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Analysis of microsatellite markers and single nucleotide polymorphisms in candidate genes for susceptibility to bipolar affective disorder in the chromosome 12Q24.31 region

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American Journal of Medical Genetics Part B (Neuropsychiatric Genetics) 135B:50 – 58 (2005)
Analysis of Microsatellite Markers and Single
Nucleotide Polymorphisms in Candidate Genes for
Susceptibility to Bipolar Affective Disorder in the
Chromosome 12Q24.31 Region
Eric Shink,1 Mario Harvey,1 Monique Tremblay,1 Bernard Gagné,1 Pascal Belleau,1 Catherine Raymond,1
Michel Labbé,1 Marie-Pierre Dubé,2 Ronald G. Lafrenière,2 and Nicholas Barden1*
1
Neuroscience, CHUL Research Center and Laval University, CHUQ Pavillon CHUL, Ste-Foy, Que´bec, Canada
Xenon Genetics Research, Inc., Montreal, Canada
2
Previous results from our genetic analyses using
pedigrees from a French Canadian population
suggested that the interval delimited by markers
D12S86 and D12S378 on chromosome 12 was the
most probable genomic region to contain a susceptibility gene for affective disorders. Here we
present a more detailed genetic analysis of a 7.7 Mb
genomic region located on 12q24.31. This region
was saturated with 20 microsatellite markers to
refine the candidate region and linkage analysis
performed in 41 families from the Saguenay-LacSt-Jean (SLSJ) region of Quebec. The results of
two point parametric analysis using MFLINK
supported the presence of a susceptibility locus
on chromosome 12q24.31. Association studies with
microsatellite markers using a case/control sample from the same population (n ¼ 401) and analyzed with CLUMP revealed significant allelic
associations between the bipolar phenotype and
markers NBG6 (P ¼ 0.008) and NBG12 (P < 103).
According to these results, we investigated candidate genes in the NBG12 area. We analyzed 32
genes for the presence of polymorphisms in
coding sequences and intron/exon junctions
and genotyped 22 non-synonymous SNPs in
the SLSJ case/control sample. Two uncommon
polymorphisms (minor allele frequency 0.03)
found in KIAA1595 and FLJ22471 genes, gave
P-values below 0.05 with the T1 statistic. Moreover, using haplotype analysis, a nearly significant haplotypic association was observed at the
HM74 gene. These results do not give strong
support for a role in the susceptibility to bipolar
disorder of any of these genes analyzed. However,
the significance of rare polymorphisms should be
explored by further analyses.
ß 2005 Wiley-Liss, Inc.
KEY WORDS: bipolar disorder; linkage analysis; association analysis; linkage
disequilibrium structure; chromosome 12q24.31 region
Grant sponsor: Canadian Institutes of Health Research; Grant
sponsor: Xenon Genetics Research, Inc.
*Correspondence to: Dr. Nicholas Barden, Neuroscience,
CHUQ pavillon CHUL, 2705 Blvd. Laurier, Ste-Foy, Québec,
G1V 4G2, Canada. E-mail: barden@crchul.ulaval.ca
Received 15 July 2004; Accepted 22 November 2004
DOI 10.1002/ajmg.b.30165
ß 2005 Wiley-Liss, Inc.
INTRODUCTION
Bipolar affective disorder (BP) is a common complex
neuropsychiatric disorder, characterized by the recurrence of
depressive and manic/hypomanic episodes, with a worldwide
prevalence of 0.4%–1.6%. While evidence for a genetic contribution to BP etiology is supported by family, twin, and
adoption studies [Tsuang and Faraone, 1990], the nature of the
underlying genetic mechanisms is less clear [Craddock and
Jones, 2001]. Despite these restrictions, consistent linkage
findings have been reported for some broad chromosomal
regions that are likely to harbor deleterious alleles for BP,
including 12q23-q24 [Craddock et al., 1994; Dawson et al.,
1995b; Ewald et al., 1998, 2002, 2003; Morissette et al., 1999;
Degn et al., 2001; Curtis et al., 2003; Ekholm et al., 2003; Shink
et al., 2005]. Some candidate genes including Darier’s disease
gene (ATP2A2) [Jacobsen et al., 2001b; Jones et al., 2002],
PLA2G1B [Dawson et al., 1995a; Jacobsen et al., 1996, 1999],
UTL2/CUX2 [Jacobsen et al., 2001a], PAH [Green et al., 2003],
LHX5 [Green et al., 2003], DUSP6 [Toyota et al., 2000], and
NOS1 [Buttenschon et al., 2004] from chromosomal region
12q23-q24 have already been investigated. Most of these genes
are localized in the neighborhood of the Darier’s disease gene.
However, no clear association with affective disorder has been
reported so far.
Our first genome-wide scan suggested the presence of a
susceptibility locus on chromosome 12q23-q24 that segregates
in two large pedigrees coming from the Saguenay-Lac-St-Jean
(SLSJ) area in Quebec [Morissette et al., 1999]. We observed
a LOD score value of 2.87 at locus D12S78 under an intraheterogeneity model with recessive segregation mode.
Although this population was initially expected to be homogeneous, subsequent analyses did not support the presence of a
shared haplotype between most BP subjects [Shink et al.,
2003]. More recently, we conducted a second genome-wide scan
with 20 families sampled from the SLSJ region and the results
again supported the presence of a susceptibility locus for BP on
chromosome 12q24 under both parametric and model-free
analyses [Shink et al., 2005].
