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Analysis of GWAS top hits in ADHD suggests association to two polymorphisms located in genes expressed in the cerebellum.

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RAPID PUBLICATION
Neuropsychiatric Genetics
Analysis of GWAS Top Hits in ADHD Suggests
Association to Two Polymorphisms Located in Genes
Expressed in the Cerebellum
Francesca Lantieri,1,2 Joseph T. Glessner,3 Hakon Hakonarson,3,4,5,6 Josephine Elia,1,7*
and Marcella Devoto4,6,8,9
1
Department of Child and Adolescent Psychiatry, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
2
Dipartimento di Scienze della Salute, Sezione Biostatistica, Universita degli Studi di Genova, Genova, Italy
3
Center for Applied Genomics, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
4
5
Division of Pulmonary Medicine, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
6
Department of Pediatrics, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania
7
Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania
CCEB, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania
8
9
Dipartimento di Medicina Sperimentale, Universita’ La Sapienza, Roma, Italy
Received 20 April 2010; Accepted 2 June 2010
Attention deficit/hyperactivity disorder (ADHD) is a common
psychiatric disorder influenced by genetic factors. Several chromosomal regions with potential linkage and candidate genes
associations have been reported, but findings are often inconsistent and non-replicated. The few genome-wide association
studies (GWAS) carried out so far differ for study design and
phenotypes analyzed, and did not detect any association significant at the genome-wide level. In the present study we examined
the top SNPs reported in the GWAS by Neale et al. [2008] in an
independent cohort. Although our sample size is smaller (415
trios vs. 909), the power was sufficient to confirm the role of
candidate markers in ADHD if a true association exists. Two
out of 36 top SNPs were significant at a ¼ 0.05 in our sample,
although none was still significant after correction for multiple
tests. These two SNPs are both located in genes coding for as
yet uncharacterized proteins expressed in the cerebellum,
XKR4 in 8q12.1, and FAM190A in 4q22.1. Three other FAM190A
SNPs have TDT P-values of <105 in our sample, a level of
significance only reached by a total of five SNPs in our genomewide data. While these findings could be due to chance, we
cannot exclude that these markers are indeed associated to
disease risk. Remarkably, brain imaging studies have shown
reduction of the posterior inferior cerebellar lobules volume
of ADHD boys and girls compared to controls, persistent
with age and not present in unaffected siblings, suggesting that
the cerebellum may be directly related to pathophysiology of
ADHD. 2010 Wiley-Liss, Inc.
Key words: ADHD; GWAS; cerebellum
2010 Wiley-Liss, Inc.
How to Cite this Article:
Lantieri F, Glessner JT, Hakonarson H, Elia J,
Devoto M. 2010. Analysis of GWAS Top Hits
in ADHD Suggests Association to Two
Polymorphisms Located in Genes Expressed
in the Cerebellum.
Am J Med Genet Part B 153B:1127–1133.
Attention deficit/hyperactivity disorder (ADHD) is a common
psychiatric disorder with a prevalence of 5–10% in school age
children [Scahill and Schwab-Stone, 2000], that causes significant
behavioral and social impairment throughout the life span
[Greenfield et al., 1988; Nigg et al., 2005; Barkley et al., 2006].
Family, twin, and adoption studies have demonstrated that ADHD
Grant sponsor: NIMH; Grant number: K23MH066275-01; Grant sponsor:
National Center for Research Resources; Grant number: UL1-RR-024134;
Grant sponsor: The Center for Applied Genomics at The Children’s
Hospital of Philadelphia.
*Correspondence to:
Dr. Josephine Elia, M.D., The Children’s Hospital of Philadelphia, Science
Center, 3440 Market St. Suite 200, Philadelphia, PA 19104.
E-mail: elia@email.chop.edu
Published online 6 July 2010 in Wiley Online Library
(wileyonlinelibrary.com).
DOI 10.1002/ajmg.b.31110
1127
1128
is influenced by genetic factors [Thapar et al., 1995], with an overall
heritability of 0.76 [Biederman and Faraone, 2005].
The initial search for ADHD genetic risk factors followed the
candidate gene approach, focusing on genes involved in neurotransmission, based on evidence from effective pharmacotherapeutic agents [Solanto, 1998], animal models [Viggiano et al.,
2003], and neuroimaging studies [Volkow et al., 2007]. Dopaminergic and catecholaminergic pathways have been the most widely
studied [Biederman and Faraone, 2005], but also the serotonergic
[Biederman and Faraone, 2005] and the glutaminergic [Mick et al.,
2008] neurotransmission pathways have been investigated, together with genes regulating vesicular neurotransmission. Although
a few confirmed associations have emerged from candidate gene
analyses [Brookes et al., 2006; Elia et al., 2009], findings are
often inconsistent and non-replicated across different studies.
Genome-wide linkage studies carried out so far have showed several
chromosomal regions with potential linkage [Smalley et al., 2002;
Bakker et al., 2003; Arcos-Burgos et al., 2004; Ogdie et al., 2004;
Hebebrand et al., 2006; Asherson et al., 2008; Romanos et al., 2008;
Zhou et al., 2008]. However, none of the loci strongly replicated in
additional studies [Bakker et al., 2003; Arcos-Burgos et al., 2004;
Hebebrand et al., 2006], and some studies did not identify any
significant linkage at all [Faraone et al., 2008].
