Metab Brain Dis DOI 10.1007/s11011-015-9693-x
RESEARCH ARTICLE
Polymorphisms within the neuronal cadherin (CDH2) gene are associated with obsessive-compulsive disorder (OCD) in a South African cohort N. W. McGregor 1,2,3 & C. Lochner 3 & D. J. Stein 4 & S. M. J. Hemmings 1
Received: 24 January 2015 / Accepted: 28 May 2015 # Springer Science+Business Media New York 2015
Abstract OCD is characterised by recurrent obsessions and compulsions that result in severe distress and increased risk for comorbidity. Recently published findings have indicated that the neuronal cadherin gene (CDH2) plays a role in the development of canine OCD, and led us to investigate the human ortholog, CDH2, in a human OCD cohort. Seven CDH2 polymorphisms were selected and genotyped in a South African Caucasian cohort of 234 OCD patients and 180 healthy controls using TaqMan assays. Polymorphisms were analysed in a single-locus and haplotypic context. Of the seven polymorphisms, two reached statistical significance for OCD under additive and codominant models of inheritance (rs1120154 and rs12605662). CDH2 SNP, rs1120154, C-allele carriers were found to be significantly associated with lower risk to develop OCD compared to TT-homozygotes (OR = 0.49; 95 % CI: 0.32–0.75; p < 0.001), and rs12605662 G-allele carriers were significantly associated with reduced risk OCD compared to TT-homozygotes (OR = 0.46; 95 % CI: 0.30–0.71; p < 0.001), Furthermore, a single haplotype was found to infer an increased risk for OCD diagnosis (*rs8087457-rs1148374: A-T). Polymorphisms
* N. W. McGregor [email protected] 1
Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
2
Department of Genetics, Faculty of Science, Stellenbosch University, Cape Town, South Africa
3
MRC Unit on Anxiety and Stress Disorders, Department of Psychiatry, Stellenbosch University, Cape Town, South Africa
4
Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
within the CDH2 gene are associated with susceptibility to OCD in a South African cohort. Keywords Obsessive-compulsive disorder . Caderhin-2 . YBOCs . Genetics
Introduction Obsessive-compulsive disorder (OCD) is a common and severely debilitating condition affecting between 1 and 3 % of the world’s population (Fontenelle and Hasler 2008). Twin studies have shown that the genetics of OCD has a heritability of between 45 and 65 % (Hettema et al. 2001; van Grootheest et al. 2005), however, the complexity underlying these genetics continue to hamper attempts at elucidating the genetic aetiology underlying OCD psychopathology (Hemmings et al. 2004; Lochner et al. 2005). Recent studies using a canine animal model have associated single nucleotide polymorphisms (SNPs) found within the neuronal cadherin gene (Cdh2) with canine compulsive disorder (CCD) in the Doberman (Dodman et al. 2010). CCD shares many similarities with OCD in humans in that both are characterised by repetitive behaviours causing functional impairment and distress, both have genetic heritability to a certain extent, and both have symptoms that can be alleviated by combinations of medication and therapy (Overall and Dunham 2002; Moon-Fanelli et al. 2011). CCD represents a naturally occurring model for human OCD that is genetically more complex than an induced animal model in which either single point mutations are induced or environmental stressors are applied during early development (Overall and Dunham 2002). In addition, dog breeds, such as the Doberman Pinscher, which present with particularly high CCD levels (Tang et al. 2014), possess limited genetic diversity,
Metab Brain Dis
facilitating the identification of candidate genes, functional variants and pathways that play a role in the development of OCD (Tang et al. 2014). In humans, the Cdh2 orthologue, CDH2, is part of the cadherin gene superfamily of cell-cell adhesion molecules which are involved in early brain morphogenesis, long-term potentiation, synaptic plasticity and synaptogenesis (Goodwin and Yap 2004; Shapiro et al. 2007; Tomaselli et al. 2008). CDH2 plays an important role in regulating glutamate receptor trafficking (Moya et al. 2013), with the role of glutamate in the psychopathology of OCD being well established (Arnold et al. 2004; Chakrabarty et al. 2005; Grant et al. 2007; Kariuki-Nyuthe et al. 2014). Other members of the cadherin gene family (CDH8-10) have previously been implicated in disorders characterised by repetitive and compulsive behaviours, such as autism spectrum disorders (Kroisel et al. 2004; Wang et al. 2009; Pagnamenta et al. 2011). Rare variants in CDH2 have recently been found to be associated with repetitive behaviours in OCD and Tourette’s disorder (Moya et al. 2013). We therefore set out to investigate whether variants within the Cdh2 human orthologue, CDH2, are associated with OCD in a South African cohort.