We undertook the characterization of the distal 12q24
genomic region and present here results obtained for a
7.7 Mb interval. We first report linkage-mapping analyses
based on 20 highly polymorphic microsatellite markers
spanning over this area. We also sought to narrow the subinterval for the screening of candidate genes by performing a
case/control study with these markers. After such refinement,
we used a candidate gene approach to search for polymorphisms in coding sequences and exon-intron junctions of 32
known genes. Twenty-two non-synonymous single nucleotide
polymorphisms (nsSNP) were identified and typed in a case/
control sample from the SLSJ area and analyzed for allelic,
genotypic, and haplotypic association with BP affected individuals. The results from this case/control study do not suggest
Bipolar Disorder Locus in12Q24.31
that any of these polymorphisms could be a major susceptibility locus for BP.
MATERIALS AND METHODS
Ascertainment and Diagnosis
BP probands selection and family extensions were described
elsewhere [Morissette et al., 1999; Shink et al., 2005].
Briefly, diagnosis was established by a panel including at least
two psychiatrists and according to the structured clinical
interview for DSM-IIIR (SCID I interviews) [Spitzer et al.,
1987]. Clarification and possible further interview were
requested until agreement was reached in case of any discordance between panel members.
For linkage analysis we ascertained 485 individuals from
41 families of the SLSJ region of Quebec. This collection
included 21 small pedigrees, totalizing 101 sampled persons
never before used for linkage analysis. Diagnoses among the
whole genotyped individuals were distributed as follows: 105
bipolar I (BPI) or schizoaffective disorder, bipolar type (Scz),
42 bipolar II (BPII), 54 recurrent unipolar major depression
(RUP), and 57 cases of single episode major depression.
A case/control sample consisting of 213 individuals from the
SLSJ region (BPI (n ¼ 182, mean age at onset 28 11 years
[mean SD], 60% female) or BPII (n ¼ 31, mean age at onset
27 11 years [mean SD], 55% female)) was established for
association analysis. Although most of the case subjects had a
family history of affective disorders, they were all unrelated
within immediate generations. Eleven spouses diagnosed as
normal and without child with major affective disorder
diagnosis were included in the control group. The remainder
of controls subjects (n ¼ 177), all from the SLSJ region, were
recruited from non-psychiatric genetic projects, since there is
no significant gain of power to screen controls for psychiatric
disorders with a lifetime risk of about 1% [Owen et al., 1997]
and, as far as we could ascertain were representative of the
general population. No attempt was made to match the
controls and cases by age. Chi-squared test on 2 2 contingency table displayed no significant difference between
distributions of gender in the case and control groups
(P ¼ 0.318).
51
Canada). Amplification reactions were done using the Taq
platinum DNA polymerase (Invitrogen). The amplification
products were purified on GF/C 384-well plates from Whatmann (VWR, Montreal, Canada). PCR products (30 ml) using
the PB buffer from Qiagen as binding buffer. The purified
samples were quantified with the PicoGreen dsDNA quantitation assay (Invitrogen) and sequenced from both strands
using the DYEnamic ET terminator cycle sequencing kit
(Amersham Biosciences, Baie D’Urfé, Canada). We slightly
modified the manufacturer’s protocol. Briefly, in a sequencing
reaction we added: 4 ng of DNA, 1 pmole of sequencing primer,
0.2 ml of ET sequencing mix, 1.8 ml of dilution buffer (65 mM
Tris, 16.3 mM MgCl2, 12.5% glycerol, pH ¼ 9.5), and completed
to 5 ml with water. We ran the following cycling program for 20–
25 cycles: 958C, 20 sec; 598C, 60 sec. The post-sequencing clean
up was done by ethanol/salt precipitation. The sequencing
products were dissolved in 7 ml of HIDI formamide (Applied
Biosystems, Inc, LA), and were resolved on an ABI PRISM
3730XL DNA analyzer (Applied Biosystems, Inc., LA). The
sequencing data were analyzed for the presence of polymorphisms by using the STADEN package [Bonfield et al., 2002].
Denaturing HPLC (DHPLC) Method
PCR primers were designed using PrimerSelect (DNASTAR,
Madison, WI) and purchased from BioCorp (Montreal,
Canada). Samples were amplified using Taq polymerase
(Qiagen). All amplified fragments were tested by agarose gel
electrophoresis, then pooled 2X, denatured by heating to 948C
for 5 min, then reannealed slowly by a controlled cooling to
258C over the course of 1.5 hr in a PE 9700 thermocycler.
Before DHPLC analysis of PCR products, amplicon sequences
were used to determine denaturation temperatures and buffer
gradients with the WAVEMAKER software for each amplicon.
Samples were then injected onto a DNASepHT column in a
model 3500HT WAVE dHPLC apparatus running WAVEMAKER 4.1 software (Transgenomic, Inc., Omaha, Nebraska).
Elution profiles were visually inspected and grouped into
‘‘types,’’ such as T1 and T2. Then, results were compiled into an
Excel worksheet for further processing. The amplification
products showing DHPLC variants were sequenced as
described earlier to determine the variations.