Recently, a few ADHD genome-wide association studies (GWAS)
have been attempted [see Franke et al., 2009 for a review]. Two of
them used samples collected by the GAIN/IMAGE collaborative
group on 958 Caucasian ADHD probands aged 5–17 and their
parents [Lasky-Su et al., 2008; Neale et al., 2008], partly overlapping
with the samples analyzed in a previous candidate genes study
[Brookes et al., 2006].
Neale et al. conducted a family-based GWAS on the categorical
definition of ADHD, using the transmission disequilibrium test
(TDT) on 909 complete trios with probands mainly with the
combined subtype of ADHD. Although they calculated that their
sample size was enough to detect at least one association significant
at genome-wide level (P < 5 108), for an odds ratio (OR) of
1.3 and a minor allele frequency (MAF) of 0.20, no SNP achieved
this level of significance indicating that an even larger sample may
be required. However, they reported the top 25 SNPs based on the
nominal P-values and the top 25 SNPs after genomic correction
[Devlin and Roeder, 1999], for a total of 36 SNPs, 14 of which
included in both lists.
Lasky-Su et al. analyzed the same population treating ADHD
symptoms as quantitative phenotypes, generated with two methods: one considering the total count of symptoms and the count of
symptoms classified as hyperactive-impulsive or inattentive obtained through the Parental Account of Childhood Symptoms (PACS)
interview, and the other based on the total count of symptoms and
the count of hyperactive-impulsive or inattentive symptoms, using
the principal component methodology implemented in the FamilyBased Association Test (FBAT-PC) [Lange et al., 2004] applied to
the Long Version of Conners’ Parent Rating Scale (CPRS-R:L). For
each of the six phenotypes thus generated, additive, dominant and
recessive models were applied, for a total of 18 analyses [Lasky-Su
et al., 2008]. The two most significant markers were located in the
CDH13 gene for the phenotype based on all symptoms under the
additive genetic model (P-value ¼ 0.005) and in the GF0D1 gene for
AMERICAN JOURNAL OF MEDICAL GENETICS PART B
the inattentive symptoms under the dominant model, using the
FBAT-PC method (P-value ¼ 0.004).
A third GWAS was carried out on a pooled sample of 343
adults (18–65 years old) with persistent ADHD and 304 controls
of German origin [Lesch et al., 2008]. In all three studies no marker
was significantly associated at the genome-wide level after appropriate correction for multiple testing, and there was scarce overlap
of findings among them and even across the different analyses
carried out by Lasky-Su et al. [2008].
At the Children’s Hospital of Philadelphia we are currently
recruiting ADHD patients and their parents for an ongoing genetic
study on the disorder. Our sample so far comprises 1,286 NorthAmerican individuals of European descent from 428 families, and
includes 426 complete trios from 386 nuclear families. Probands
were recruited from pediatric and behavioral health centers in the
Philadelphia area based on age range of 6–18 years, and KSADS-P
IVR interview were administered to the parents and child separately. Details on the recruitment methods are described elsewhere [Elia
et al., 2009]. The study was approved by the Institutional Review
Boards of The Children’s Hospital of Philadelphia (Protocol #20031-3125) and the University of Pennsylvania School of Medicine
(Protocol #707843). This research was performed in compliance
with the Code of Ethics of the World Medical Association
(Declaration of Helsinki).
Given that our study design is similar to the one by Neale et al.
[2008] (henceforth referred to simply as Neale), that is a familybased study of ADHD children and adolescents, and that we elected
to perform the analysis on the categorical definition of ADHD based
on the DSM-IV criteria, we examined in our sample the association
with their 36 top SNPs. Although our current sample size is smaller
than the one analyzed by Neale, and thus clearly underpowered for
a genome-wide study, it should be sufficient to confirm the role of
candidate markers in ADHD if a true association exists. We
estimated the statistical power to confirm the association reported
by Neale for our final sample size of 415 trios, for the values of OR
reported by them, and the MAF observed in the HapMap CEU
sample for each SNP, using the software Quanto [Gauderman and
Morrison, 2006]. Fixing the prevalence of ADHD at 5%, assuming
the log-additive genetic risk model, and setting one-side P-value of
0.05 as significance threshold, we found that, with the exception of
one case with a power of 72%, we had power above 85% for all
markers (Table I).
Lymphoblastoid cell lines were established from peripheral
blood and DNA was extracted at Rutgers University Cell and DNA
Repository in New Jersey (RUCDR). The Center for Applied
Genomics at The Children’s Hospital of Philadelphia carried out
the genotyping using the InfiniumII HumanHap550 k BeadChip
technology (Illumina, San Diego, CA) [Steemers et al., 2006].
Following genotyping, careful data cleaning was carried out
checking for gender discrepancies, identity by descent (IBDs), and
sample heterozygosity. To ensure genotyping quality we excluded
from the analysis five individuals that had more than 10% of
missing genotyping calls. None of the families showed Mendelian
error rates of more than 1%. In fact, in all families except one the
Mendelian error rate was <0.05%. Following quality control, 374
nuclear families with both parents (338 with one affected child,
31 with two, and 5 with three), for a total of 415 trios (comprising
LANTIERI ET AL.
