturbance,

including attention problems, among children in an

Australian longitudinal study. Costello et al. (1996)

found that poverty was the strongest demographic

correlate to child psychiatric disorders, including

hyperactivity, among children aged 9, 11, and 13 in

both urban and rural populations. Additional links

were found between socioeconomic adversity and

hyperactive 8-year-old boys with reading disabilities

in a longitudinal study (Chadwick et al. 1999).

Although there appears to be a relationship between

low SES and ADHD, these findings must be interpreted

cautiously for several reasons. First, a gene that plays a

role in the etiology of ADHD may also influence SES

through other indirect factors, resulting in the observed

association. SES is a complicated construct that incor-

porates measures including education and income and

is also indirectly related to many other variables, some

with genetic etiologies. Therefore, it is possible that the

observed relationship between low SES and ADHD is

actually due to genetic factors. To clearly differentiate

genetic and environmental effects of SES, twin or

adoption studies are necessary. In addition to the

possibility that genes could be indirectly responsible

for the observed ADHD–SES association, complex

interactions between genes and the environment could

be involved in the observed associations, which may

warrant additional study. When investigating a gene-

by-environment interaction between SES and ADHD,

it is important to control for parental psychopathology

because of the known associations between parental

psychopathology and both low SES (Dohrenwend et al.

1992) and psychopathology in offspring of these

individuals (Faraone et al. 1999).

Recent animal research suggests that brain-derived

neurotrophic factor (BDNF) may mediate response to

different environmental stimuli (Berton et al. 2006;

Tsankova et al. 2006). Specifically, research showed that

mice who were continually exposed to bouts of social

defeat developed long-lasting aversion to social contact.

Using knockout mice these researchers showed that

BDNF is necessary to develop this aversion to social

contact (Berton et al. 2006). Such research suggests that

BDNF would be a good candidate gene to study in the

context of gene-by-environment interactions.

Brain-derived neurotrophic factor, located on

11p14.1, represents a viable candidate gene for

ADHD, primarily because of the biologic plausibility

of the gene and the observed relationship BDNF has

had with ADHD and closely related psychiatric disor-

ders such as bipolar disorder and obsessive compulsive

disorder (Mossner et al. 2005; Neves-Pereira et al.

2002; Sklar et al. 2002) BDNF regulates neuronal

development and function. When BDNF is eliminated

from the brain after birth or in the early stages of

development in mice mutants, the mice are hyperactive

(Monteggia et al. 2004; Rios et al. 2001). One

polymorphism in BDNF in particular has been associ-

ated with ADHD. Val66met is a nonsynonymous

polymorphism that produces a valine to methionine

substitution at codon 66. An association has also been

found between the paternal valine allele and ADHD

susceptibility (Kent et al. 2005). Research has found

that Val66met also affects memory and hippocampal

function by impacting intracellular trafficking and

activity-dependent secretion of BDNF (Egan et al.

2003). Although it is biologically plausible that BDNF

is involved in the etiology of ADHD, some studies

have not observed this association. A recent study that

examined two common polymorphisms in BDNF,

Val66Met and rs6265, failed to find an association with

ADHD individuals (Friedel et al. 2005).

Other researchers use other biologic arguments to

suggest that BDNF is important in the etiology of

ADHD. Tsai (2003) argues that two classes of medica-

tions commonly used to treat ADHD, psychostimulants

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488 Behav Genet (2007) 37:487–497

and antidepressants, act to elevate central BDNF in

animal studies and therefore BDNF may play a role in

the therapeutic mechanisms underlying these drugs.

Tsai also points out the similarity between the reduced

amount of BDNF in ADHD individuals through

smaller brain volume and the hyperactivity that results

when BDNF is eliminated from the brains of postnatal

mice. Recently, Tsai again advocated studying the

relationship between BDNF and ADHD (Tsai 2006).

In addition, BDNF has been named as a possible

candidate gene for ADHD that merits further study

(Slopien et al. 2006). In this manuscript we further

study the relationships between BDNF, SES, and

ADHD in the context of gene by environment

interactions.

Methods

Study population

Two hundred and twenty-nine ADHD families were

recruited from various continuing research studies

being conducted through Massachusetts General Hos-

pital (MGH) pediatric psychopharmacology clinic.

Ninety families were ascertained from the longitudinal

case–control family studies of ADHD boys and girls. In

this study, probands were recruited from MGH general

pediatric and pediatric psychopharmacology clinics or

from health maintenance organizations throughout the

Boston area. Ascertainment of the probands and their

relatives was based on DSM-III-R criteria as subjects

were recruited before the publication of DSM-IV.

