Elucidating the genetic architecture of familial schizophrenia using rare copy number variant and linkage scans

Article (PDF Available)inProceedings of the National Academy of Sciences 106(39):16746-51 · September 2009with28 Reads
DOI: 10.1073/pnas.0908584106 · Source: PubMed
Abstract
To elucidate the genetic architecture of familial schizophrenia we combine linkage analysis with studies of fine-level chromosomal variation in families recruited from the Afrikaner population in South Africa. We demonstrate that individually rare inherited copy number variants (CNVs) are more frequent in cases with familial schizophrenia as compared to unaffected controls and affect almost exclusively genic regions. Interestingly, we find that while the prevalence of rare structural variants is similar in familial and sporadic cases, the type of variants is markedly different. In addition, using a high-density linkage scan with a panel of nearly 2,000 markers, we identify a region on chromosome 13q34 that shows genome-wide significant linkage to schizophrenia and show that in the families not linked to this locus, there is evidence for linkage to chromosome 1p36. No causative CNVs were identified in either locus. Overall, our results from approaches designed to detect risk variants with relatively low frequency and high penetrance in a well-defined and relatively homogeneous population, provide strong empirical evidence supporting the notion that multiple genetic variants, including individually rare ones, that affect many different genes contribute to the genetic risk of familial schizophrenia. They also highlight differences in the genetic architecture of the familial and sporadic forms of the disease.

Figures

Figure
Figure
Elucidating the genetic architecture of familial
schizophrenia using rare copy number variant
and linkage scans
Bin Xu
a,b,1
, Abigail Woodroffe
c,1
, Laura Rodriguez-Murillo
a
, J. Louw Roos
d
, Elizabeth J. van Rensburg
e
,
Gonc
¸alo R. Abecasis
f
, Joseph A. Gogos
b,g,2
, and Maria Karayiorgou
a,2
Departments of aPsychiatry, bPhysiology and Cellular Biophysics, and gNeuroscience, Columbia University Medical Center, New York, NY 10032; Departments
of cEpidemiology and fBiostatistics, University of Michigan, Ann Arbor, MI 48109; dDepartment of Psychiatry, Weskoppies Hospital, Pretoria, Republic of
South Africa 0001; and eDepartment of Genetics, University of Pretoria, Pretoria, Republic of South Africa 0001
Communicated by David E. Housman, Massachusetts Institute of Technology, Cambridge, MA, August 8, 2009 (received for review June 16, 2009)
To elucidate the genetic architecture of familial schizophrenia we
combine linkage analysis with studies of fine-level chromosomal
variation in families recruited from the Afrikaner population in
South Africa. We demonstrate that individually rare inherited copy
number variants (CNVs) are more frequent in cases with familial
schizophrenia as compared to unaffected controls and affect al-
most exclusively genic regions. Interestingly, we find that while
the prevalence of rare structural variants is similar in familial and
sporadic cases, the type of variants is markedly different. In
addition, using a high-density linkage scan with a panel of nearly
2,000 markers, we identify a region on chromosome 13q34 that
shows genome-wide significant linkage to schizophrenia and show
that in the families not linked to this locus, there is evidence for
linkage to chromosome 1p36. No causative CNVs were identified in
either locus. Overall, our results from approaches designed to
detect risk variants with relatively low frequency and high pen-
etrance in a well-defined and relatively homogeneous population,
provide strong empirical evidence supporting the notion that
multiple genetic variants, including individually rare ones, that
affect many different genes contribute to the genetic risk of
familial schizophrenia. They also highlight differences in the ge-
netic architecture of the familial and sporadic forms of the disease.
rare mutations chromosome 13q34 chromosome 1p36
RAPGEF gene family
Schizophrenia (SCZ) is a chronic, psychiatric disorder that has
an estimated worldwide prevalence of 1%. The genetic
architecture of the disease remains largely unknown. The stron-
gest predictor of SCZ is having an affected first-degree relative
(1). In addition to the familial forms, nonfamilial (sporadic)
forms of the disease also exist (1, 2). The exact proportion of
each form is largely unknown, but it is thought that at least 60%
of cases are sporadic (3, 4). Genome-wide linkage scans have
been conducted to identify loci harboring rare mutations/
variants that increase susceptibility to familial SCZ. Loci have
been identified on almost every chromosome, but only a few
regions have been replicated across studies. One such region is
near the telomere of chromosome 13q (5–12). Across studies the
region of peak linkage is broad, from 13q12 to 13q34. This region
has also been linked to bipolar disorder (13). In addition to
linkage studies, a number of earlier (14) as well as more recent
studies (15–18) have provided strong evidence supporting the
importance of rare structural mutations/variants in SCZ vulner-
ability. Rare inherited structural lesions, in particular, are ex-
pected to be prominent in familial SCZ, but their full contribu-
tion to transmitted liability in familial SCZ cases has not been
examined so far in a systematic manner (19).