1129
TABLE I. Features of the SNPs Analyzed and Statistical Power to Detect Association in Our Sample
Reference
SNP rs#
rs10807124
rs11221064
rs12505502
rs12772737
rs1427324
rs1539549
rs1541665
rs17673653
rs17689952
rs17722514
rs17754282
rs2295426
rs2311120
rs2323262
rs2439832
rs2678787
rs2747100
rs2939678
rs3782309
rs4241112
rs4913069
rs6570426
rs6657749
rs6919857
rs7187223
rs7528615
rs7657608
rs876477
rs922781
rs9389835
rs9484448
rs957795
rs9608617
rs964647
rs9676447
rs9973180
chr
6
11
4
10
14
13
5
5
4
13
11
14
18
4
15
18
14
8
12
2
5
6
1
6
16
1
4
4
15
6
6
21
22
6
19
18
Physical
positiona
33512042
127192523
91787046
116731491
58434446
35349881
170075495
170099172
167158604
89511946
87622650
58446208
50628121
21753534
41566978
26436326
57534739
56409692
26750663
122378682
139525294
141239039
212643136
137182147
81015234
84505551
168390756
20766026
58857636
141312353
141260753
45583147
26236244
88993111
54116059
55105194
Alleles
G>A
G>C
T>G
C>G
C>T
C>T
C>T
A>T
A>G
C>T
T>G
T>C
A>G
G>A
C>T
T>C
G>C
G>A
G>T
C>T
G>A
A>T
C>T
C>T
A>G
T>C
C>T
C>T
C>G
T>C
C>T
G>A
G>C
A>T
T>C
C>T
Lists
of top
SNPs
from
Neale
u
c
Both
c
Both
Both
c
u
u
c
Both
c
c
u
u
c
u
u
Both
Both
u
c
c
Both
c
u
Both
Both
Both
Both
c
u
Both
Both
Both
u
Genotyped
or imputed
in our
sample
Imputed
Imputed
Imputed
Imputed
Genotyped
Imputed
Imputed
Imputed
Imputed
Imputed
Imputed
Genotyped
Imputed
Genotyped
Imputed
Imputed
Imputed
Genotyped
Genotyped
Imputed
Genotyped
Imputed
Imputed
Imputed
Imputed
Genotyped
Genotyped
Imputed
Imputed
Genotyped
Genotyped
Imputed
Imputed
Imputed
Imputed
Imputed
No. of
polymorphicb
SNPs in the 50 kb
region around the
ref. SNP in HapMapc
28
91
45
44
—
79
115
76
39
161
61
—
53
—
33
79
96
—
—
20
—
48
74
32
112
—
—
66
95
—
—
46
136
60
41
76
No. of SNPs in
the 50kb region
around the ref.
SNP in our
dataset
6
16
16
4
—
19
15
15
8
18
17
—
9
—
7
15
16
—
—
2
—
11
23
5
23
—
—
15
27
—
—
12
35
11
11
19
MAFd
0.27
0.16
0.47
0.32
0.27
0.26
0.17
0.17
0.22
0.07
0.19
0.27
0.23
0.19
0.06
0.23
0.58
0.20
0.14
0.25
0.13
0.31
0.17
0.63
0.04
0.35
0.31
0.07
0.54
0.35
0.34
0.08
0.19
0.07
0.07
0.28
ORs from
Neale
0.744
1.439
1.303
0.725
0.724
0.731
0.634
0.661
0.722
1.648
1.441
0.710
0.680
0.697
0.657
0.723
0.764
0.722
1.527
0.721
0.648
0.735
0.664
0.749
0.446
0.756
1.356
1.903
1.315
0.731
0.739
0.619
1.376
1.717
2.078
0.749
Power
0.87
0.89
0.85
0.93
0.92
0.89
0.98
0.96
0.89
0.88
0.92
0.94
0.96
0.92
0.72
0.89
0.86
0.87
0.94
0.91
0.94
0.91
0.95
0.89
0.98
0.87
0.90
0.98
0.87
0.93
0.91
0.88
0.85
0.92
1.00
0.86
u, from the uncorrected list; c, from the corrected list; both, present on both lists.aPosition based on the human assembly NCBI B36, hg18.
b
MAF > 105.
c
Release 24 and 27 merged together.
d
For CEU sample from HapMap.
309 male and 106 female cases) were used in the analysis. Of these,
258 patients were combined-ADHD subtype (C), 126 inattentive
subtypes (IA), 29 hyperactive (HI), and 2 not specified. We used all
subtypes for the main association analysis, but we also repeated the
analysis for the combined subtype alone, since the sample analyzed
by Neale was mainly composed of patients with this subtype.
Ten SNPs of the 36 reported by Neale were also genotyped in our
sample (Table I). We imputed the 26 missing SNPs based on
HapMap data from phase I þ II (release 24) merged with phase
III (release 2 http://hapmap.ncbi.nlm.nih.gov/). To this aim, we
selected from HapMap the SNPs included in the 50 kb genomic
region around each SNP reported by Neale not genotyped in our
sample (Table I), and used genotypes from the HapMap CEU
sample as the reference panel to impute those SNPs in our sample.
Imputation was done with Beagle version 3.0.4 [Browning and
Browning, 2009], that reports phased genotypes for parents, deriving the sibling genotypes from the haplotypes transmitted from
parents. Data obtained in this way were merged to the genotyped
SNPs.
Following standard SNP data quality procedures (checking for
Hardy–Weinberg equilibrium in the parents with P > 105, call
rate >90%, MAF >1%, Mendelian error rate <1%), only one SNP
1130
AMERICAN JOURNAL OF MEDICAL GENETICS PART B
with MAF lower than 0.01 (rs7187223) and another that was not
polymorphic at all (rs9676447), were excluded from the analysis.