Individuals were required to be between 6 and 18 years

of age to participate in the study. Potential subjects

were excluded if they were adopted, had major

sensorimotor handicaps, psychosis, autism, inadequate

command of the English language, an IQ < 70, or their

nuclear family was not able to participate in the study.

All of the ADHD probands met DSM-III-R diagnostic

criteria for ADHD at the time of the clinical referral

and had active ADHD symptoms at the time of

recruitment.

Additional families were also ascertained from an

affected sibling pair linkage study of ADHD (83

families), a sample of bipolar families (37 families), a

family study of ADHD adults (17 families), and a

sample of substance abuse families (2 families). Recruit-

ment, inclusion, and exclusion criteria for these other

studies were the same as the longitudinal study for

ADHD boys and girls with the following exceptions: (1)

ADHD cases were obtained from the MGH pediatric

psychopharmacology clinic, the child psychiatry clinic at

Children’s Hospital in Boston, or by referrals from

individual child psychiatrists throughout the community;

(2) ascertainment was based on DSM-IV diagnoses; (3)

the pediatric bipolar studies ascertained cases for

bipolar disorder and did not screen out cases with

psychosis. Individuals 18 years of age or older provided

written informed consent, mothers provided written

informed consent for minor children and children

provided written assent to participate in this study.

Because the same research group conducted these

studies, the ascertainment criterion for ADHD did not

differ among studies (e.g., children enrolled as bipolar

probands for the family study of bipolar disorder would

have qualified for enrollment in the ADHD studies if

they also met criteria for ADHD), thereby limiting

potential heterogeneity.

ADHD symptom assessment

Psychiatric information was collected from children

using the K-SADS-E (Epidemiologic Version) for

either DSM-III-R or DSM-IV. The K-SADS-E is a

widely used semi-structured psychiatric diagnostic

interview with established psychometric properties

(Orvaschel 1994; Orvaschel and Puig-Antich 1987).

The interview inquires about the child’s lifetime

history of psychopathology and provides a standard-

ized method of obtaining and recording symptoms

necessary for the assessment of several disorders,

including ADHD. These data were collected from

the mother for all children; children 12 and older were

also evaluated directly. We did not directly interview

children younger than 12 because this has been shown

to be unreliable (Achenbach and McConaughy 1987;

Breton et al. 1995; Edelbrock et al. 1985; Schwab-Stone

et al. 1994).

We used the K-SADS-E to collect information on

nine inattentive and nine hyperactive-impulsive symp-

toms in individuals. The inattentive symptoms

included an inability to pay attention to details,

difficulty with sustained attention in tasks or play

activities, apparent listening problems, difficulty

following instructions, problems organizing tasks and

activities, avoidance or dislike of tasks that require

mental effort, tendency to lose things, distractibility,

and forgetfulness in daily activities. Symptoms of

hyperactive-impulsivity included fidgeting or squirm-

ing, difficulty remaining seated, restlessness, difficulty

playing quietly, always seeming to be ‘‘on the go,’’

excessive talking, blurting out answers before hearing

the full question, difficulty waiting for a turn or in line,

and problems with interrupting or intruding. Each

symptom was recorded as present or absent (1 and 0,

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Behav Genet (2007) 37:487–497 489

respectively); in the few cases when a symptom was

exhibited, but very rarely, the symptom was coded in

between these two categories (a value of 0.5 was

assigned). This information was used to generate the

total number of inattentive symptoms the child

exhibits, the total number of hyperactive-impulsive

symptoms the child exhibits, and the total number of

symptoms the child exhibits overall. In the case where

there was incomplete information on symptoms,

symptoms counts were generated using the available

symptom information. In addition, children over 12

years of age had data collected using themselves and

their mother as the informant. In this case, if either

person reported the presence of an ADHD symptom,

it was counted as present in the analysis.

SES assessment

The environmental variable, SES was generated using

a five-point scale developed by Hollingshead, where a

high score represents a low SES (Hollingshead 1975).

This variable was determined by using information on

occupation, education, sex, and marital status. This

information is combined according to a formula to

generate an ordinal scale for social class.