Methods Study participants Unrelated South African Caucasian individuals with a primary diagnosis of OCD (n = 234) were recruited through physician referral, media advertisements, the Mental Health Information Centre, and the OCD Association of South Africa. OCD was considered the primary diagnosis if the participant indicated that this was the condition that caused them the most functional impairment or distress at the time of the assessment. Healthy controls (n = 153) were recruited from both university and non-university settings. For the purposes of this study, South African Caucasians were classified as those participants who were white and had self-reported home languages of English and/or Afrikaans. All participants provided written and informed consent for the study. Ethical approval was obtained from the Health Research Ethics Committee (HREC) at Stellenbosch University. OCD participants were interviewed by a clinician with expertise in the field, and met the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) criteria (APA 2000) for OCD on the Structured Clinical Interview for Axis I Disorders (SCID-I) (First et al. 1998). Referring clinicians were contacted to establish, where possible, a longitudinal expert opinion on the diagnostic status of the patient. Patients were included irrespective of whether they were receiving treatment for OCD and irrespective of the presence of psychiatric
comorbidity (except for psychoses), provided OCD was the primary diagnoses. Age of onset of OCD was defined as the age at which the obsessive-compulsive symptomatology became functionally impairing. The Yale-Brown ObsessiveCompulsive Severity Scale (YBOCS) was used to assess of the severity of obsessive-compulsive symptoms (Goodman et al. 1989). Controls were considered a sample of convenience, and did not undergo comprehensive diagnostic interviews. They were recruited countrywide and can be taken as representative of the local general population. Blood extraction and SNP selection Peripheral blood was drawn from all participants, and genomic DNA was subsequently extracted from each blood sample using the standard phenol/chloroform method. SNPbrowser version 3.25 (Applied Biosystems, Foster City, Calif; www. appliedbiosystems.com) was used to select single-nucleotide polymorphisms (SNPs) that stretched across CDH2. Validated SNPs with a minor allele frequency (MAF) of higher than 0.2 in the Centre d’Etude du Polymorphisme Human population collection (CEU; Utah residents with ancestry from northern and western Europe) were used (www.hapmap.org). Genotyping was performed using predesigned TaqMan probed-based assays (Applied Biosystems, Germany), according to the manufacturer’s instructions. Genotyping Seven SNPs were selected for genotyping analysis using the TaqMan 5′ nuclease assay. All TaqMan genotyping assays used in this study are predesigned and commercially available (Applied Biosystems; www.allsnps.com). The polymerase chain reaction (PCR) for the TaqMan SNP genotyping assays consisted of 1 μL DNA, 2.5 μL (2×) TaqMan Genotyping Master Mix (Applied Biosystems), 0.25 μL (20×) TaqMan SNP Genotyping Assay Mix (Applied Biosystems), and 1. 25 μL dH2O. These 5 μL reactions were amplified in a GeneAmp PCR system 9700 (Applied Biosystems) under the following conditions: 50 °C for 2 min, 95 °C for 10 min, 40 cycles of 92 °C for 15 s, followed by 60 °C for 1.5 min. The fluorescent intensity of the final reaction product was measured using the 7900HT Fast Real-Time PCR System (Applied Biosystems) and analysed with Sequence Detection Systems version 2.3 (Applied Biosystems). The Sequence Detection Systems software performs automatic allele calling, based on fluorescence signals. All genotypes with a confidence level of less than 95 % are marked as undetermined, ensuring that ambiguous genotypes are not entered into the data analyses. Any genotypes that had a confidence level of less than 95 % were removed from the present dataset.