None of the 36 SNPs investigated showed Mendelian inconsistency
in any of the families. At the genome-wide level, only 2,240 SNPs
(<0.4%) had Mendelian error rates >1%, confirming the overall
good quality of our genotype data.
Since our aim was to test association with the same alleles
reported by Neale, we evaluated the probability of observing a
distortion from random segregation of the 34 remaining SNPs in
the same direction as they reported. To this aim we calculated the
P-values using the binomial distribution on the transmitted:untransmitted (T:U) allele frequencies calculated by plink [Purcell
et al., 2007] with respect to the expected ratio of 50:50 (Table II).
Two SNPs were significant at a ¼ 0.05 (rs12505502 and
rs2939678). After correcting for the number of tests performed no
SNP was still significant. Overall, odd-ratios ranged between 0.821
and 1.455, and for 15 SNPS were opposite in direction to those
reported by Neale (in italics in Table II). Since the sample analyzed by
Neale presented a marked excess of children with the combined
ADHD subtype, we also performed the same tests on the combined
TABLE II. Results for the 36 Reference SNPs
From Neale
SNP
rs10807124
rs11221064
rs12505502
rs12772737
rs1427324
rs1539549
rs1541665
rs17673653
rs17689952
rs17722514
rs17754282
rs2295426
rs2311120
rs2323262
rs2439832
rs2678787
rs2747100
rs2939678
rs3782309
rs4241112
rs4913069
rs6570426
rs6657749
rs6919857
rs7187223
rs7528615
rs7657608
rs876477
rs922781
rs9389835
rs9484448
rs957795
rs9608617
rs964647
rs9676447
rs9973180
A1:A2a
A:G
C:G
G:T
G:C
T:C
T:C
T:C
T:A
G:A
T:C
G:T
C:T
G:A
A:G
T:C
C:T
G:C
A:G
T:G
T:Cc
A:G
T:A
T:C
C:T
G:A
C:T
T:C
T:C
C:G
C:T
T:C
A:G
C:G
T:A
C:T
T:C
T:Ua
313:421
305:212
525:403
292:403
301:416
328:449
135:213
160:242
270:374
178:108
294:204
304:428
229:337
207:297
153:233
279:386
391:512
273:378
223:146
277:384
136:210
344:468
184:277
370:494
37:83
353:467
434:320
118:62
493:375
350:479
353:478
117:189
399:290
170:99
106:51
328:438
ORs
0.74
1.44
1.30
0.72
0.72
0.73
0.63
0.66
0.72
1.65
1.44
0.71
0.68
0.70
0.66
0.72
0.76
0.72
1.53
0.72
0.65
0.74
0.66
0.75
0.45
0.76
1.36
1.90
1.32
0.73
0.74
0.62
1.38
1.72
2.08
0.75
Whole sample (415 trios)
TDT corrected
(uncorrected)
P-value
(6.71E 05)
3.89E 05
5.62E 05 (6.21E 05)
4.96E 05
3.50E 05 (1.75E 05)
2.88E 05 (1.42E 05)
5.60E 05
(4.32E 05)
(4.16E 05)
3.14E 05
4.97E 05 (5.51E 05)
1.00 E 05
1.22 E 05
(6.10E 05)
(4.66E 05)
6.36 E 05
(5.66E 05)
(3.87E 05)
5.53E 05 (6.11E 05)
6.06E 05 (3.16E 05)
(6.94E 05)
2.74 E 05
2.99 E 05
4.79E 05 (2.46E 05)
5.21 E 05
(6.86E 05)
2.96E 05 (3.30E 05)
2.69E 05 (2.99E 05)
5.62E 05 (6.20E 05)
1.58E 05 (7.45E 06)
2.94 E 05
(3.86E 05)
2.96E 05 (3.29E 05)
1.34E 05 (1.50E 05)
1.01E 05 (1.14E 05)
(7.05E 05)
T:Ua
168:147
114:127
229:194
184:159
163:166
134:118
104:113
106:113
139:144
62:56
105:101
162:167
155:136
110:100
83:78
163:163
215:220
128:156
96:98
164:188
77:78
168:165
81:79
200:195
—
198:170
173:191
43:37
211:205
170:179
166:170
64:44
157:176
57:49
—
168:175
ORsb
1.14
0.90
1.18
1.16
0.98
1.14
0.92
0.94
0.97
1.11
1.04
0.97
1.14
1.10
1.06
1.00
0.98
0.82
0.98
0.87
0.99
1.02
1.03
1.03
—
1.17
0.91
1.16
1.03
0.95
0.98
1.46
0.89
1.16
—
0.96
Binomial
P-value
0.89
0.82
0.05
0.92
0.46
0.86
0.29
0.34
0.41
0.32
0.42
0.41
0.88
0.78
0.68
0.52
0.42
0.05
0.59
0.11
0.50
0.59
0.59
0.62
—
0.93
0.84
0.29
0.40
0.33
0.44
0.98
0.86
0.25
—
0.37
C-subtype (257 trios)
T:Ua
104:93
65:87
140:115
109:107
99:103
75:73
69:64
71:64
80:94
42:34
71:67
99:102
95:80
66:60
48:50
108:106
131:140
86:94
63:62
116:97
52:48
101:98
53:47
121:120
—
137:108
109:115
22:21
135:118
104:102
102:108
43:29
92:110
36:32
—
108:102
ORsb
1.12
0.75
1.22
1.02
0.96
1.03
1.08
1.11
0.85
1.24
1.06
0.97
1.19
1.10
0.96
1.02
0.94
0.91
1.02
0.84
1.08
1.03
1.13
1.01
—
1.27
0.95
1.05
1.14
1.02
0.94
1.48
0.84
1.13
—
1.06
Binomial
P-value
0.80
0.97
0.07
0.58
0.42
0.60
0.70
0.75
0.16
0.21
0.40
0.44
0.89
0.73
0.46
0.58
0.31
0.30
0.50
0.91
0.69
0.61
0.76
0.55
—
0.97
0.68
0.50
0.16
0.58
0.37
0.96
0.91
0.36
—
0.69
Binomial P-values are reported for the allele over-transmitted (or under-transmitted) in Neale et al. [2008].aA1 is the minor allele, while A2 is the major allele for Neale et al. [2008]. The
transmitted:untransmitted rate refers to the minor allele.