Genotyping methods

A description of the genotyping methods was reported

previously elsewhere (Sklar et al. 2002). Genotyping of

single nucleotide polymorphisms (SNPs) in BDNF was

performed by mass spectrometry as follows. Primers

were designed using SpectroDESIGNER software

(Sequenom, CA, USA) to have a Tm of 56–60� with

a mass range between 5,000 and 8,000 Da as described

(Buetow et al. 2001). PCR amplification was per-

formed as follows. Each reaction contained AmpliT-

AQ Gold (0.1 U, Perkin Elmer), dNTPs (0.2 mM),

MgCl2 (1.5 mM), genomic DNA (5 ng), locus specific

primers (0.2 M final concentration of each primer), in

the supplied buffer in a final volume of 6 l using the

following PCR conditions (92�C for 9 min, 46 cycles of

94�C for 20 s, 56�C for 30 s, and 72�C for 30 s followed

by a final extension of 72�C for 3 min). Following the

PCR, dNTPs were removed by shrimp alkaline phos-

phatase (SAP) by adding 2 l of SAP (0.3 U) in

Thermosequenase buffer and incubating at 37�C for

20 min, followed by inactivation at 85�C for 5 min. The

homogeneous MassEXTEND reaction was performed

by adding to the SAP-treated product 2 l of a solution

containing ddNTPs (0.50 M each), dNTPs (0.50 M

each), MassEXTEND primers (0.6 nM), Thermose-

quenase buffer (Pharmacia), and Thermosequenase

(0.063 U l–1). The termination mix of ddNTPs and

dNTPs was predicted by the SpectroDESIGNER

software and was specific for each SNP genotyped.

The reactions were thermocycled under the following

conditions: 94�C for 2 min, 40 cycles of 94�C for 5 s,

40�C for 5 s, 72�C for 5 s, then 72�C for 5 min.

SpectroCLEAN, a proprietary ion-exchange resin, is

added to remove salt. The sample plate is rotated for

4 min at RT and then centrifuged for 1 min at

1,400 rpm. Using a 24-pin SpectroPOINT, 7 nl of each

reaction was then loaded onto each position of a 384-

well SpectroCHIP preloaded with 7 nl of matrix (3-

hydroxypicolinic acid). SpectroCHIPs were analyzed in

automated mode by a MassARRAY RT mass spec-

trometer (Bruker-Sequenom) (Buetow et al. 2001).

The resulting spectra were analyzed by SPECTRO-

TYPER software (Sequenom) after baseline correction

and peak identification. The minimum acceptable

signal to noise ratio was 5:1. Clusters were verified by

two independent observers.

Ten SNPs spanning 217 kb (average ~1SNP/21.7 kb)

in and around BDNF were used in this analysis; seven

SNPs were selected surrounding BDNF and three

SNPs were selected in BDNF. These SNPs were

screened in 12 multigenerational CEPH pedigrees to

evaluate SNP assay quality and characterize the

linkage disequilibrium (LD) relationships. The CEPH

pedigrees are a group of extended pedigree families

with a large number of children that have often been

used to generate haplotypes for Caucasian populations

in the HAPMAP project. SNPs used in the analysis

met the following conditions: (a) genotyping call

rate > 90%; (b) genotypes in Hardy–Weinberg equi-

librium; and (c) no Mendelian errors. Although the

minimum accepted call rate was 90%, the actual call

rates observed for this sample were substantially

higher, ranging from 97.6 to 99.0%.

Haplotype blocks of LD were reconstructed in the

sample used here and in CEPH pedigrees. The EM

algorithm and the haplotype block criteria of Gabriel

et al. (2002) as implemented in Haploview (Barrett

et al. 2005) were used to determine the haplotype

blocks in both of these samples. The final haplotype

block structure was determined by incorporating the

results from both these data and the CEPH families.

Genotyping of the SNPs was done by MALDI-TOF

mass spectrometry (Buetow et al. 2001).

Parental psychopathology

Parental psychopathology was evaluated using the

Structured Clinical Interview for DSM-III-R and

DSM-IV (Spitzer et al. 1992; Williams et al. 1992)

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490 Behav Genet (2007) 37:487–497

supplemented by the K-SADS-E for childhood diag-

noses. This interview has been shown to have high

inter-rater and test–retest reliabilities (Skre et al. 1991;

Williams et al. 1992). Two approaches were used to

adjust the analysis for parental psychopathology. First,

a quantitative measure of parental psychopathology

was generated for each family by summing the number

of lifetime psychiatric diagnoses given to each parent.