Genomic DNA Preparation
Blood samples from each individual were collected in 10-ml
K3 EDTA Vacutainer tube (Becton–Dickinson) and genomic
DNA was isolated by Puregene DNA Isolation kit (Gentra
Systems) according to the manufacturer’s instructions. DNA
was solubilized in 500 ml of DNA Hydration Solution and the
final concentration was adjusted to 300–400 mg/ml by spectroscopy at 260 nm.
Mutation Analysis
We searched for single nucleotide polymorphisms and other
variations in coding sequences and exon–intron boundaries of
genes using direct sequencing and dHPLC strategies. The
starting sample was composed of 16 unrelated BP affected
individuals selected to maximize the number of different
haplotypes and also according to their link with families that
gave positive genetic linkages on chromosome 12q24. The
power to identify a diallelic polymorphism with this sample
was established at 80% assuming a minor allele frequency of
5% with simple binomial sampling statistics. All novel SNPs
identified have been submitted to the NCBI dbSNP.
Sequencing Method
The targeted sequences were amplified by PCR. Amplification primers were purchased from Invitrogen (Burlington,
Genotyping of Microsatellite Markers
We used a fluorescent-based method for the genotyping of
microsatellite markers. Briefly, the region encompassing each
repeated sequence was amplified by PCR using an unlabeled
primer and a fluorescent-labeled primer (Applied Biosystems,
Inc, CA). The marker-associated dyes and the corresponding amplification product length are listed in Table I. The
amplification reaction is performed using 50 ng of DNA sample,
0.2 unit of Taq platinum DNA polymerase (Invitrogen,
Burlington, Canada), 20 mM Tris-Cl (pH 8.4), 50 mM KCl,
1.5 mM MgCl2, 100 mM of each dNTP, and 1.5 mM of each primer
in a final volume of 7 ml. The samples are incubated at 958C for
3 min to activate the Taq platinum DNA polymerase, then
10 cycles of PCR amplification were done as follow: 958C, 15 sec;
588C, 15 sec, 728C, 30 sec; after that 15 cycles as follow: 898C,
15 sec; 588C, 15 sec, 728C, 30 sec. Finally, the samples are
incubated at 728C for 30 min. Then, they were pooled according
to their dye-labeled primer and their PCR product length
(pool of four samples), resolved on an ABI 3100 DNA analyzer
(Applied Biosystems, Inc, CA). The raw data were analyzed
using Genemapper2 (Applied Biosystems, Inc, CA), and
compiled in a 4 D database (ACIUS) designed in a Macintosh
environment as previously described [Morissette, 1992]. Allelic
frequencies were estimated with VITESSE [O’Connell and
Weeks, 1995] from the pedigree sample [Boehnke, 1991].
52
Shink et al.
TABLE I. List of Microsatellite Markers Used for Linkage Analyses and Association Studies
Dyea
Locus
Allele
length (bp)
Het(%)
Position (bp)
NBG_11
VIC
204–218
66
119963576
D12S1666
NBG_5
6FAM
VIC
241–281
253–261
67
38
120066644
120114441
D12S1721
NBG_8
VIC
VIC
263–299
166–188
72
73
120469198
121097458
NBG_6
NED
182–218
74
121396927
NBG_9
VIC
156–180
69
121695884
NBG_10
6FAM
174–186
50
121976850
D12S1349
NBG_12
NED
NED
172–196
165–207
79
64
122090292
122470449
NBG_4
NED
171–199
67
124214016
NBG_3
VIC
182–206
65
124327585
D12S378
NBG_2
VIC
VIC
152–176
128–160
73
54
124387047
124923002
D12S1614
D12S342
D12S340
D12S1639
D12S1634
D12S2075
NED
VIC
PET
6FAM
PET
6FAM
167–177
218–240
242–250
221–255
151–169
275–311
72
74
62
77
51
71
125599529
125607947
125865825
126102199
126888404
127685134
Primersb
TCATTTGTCCTATTCCCATTCTG(F)
GTTTCTT-TGTAGAGTTTTCTGAATGTCCGC(R)
UniSTS:5781
TAAGCCAGCTGATTCCCAAG(F)
GTTTCTT-CTGGACAATATAGCGAGGCT(R)
UniSTS:16176
TCACGTCCTTCTTTCTTTGC(F)
GTTTCTT-GCATGCAGCTAAAAAGTGCT(R)
CGAACCAGCCCTAACCTAGC(F)
GTTTCTT-GGACAGGATGTCGTGGGAT(R)
TTGTGTGTCCATTTTTGACCA(F)
GTTTCTT-TGACGAAATTGCCTAATGATG(R)
GTGACCCTGGGTTCCAAGCA(F)
GTTTCTT-GACCTGGGAGGTCAGGG(R)
UniSTS:31135
GGTGAGAGAGCAAGATCCTTTCTATT(F)
GTTTCTT-AGATGAGAACCCTAGCCAGCC(R)
GTTTGTTACTTCACATTTTGACTGG(F)
GTTTCTT-TCTTGGAACTTCAGGCCAAC(R)
ATGTACCCAGGAAACCAGCA(F)
GTTTCTT-GTTTGACTGTTGCAATCATGTTACT(R)
UniSTS:48801
CTGGGCAAGAGAGTGAAACC(F)
GTTTCTT-TGAACTCCACCAGGGTTTGT(R)
UniSTS:66768
UniSTS:34698
UniSTS:70202
UniSTS:14122
UniSTS:62030
UniSTS:59386
The markers were from public databases (D12SXXXX) or our laboratory (NBG_XX). Novel microsatellite markers (internal code NBG) have been submitted
to the public database (NCBI UniSTS). For each marker, we indicate the fluorescent dye used to label the forward primer; the observed amplification product
size; the allele heterogeneity; the position on chromosome 12 (at the end of repeats) based on build 114 of the human genome assembly; and either primer
sequences or marker references in the UniSTS database (http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db¼unists). All markers contain CA repeats except
for D12S378 and D12S2075, which are tetranucleotide repeats.
a
The fluorescent dyes are trademarks of PE Corporation.
b
GTTTCTT, pig-tail sequence.