b
In italics ORs with opposite sign to those found by Neale et al. [2008].
c
In our sample the minor allele was C (MAF ¼ 0.4365).
LANTIERI ET AL.
subset only, comprised of 258 trios. In this analysis, no SNP had Pvalues below 0.05, likely due to the small sample size. The smallest
P-value of 0.07 was observed for rs12505502.
Our results did not show any evident bias in the distribution of
association test statistics calculated on the genome-wide data, with
a lambda factor of 1.015. However, we also applied the genomic
correction, without any difference in the results, as expected (data
not shown).
The two SNPs significant at a ¼ 0.05 in our sample are both
located in genes coding for as yet uncharacterized proteins.
rs2939678 is in the XKR4 gene (XK, Kell blood group complex
subunit-related family, member 4), in 8q12.1, a gene expressed in
the cerebellum. In a recent GWAS, a SNP upstream of this gene
has been described as associated to the efficacy of the antipsychotic
iloperidone in schizophrenia treatment [Lavedan et al., 2009].
rs12505502 is in the FAM190A gene (family with sequence similarity 190 member A), in 4q22.1, a gene highly expressed in ovary and
testis but also expressed in brain and cerebellum. Notably, three
other FAM190A SNPs, namely rs11097273, rs7683787, and rs1551773,
have TDT P-values of <105 in our sample, a level of significance only
reached by a total of five SNPs in our genome-wide data.
In conclusion, hundreds of genes have been reported as associated to ADHD [Bobb et al., 2005; Khan and Faraone, 2006], but,
despite strong evidence for a large genetic contribution, there is a
high rate of inconsistency and lack of replication across different
studies. These findings suggest that many genes contribute to the
disorder in a modest way and therefore extremely large sample sizes
will be necessary to reach a definite conclusion about their role. Very
few GWAS have been published so far, and they generally differ for
study design and phenotypes analyzed [Franke et al., 2009]. In the
present study we examined the top SNPs reported by Neale et al.
[2008] in a group of ADHD trios of European descent, using a
similar study design and the same phenotype definition. We could
not unequivocally confirm any of the SNPs among their top
significant ones as associated to the disorder in our independent
sample after correction for multiple tests. However, two markers,
rs12505502 and rs2939678, had nominal P-value ¼ 0.05. These
SNPs are located in genes whose function is still unknown, but
that are expressed in cerebellum, known to play a role in a variety of
cognitive processes, such as executive functioning, memory, learning, attention, visuo-spatial regulation, language and behavioralaffective modulation [Baillieux et al., 2008].
Remarkably, the most consistent findings in ADHD brain imaging studies are decreases in total cerebral volume [Castellanos et al.,
1996, 2001, 2002; Filipek et al., 1997; Kates et al., 2002; Mostofsky et
al., 2002; Hill et al., 2003; Durston et al., 2004], and in posterior
inferior cerebellar vermis (lobes VIII–X) volume [Castellanos et al.,
1996, 2001, 2002; Berquin et al., 1998; Bussing et al., 2002; Hill et al.,
2003; Durston et al., 2004]. The reduction of the posterior inferior
lobes of the vermis, confirmed in both males and females
[Castellanos et al., 2001, 2002; Hill et al., 2003], persisted with age
in a longitudinal study that scanned each patient one to four times
between ages 5 and 18 and was found to correlate with parent- and
clinician-rated severity measures of ADHD [Castellanos et al.,
2002]. The only imaging study that also included unaffected siblings
of ADHD boys reported intracranial volume reduction as well as
reductions in right prefrontal gray matter and left occipital gray and
1131
white matter also in the unaffected siblings. The right cerebellar
volume was the only measure reduced in the ADHD children but
not in the unaffected siblings [Durston et al., 2004], suggesting that
the cerebellum may be more directly related to pathophysiology.
While the finding of a genetic association with SNPs located in genes
expressed in the cerebellum may be due to chance, we cannot
exclude that these markers are indeed associated to the disease albeit
exerting a low risk.
ACKNOWLEDGMENTS
This research is supported by NIMH (K23MH066275-01), UL1-RR
-024134 from the National Center for Research Resources, and The
Center for Applied Genomics at The Children’s Hospital of Philadelphia. We want to thank all the families that have participated,
the referring clinicians, our research coordinator Tamika Scott, and
the technical staff in the Center for Applied Genomics at The
Children’s Hospital of Philadelphia for generating the genotypes
used in this study.