Residuals were generated by regressing this parental

psychopathology variable on SES. The residuals were

used to adjust the gene-by-environment interaction

analyses for potential confounding that may exist

between high parental psychopathology and low SES

and the child’s psychopathology. The following psy-

chiatric diagnoses were used to generate this summary

measure of parental psychopathology: ADHD, agora-

phobia, anorexia, alcohol dependence/abuse, bipolar

disorder, bulimia, conduct disorder, dysthymia, gener-

alized anxiety disorder, major depressive disorder,

obsessive compulsive disorder, oppositional defiant

disorder, panic disorder, psychosis, simple phobia,

social phobia, and substance dependence/abuse. Sec-

ond, we adjusted for parental psychopathology by

adding individual covariates for alcohol abuse/depen-

dence, substance abuse/dependence, major depression,

conduct disorder, and bipolar disorder. We created

binary variables for these disorders; if either parent

had a given disorder, the covariate was given a value of

1 and a value of 0 was assigned otherwise. The analysis

was then rerun adjusting for these covariates.

Main genetic effects and gene–environment

interactions in family-based association tests

Previous methodologies for gene-by-environment

interactions in the setting of family based designs have

been restricted to single SNPs and dichotomous traits.

Recently, Vansteelandt et al. (submitted for publica-

tion, 2006) proposed a new approach where gene-by-

environment interactions can be modeled using SNPs

as well as haplotypes, quantitative or dichotomous

phenotypes, and complex exposure variables. Vanstee-

landt et al. (submitted for publication, 2006) introduce a

general methodology that uses an estimate of the main

genetic effect size, b1, and an estimate of the gene-by-

environment interaction, b2, to test of the gene-by-

environment interaction after accounting for the main

genetic effect (FBAT-Interaction). A general model is

given as follows for n nuclear families with m offspring:

E(Yij) ¼ b k1X(Gij) þ b k

2X(Gij)Zij

þ x ij(Zij, Si)

j ¼ 1; . . . ;mi

i ¼ 1; . . . ; n

k ¼ 1; . . . ; p

Yij is the mean-centered phenotype (in this case either

the total number of ADHD symptoms, the number of

hyperactive-impulsive symptoms, or the number of inat-

tentive symptoms) for the jth offspring in the ith family.

X(Gij) is the phased set of alleles in the haplotype or the

alleles at the given SNP for the jth offspring in the ith

family. Zij is the environmental exposure variable of

interest (in this case SES) for the jth offspring in the ith

family. The parameter vector b1 represents the main

genetic effect and the parameter vector b2 represents the

gene-by-environment interaction effect. The kth compo-

nent of b1 gives the main genetic effect of the kth

haplotype/SNP over and above the effect of the other

haplotypes/SNPs. Similarly, the kth component of b2

represents whether the effect of the kth haplotype/SNP

on the phenotypic trait interacts with the environmental

exposure. x ij is an unknown function that describes the

dependent relationship of the phenotypic trait on factors

that are not explicitly stated in the model. Many variables

are incorporated into x ij, including the main environ-

mental effect, among other possible covariates. Using this

analytical technique, it is therefore not possible to parse

out the environmental effects from the other variables

that are incorporate into x ij.

Vansteelandt et al. (submitted for publication, 2006)

uses causal inference methodology to derive estimating

equations that are used to generate an estimate of the

main genetic effect, b1, and the gene-by-environment

interaction, b2. Details of causal inference methodol-

ogy are described in detail elsewhere (Pearl 2000). We

provide a brief description here.

The goal of most genetic epidemiologic studies is to

quantify the causal effect of alleles on a disease

outcome; however, genetic epidemiologic methods

currently provide measures of association for expo-

sure/outcome relationships, but lack a causal interpre-

tation. Causal inference methodology uses

counterfactual theory and graph theory to define

causal concepts and identify confounding variables.

This information is then incorporated into the statis-

tical analysis of data so that the generated estimates

can have a causal interpretation. One advantage of this

methodology is that the parameter estimates are robust

against population admixture and do not depend on

the unknown function x ij. Therefore, the estimation

of b1 and b2 are valid regardless of x ij. Vansteelandt

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Behav Genet (2007) 37:487–497 491

et al. (submitted for publication, 2006) also uses causal

inference methodology to derive a score test (FBAT-

Interaction) for the gene-by-environment interaction

after accounting for the main genetic effect (Ho:

b1 „ 0 or b1 = 0 and b2 = 0). The general principle

behind FBAT-Interaction is that after removing the

overall main genetic effect, the phenotype should not

depend on the genotypes conditional on the environ-

mental exposure under the null hypothesis. Such a test

is therefore valid regardless of the estimate for the

main genetic effect. This class of estimates and tests (b1

estimate, and b2 estimate, and FBAT-Interaction) is

available in the program PBAT (Lange et al. 2004)

(http://biosun1.harvard.edu/~clange/pbat.htm).