To understand the genetic architecture of familial SCZ and
the pattern of transmission of rare risk alleles in affected
families, we combine high-resolution linkage analysis with stud-
ies of fine-level chromosomal variation in families recruited from
the Afrikaner population in South Africa, a genetically and
environmentally homogeneous population who have descended
from mostly Dutch immigrants who settled in South Africa
beginning in 1652 (20). In addition to the genetic homogeneity,
the Afrikaners are valuable for genetic studies because they
present a close-knit family structure and offer the potential to
perform detailed genealogical analysis, which affords reliable
discrimination of familial and nonfamilial forms of the disease
and facilitates family-based genetic studies. Here, using ap-
proaches designed to detect risk variants with relatively low-
frequency and high penetrance, we provide strong empirical
evidence supporting the notion that multiple genetic variants,
including individually rare ones that affect many different genes,
contribute to the genetic risk of familial SCZ.
Results
Patient Cohorts. We performed a genome-wide survey of rare
inherited copy number variants (CNVs) in a total of 182
individuals, consisting of 48 probands with familial SCZ [positive
disease history in a first-degree (n33) or second-degree (n
15) relative; Fig. S1] and both of their biological parents, as well
as all additional affected relatives that were available for geno-
typing. Of the 48 probands, 40 are diagnosed as affected in the
narrow category and eight in the broad category (see Methods).
The familial cases cohort was compared to a control cohort (n
159 triad families) as well as to a cohort enriched in sporadic
cases (n152 triad families), defined as cases with negative
family history of SCZ in a first- or second-degree relatives, also
recruited from the Afrikaner community as previously described
(15). In that respect, it should be noted that there were no
significant differences in the average number of first- or second-
degree relatives among families with and without family history.
Specifically, in the 48 families with positive family history of SCZ
in first- or second-degree relatives reported here, the average
proband sibship was comprised of 3.4, the average maternal
sibship of 4.3, and the average paternal sibship of 4.2 individuals.
In the cohort enriched in sporadic cases (15), these numbers are
3.3, 4.3, and 4.6, respectively. Negative or positive family history
or availability of additional affecteds was not a screening crite-
rion (see Methods).
For our linkage studies, we genotyped 479 subjects from 130
families. Sixty-nine families are informative. In these, 112 indi-
Author contributions: B.X., A.W., G.R.A., J.A.G., and M.K. designed research; B.X. and A.W.
performed research; J.L.R. and E.J.v.R. contributed new reagents/analytic tools; B.X., A.W.,
L.R.-M., and G.R.A. analyzed data; and B.X., A.W., G.R.A., J.A.G., and M.K. wrote the paper.
The authors declare no conflict of interest.
1B.X. and A.W. contributed equally to this work.
2To whom correspondence may be addressed. E-mail: mk2758@columbia.edu or
jag90@columbia.edu.
This article contains supporting information online at www.pnas.org/cgi/content/full/
0908584106/DCSupplemental.
16746–16751
PNAS
September 29, 2009
vol. 106
no. 39 www.pnas.orgcgidoi10.1073pnas.0908584106
viduals are diagnosed as affected in the narrow categor y, and 128
individuals are classified as broadly affected. In the 54 informa-
tive families with at least two affected members, there are 60 and
79 affected relative pairs for the narrow and broad affection
categories, respectively (Table S1); this is 43% more affected
relative pairs than in our previous, 9-cM, linkage study (12). A
subset of the families (67%) used in the CNV studies (n32)
was also included in the linkage scan. The appropriate Institu-
tional Review Boards and Ethics Committees at University of
Pretoria and Columbia University have approved all procedures
for this study.
Genome-Wide Survey of Rare Inherited CNVs. We surveyed single
nucleotide polymorphisms (SNPs) and CNVs using the Af-
fymetrix Genome-Wide Human SNP 5.0 arrays and used inten-
sity and genotype data from both SNP and CN probes to identify
autosomal deletions and duplications as described previously
(15). The estimated rare inherited mutation rate was compared
to the collective rate of inherited CNVs among sporadic cases
and unaffected individuals from the same population (15). Rare
inherited CNVs detected in familial cases and their parents were
considered only if they involved at least 10 consecutive probe sets
(average resolution of 30 kb) and did not show 50% overlap
with a CNV detected in any parental chromosome (other than
those of the biological parents) in the familial, sporadic, or
control cohorts (n1,432 chromosomes). Using these criteria,
we identified 24 rare inherited CNVs in 19 familial cases
affecting 52 genes (Tables S2 and S3). The frequency of carriers
of rare inherited structural lesions is 40% (19 out of 48) in our
cohort of familial cases as compared to the 20% (32 out of 159)
collective rate of inherited CNVs among unaffected individuals
from the same population (15) (relative enrichment 1.97, Fish-
er’s Exact Test P0.01) (Table 1). Cases and controls carry on
average 0.5 (24 CNVs in 48 cases) and 0.2 (32 in 159 controls)
rare CNVs per person, respectively, a 2-fold difference in rare
CNV burden. It should be noted that our population-specific
filtering process is preferable to the one based on the diverse set
of CNVs present in the database of genomic variants (DGV) (16)
because DGV includes samples that have not been screened for
psychiatric phenotypes and likely includes several pathogenic
variants, and in addition, CNV frequency and disease-
penetrance may vary across human populations (21). Neverthe-
less, essentially identical enrichment (relative enrichment 1.85,
27% vs. 14.5%, Fisher’s Exact Test P0.05) was obtained upon
further filtering that removed rare inherited variants overlapping
with DGV (hg18 version 4). Familial clustering offers an im-
portant validation measure for all identified CNV regions (22).