Metab Brain Dis
Statistics Numeric variables (age at interview and age at onset of OCD) were analysed using linear models and were summarised as median (interquartile ranges), since their distributions deviated from normality. Data on OCD symptom severity, as assessed using the YBOCS, was analysed as mean and standard errors (SE). Age at interview was compared between the diagnostic groups (OCD or control) using Kruskal-Wallis test, implemented using base R functions in R Studio (version 0.98.501). Pearson’s Chi square test was used to compare gender and home language (Afrikaans, English or both) between the diagnostic groups. No statistically significant differences between diagnostic groups were observed for age at interview or for gender; however, statistically significant differences with regard to home language were observed (p = 0.006). Therefore, all genetic analyses were adjusted for home language, which served as a proxy for possible population structure. Hardy Weinberg Equilibrium (HWE) and association of polymorphisms with OCD were assessed using the R Genetics package SNPassoc (González et al. 2013), implemented in RStudio statistical software (Version 0.98.501). Statistical association was assessed using the additive, dominant and recessive genetics models, and the most parsimonious model was selected using Akaike’s Information Criteria (AIC). Haplotype frequencies were only inferred in the blocks constructed using the solid spine of linkage disequilibrium (LD) method, implemented in Haploview software (Version 4.2) (Gabriel et al. 2002). Linkage disequilibrium values were represented as D’ values. Haplotype freq ue n ci es w er e i n f er r e d u s i ng th e ex p ec t a t i on maximization (EM) algorithm implemented in the R package, haplo.stats (Schaid et al. 2005). Haplotype analysis was also performed using this package, and the haplo.score function was used to derive score statistics for each haplotype that occurred at a frequency of >0.05. The haplotype score tests are derived from generalised linear models, and allow for global and haplotype-specific tests of association. The global (overall) test analyses all haplotypes above a userdefined threshold simultaneously, while the haplotypespecific test interrogates the association phenotype and haplotype, compared to the remaining haplotypes. Haplotype All haplotype analyses were adjusted for home language (population), age and sex. For all haplotype analyses, we assumed additive effects. Haplotype analyses were performed using Haploview 4.2 (Gabriel et al. 2002). Haplotype blocks were designated based on 95 % confidence bounds on D’ indicating
strong linkage disequilibrium (LD) (Gabriel et al. 2002). Markers with MAF < 0.05 were excluded.
Results Demographic characteristics Two hundred and thirty-four OCD patient (234 [49.1 %] male) and 180 control (71 [39.4 %] male) participants were investigated in the study. The median age was 32.5 years (interquartile range [IQR] = 22 years). No statistically significant differences were observed between OCD and control groups for gender (p = 0.423) or age at interview (p = 0.133). The frequency of home language (which can be used as a proxy for population group) was, however, found to be significantly different between OCD and control groups (p = 0.006). All subsequent genetic analyses were thus adjusted for home language. Clinical characteristics YBOCS scores were recorded in 222 (94.8 %) OCD patients, whilst information pertaining to age at onset of OCD was recorded in 224 (95.7 %). Patients presented with mean YBOCS scores of 19.93 (SD = 7.4) and median age at onset of OCD was 15 years (IQR = 14.25 years). Neither YBOCS nor age at onset of OCD was found to differ significantly by gender (p = 0.423 and p = 0.149, respectively). One hundred and fifty-four (65.8 %) of the OCD patients presented with lifetime comorbid major depression, which was subsequently adjusted for in case-only genetic association analyses. Genetics All SNPs were in HWE (p > 0.05). Seven CDH2 polymorphisms were investigated for possible association with OCD in the current sample (rs1041985, rs7240351, rs1220154, rs1662731, rs8087457, rs1148374, rs12605662). Single nucleotide polymorphism rs12605662 was found to be associated with OCD under the additive allelic model (p < 0.001). Here, the addition of each G-allele significantly reduced the probability that the individual would have OCD (OR = 0.46; 95 % CI: 0.30–0.71). In addition, the rs1220154 polymorphism was also found to be associated with OCD under the additive allelic model of inheritance. Here, C-allele carriers were found be at significantly lower risk of having OCD compared to those individuals who were TT-homozygotes (OR = 0.49, 95 % CI: 0.32–0.75; p < 0.001) (Table 1). When OCD symptom severity was investigated, we found a negative association between rs8087457 and the YBOCS score. Individuals possessing the rs8087457 AT genotype
Metab Brain Dis Table 1
YBOCS, age and onset and case–control association analyses for CDH2 polymorphisms
CDH2 SNP
rs1041985 rs7240351 rs1220154 rs1662731 rs8087457 rs1148374 rs12605662
Alleles
G/A G/A T/C C/T A/T T/A A/G
Major allele freq.