In general, the FBAT/PBAT approach is a generalized

version of the TDT that allows for many different

scenarios such as multiple siblings in a family. This

approach can even analyze extended pedigrees. If multi-

ple offspring are phenotyped, they will contribute to the

test statistic in a way that accounts for sibling correlation

in each family. If offspring are not phenotyped, they may

also contribute by helping to determine parental geno-

types if the parental genotypes are incomplete.

We assess possible SNP-by-SES interactions and the

haplotype-by-SES interactions using three quantitative

phenotypes: (1) the number of inattentive symptoms;

(2) the number of hyperactive-impulsive symptoms;

and (3) the total number of inattentive and hyperac-

tive-impulsive symptoms. In this analysis we use the

estimates for b1 and b2 to generate the FBAT-Inter-

action p-value for the SNP/haplotype-by-SES interac-

tion. Because this methodology does not estimate the

main environmental effect, this is not reported; how-

ever, the FBAT-Interaction test presented here is valid

regardless of what the main environmental effect is, as

this effect is incorporated into x ij. The significance of

the main genetic effects between each SNP and the

three ADHD phenotypes is also reported. Association

analyses were adjusted for parental psychopathology as

measured by the number of parental disorders.

Because BDNF is not located in a genomic region

linked to ADHD-related phenotypes in past research,

the null hypothesis of our PBAT analyses assumed no

association and no linkage. An additive mode of

inheritance was used and at least 20 informative

families were required at any given SNP to be included

in the analysis. We only considered one model to

reduce the number of statistical tests. The ADHD

symptoms counts, SES, and the number of parental

disorders were all transformed to Z-scores in the

analysis. Analyses were adjusted for multiple testing

within the ADHD symptom count classes using the

false discovery rate (FDR) (Benjamini and Hochberg

1995). The FDR controls the proportion of false

positives that are observed. This is in contrast to other

multiple comparison approaches, such as Bonferroni,

that controls the probability of generating a single false

positive result. Significant finding are determined using

q-values, the FDR equivalent to p-values. An overall

FDR of 0.05 was used to determine significance.

Results

Genotype results

A total of ten SNPs spanning 217 kb (average ~1SNP/

21.7 kb) passed the specifications to be used in the

analysis. Seven SNPs were in the region surrounding

BDNF (hCV102787, hCV1177022, hCV1177024,

rs1038660, rs1013442, rs1565228, and rs1387144) and

three SNPs were within the gene itself (BDNFa30,

Val66Met, and BDNFa44). None of the seven SNPs

surrounding BDNF were in the promoter region or

functional. Two of the three SNPs within BDNF,

BDNFa30, Val66Met, were in one of the two exons in

BDNF and the functionality of Val66Met has been

studied previously (Bath and Lee 2006; Pezawas et al.

2004). Using the CEPH families, the conservative

Gabriel et al. (2002) criteria divided the SNPs into

three haplotype blocks with strong LD: (1) hCV102787,

hCV1177022, hCV1177024; (2) rs1038660, rs1013442,

rs1565228; and (3) BDNFa30, Val66Met, BDNFa44.

Using the same criteria, our sample identified two

haplotype blocks: (1) rs1038660, rs1013442, rs1565228

and (2) BDNFa30, Val66Met, BDNFa44. We decided to

use the haplotype block structure generated from our

data, as it was consistent with the CEPH data but

suggested a few SNPs in our sample were not in as strong

LD as in the CEPH families. Although the SNPs were

separated into two haplotype blocks, notable LD still

existed between these groupings. D¢ is a measure of the

LD between two SNPs that ranges between 0 and 1, with

higher values indicating more LD. Many of the D¢estimates between SNPs on different haplotype blocks

were > 0.8, suggesting that the SNPs in these haplotype

blocks are not acting completely independent of one

another.

Descriptive statistics

Of the initial 229 families with genotype information,

228 families had adequate information to be used in

the analysis. Descriptive information on these families

is listed in Table 1.

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492 Behav Genet (2007) 37:487–497

The Pearson correlation between number of parental

disorders and SES was significant (r = 0.31, p < 0.001).