Nevertheless, we also confirmed, in four out of four tested
families, the observed patterns of inheritance using an indepen-
dent approach, the multiplex ligation-dependent probe amplifi-
cation (MLPA) assay (23) (see SI Methods and Fig. S2).
Two lines of evidence suggest that the observed 2-fold
enrichment of rare inherited CNVs in familial SCZ has a bone
fide pathogenic basis. First, the observed enrichment in inherited
structural mutations is highly specific, since we did not find any
enrichment of de novo structural lesions in the same familial
cases cohort (15). Second, as would be expected for pathogenic
lesions, the enrichment in inherited CNVs among familial cases
is confined exclusively to CNVs overlapping at least one gene,
either partly or in its entirety (herein referred to as genic CNVs).
Specifically, when we analyzed CNVs separately according to
their gene composition, we found that individuals with familial
SCZ were 2.7 times as likely as controls to harbor rare genic
CNVs (relative enrichment 2.7, Fisher’s Exact Test P10
3
,
Table 1). Essentially identical enrichment in genic CNVs (rel-
ative enrichment 2.6, 25% vs. 9.5%, Fisher’s Exact Test P
0.012) was obtained upon further filtering that removed rare
inherited variants overlapping with DGV (hg18 version 4). In
contrast, there was no significant difference in the proportion of
cases versus controls carrying rare nongenic CNVs (Table 1).
Moreover, no such enrichment in inherited genic CNVs was
observed in the cohort enriched for sporadic cases (relative
enrichment 1.2, P0.52), where the contribution of inherited
CNVs appears to be relatively minor. In that respect, it is
noteworthy that while the overall frequency of carriers of all rare
structural variants is the same between the familial and sporadic
cases (15) (40%), the type of variants is markedly different
(Fig. 1A). Sporadic SCZ is characterized by a marked enrichment
of rare de novo mutations and only a modest increase in the rate
of rare inherited CNVs, which do not appear to preferentially
affect genes. By contrast, familial SCZ is characterized by
enrichment in rare inherited genic CNVs (predicted to have
higher penetrance), while de novo mutations are less prominent.
None of the rare CNVs found in familial cases are present in
control chromosomes. To identify which of these CNVs are most
likely to be pathogenic, we investigated the relationship between
rare inherited CNVs and disease status within each family. For
12 of the 19 CNV carriers, DNA samples and genotypic infor-
mation were available for at least one more affected relative. In
nine of these 12 families, the CNV segregated to all genotyped
affected members (only two of the 23 affected members of these
nine families were not available for genotyping). Thus, CNVs in
these nine families showed clear co-segregation with the clinical
diagnosis, in a manner consistent with incomplete penetrance
models (i.e., present also in some unaffected family members)
(Fig. 1B). The remaining three CNVs segregated only to one of
the genotyped affected members in each family. Given the
20% basal rate of inherited CNVs in unaffected controls (15),
these are likely to be neutral variants. Alternatively, these cases
may be indicative of more complex modes of inheritance, such
as bilineal transmission of risk alleles. To exclude that the
observed pattern of co-inheritance of CNVs and diagnosis in the
informative families is due to chance alone, we conducted a
simulation study where we disrupted the relationship between
CNVs and diagnosis by permuting the diagnosis, while keeping
constant the pedigree structure, the inherited pattern of CNVs
Table 1. Increased frequency of rare inherited genic CNVs in familial SCZ
Cohort
No. of
families
Total rare inherited CN mutation
carriers Genic inherited CN mutation carriers
Non-genic inherited CN mutation
carriers
n%
P
n%
P
n%
P
vs.
controls
vs.
sporadic
vs.
controls
vs.
sporadic
vs.
controls
vs.
sporadic
Familial 48 19 39.6 0.012 NS 17 35.4 0.001 0.007 2 4.2 NS NS
Sporadic 152 46 30.3 24 15.8 22 14.5
Controls 159 32 20.1 21 13.2 11 6.9
Statistical significance was determined using Fisher’s exact probability test; NS non-significant.
Xu et al. PNAS
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GENETICS
and the number of affected individuals in each family. Co-
inheritance of CNVs and diagnosis in nine out of 12 families was
observed only once in 10,000 permutation runs (empirical P
value 0.0001) and, therefore, it is unlikely to be due to chance.
The nine CNVs segregating to all genotyped affected mem-
bers within the respective families, alter 12 known genes: PEX13,
KIAA1841,AHSA2,USP34,C4orf45,RAPGEF2,PTPRN2,
CSMD1,NRG3,MACROD2,A26B3, and LOC441956, all of
which are candidates for follow-up studies. Convergence with
previous studies highlights at least two of these genes as partic-
ularly worthy of follow-up. Specifically, the neuregulin-3 (NRG3)
gene is implicated by a 73.6-kb-long duplication in the first
intron, which may cause regulatory deficits. An overlapping
CNV has been reported previously in one case with SCZ (24)
(Table S2). Three SNPs in the same intron were recently
associated with SCZ-related quantitative traits (24). The homol-
ogous gene, NRG1, is also a well-known candidate gene (25). In
addition, the RAPGEF2 gene, encoding for a GTP exchange
factor, is implicated by a 716.4-kb duplication that encompasses
this gene. Mutation of the RAPGEF2 ortholog in mice affects the
formation of the cerebral cortex, reduces the threshold for the
induction of epileptic seizures and results in commissural fiber
defects (26). Previously, we have identified in a patient with SCZ
a de novo exonic microdeletion affecting another member of the
RAPGEF family (RAPGEF6) located within a SCZ suscepti-
bility locus at chromosome 5q (15). Mutations in another
member of this family (RAPGEF4) have been described in
autism (27).