HWE pvalue
Missing (%)
68.8 54.0 60.6 72.4 55.1 55.0 57.9
0.47 0.15 0.83 0.89 0.38 1.00 0.06
10.6 18.8 13.0 15.2 17.9 22.0 14.7
p-value: case–control
p-value: YBOCS
p-value: onset
Additive
Codominant
Additive
Codominant
Additive
Codominant
0.653 0.258
RESEARCH ARTICLE
Polymorphisms within the neuronal cadherin (CDH2) gene are associated with obsessive-compulsive disorder (OCD) in a South African cohort N. W. McGregor 1,2,3 & C. Lochner 3 & D. J. Stein 4 & S. M. J. Hemmings 1
Received: 24 January 2015 / Accepted: 28 May 2015 # Springer Science+Business Media New York 2015
Abstract OCD is characterised by recurrent obsessions and compulsions that result in severe distress and increased risk for comorbidity. Recently published findings have indicated that the neuronal cadherin gene (CDH2) plays a role in the development of canine OCD, and led us to investigate the human ortholog, CDH2, in a human OCD cohort. Seven CDH2 polymorphisms were selected and genotyped in a South African Caucasian cohort of 234 OCD patients and 180 healthy controls using TaqMan assays. Polymorphisms were analysed in a single-locus and haplotypic context. Of the seven polymorphisms, two reached statistical significance for OCD under additive and codominant models of inheritance (rs1120154 and rs12605662). CDH2 SNP, rs1120154, C-allele carriers were found to be significantly associated with lower risk to develop OCD compared to TT-homozygotes (OR = 0.49; 95 % CI: 0.32–0.75; p < 0.001), and rs12605662 G-allele carriers were significantly associated with reduced risk OCD compared to TT-homozygotes (OR = 0.46; 95 % CI: 0.30–0.71; p < 0.001), Furthermore, a single haplotype was found to infer an increased risk for OCD diagnosis (*rs8087457-rs1148374: A-T). Polymorphisms
* N. W. McGregor [email protected] 1
Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
2
Department of Genetics, Faculty of Science, Stellenbosch University, Cape Town, South Africa
3
MRC Unit on Anxiety and Stress Disorders, Department of Psychiatry, Stellenbosch University, Cape Town, South Africa
4
Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
within the CDH2 gene are associated with susceptibility to OCD in a South African cohort. Keywords Obsessive-compulsive disorder . Caderhin-2 . YBOCs . Genetics
Introduction Obsessive-compulsive disorder (OCD) is a common and severely debilitating condition affecting between 1 and 3 % of the world’s population (Fontenelle and Hasler 2008). Twin studies have shown that the genetics of OCD has a heritability of between 45 and 65 % (Hettema et al. 2001; van Grootheest et al. 2005), however, the complexity underlying these genetics continue to hamper attempts at elucidating the genetic aetiology underlying OCD psychopathology (Hemmings et al. 2004; Lochner et al. 2005). Recent studies using a canine animal model have associated single nucleotide polymorphisms (SNPs) found within the neuronal cadherin gene (Cdh2) with canine compulsive disorder (CCD) in the Doberman (Dodman et al. 2010). CCD shares many similarities with OCD in humans in that both are characterised by repetitive behaviours causing functional impairment and distress, both have genetic heritability to a certain extent, and both have symptoms that can be alleviated by combinations of medication and therapy (Overall and Dunham 2002; Moon-Fanelli et al. 2011). CCD represents a naturally occurring model for human OCD that is genetically more complex than an induced animal model in which either single point mutations are induced or environmental stressors are applied during early development (Overall and Dunham 2002). In addition, dog breeds, such as the Doberman Pinscher, which present with particularly high CCD levels (Tang et al. 2014), possess limited genetic diversity,
Metab Brain Dis
facilitating the identification of candidate genes, functional variants and pathways that play a role in the development of OCD (Tang et al. 2014). In humans, the Cdh2 orthologue, CDH2, is part of the cadherin gene superfamily of cell-cell adhesion molecules which are involved in early brain morphogenesis, long-term potentiation, synaptic plasticity and synaptogenesis (Goodwin and Yap 2004; Shapiro et al. 2007; Tomaselli et al. 2008). CDH2 plays an important role in regulating glutamate receptor trafficking (Moya et al. 2013), with the role of glutamate in the psychopathology of OCD being well established (Arnold et al. 2004; Chakrabarty et al. 2005; Grant et al. 2007; Kariuki-Nyuthe et al. 2014). Other members of the cadherin gene family (CDH8-10) have previously been implicated in disorders characterised by repetitive and compulsive behaviours, such as autism spectrum disorders (Kroisel et al. 2004; Wang et al. 2009; Pagnamenta et al. 2011). Rare variants in CDH2 have recently been found to be associated with repetitive behaviours in OCD and Tourette’s disorder (Moya et al. 2013). We therefore set out to investigate whether variants within the Cdh2 human orthologue, CDH2, are associated with OCD in a South African cohort.