The correlation between the number of inattentive

symptoms and hyperactive-impulsive symptoms was

high (r = 0.70, p < 0.001). As expected, the number of

inattentive and the number of hyperactive-impulsive

symptoms were both strongly correlated with the total

number of ADHD symptoms (r = 0.92 and 0.92, respec-

tively, p < 0.001). Because these symptom measures

were strongly correlated, we adjusted for the multiple

comparisons within each symptom measure. The corre-

lation between SES and qualitative and quantitative

measures of ADHD (ADHD diagnosis and total num-

ber of ADHD symptoms) were 0.10 (p = 0.006) and 0.14

(p = 0.0003), respectively. This suggests that there is an

increase in ADHD with decreasing SES (higher Hol-

lingshead scores refer to lower SES).

SNP-by-SES interactions

We observed nominally significant associations for

seven of the ten SNPs in BDNF with one of the

ADHD phenotypes. For the hyperactive-impulsive

symptom count, we observed nominally significant

associations between hCV102787, hCV1177022,

hCV1177024 (unadjusted p-values = 0.025, 0.046, and

0.041, respectively). For the inattentive symptom

count, we observed nominally significant associations

between rs1013442, rs1387144, Val66Met (unadjusted

p-values = 0.026, 0.019, and 0.029, respectively) and for

total ADHD symptoms we observed a nominally

significant association with BDNFa40 (unadjusted p-

value = 0.040). Although there were several nominally

significant results, none of these findings remained

significant after using the FDR adjustment.

Table 2 shows the findings for the SNP-by-SES

interaction while using the three different ADHD

symptom counts and adjusting for number of parental

disorders. Presented is the number of informative

families at each SNP, the physical location of the SNP

(Human Genome Working Draft, http://genome.ucs-

c.edu/May 2004 freeze), and the uncorrected FBAT-

Interaction p-values. After adjusting for multiple

comparisons, the significant SNPs are denoted using

an asterisks and the interaction estimate, b2, is also

provided.

Although several SNPs met nominal significance,

analyses using inattentive symptom counts were the

only ones that met overall significance. After account-

ing for the main genetic effect, rs1013442 (nominal

p-value = 0.011; FDR q-value = 0.041), rs1387144

(nominal p-value = 0.009; FDR q-value = 0.041), and

Val66Met (nominal p-value = 0.012; FDR q-va-

lue = 0.041) had significant SNP-by-SES interactions

using the inattentive symptom count phenotype. Allele

a in rs1013442 and allele t in Val66Met and allele c in

rs1387144 were associated with an increase in number

of inattentive symptoms in lower SES environments.

This analysis was also redone with age as a covariate to

account for the possibility that SES might impact the

genetic risk differently in different age groups. Negli-

gible changes of the overall findings resulted. Similarly,

because it has been suggested that BDNF may play a

role in bipolar disorder and substance abuse, these

diagnoses were included as covariates in the analysis to

insure that the observed effects were due to ADHD

and not one of these disorders. When these covariates

were included, the findings remained unchanged. The

results presented include a quantitative measure of

parental psychopathology as a covariate; however,

when individual covariates for each parental psychiat-

ric disorder were used, the findings were similar.

Table 3 lists the haplotype block, the haplotype, the

number of informative families, the haplotype fre-

quency, and the unadjusted FBAT-Interaction p-val-

ues for the haplotype-by-SES interaction using the

three ADHD phenotypes. Although several haplo-

types met nominal significance, none reach overall

significance after adjusting for multiple comparisons.

Table 1 Descriptive statistics on the sample used in the SNP/haplotype-by-SES analysis

Total number of individuals 701

Number of offspring 345Boys 210 (60.9%)Girls 135 (39.1%)

Number of families 228Families with one affected

offspring (%)129 (56.6%)

Families with two affectedoffspring (%)

84 (36.8%)

Families with three affectedoffspring (%)

12 (5.3%)

Families with four affectedoffspring (%)

3 (1.3%)

Number of affected offspring ineach SES category (%)1 high SES (%) 107 (31.0%)2 152 (44.1%)3 56 (16.2%)4 28 (8.1%)5 low SES (%) 2 (0.6%)

Mean SES of families (SD) 2.03 (SD = 0.9)Mean number of symptoms among

affected offspring (SD)Number of inattentive symptoms 7.74 (SD = 1.5)Number of hyperactive-impulsive

symptoms6.16 (SD = 2.3)

Number of total symptoms 13.77 (SD = 2.9)

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Behav Genet (2007) 37:487–497 493

Discussion

This analysis is one of the first to apply the method-

ology proposed by Vansteelandt et al. (submitted for

publication, 2006) that evaluates gene-by-environment

interactions using quantitative phenotypes. We found

three significant SNP-by-SES interactions using the

inattentive phenotype. Continued research is necessary

to replicate these findings and identify what functional

roles these SNPs may be detecting in the SNP-by-SES

associations. From a treatment perspective, it would be

difficult to incorporate this information directly into a

management plan for ADHD; however this informa-

tion could help in screening individuals with the

disorder.