Genotyping of SNPs
SNPs were genotyped by direct sequencing of the amplification products. The sequencing traces for each individual were
automatically typed for the corresponding SNP using a homedeveloped program, GENO.pl and results of SNP genotyping
were compiled in the 4 D database cited above. SNP allelic
frequencies were computed directly from the control group and
deviation from Hardy–Weinberg equilibrium (HWE) was
assessed with the exact test from Genepop software package
(http://wbiomed.curtin.edu.au/genepop).
Microsatellite Marker Statistical Analysis
Two hierarchical affection status models (ASM) were used
to define the affected phenotype for linkage analysis since BP
are made up of a ‘‘spectrum’’ of associated disorders that could
be modulated by common susceptibility genes [Tsuang and
Faraone, 1990; Berrettini, 1997]. The first model (ASMI)
classified BPI, Scz and BPII individuals as affected, and
single or recurrent episode major depression diagnosis as
phenotype unknown. This last phenotype was extended to
include subjects whose status was unclear. All other diagnoses
were considered as unaffected. The second model (ASMII) was
similar to ASMI but the affected category was broadened to
include individuals with recurrent episode major depression.
The MFLINK software package (version 2.0) was used for
linkage analysis with microsatellite markers [Curtis and
Sham, 1995; Curtis et al., 1999; Sham et al., 2000]. This
software maximizes the LOD score over a set of disease-model
parameters subject to constraints (MLOD) such that the
statistic 4.6xMLOD has an asymptotic two-tailed chi-squared
distribution with one degree of freedom. The disease prevalence was set to 1% and the mode of inheritance varied from
recessive to dominant during the process of maximization.
MLODs were computed for multiplex pedigrees and for their
nuclear components since the sample contained large densely
affected kindred, which are more likely to characterize the
segregation of multiple susceptibility genes [Durner et al.,
1992]. No correction was applied to take into account the
presence of two ASMs and the use of the whole pedigree sample
and its nuclear components. Indeed, there was no clear way to
correct for multiple testing since these tests were correlated.
The association studies were done with our dense set of
microsatellite markers to reduce the region of interest [Weeks
and Lathrop, 1995; Hardy and Singleton, 2000]. The association hypothesis, which assumes correlated occurrence of DNA
sequence and bipolar phenotype on a population scale, was
tested with the software package CLUMP Version 1.6 [Sham
and Curtis, 1995]. The usual chi-squared statistic on the raw
contingency table (T1 statistic) and the largest chi-squared
statistic calculated by comparing one column of the original
table against the total of the other columns (T3 statistic) were
utilized. One thousand simulations were done to establish
empirical P-value with CLUMP.
Bipolar Disorder Locus in12Q24.31
Haplotyping was done on the pedigrees with the GENEHUNTER software package [Kruglyak et al., 1996].
SNPs Statistical Analysis
As for microsatellite markers, the association hypothesis
with SNPs was tested with CLUMP. Only the T1 statistic was
used for allelic association, while both T1 and T3 statistics were
used to test genotypic association. SNP haplotypes were
assessed within genes with the software package PHASE
Version 1.0 [Stephens et al., 2001]. Considering the predicted
haplotypes as alleles, T1 and T3 statistics were computed for
allelic and genotypic association analysis. No correction for
multiple testing was applied for association analysis since this
work was prospective.
RESULTS
Linkage and Association Results
With Microsatellite Markers
The linkage results from the whole pedigree series and their
nuclear components are shown in Figure 1 for ASMI and
ASMII. Most maximum MLOD scores were observed when
pedigrees were broken into nuclear families (15/20). All
microsatellite markers show at least one MLOD value with
P-values less than 0.05 except at D12S1634. The maximum
MLOD value of 3.85 (P ¼ 0.00003) was reached at D12S1721
under ASMI and pedigrees broken into branches. Under
ASMII and broken pedigrees, its MLOD value decreased to
3.18 (P ¼ 0.00013) but two close markers, NBG11 and NBG6,
gave MLOD values greater or equal to 3.28 with associated
P-values less or equal to 0.0001. No correction for multiple
testing was applied because of correlated tests. NBG3 was the
most distal marker with maximum MLOD value exceeding
53
the threshold value of 3. Maximum MLOD values with jointed
P-values of 0.00154 and 0.00444 were reported respectively for
markers D12S342 and D12S1639 previously reported by
others groups as described above.
HWE hypothesis was satisfied at the 5% level for each
microsatellite marker after application of the Bonferroni
corrections for multiple testing. Table II lists empirical
P-values observed with CLUMP for allele and genotype
association analysis. Empirical P-values less than 0.005 were
observed at marker NBG12 for T1 and T3 statistics under
allelic association analysis. T1 statistic suggested allelic
association between BP and NBG6 (empirical P ¼ 0.008).