REFERENCES
Arcos-Burgos M, Castellanos FX, Pineda D, Lopera F, Palacio JD,
Palacio LG, Rapoport JL, Berg K, Bailey-Wilson JE, Muenke M.
2004. Attention-deficit/hyperactivity disorder in a population isolate: Linkage to loci at 4q13.2, 5q33.3, 11q22, and 17p11. Am J Hum
Genet 75:998–1014.
Asherson P, Zhou K, Anney RJ, Franke B, Buitelaar J, Ebstein R, Gill M,
Altink M, Arnold R, Boer F, Brookes K, Buschgens C, Butler L, Cambell D,
Chen W, Christiansen H, Feldman L, Fleischman K, Fliers E, HoweForbes R, Goldfarb A, Heise A, Gabri€els I, Johansson L, Lubetzki I, Marco
R, Medad S, Minderaa R, Mulas F, M€
uller U, Mulligan A, Neale B, Rijsdijk
F, Rabin K, Rommelse N, Sethna V, Sorohan J, Uebel H, Psychogiou L,
Weeks A, Barrett R, Xu X, Banaschewski T, Sonuga-Barke E, Eisenberg J,
Manor I, Miranda A, Oades RD, Roeyers H, Rothenberger A, Sergeant J,
Steinhausen HC, Taylor E, Thompson M, Faraone SV. 2008. A highdensity SNP linkage scan with 142 combined subtype ADHD sib pairs
identifies linkage regions on chromosomes 9 and 16. Mol Psychiatry
13(5):514–521.
Baillieux H, De Smet HJ, Paquier PF, De Deyn PP, Mari€en P. 2008.
Cerebellar neurocognition: Insights into the bottom of the brain. Clin
Neurol Neurosurg 110(8):763–773.
Bakker SC, van der Meulen EM, Buitelaar JK, Sandkuijl LA, Pauls DL,
Monsuur AJ, van ’t Slot R, Minderaa RB, Gunning WB, Pearson PL, Sinke
RJ. 2003. A whole-genome scan in 164 Dutch sib pairs with attentiondeficit/hyperactivity disorder: Suggestive evidence for linkage on chromosomes 7p and 15q. Am J Hum Genet 72:1251–1260.
Barkley RA, Fischer M, Smallish L, Fletcher K. 2006. Young adult outcome
of hyperactive children: Adaptive functioning in major life activities. J
Am Acad Child Adolesc Psychiatry 45(2):192–202.
Berquin PC, Giedd JN, Jacobsen LK, Hamburger SD, Krain AL, Rapoport
JL, Castellanos FX. 1998. Cerebellum in attention-deficit hyperactivity
disorder: A morphometric MRI study. Neurology 50(4):1087–1093.
Biederman J, Faraone SV. 2005. Attention-deficit hyperactivity disorder.
Lancet 366(9481):237–248. Erratum in: Biederman J, Faraone SV. Lancet. 2006 367(9506):210.
Bobb AJ, Castellanos FX, Addington AM, Rapoport JL. 2005. Molecular
genetic studies of ADHD: 1991 to 2004. Am J Med Genet Part B
132B(1):109–125.
1132
Brookes K, Xu X, Chen W, Zhou K, Neale B, Lowe N, Aneey R, Franke B, Gill
M, Ebstein R, Buitelaar J, Sham P, Campbell D, Knight J, Andreou P,
Altink M, Arnold R, Boer F, Buschgens C, Butler L, Christiansen H,
Feldman L, Fleischman K, Fliers E, Howe-Forbes R, Goldfarb A, Heise A,
Gabriels I, Korn-Lubetzki I, Marco R, Medad S, Minderaa R, Mulas F,
Muller U, Mulligan A, Rabin K, Rommelse N, Sethna V, Sorohan J, Uebel
H, Psychogiou L, Weeks A, Barrett R, Craig I, Banaschewski T, SonugaBarke E, Eisenberg J, Kuntsi J, Manor I, McGuffin P, Miranda A, Oades
RD, Plomin R, Roeyers H, Rothenberger A, Sergeant J, Steinhausen HC,
Taylor E, Thompson M, Faraone SV, Asherson P, Johansson L. 2006. The
analysis of 51 genes in DSM-IV combined type attention deficit hyperactivity disorder: Association signals in DRD4, DAT1 and 16 other genes.
Mol Psychiatry 11:934–953.
Browning BL, Browning SR. 2009. A unified approach to genotype
imputation and haplotype phase inference for large data sets of trios
and unrelated individuals. Am J Hum Genet 84:210–223.
Bussing R, Grudnik J, Mason D, Wasiak M, Leonard C. 2002. ADHD and
conduct disorder: An MRI study in a community sample. World J Biol
Psychiatry 3(4):216–220.
Castellanos FX, Giedd JN, Marsh WL, Hamburger SD, Vaituzis AC,
Dickstein DP, Sarfatti SE, Vauss YC, Snell JW, Lange N, Kaysen D, Krain
AL, Ritchie GF, Rajapakse JC, Rapoport JL. 1996. Quantitative brain
magnetic resonance imaging in attention-deficit hyperactivity disorder.
Arch Gen Psychiatry 53(7):607–616.