As stated previously, Val66Met, which produces a

valine to methionine substitution at codon 66, is

contained within BDNF and represents a functional

variation in the transcript that affects memory and

hippocampal function by impacting intracellular traf-

ficking and activity-dependent secretion of BDNF

(Egan et al. 2003). Previous associations have been

found between the paternal valine allele and ADHD

susceptibility (Kent et al. 2005). Our results did not

directly replicate this finding but we did observe and

association with Val66Met and the number of inatten-

tive symptoms. Furthermore, rs1038660 and BDNFa30

both had nominally significant associations with the

paternal allele (unadjusted p-values = 0.029 and 0.046,

respectively).

Although the genetic associations between the

BDNF SNPs and the various ADHD symptom counts

did not withstand multiple testing, it is noteworthy that

seven of the ten SNPs achieved nominal significance

with one of the ADHD symptom counts. In addition,

there was no overlap in the SNPs that achieved

nominal significance for each of the symptom counts

which might suggest that different regions of BDNF

have different influences on the ADHD symptoms, if

this finding can be substantiated through replication.

If our results can be replicated, this polymorphism

should be examined further for potential moderating

effects. rs1013442 and rs1387144 are nonfunctional

SNPs that surround BDNF. Although the Gabriel et al.

(2002) criteria separated these SNPs to different

haplotype blocks, the overall extent of LD within this

region is still notable. Therefore, these SNPs outside of

BDNF may be in LD with the same functional variant

Table 2 SNP-by-SES resultsthroughout BDNF

a Significant finding afteradjusting for multiplecomparisons using FDR

SNP No. ofinformativefamilies

Physicallocation(bp)

Nominal FBAT-Interaction p-value b2 estimates forthe significant interactions

Totalsymptoms

Hyperactive-impulsivesymptoms

Inattentivesymptoms

hCV102787 73 27,421,698 0.307 0.100 0.387hCV1177022 60 27,453,547 0.074 0.041 0.771hCV1177024 130 27,457,093 0.5784 0.487 0.054rs1038660 95 27,506,218 0.022 0.074 0.132rs1013442 116 27,535,772 0.297 0.984 0.011a b2 = –0.647rs1565228 50 27,543,262 0.260 0.450 0.077rs1387144 128 27,592,145 0.994 0.171 0.009a b2 = –0.455BDNFa30 110 27,633,867 0.750 0.804 0.094Val66Met 98 27,636,742 0.429 0.891 0.012a b2 = –0.636BDNFa44 102 27,638,422 0.038 0.071 0.095

Table 3 Haplotype-by-SESresults throughout BDNF

Haplotypeblock

Haplotype No. ofinformativefamilies

Haplotypefrequency

Nominal FBAT-Interaction p-value b2

estimates for the significant interactions

Totalsymptoms

Hyperactive-impulsivesymptoms

Inattentivesymptoms

rs103866;rs101344;rs1565228

C:T:G 138 0.578 0.130 0.010 0.177C:A:G 111 0.224 0.536 0.762 0.010A:T:G 62 0.121 0.013 0.010 0.763A:T:C 44 0.071 0.386 0.536 0.141

BDNFa30;Val66Met;BDNFa44

T:C:C 135 0.537 0.542 0.106 0.138C:C:G 95 0.206 0.047 0.073 0.144T:T:C 95 0.175 0.451 0.910 0.014C:C:C 37 0.064 0.244 0.540 0.320

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494 Behav Genet (2007) 37:487–497

as the SNPs within BDNF. Alternatively, these SNPs

could be in LD with another functional variant of the

gene, such as the function of the other BDNF exon.