Moreover, a borderline significant empirical P-value of 0.023
was observed at the most distal marker D12S2075.
Based on these results and also on previous linkage studies,
the candidate region on chromosome 12q24.31 must include
sequences between NBG11 and D12S2075. However, the distal
region from the associated marker NBG12 was chosen as
the best to begin our mutation screening on the basis of an
extensive haplotype analysis (results not shown).
Mutation Screening in Candidate Genes
We undertook a large mutation analysis on almost all known
genes located between NBG12 and D12S342. This 2.5 Mb
genomic region, of which 38 known genes have been annotated,
is included in the contig NT_09755.15 from the NCBI. In this
case, known genes are defined as protein-coding genes supported by at least one experimental mRNA, even though
function may be known or unknown. We selected 32 of these 38
genes to be screened for the presence of sequence variations,
mostly in coding regions (see Table III). When known, the 50
and 30 -untranslated sequences were also partially or totally
analyzed. We did not analyze any promoter region. The
Fig. 1. Two-point MLOD observed for the whole pedigree series and their nuclear components using MFLINK. Analysis was conducted by using ASMI
(*–* for whole pedigree; &- - -&for nuclear components) and ASMII (~–~for whole pedigree; ^- -^for nuclear components).
54
Shink et al.
TABLE II. Empirical P-values Observed With CLUMP for Statistics T1 and T3 for Allelic and
Genotypic Analyses of Microsatellite Markers (1000 Simulation Replicates)
Effective
Alleles
Genotypes
Locus
Case
Control
T1 (P-value)
T3 (P-value)
T1 (P-value)
T3 (P-value)
NBG11
D12S1666
NBG5
D12S1721
NBG8
NBG6
NBG9
NBG10
D12S1349
NBG12
NBG4
NBG3
D12S378
NBG2
D12S1614
D12S342
D12S340
D12S1639
D12S1634
D12S2075
204
208
213
210
213
213
213
213
212
213
207
209
211
210
210
211
209
209
211
203
98
175
179
176
179
179
175
175
180
175
178
175
180
170
179
180
179
180
181
181
0.250
0.366
0.969
0.693
0.754
0.008
0.759
0.521
0.887
0.002
0.418
0.171
0.171
0.896
0.803
0.394
0.890
0.087
0.361
0.023
0.421
0.543
0.934
0.463
0.921
0.356
0.768
0.178
0.864
<103
0.506
0.829
0.405
0.749
0.692
0.740
0.869
0.170
0.248
0.157
0.680
0.393
0.997
0.805
0.973
0.172
0.690
0.122
0.782
0.018
0.813
0.601
0.540
0.210
0.710
0.445
0.895
0.652
0.505
0.085
0.553
0.476
1.000
0.838
0.929
0.449
0.606
0.173
0.816
0.552
0.545
0.897
0.560
0.613
0.831
0.622
0.838
0.295
0.590
0.451
TABLE III. List of Genes Analyzed for the Presence of Mutations
Locus
B3GNT4
SMAC
VPS33A
RSN
FLJ14796
FLJ11021
KNTC1
HM74a
HM74
GPR81
DENR
HIP1R
FLJ12750
ABCB9
FLJ13491
ARL6IP4/SRP25
PITPNM2/NIR3
MPHOSPH9
FLJ22255/FLJ38663
CDK2AP1
SBNO1
MGC7036/FLJ32372
RNP24
KIAA1595/DDX55
EIF2B1
GTF2H3
FLJ12975
ATP6V0A2/TJ6
KIAA2017/DNAH10
FLJ22471
DKFZp761B128
SMRT/N-CoR2
RefSeq
Orientation
Non-coding and
coding exons
UTR
Coding
length (bp)
NM_030765.1
NM_019887.2
NM_022916.2
NM_002956.1
AK027702.1
NM_023012.2
NM_014708.2
NM_177551.2
NM_006018.1
NM_032554.2
NM_003677.2
XM_290592.1
NM_024667.1
NM_019625.1
NM_024623.1
NM_016638.1
NM_020845.1
NM_022782.1
NM_152269.1
NM_004642.2
AB014772.1
NM_145058.1
NM_006815.2
BC030020.1
NM_001414.1
NM_001516.3
NM_024809.2
NM_012463.1
XM_113706
NM_025140.1
NM_152437.1
NM_006312.1
þ
þ
þ
þ
þ
þ
þ
þ
þ
þ
þ
þ
þ
þ
2
6
13
24
14
10
(1)63
1
1
1(1)
(1)7
32
4
(1)11
(3)6
5
24
(1)16(2)
(1)2
4
31*
4
4
14
8
13
18
20
7*
(1)4
(4)1
47
T
T
T
T
T
3P
T
T
T
3P
3P
T
3P
T
T
T
3P
T
T
T
5NA
3P
T
T
T
3NA
T
3NA
T
5NA
T
T
1,137
720
1,791
4,284
2,124
1,305
6,630
1,092
1,164
1,041
598
3,207
858
2,172
873
657
4,050
2,079
501
348
4,179
636
606
1,803
918
927
2,094
2,571
1,155
996
786
7,554
The genes are listed from centromere to telomere. The column orientation indicates the strand corresponding to
mRNA sequences. The number (in brackets) at left or at center of coding exons specifies the number of 50 or 30 untranslated exons, respectively. The asterisks (*) designate the sequence partially annotated into genomic contig.