We examined the familial cases for differences in clinical and
phenotypic variables (history of developmental delays or learn-
ing disabilities, mental retardation, age at onset, and disease
severity) that may discriminate inherited CN mutation carriers.
We focused our analysis on probands from families where CNVs
show apparent co-segregation with the illness and are therefore
more likely to be pathogenic. Among them, the male to female
ratio is identical to that of the entire familial cases cohort, and
there is no statistically significant evidence for parental origin
effects. None of these probands had a history of developmental
delays or learning disabilities, or presence of mental retardation.
In addition, there was no difference in the age at disease onset
between these probands and noncarriers of rare CNVs, but we
found some suggestive differences in indices of disease severity,
including co-morbid substance abuse (67% versus 23%, P0.03,
uncorrected), duration of illness (141.6 months versus 92.2
months, P0.19), and number of hospitalizations (4.9 com-
pared to 2.9, P0.13), indicating a more debilitating or
treatment-resistant form of illness among CNV carriers.
High-Density Linkage Analysis. Previously, a low-density (9-cM)
linkage scan in our Afrikaner sample identified suggestive
Fig. 1. Inherited CNVs in families with SCZ. (A) Frequency distribution of rare CNVs identified in familial and sporadic cases of SCZ at the resolution afforded
by the Affymetrix Genome-Wide Human SNP 5.0 arrays. There is a 20% basal rate of inherited CNVs in unaffected controls, while the overall frequency of carriers
of all rare CNVs is the same between familial and sporadic cases (40%). (B) Rare inherited CNVs showing co-segregation with the clinical diagnosis in the
respective affected families. For each CNV, the structure of the affected family, as well as the genomic position of the CNV, are indicated. Affected individuals
are marked in black. Probands are indicated by red arrows. Individuals who carry rare CNVs are indicated by an asterisk. Individuals where no genotype
information is available are indicated by question marks.
16748
www.pnas.orgcgidoi10.1073pnas.0908584106 Xu et al.
evidence for linkage at 13q34 and 1p36 (maximum nonparamet-
ric LOD scores 2.99 and 2.23, respectively) (12). Here we
genotyped 2005 di-, tri-, and tetra-nucleotide repeat microsat-
ellite markers, in one of a few linkage scans of psychiatric
disorders to attain this level of genome-wide coverage. The
average inter-marker distance was 1.9 cM (1.4), and the
average heterozygosity for the autosomal markers was 0.71
(0.12) resulting in an information content of 0.84 (0.046)
(Figs. S3, S4). We conducted both parametric and nonparamet-
ric analyses. For our parametric analyses, we used the algorithm
implemented in LAMP to estimate the disease allele frequency
and genotype penetrances by maximum likelihood. We used the
optimized parameters to calculate model maximized LOD
(MOD) scores, which are more powerful than LOD scores when
the disease parameters are unknown (28).
Singlepoint Parametric Results. We found three markers with a
MOD score of at least 3.0 in either affection category: D13S285
on 13q34, D9S50 on 9p13, and D21S270 on 21q22. The highest
MOD scores for both affection statuses are for D13S285, located
at 127 cM. For the narrow affection status the MOD score is 3.30
and for the broad classification is 3.67. D9S50 at 60 cM has a
MOD score of 3.56 for the broad classification and 2.51 for the
narrow. For D21S270 at 46 cM, the MOD score for the narrow
classification is 3.0 and for the broad is 1.88 (Table S4).
Multipoint Parametric Results. For our multipoint parametric anal-
ysis (Fig. 2), the maximum MOD scores for both affection
classifications are also on 13q34. The maximum MOD score for
the narrow affection status is 3.13 at 126 cM. When we repeated
the linkage analyses on 1,000 simulated data sets, we calculated
an empirical Pvalue of 0.093. For the broad affection status, the
highest MOD score is 3.76 at 131 cM, near D13S293, and
the 1-MOD region spans from 115 cM to the q terminus. The
empirical Pvalue for the broad affection status alone is 0.025.
When we include both the narrow and broad classifications in
our simulations, 42 of the data sets resulted in a MOD score
greater than or equal to 3.76. This empirical genome-wide P
value of 0.042 meets the criteria for a significant linkage result
(29). For the broad phenotype, the maximum likelihood estimate
for the disease penetrance for an individual with two copies of
the disease allele is 1.0, for an individual with one copy is 0.073,
and for an individual with no copies is 0.005. Although the
disease allele frequency is estimated to be fairly rare (f
d
0.030),
the relative risk is very high (RR 13.77). In addition to the 13q
locus, we identified linkage peaks on chromosomes 21q22 at 46
cM, near D21S1900, with a 1-MOD interval from 41 to 48 cM
and on 9q21 at 85 cM, near D9S1877, with a 1-MOD interval
from 80 to 96 cM (Table 2).