Castellanos FX, Giedd JN, Berquin PC, Walter JM, Sharp W, Tran T,
Vaituzis AC, Blumenthal JD, Nelson J, Bastain TM, Zijdenbos A, Evans
AC, Rapoport JL. 2001. Quantitative brain magnetic resonance imaging
in girls with attention-deficit/hyperactivity disorder. Arch Gen Psychiatry 58(3):289–295.
Castellanos FX, Lee PP, Sharp W, Jeffries NO, Greenstein DK, Clasen LS,
Blumenthal JD, James RS, Ebens CL, Walter JM, Zijdenbos A, Evans AC,
Giedd JN, Rapoport JL. 2002. Developmental trajectories of brain volume
abnormalities in children and adolescents with attention-deficit/hyperactivity disorder. JAMA 288(14):1740–1748.
Devlin B, Roeder K. 1999. Genomic control for association studies.
Biometrics 55(4):997–1004.
Durston S, Hulshoff Pol HE, Schnack HG, Buitelaar JK, Steenhuis MP,
Minderaa RB, Kahn RS, van Engeland H. 2004. Magnetic resonance
imaging of boys with attention-deficit/hyperactivity disorder and their
unaffected siblings. J Am Acad Child Adolesc Psychiatry 43(3):332–340.
Elia J, Capasso M, Zaheer Z, Lantieri F, Ambrosini P, Berrettini W, Devoto M,
Hakonarson H. 2009. Candidate gene analysis in an on-going genome-wide
association study of attention-deficit hyperactivity disorder: Suggestive
association signals in ADRA1A. Psychiatr Genet 19(3):134–141.
Faraone SV, Doyle AE, Lasky-Su J, Sklar PB, D’Angelo E, GonzalezHeydrich J, Kratochvil C, Mick E, Klein K, Rezac AJ, Biederman J.
2008. Linkage analysis of attention deficit hyperactivity disorder. Am J
Med Genet Part B 147B:1387–1391.
Filipek PA, Semrud-Clikeman M, Steingard RJ, Renshaw PF, Kennedy DN,
Biederman J. 1997. Volumetric MRI analysis comparing subjects having
attention-deficit hyperactivity disorder with normal controls. Neurology
48(3):589–601.
Franke B, Neale BM, Faraone SV. 2009. Genome-wide association studies
in ADHD. Hum Genet 126(1):13–50. Review.
Gauderman WJ, Morrison JM. 2006. QUANTO 1.1: A computer program
for power and sample size calculations for genetic-epidemiology studies.
http://hydra.usc.edu/gxe.
Greenfield B, Hechtman L, Weiss G. 1988. Two subgroups of hyperactives
as adults: Correlations of outcome. Can J Psychiatry 33(6):505–508.
Hebebrand J, Dempfle A, Saar K, Thiele H, Herpertz-Dahlmann B, Linder
M, Kiefl H, Remschmidt H, Hemminger U, Warnke A, Knolker U, Heiser
AMERICAN JOURNAL OF MEDICAL GENETICS PART B
P, Friedel S, Hinney A, Schafer H, Nurnberg P, Konrad K. 2006. A genomewide scan for attention-deficit/hyperactivity disorder in 155 German
sib-pairs. Mol Psychiatry 11:196–205.
Hill DE, Yeo RA, Campbell RA, Hart B, Vigil J, Brooks W. 2003. Magnetic
resonance imaging correlates of attention-deficit/hyperactivity disorder
in children. Neuropsychology 17(3):496–506.
Kates WR, Frederikse M, Mostofsky SH, Folley BS, Cooper K, MazurHopkins P, Kofman O, Singer HS, Denckla MB, Pearlson GD, Kaufmann
WE. 2002. MRI parcellation of the frontal lobe in boys with attention
deficit hyperactivity disorder or Tourette syndrome. Psychiatry Res
116(1–2):63–81.
Khan SA, Faraone SV. 2006. The genetics of ADHD: A literature review of
2005. Curr Psychiatry Rep 8(5):393–397.
Lange C, van Steen K, Andrew T, Lyon H, DeMeo DL, Raby B, Murphy A,
Silverman EK, MacGregor A, Weiss ST, Laird NM. 2004. A family-based
association test for repeatedly measured quantitative traits adjusting for
unknown environmental and/or polygenic effects. Stat Appl Genet Mol
Biol 3(1):1–27.
Lasky-Su J, Neale BM, Franke B, Anney RJ, Zhou K, Maller JB, Vasquez AA,
Chen W, Asherson P, Buitelaar J, Banaschewski T, Ebstein R, Gill M,
Miranda A, Mulas F, Oades RD, Roeyers H, Rothenberger A, Sergeant J,
Sonuga-Barke E, Steinhausen HC, Taylor E, Daly M, Laird N, Lange C,
Faraone SV. 2008. Genome-wide association scan of quantitative traits
for attention deficit hyperactivity disorder identifies novel associations
and confirms candidate gene associations. Am J Med Genet Part B
147B(8):1345–1354.
Lavedan C, Licamele L, Volpi S, Hamilton J, Heaton C, Mack K, Lannan R,
Thompson A, Wolfgang CD, Polymeropoulos MH. 2009. Association of
the NPAS3 gene and five other loci with response to the antipsychotic
iloperidone identified in a whole genome association study. Mol Psychiatry 14:804–819.