The column UTR describes the status for the analysis of untranslated regions; T, totally analyzed; 3P, 30 UTR
partially analyzed; 5NA, 50 UTR not analyzed; 3NA, 30 UTR not analyzed.
Bipolar Disorder Locus in12Q24.31
remaining six genes were eliminated according to these
features: The incompleteness of the genomic annotation
(MONDOA), the function and expression (TSP-NY), overlapping with well-known locus (FLJ213769), not annotated
when this study was started (MGC35140, Loc347945, and
Loc144347). The sequences to be screened were dictated by
the mRNA Reference Sequence (RefSeq) from the NCBI,
except for FLJ14796, SBNO1, SMRT/N-CoR2, and KIAA1595
of which the RefSeq did not correctly represent the most
probable full length coding sequences. Moreover, EST clustering analyses (http://genenest.molgen.mpg.de/) suggested
that some genes (ABCB9, CDK2AP1, EIF2B1, FLJ11021,
FLJ13491, FLJ14796, FLJ22471, MPHOSPH9, SRp25, and
SMRT/N-CoR2) could generate different protein variants by
alternative exon usage. As already mentioned, the RefSeq
associated to these loci corresponds to the most probable
transcripts (Cluster having the largest number of associated
mRNA and EST), but we screened for exons generating all
possibilities of transcription variants.
Among listed genes, we could point out some of the most
interesting candidates based on their known or suspected
function. HM74 and GPR81 belong to the G protein-coupled
receptor superfamily. This type of receptor is involved in
regulation of intracellular second messengers, such as cAMP,
inositol phosphates, diacylglycerol, and calcium ions [Karasinska et al., 2003]. Interestingly, the HM74 locus contains a
duplicate genomic region, coding for two HM74 variants,
HM74 and HM74a, which are 94% identical. The identification
of mRNA and ESTs associated to each form suggest that both
variants could be expressed. Recently, nicotinic acid has been
identified as a ligand for HM74 and HM74a in adipocytes [Wise
et al., 2003]. On the other hand, GPR81 remains an orphan
receptor. NIR3 is also another interesting candidate gene
based on its expression in the dentate gyrus and its potential
involvement in phosphoinositol signaling. Lithium is known to
directly inhibit the enzyme monophosphate phosphatase
implicated in phosphoinositide metabolism [Lenox and Wang,
2003].
In this mutation analysis we identified 169 polymorphisms,
including 13 in 50 -untranslated sequences (50 -UTR), 78 in
introns, 28 in 30 -UTR, and 50 in coding regions (data not
shown). Among these coding SNPs, 25 (50%) are non-synonymous polymorphisms (nsSNPs) and we found one glutamine
repeat (CAG12-16) in the SMRT/N-CoR2 gene. The latter and
other point mutations in SMRT/N-CoR2 gene led us to independently investigate this locus in more detail [Shink et al.,
2005]. All other missense mutations, listed in Table IV, have
been genotyped and analyzed for association with BP.
SNP Association Results
All non-synonymous SNPs identified in our mutation
screening were typed in a case/control sample (401 individuals)
from the SLSJ region. Allelic and genotypic association results
are shown in Table V. Considering the power of our study,
we selected to maximize the number of positive associations
(type I error) by setting up a threshold for significant results at
P-values ¼ 0.05 without correction for multiple testing and to
replicate positive results in an independent sample. We found
15 of 22 SNPs had minor allele frequencies of less than 20%.
Empirical P-value was less than 0.05 for T1 statistic under
genotypic analysis at SNPs GC29E06A and GC32E04A located
within the KIAA1595/DDX55 and the FLJ22471 loci respectively. The SNP GC29E06A describes the amino acid change
Glu154Lys. The DDX55 protein is a member of the DEAD-box
family, which is mostly involved in ribosome biogenesis and
translation initiation [Abdelhaleem et al., 2003]. The glutamic
acid 154 residue is conserved between three DDX55 orthologs
(human, rat, and mouse), and is replaced by the other
negatively charged amino acids, aspartic acid, in the Tetraodon
homolog (data not shown). This residue is included in the
DEAD box domain, but is not conserved among DEAD boxcontaining proteins. Evidence for a susceptibility gene was
observed at SNP GC32E04A with an empirical P-values of
0.001, but the control sample did not satisfy HWE even after
correction for multiple testing (uncorrected P < 0.001) since all
TABLE IV. List of Genotyped Missense Variations
Locus
SNP
RSN
GC04E19A
GC04E14A
GC04E12A
GC06E04A
GC07E08A
GC07E25A
GC07E57A
GC09E01B
GC09E01A
GC10E01E
GC10E01C
GC10E01D
GC10E01B
GC10E01A
GC18E01A
GC22E16A
GC29E04A
GC29E06A
GC29E08A
GC24E19A
GC32E04A
GC32E03A
FLJ11021
KNTC1
HM74v02
HM74v01
NIR3
SBNO1
KIAA1595
TJ6
FLJ22471
55
Alleles
A-C (A1178S)
G-C (E1034D)
T-C (S895P)
A-G (H88R)
G-T (K245N)
G-C (E738D)
T-G (V2021G)
G-A (M317I)
C-T (R311C)
G-C (M346I)
G-A (M317I)
G-A (R253H)
C-T (L198F)
C-A (P173T)
C-T (P9L)
G-A (S728N)
G-C (V101L)
G-A (E154K)
A-G (N264S)
C-T (A813V)
G-A (R281H)
A-T (S70C)
Genomic
position (bp)
dbSNP
122572096
122620929
122626006
122811947
122839214
122866189
122911347
122995306
122995326
123008673
123008760
123008953
123009119
123009194
123327538
123614645
123815976
123817228
123821709
123965449
124145702
124151249
—
rs1129167
—
—
—
—
—
—
—
rs1696351
rs2454727
rs2454726
rs676823
rs1798192
—
rs1060105
—
—
—
—
—
rs11057401
The chromosomal locations are dictated by the contig assembly build 33 from the NCBI. The column ‘Alleles’
describes the punctual nucleotide variation (WT-mutant), and the corresponding amino acid modification. The
wild type sequences correspond to RefSeq in Table III. Novel SNPs have been submitted to the NCBI dbSNP and
the reference SNP ID is given for SNPs that have already been characterized.