Singlepoint Nonparametric Results. The highest LOD score is for
marker D1S2885 at 46 cM on 1p36. The LOD score for the
narrow classification is 2.30, and is the genome-wide maximum
at 2.87 for the broad classification. D13S285, at 127 cM on 13q34,
has elevated LOD scores for both the narrow and broad classi-
fications; the LOD scores are 2.80 and 2.70, respectively. Also
noteworthy are three adjacent markers on 21q22 with LOD
scores greater than 2.0 using the narrow category: D21S1900
(LOD 2.30), D21S1919 (LOD 2.55), and D21S270 (LOD
2.31). For the broad category these three markers have LOD
scores ranging from 1.20 to 1.53 (Table S5).
Multipoint Nonparametric Results. The maximum multipoint LOD
score is located about 2 cM away from D13S285 at 125 cM on
Fig. 2. MOD score analysis. Green line shows MOD scores for the narrow
classification. Blue line shows MOD scores for the broad classification.
Table 2. Parametric multipoint MOD scores >1.5 for either SCZ status (multiplicative model)
Chr Position (1-MOD) Nearest Marker Aff MOD Freq of A RR Pen of AA Pen of Aa Pen of aa
3q21 137 cM (128–147) D3S1589 N
B
1.95
1.13
0.39
0.53
99.84
99.97
0.065
0.035
0.00065
0.00035
6.5 10
6
3.5 10
6
9p24 12 cM (3–19) D9S1686 N
B
1.40
1.68
0.01
0.01
9.86
11.03
0.77
.99
0.078
0.091
0.0079
0.0082
9q21 85 cM (80–96) D9S1877 N
B
1.05
2.20
.01
.01
10.44
10.72
.99
.99
0.096
0.093
0.0092
0.0087
10q22 92 cM (77–112) D10S537 N
B
1.58
0.71
0.01
0.17
11.32
4.04
.99
0.072
0.088
0.018
0.0078
0.0044
13q34 131 cM (115-qter) D13S293 N
B
3.13
3.76
0.03
0.03
14.76
13.77
.99
.99
0.068
0.073
0.0046
0.0053
15q21 56 cM (54–65) D15S1022 N
B
0.87
1.69
.01
.01
10.35
10.39
.99
.99
0.097
0.096
0.0094
0.0093
16p13 31 cM (27–45) D16S3047 N
B
1.31
1.64
0.17
0.10
5.60
5.64
0.10
0.14
0.018
0.026
0.0032
0.0046
21q22 46 cM (41–48) D21S1900 N
B
2.72
1.18
0.36
0.52
99.95
99.86
0.073
0.037
0.00073
0.00037
7.4 10
6
3.7 10
6
22q11 3 cM (2–9) D22S420 N
B
1.47
1.62
0.44
0.45
99.99
99.99
0.049
0.048
0.00050
0.00048
5.0 10
6
4.8 10
6
‘A’ is the disease allele, ‘a’ is the non-disease allele. 1-MOD, region in which the MOD score is within 1 MOD score of the highest MOD score; Aff, affection
classification; N, narrowly affected; B, broadly affected; Freq, allele frequency; RR, relative risk based on a 1% prevalence of SCZ; Pen, penetrance of SCZ for the
given genotype. Therefore, Pen of AA is the probability of having SCZ, given two copies of the disease allele.
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GENETICS
13q34. At this location, the LOD score for the narrow classifi-
cation is 2.65; for the broad classification the LOD score is
slightly higher at 2.66. The 1-LOD interval around this peak
extends from 119 cM to the q terminus. However, the empirical
significance based on 1,000 simulations is not significant (P
0.25). The nonparametric multipoint analysis also provides
evidence for linkage at 21q22. The LOD scores are 2.16 for the
narrow and 0.83 for the broad classification (Table S6). This is
the region where three markers showed evidence for linkage in
the singlepoint analysis.
Targeted Analyses in Families Nonlinked to Chromosome 13. Twenty-
five of our families in our sample exhibited a MOD score of 0.0
at the location of our strongest linkage peak, at 131 cM on
chromosome 13q34 near marker D13S293. To examine evidence
for additional susceptibility loci, we carried out a series of
analysis targeted at these 25 families (Table 3). Notably, the
maximum MOD score in these 25 families occurs on chromo-
some 1p36 at 35 cM (1 cM from D1S2644), near the peak that
was identified in our previous 9-cM scan. The MOD score for the
narrow classification at that position is 3.21 (empirical Pvalue
0.15); the MOD score for the broad classification is 1.74. The
nonparametric LOD scores in the region (both at 29 cM, near
D1S2697) were only 1.32 and 0.54 for the narrow and broad
classifications, respectively. The MOD score analysis results
suggest a dominant mode of inheritance, with the estimated
disease allele frequency of 0.003 and penetrances/genotype
relative risks of 1.0, 0.93, and 0.004.