Lesch KP, Timmesfeld N, Renner TJ, Halperin R, R€
oser C, Nguyen TT,
Craig DW, Romanos J, Heine M, Meyer J, Freitag C, Warnke A, Romanos
M, Sch€afer H, Walitza S, Reif A, Stephan DA, Jacob C. 2008. Molecular
genetics of adult ADHD: Converging evidence from genome-wide
association and extended pedigree linkage studies. J Neural Trans
115(11):1573–1585.
Mick E, Neale B, Middleton FA, McGough JJ, Faraone SV. 2008. Genomewide association study of response to methylphenidate in 187 children
with attention-deficit/hyperactivity disorder. Am J Med Genet Part B
147B(8):1412–1418.
Mostofsky SH, Cooper KL, Kates WR, Denckla MB, Kaufmann WE. 2002.
Smaller prefrontal and premotor volumes in boys with attention-deficit/
hyperactivity disorder. Biol Psychiatry 52(8):785–794.
Neale BM, Lasky-Su J, Anney R, Franke B, Zhou K, Maller JB, Vasquez AA,
Asherson P, Chen W, Banaschewski T, Buitelaar J, Ebstein R, Gill M,
Miranda A, Oades RD, Roeyers H, Rothenberger A, Sergeant J, Steinhausen HC, Sonuga-Barke E, Mulas F, Taylor E, Laird N, Lange C, Daly
M, Faraone SV. 2008. Genome-wide association scan of attention deficit
hyperactivity disorder. Am J Med Genet Part B 147B(8):1337–1344.
Nigg JT, Stavro G, Ettenhofer M, Hambrick DZ, Miller T, Henderson JM.
2005. Executive functions and ADHD in adults: Evidence for selective
effects on ADHD symptom domains. J Abnorm Psychol 114:706–717.
Ogdie MN, Fisher SE, Yang M, Ishii J, Francks C, Loo SK, Cantor RM,
McCracken JT, McGough JJ, Smalley SL, Nelson SF. 2004. Attention
deficit hyperactivity disorder: Fine mapping supports linkage to 5p13,
6q12, 16p13, and 17p11. Am J Hum Genet 75(4):661–668.
Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller
J, Sklar P, de Bakker PI, Daly MJ, Sham PC. 2007. PLINK: A tool set for
whole-genome association and population-based linkage analyses. Am J
Hum Genet 81(3):559–575. http://pngu.mgh.harvard.edu/purcell/plink/.
LANTIERI ET AL.
Romanos M, Freitag C, Jacob C, Craig DW, Dempfle A, Nguyen TT,
Halperin R, Walitza S, Renner TJ, Seitz C, Romanos J, Palmason H, Reif
A, Heine M, Windemuth-Kieselbach C, Vogler C, Sigmund J, Warnke A,
Sch€afer H, Meyer J, Stephan DA, Lesch KP. 2008. Genome-wide linkage
analysis of ADHD using high-density SNP arrays: Novel loci at 5q13.1
and 14q12. Mol Psychiatry 13(5):522–530.
Scahill L, Schwab-Stone M. 2000. Epidemiology of ADHD in school-age
children. Child Adolesc Psychiatr Clin N Am 9(3):541–555.
Smalley SL, Kustanovich V, Minassian SL, Stone JL, Ogdie MN, McGough
JJ, McCracken JT, MacPhie IL, Francks C, Fisher SE, Cantor RM, Monaco
AP, Nelson SF. 2002. Genetic linkage of attention-deficit/hyperactivity
disorder on chromosome 16p13, in a region implicated in autism. Am J
Hum Genet 71(4):959–963.
Solanto MV. 1998. Neuropsychopharmacological mechanisms of stimulant drug action in attention-deficit hyperactivity disorder: A review and
integration. Behav Brain Res 94:127–152.
Steemers FJ, Chang W, Lee G, Barker DL, Shen R, Gunderson KL. 2006.
Whole-genome genotyping with the single-base extension assay. Nat
Methods 3(1):31–33.
1133
Thapar A, Hervas A, McGuffin P. 1995. Childhood hyperactivity scores are
highly heritable and show sibling competition effects: Twin study evidence. Behav Genet 25(6):537–544.
Viggiano D, Ruocco LA, Sadile AG. 2003. Dopamine phenotype and
behaviour in animal models: In relation to attention deficit hyperactivity
disorder. Neurosci Biobehav Rev 27:623–637.
Volkow ND, Wang GJ, Newcorn J, Fowler JS, Telang F, Solanto MV, Logan
J, Wong C, Ma Y, Swanson JM, Schulz K, Pradhan K. 2007. Brain
dopamine transporter levels in treatment and drug naive adults with
ADHD. Neuroimage 34:1182–1190.
Zhou K, Asherson P, Sham P, Franke B, Anney RJ, Buitelaar J, Ebstein R,
Gill M, Brookes K, Buschgens C, Campbell D, Chen W, Christiansen H,
Fliers E, Gabriels I, Johansson L, Marco R, Mulas F, Muller U, Mulligan A,
Neale BM, Rijsdijk F, Rommelse N, Uebel H, Psychogiou L, Xu X,
Banaschewski T, Sonuga-Barke E, Eisenberg J, Manor I, Miranda A,
Oades RD, Roeyers H, Rothenberger A, Sergeant J, Steinhausen HC,
Taylor E, Thompson M, Faraone SV. 2008. Linkage to chromosome 1p36
for attention-deficit/hyperactivity disorder traits in school and home
settings. Biol Psychiatry 64:571–576.
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