56
Shink et al.
TABLE V. Empirical P-Values Observed With CLUMP for Allelic and Genotypic Analyses of Coding SNPs (1000 Simulation Replicates)
Effective
Gene (a; b)*
RSN (5; 2)
FLJ11021
KNTC1 (5; 1)
HM74a (4; 2)
HM74 (7; 2)
NIR3
SBNO1
KIAA1595 (4; 1)
TJ6
FLJ22471 (4; 2)
Alleles
Genotypes
Locus
Allele frequencies
Case
Control
T1 (P-value)
T1 (P-value)
T3 (P-value)
GC04E19A
GC04E14A
GC04E12A
GC6E04A
GC07E08A
GC07E25A
GC07E57A
GC09E01B
GC09E01A
GC10E01E
GC10E01C
GC10E01D
GC10E01B
GC10E01A
GC18E01A
GC22E16A
GC29E04A
GC29E06A
GC29E08A
GC24E19A
GC32E04A
GC32E03A
A (0.018); C (0.982
C (0.646); G (0.354)
A (0.879); G (0.121)
A (0.997); G (0.003)
G (0.906); T (0.094)
C (0.972); G (0.028)
G (0.088); T (0.912)
C (0.679); T (0.321)
A (0.118); G (0.882)
C (0.062); G (0.938)
A (0.347); G (0.653)
A (0.446); G (0.554)
C (0.523); T (0.477)
A (0.463); C (0.537)
A (0.045); G (0.955)
A (0.204); G (0.796)
C (0.020); G (0.980)
A (0.022); G (0.978)
A (0.898); G (0.102)
A (0.012); G (0.988)
A (0.030); G (0,970)
A (0,607); T (0,393)
201
205
203
196
204
201
200
204
204
202
202
200
200
200
203
192
205
200
205
204
193
205
169
171
170
159
170
162
170
179
178
178
177
175
177
178
177
164
175
160
176
166
167
169
0.325
0.776
0.845
1.000
1.000
0.806
1.000
0.350
0.289
0.520
0.079
0.714
0.240
0.644
0.230
0.779
0.572
0.129
0.178
0.760
1.000
0.228
0.319
0.071
0.652
1.000
0.251
0.835
0.097
0.645
0.382
0.533
0.213
0.785
0.148
0.660
0.235
0.908
0.587
0.027
0.393
0.754
0.001
0.365
0.319
0.073
0.621
1.000
0.462
0.835
0.363
0.631
0.207
0.383
0.215
0.781
0.168
0.659
0.235
0.888
0.587
0.078
0.354
0.754
0.287
0.385
*a, maximum number of haplotypes estimated by PHASE over the case and the control sample for the gene; b, number of major haplotype that accounted for
an estimated 80% of the observed chromosomes in both sample.
heterozygous individuals (10) had BPI or BPII diagnosis. We
verified the typing for the SNP GC32E04A to eliminate the
possibility of experimental errors. A slight increase of heterozygous genotype was observed at GC29E06A in the control
group (4%) versus the case group (0.5%).
Table V shows the maximum number of haplotypes
estimated by PHASE over the case and the control sample for
each gene with more than one genotyped SNP. It appeared that
one or two major haplotypes accounted for an estimated 80% of
the observed chromosomes in each sample. Case/control
studies of SNP haplotypes led to an empirical P-value of
0.030 for T1 statistic with the gene HM74. PHASE allowed us
to reconstruct only 7 of 32 possible haplotypes within HM74,
the largest difference between case and control haplotype
frequencies was an increase of 4% in the case group. Moreover,
a haplotype found in the controls with a frequency of 2% was not
observed in cases. An empirical P-value of 0.020 was obtained
with T1 statistic when the genotypes of haplotypes distribution
were compared between cases and controls at gene FLJ22471.
No other association was significant at the 5% level, including
KIA1595.
DISCUSSION
These results present the first exhaustive candidate genes
analysis distal to the Darier’s disease gene locus, and point out
possible deleterious alleles in, or close to, three genes that
could have a small effect on the incidence of BP. We first
showed a linkage analysis over 7.7 Mb, using 20 microsatellite
markers from NBG11 to D12S2075. Most maximum MLOD
values (15/20) have been shown with nuclear families that
could be explained by intra-family heterogeneity. This result is
coherent with our previous report suggesting that different
ancestors have introduced one or more common deleterious
alleles [Shink et al., 2003]. In the same way, the perspective
that a susceptibility gene to BP can segregate in many different
ethnic groups argues in favor of a common deleterious allele.