Exploration of the Relation Between Linkage Signals and CNVs. We
first assessed the contribution of copy number mutations to the
two primary and consistent among studies linkage signals at
13q34 and 1p36. Whole genome CNV annotation, conducted
using dCHIP program, identified 788 putative CNVs in the 241
cases included in our scan. In the 13q34 region, there was only
one CNV identified, which did not overlap with any gene (Table
S7). This CNV was found in two out of 224 cases (0.89%) and
three out of 361 parents of unaffected controls (0.83%). In the
1p36 region, there were seven CNVs including six genomic gains
and one loss (Table S7). There was no statistical difference in the
frequencies of these CNVs between cases and controls. None of
the identified CNVs was present in families linked to the
respective linkage loci. Thus, at the level of resolution of our
scan, we could not identify any CNV that accounts for the
linkage signals identified.
Because only a subset of the families used in the linkage
studies (n32) were also included in the CNV scan and
additional, yet unidentified rare CNV carrying families are likely
to be part of the linkage cohort, it is not possible at this point to
conclusively evaluate the effect that removal of families, which
carry at least one rare CNV, has on linkage. Nevertheless,
preliminary analysis shows that, despite the decrease in the
sample size, after removing CNV carr ying families (n14, eight
of them with CNVs co-segregating with the clinical diagnosis),
the evidence for linkage in 13q34 remained unchanged (MOD
3.77). To estimate the expected change, under the assumption
that the families removed from the analysis are contributing to
the 13q34 signal, we randomly removed 14 families from among
the ones showing linkage to 13q34. In 100 trials, the MOD scores
varied from 0.53 to 2.76 for the narrow definition, with an
average of 1.50, and from 0.63 to 2.99 for the broad definition,
with an average of 1.74. This notable reduction in the MOD
score suggests that at least compared to the families linked to
13q34, the rare CNV containing families may represent a largely
distinct subset of genetic liability. Interestingly, our exploratory
analysis shows that in a number of loci, weak linkage signals are
amplified following removal of rare CNV families despite de-
crease in power (for example, at 3q21 MOD increases from 1.95
to 2.3 for the narrow classification, at 9p24 MOD increases from
1.68 to 2.13 for the broad classification, and at 16p13 MOD
increases from 1.64 to 2.3 for the broad classification). These may
reflect true linkage signals, which are masked by the heteroge-
neity introduced by the rare CNV families.
Discussion
Our results offer a comprehensive picture of the genetic archi-
tecture of familial SCZ in a relatively homogeneous population.
Our chromosomal variation analysis provides evidence for a role
of inherited structural lesions in familial SCZ and highlights
some important differences in the genetic architecture of familial
and nonfamilial forms of the disease. The majority of the
identified inherited CNVs, co-segregate with disease in a man-
ner consistent with necessary but not always sufficient genetic
‘‘hits,’’ which lead to a disease state only in combination with
additional, inherited structural or sequence variation or envi-
ronmental factors. Patient stratification suggests that inherited
CNVs do not correlate with histor y of developmental delays,
learning disabilities, or presence of mental retardation, but
appear to be enriched in more debilitating or treatment-resistant
form of illness. Although our study is statistically underpowered
to prove the involvement of any specific CNV, analysis of
co-segregation with the clinical diagnosis, as well as convergence
with previous studies, highlights a number of genes, gene fam-
ilies, and related pathways (such as the contactin family, the
CSMD1,ADARB1,RXFP2,LRFN5, and NRG3 genes) as par-
ticularly worthy of follow-up (see SI Text). In particular, we
provide additional evidence strongly supporting a previously
unknown role of a family of Rap1 guanine nucleotide exchange
factors (RAPGEF family) and Rap1-mediated processes (30) in
psychiatric disorders.
Our linkage analyses indicated one or more genes that in-
creases susceptibility to SCZ on chromosome 13q34 in our
broadly affected individuals. It is noteworthy that three of the
four other SCZ linkage scans that identified a LOD score greater
than 2.0 at 13q34 included schizoaffective disorder, bipolar type
in the affection category (7–10). That our broad classification
also includes this diagnosis supports the hypothesis that one or
more genes on 13q34 increases susceptibility to SCZ and bipolar
spectrum disorders. Based on the results of this study and
previous ones, there is considerable evidence for linkage to 13q.
When we analyzed families that do not show evidence for linkage
to 13q34, we identified another linkage peak on 1p36. This result
was much stronger when we used the narrow definition of SCZ
that did not include schizoaffective disorder, bipolar subtype.
Compared to the 13q34 locus, this could indicate different causal
alleles and mechanisms in subjects with and without symptoms
Table 3. Parametric linkage to chromosome 1p36 (dominant model)
Family group Number of families Position Nearest Marker Narrow MOD Broad MOD
All* 69 35cM D1S2826 1.28 0.20
Unlinked to 13q 25 35cM D1S2826 3.21 1.74
Linked to 13q 43 35cM D1S2826 0.12 0.00
*One family had a MOD score of 0.000 and was not used in the subset analysis.
16750
www.pnas.orgcgidoi10.1073pnas.0908584106 Xu et al.
of bipolar disorder. Notably, at the level of resolution of our scan,
our analysis indicates that CNVs within both linkage signal
regions are likely to be neutral and unlikely to account for the
linkage signals identified. Finally, although the sample sizes are
relatively small, our findings suggest that compared to the
families linked to 13q34, the rare CNV carrying families repre-
sent a largely distinct subset of genetic liability. CNV carrying
families may be a source of heterogeneity and future studies will
focus on identifying all rare CNVs as putative risk loci and as a
tool to stratify the samples to reduce genetic heterogeneity for
linkage analyses and improve detection of weak signals.