Given the clinical distinctions between individuals

who exhibit predominantly inattentive, hyperactive-

impulsive, and a combination of these symptoms, it is

also likely that genetic heterogeneity exists between

these groups or that one of these phenotypes is more

strongly associated with the observed SNP/haplotype-

by-SES interactions. In this analysis, we observed that

the inattentive phenotype is the only one with signif-

icant interaction findings after adjusting for multiple

comparisons. This suggests that using the number of

inattentive symptoms may be the optimal phenotype to

further investigate the interaction findings in and

around BDNF.

Low SES has long been associated with psychiatric

disorders (Dohrenwend et al. 1992; Kessler et al. 1994)

and our findings confirm a correlation between ADHD

and low SES using continuous and discrete phenotypes

(p < 0.01 in all cases). Researchers have long ques-

tioned whether low SES results in increased rates of

psychiatric illness or whether the burden of psychiatric

illness results in a decrease in SES. These findings

suggest that in the case of ADHD, SES may be acting

in an additional way than both of these theories

propose. Low SES, or a proxy that SES is measuring,

may not only be influential whether one develops

ADHD, but SES may be modifying the effect of

specific genes to increase or decrease the likelihood of

ADHD. Such an interaction could be explained many

ways. For example, low levels of SES are associated

with decreased income which likely results in de-

creased health care. Reduced health care often results

in a higher incidence of low birth weight, which has

been associated with a threefold increase in incidence

of ADHD (Mick et al. 2002). Although it is not known

at this time why the low birth weight–ADHD associ-

ation exists, one explanation could be that the envi-

ronment that causes low birth weight also activates

genes that increase ones susceptibility to ADHD. After

the SNP/haplotype-by-SES interactions in this paper

are replicated, careful study is necessary to determine

that specific environmental and genetic mechanisms at

work.

Because of the complex relationships involved in a

measure like SES, it is possible that a relationship

between the SNP genotypes and SES exists, which

would complicate the interpretation of the interaction

we are studying. The statistical model used in to

calculate the interaction term incorporates the main

environmental effect. Therefore, the statistical model

used protects against a false positive interaction finding

that would result from a SES–BDNF association.

Despite this, we looked at the correlation between

these genotypes and SES to determine if such a

confounding relationship exists. The correlations be-

tween SES and the BDNF SNPs were low, ranging

from 0.01 to –0.16. Four of the correlations were

significant but only one coincided with one of the

observed interaction effects.

There are several limitations of this study. To our

knowledge there is no prior research or a priori reason

why BDNF should specifically interact with SES, which

is a clear limitation of this study. Nevertheless, we

wanted to examine this hypothesis because SES is a

well accepted, albeit nonspecific, marker of environ-

mental stress and deprivation, which is why we used

this as a possible interaction variable in this analysis.

Another clear disadvantage is how SES was measured.

Although the Hollingshead scale is often used in

psychiatric research, this measure is over 30 years old

and may not adequately represent SES currently. SES

can be a proxy for many variables such as poor health

care, poor prenatal care, increased use of substance use

during pregnancy, reduced resources for their children,

and poor public school systems. In addition, our SES

measure does not incorporate geocoding, which may

result in a more accurate measure for SES. Given the

limited information that was used in generating SES

and general difficulty in quantifying SES, it is unclear

how well we are measuring this construct. A general

disadvantage of gene-by-environment interaction anal-

yses is that the interaction is sensitive to how the

interaction variable is being modeled. This methodol-

ogy provides one advantage over other methods. The

biometric model used in this analysis estimates the

linear relationship between the phenotype and SNP/

haplotype-by-SES interaction using the parameter b2.

The xij function in the biometric model allows for

other functions of the interaction term to exist (e.g.,

higher order interaction terms), but even with the

presence of these other terms, the linear estimate of

the interaction term remains valid. Finally, our sample

used individuals from several different studies, which

could increase the heterogeneity of the sample. We

believe this is limited however, because the ascertain-

ment conditions for these separate studies were virtu-

ally identical.

In summary, this study evaluated the interaction

between ten SNPs in and around BDNF and SES in

association analyses that used three quantitative mea-

sures of ADHD symptoms as the phenotypes of

interest. These results found that there were several

significant SNP-by-SES interactions with the inatten-

tive phenotype. It appears as if different SES classes

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Behav Genet (2007) 37:487–497 495

may modify the effect of the functional variant(s) to

have an impact on the number of ADHD symptom

counts that are observed. The two exons within BDNF

represent potential functional variants that may be

causing the observed associations; however, before any

conclusions about function in this region can be made

both replication of these findings with a larger sample

size and a more detailed study of the potential

functions in this chromosomal region are necessary.

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