However, as mentioned elsewhere [Curtis et al., 2003], we may
not exclude the possibility of a rare allele-causing disease since
this genomic region has been highlighted in very large
pedigrees.
The highest two-point MLOD scores, greater or equal to
3.28 (P-value 0.0001), were observed at markers NBG11,
D12S1721, and NBG6, which are more proximal than those
previously reported. For many reasons, the replication of
significant linkage results is not a foregone conclusion [Suarez
et al., 1994; Altmuller et al., 2001] and some positive peaks
may drift over 15 cM from one study to another [Hauser et al.,
1996; Roberts et al., 1999]. Consequently, the presence of
maximum MLOD values displayed at several distant markers
may reflect a common susceptibility gene that segregates in
many different populations, such as Danish, English, Faroese,
French Canadian, and Icelandic [Ewald et al., 1998; Degn et al.,
2001; Curtis et al., 2003; Ekholm et al., 2003]. In fact, the
hypothesis that a susceptibility gene to BP is common to Faroe
Islands and SLSJ populations is supported by results observed
at D12S2075 by Degn et al. [2001] and this report. Accordingly,
the recent genome-wide scan by Curtis et al. [2003] supports
linkage at marker D12S342. Unfortunately, D12S342 was
their unique locus that overlapped with our studied markers
and it will be interesting to see a more detailed linkage analysis
surrounding this marker. Obviously, it is possible that the
12q23-24 region could contain more than one locus involved in
BP.
The analysis of the genomic region surrounding the
positively associated marker NBG12 was based on a candidate
gene approach and prioritized non-synonymous SNPs for
case/control studies. This strategy is mainly supported by the
hypothesis that such polymorphisms are likely to affect the
risk of an affected phenotype, and by mutational studies of
Mendelian diseases indicating that missense/nonsense polymorphisms are the most frequent disease-causing mutations
(59%) [Tabor et al., 2002; Botstein and Risch, 2003]. By this
approach we also wanted to maximize the chance of finding a
biologically relevant association. The association studies with
22 non-synonymous mutations revealed significant genotypic
associations at the 5% level for two uncommon variations
located in genes KIAA1595 and FLJ22471, which encode for
Bipolar Disorder Locus in12Q24.31
DDX55 and limkain beta 2 proteins, respectively. The distance
between SNPs is about 328 Kb and they did not show LD (data
not shown). The exact function of DDX55 is unknown, but
as mentioned earlier, it belongs to the DEAD-box family of
proteins involved in ribosome biogenesis and translation
initiation. The yeast homolog, Spb4p, plays an important role
in maturation process of the 25S and 5.8S ribosomal RNA [de la
Cruz et al., 1998]. Moreover, Spb4p is a RNA helicase essential
for cell viability [Sachs and Davis, 1990]. Considering this, it is
unlikely that KIAA1595/DDX55 would be associated to
affective disorders. The most striking result was observed for
SNP GC32E04A in Limkain beta 2 gene where all heterozygous individuals were affected. The typing results for this
polymorphism deviate from the HW equilibrium and we
excluded any cause by experimental errors. Accordingly, this
deviation could be due to a chance sampling error or, on the
other hand, it could be interpreted as the presence of a diseaseassociated genotype. Considering the latter hypothesis, the
low frequency of this polymorphism and the BP prevalence
of about 1%, it is not likely that we would observe any heterozygote in the control sample. The contribution of heterozygous
mutations in disease susceptibility has already been reported
[Morissette et al., 1998]. For the limkain beta 2 proteins,
we noted a coil-coiled motif that could permit either selfinteractions or with other cytoskeleton-associated proteins,
thus we could postulate that diverging alleles would generate a
heterogeneous pool of proteins, which improperly interact
one with another. Alternatively, the heterozygotes at locus
GC32E04A could potentiate the effect of preliminary mutated
alleles as already described in recent reports about heterozygous mutations on Bardet–Biedl syndrome [Badano et al.,
2003]. However, since this represents only 5% (10/193) of
bipolar subjects this could not account for a majority of BP
cases in our sample, and no specific BP sub-phenotype has been
associated to this genotype. This finding has to be replicated in
an independent sample.
In conclusion, the data reported here present a major fine
mapping effort over the chromosomal 12q24.31 area. Our
association studies were focused on a narrow portion of this
genomic region, motivated by a positive allelic association with
the microsatellite marker NBG12. We analyzed 32 genes for
mutation detection, prioritized nsSNPs for genotyping and
found polymorphisms associated with bipolar disorder in
three genes. While we do not believe that any of them could
be responsible for the linkage peak observed in our family
studies, they could play a role in the promotion of BP severity,
or they could be in LD with more frequent disorder-causing
alleles. Finally, while this study shows the necessity to consider
candidate genes around D12S2075 since it has been implicated
in two independent studies, the positively associated region
upstream of NBG12 cannot be ignored.
ACKNOWLEDGMENTS
The authors thank patients and their families for participating in the study as well as Dr. Laberge, Dr. Raymond,
Dr. Brown, and Dr. M. Morissette for control samples.
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markers, nucleotide, disorder, regions, candidatus, susceptibility, 12q24, polymorphism, affective, single, microsatellite, analysis, genes, chromosome, bipolar
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