Irrespective of the pathogenic potential and the precise mode
of action of each risk locus, our results highlight the difference
in the genetic architecture of the familial and sporadic forms of
the disease and support the notion that multiple genetic variants,
including individually rare ones (often unique to a single patient)
that affect many different genes contribute to the genetic risk of
familial SCZ. This heterogeneity (present to some degree even
in founder populations) is consistent with the hypothesis that
there are many genes that contribute to SCZ and may account
for past and present difficulties in finding bone fide genetic
variants. Because there are significant clinical similarities of SCZ
cases diagnosed in Afrikaners and those diagnosed in more
heterogeneous populations (such as the U.S.) (20), our results
are likely to have general implications regarding the genetic
architecture of SCZ.
Samples and Methods
Cohorts. Both affected and control families were recruited and diagnosed as
part of our ongoing, large-scale genetic study of SCZ in the Afrikaner popu-
lation in South Africa, as previously described (12, 15, 20) (see also SI Methods).
For our linkage study, affected subjects were classified as either narrowly or
broadly affected. The narrow diagnosis includes subjects with SCZ or schizo-
affective disorder-depressive type, as previously described (12). The broad
diagnosis includes all individuals classified as affected under the narrow
definition as well as individuals with schizoaffective disorder-bipolar type.
Compared to our previous classification (12), it is more encompassing than LCI,
but not as broad as LCII. For our CNV studies, the criteria for inclusion in the
affected cohort are: (i) Afrikaner heritage; (ii) proband meeting full diagnostic
criteria for SCZ or schizoaffective disorder; and (iii) both biological parents
alive and willing to participate. It should be noted that presence of negative
or positive family history or availability of additional affected relatives is not
a screening criterion. Nevertheless, for all recruited subjects, detailed infor-
mation about family history of any psychiatric or medical illness was solicited
from at least three sources (proband and each participating parent) by two
independent raters [the nursing sister, who completes the Medical and Per-
sonal History form with each study participant and also draws a detailed
pedigree for at least three to four generations for each family, as well as the
psychiatrist who administers the Diagnostic Instrument for Genetic Studies
(DIGS) to the proband]. In addition, since we also trace the ancestry of all
recruited families, we routinely use several informants in each family to
inquire about all relatives’ names, date and place of birth, and death and
psychiatric status. Because of the close-knit family structure of the Afrikaner
families and the availability of detailed psychiatric records over several gen-
erations due to the large catchment area and long-term care provided by the
local recruiting hospital, we are typically able to obtain information about
psychiatric status for at least three to four generations removed from the
proband. For any relative identified as possibly having symptoms of SCZ or
schizoaffective disorder, or a history of treatment or hospitalization for a
psychiatric condition, every effort was made to include that relative in the
study, if alive and willing to participate, by obtaining a blood sample and
administering an in-person diagnostic interview. In a few instances where the
exact nature of a reported psychiatric diagnosis in a first- or second-degree
relative could not be substantiated (i.e., because the person was not alive or
access to records was not possible), the family history status was left unknown.
Such families are not considered in the present study or in the Xu et al. study
(15). Finally, in addition to being matched by ancestry, a subset of the control
families (three informants per family inquiring for up to three generations)
completed a detailed self-report questionnaire that inquired about several
psychiatric conditions, including psychosis, phobias, anxiety, and depression
(see also SI Methods).
Genotyping procedures, linkage analysis and CNV identification, and ver-
ification are outlined in detail in the SI Methods.
ACKNOWLEDGMENTS. We thank Alexandra Abrams-Downey and Yan Sun for
expert technical assistance. This work was supported in part by National
Institute of Mental Health Grant MH061399 (to M.K.) and MH077235 (to
J.A.G.) and the Lieber Center for Schizophrenia (SCZ) Research at Columbia
University Medical Center (CUMC).
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Xu et al. PNAS
September 29, 2009
vol. 106
no. 39
16751
GENETICS
    • "Linkage studies in families have highlighted chromosomic regions of interest related to the psychosis continuum. Existing studies suggest that both schizophrenia and schizotypal traits share some common chromosome regions (such as 4q, 5q; 6p, 6q, 8p, 9q, 10p, 10q, 11q, 15q), with an average significance generally lower in schizotypal traits than schizophrenia, regardless of ethnicity of the sample and schizotypy measure used (Levinson et al., 2000; Baron, 2001; DeLisi et al., 2002; Linney et al., 2003; Fanous et al., 2007; Xu et al., 2009; Lien et al., 2010). However, an Irish study reported higher significance schizotypal traits compared to patients with schizophrenia, highlighting chromosomal regions 5q, 9q, and 10p (Fanous et al., 2007). "
    [Show abstract] [Hide abstract] ABSTRACT: Schizotypal traits are of interest and importance in their own right and also have theoretical and clinical associations with schizophrenia. These traits comprise attenuated psychotic symptoms, social withdrawal, reduced cognitive capacity, and affective dysregulation. The link between schizotypal traits and psychotic disorders has long since been debated. The status of knowledge at this point is such schizotypal traits are a risk for psychotic disorders, but in and of themselves only confer liability, with other risk factors needing to be present before a transition to psychosis occurs. Investigation of schizotypal traits also has the possibility to inform clinical and research pursuits concerning those who do not make a transition to psychotic disorders. A growing body of literature has investigated the genetic underpinnings of schizotypal traits. Here, we review association, family studies and describe genetic disorders where the expression of schizotypal traits has been investigated. We conducted a thorough review of the existing literature, with multiple search engines, references, and linked articles being searched for relevance to the current review. All articles and book chapters in English were sourced and reviewed for inclusion. Family studies demonstrate that schizotypal traits are elevated with increasing genetic proximity to schizophrenia and some chromosomal regions have been associated with schizotypy. Genes associated with schizophrenia have provided the initial start point for the investigation of candidate genes for schizotypal traits; neurobiological pathways of significance have guided selection of genes of interest. Given the chromosomal regions associated with schizophrenia, some genetic disorders have also considered the expression of schizotypal traits. Genetic disorders considered all comprise a profile of cognitive deficits and over representation of psychotic disorders compared to the general population. We conclude that genetic variations associated with schizotypal traits require further investigation, perhaps with targeted phenotypes narrowed to assist in refining the clinical end point of significance.
    Full-text · Article · Nov 2016
    • "downregulated in brains and lymphoblastoid cell lines of patients with ASD [Cheng, Quinn, & Weiss, 2013; Melin et al., 2006; Weiss et al., 2009] . Conversely, deletions encompassing MACROD2, encoding a protein with hydrolase and deacetylase activity, were described in two families with ADHD and in another one with schizophrenia (SCZ) [Lionel et al., 2011; Xu et al., 2009]. A de novo microdeletion in this gene was also described in a patient with Kabuki syndrome that presents congenital intellectual disability. "
    [Show abstract] [Hide abstract] ABSTRACT: Common variants contribute significantly to the genetics of autism spectrum disorder (ASD), although the identification of individual risk polymorphisms remains still elusive due to their small effect sizes and limited sample sizes available for association studies. During the last decade several genome-wide association studies (GWAS) have enabled the detection of a few plausible risk variants. The three main studies are family-based and pointed at SEMA5A (rs10513025), MACROD2 (rs4141463) and MSNP1 (rs4307059). In our study we attempted to replicate these GWAS hits using a case-control association study in five European populations of ASD patients and gender-matched controls, all Caucasians. Results showed no association of individual variants with ASD in any of the population groups considered or in the combined European sample. We performed a meta-analysis study across five European populations for rs10513025 (1,904 ASD cases and 2,674 controls), seven European populations for rs4141463 (2,855 ASD cases and 36,177 controls) and five European populations for rs4307059 (2,347 ASD cases and 2,764 controls). The results showed an odds ratio (OR) of 1.05 (95% CI = 0.84-1.32) for rs10513025, 1.0002 (95% CI = 0.93-1.08) for rs4141463 and 1.01 (95% CI = 0.92-1.1) for rs4307059, with no significant P-values (rs10513025, P = 0.73; rs4141463, P = 0.95; rs4307059, P = 0.9). No association was found when we considered either only high functioning autism (HFA), genders separately or only multiplex families. Ongoing GWAS projects with larger ASD cohorts will contribute to clarify the role of common variation in the disorder and will likely identify risk variants of modest effect not detected previously. Autism Res 2016. © 2016 International Society for Autism Research, Wiley Periodicals, Inc.
    Full-text · Article · Jul 2016
    • "In addition, uncommon, non-SNP structural variants of moderate effect size [mutations/duplications/deletions, including copy number variations (CNVs)] account for a proportion of cases of schizophrenia, autism, epilepsy, and intellectual disability (O'Donovan et al., 2009 ), albeit rather non-specifically. Rarevariant/large effect size and CDCV models are not mutually exclusive; likely, both possess independent explanatory power and can be combined in a " mixed economy, " e.g., (Reich and Lander, 2001; Schork et al., 2009; Xu et al., 2009; Owen et al., 2010; Visscher et al., 2012). In future, it is likely that p-ICA will be applied to additional non brain-based common complex diseases. "
    [Show abstract] [Hide abstract] ABSTRACT: Complex inherited phenotypes, including those for many common medical and psychiatric diseases, are most likely underpinned by multiple genes contributing to interlocking molecular biological processes, along with environmental factors (Owen et al., 2010). Despite this, genotyping strategies for complex, inherited, disease-related phenotypes mostly employ univariate analyses, e.g., genome wide association. Such procedures most often identify isolated risk-related SNPs or loci, not the underlying biological pathways necessary to help guide the development of novel treatment approaches. This article focuses on the multivariate analysis strategy of parallel (i.e., simultaneous combination of SNP and neuroimage information) independent component analysis (p-ICA), which typically yields large clusters of functionally related SNPs statistically correlated with phenotype components, whose overall molecular biologic relevance is inferred subsequently using annotation software suites. Because this is a novel approach, whose details are relatively new to the field we summarize its underlying principles and address conceptual questions regarding interpretation of resulting data and provide practical illustrations of the method.
    Full-text · Article · Oct 2015
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