Methods Study participants Unrelated South African Caucasian individuals with a primary diagnosis of OCD (n = 234) were recruited through physician referral, media advertisements, the Mental Health Information Centre, and the OCD Association of South Africa. OCD was considered the primary diagnosis if the participant indicated that this was the condition that caused them the most functional impairment or distress at the time of the assessment. Healthy controls (n = 153) were recruited from both university and non-university settings. For the purposes of this study, South African Caucasians were classified as those participants who were white and had self-reported home languages of English and/or Afrikaans. All participants provided written and informed consent for the study. Ethical approval was obtained from the Health Research Ethics Committee (HREC) at Stellenbosch University. OCD participants were interviewed by a clinician with expertise in the field, and met the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) criteria (APA 2000) for OCD on the Structured Clinical Interview for Axis I Disorders (SCID-I) (First et al. 1998). Referring clinicians were contacted to establish, where possible, a longitudinal expert opinion on the diagnostic status of the patient. Patients were included irrespective of whether they were receiving treatment for OCD and irrespective of the presence of psychiatric
comorbidity (except for psychoses), provided OCD was the primary diagnoses. Age of onset of OCD was defined as the age at which the obsessive-compulsive symptomatology became functionally impairing. The Yale-Brown ObsessiveCompulsive Severity Scale (YBOCS) was used to assess of the severity of obsessive-compulsive symptoms (Goodman et al. 1989). Controls were considered a sample of convenience, and did not undergo comprehensive diagnostic interviews. They were recruited countrywide and can be taken as representative of the local general population. Blood extraction and SNP selection Peripheral blood was drawn from all participants, and genomic DNA was subsequently extracted from each blood sample using the standard phenol/chloroform method. SNPbrowser version 3.25 (Applied Biosystems, Foster City, Calif; www. appliedbiosystems.com) was used to select single-nucleotide polymorphisms (SNPs) that stretched across CDH2. Validated SNPs with a minor allele frequency (MAF) of higher than 0.2 in the Centre d’Etude du Polymorphisme Human population collection (CEU; Utah residents with ancestry from northern and western Europe) were used (www.hapmap.org). Genotyping was performed using predesigned TaqMan probed-based assays (Applied Biosystems, Germany), according to the manufacturer’s instructions. Genotyping Seven SNPs were selected for genotyping analysis using the TaqMan 5′ nuclease assay. All TaqMan genotyping assays used in this study are predesigned and commercially available (Applied Biosystems; www.allsnps.com). The polymerase chain reaction (PCR) for the TaqMan SNP genotyping assays consisted of 1 μL DNA, 2.5 μL (2×) TaqMan Genotyping Master Mix (Applied Biosystems), 0.25 μL (20×) TaqMan SNP Genotyping Assay Mix (Applied Biosystems), and 1. 25 μL dH2O. These 5 μL reactions were amplified in a GeneAmp PCR system 9700 (Applied Biosystems) under the following conditions: 50 °C for 2 min, 95 °C for 10 min, 40 cycles of 92 °C for 15 s, followed by 60 °C for 1.5 min. The fluorescent intensity of the final reaction product was measured using the 7900HT Fast Real-Time PCR System (Applied Biosystems) and analysed with Sequence Detection Systems version 2.3 (Applied Biosystems). The Sequence Detection Systems software performs automatic allele calling, based on fluorescence signals. All genotypes with a confidence level of less than 95 % are marked as undetermined, ensuring that ambiguous genotypes are not entered into the data analyses. Any genotypes that had a confidence level of less than 95 % were removed from the present dataset.
Metab Brain Dis
Statistics Numeric variables (age at interview and age at onset of OCD) were analysed using linear models and were summarised as median (interquartile ranges), since their distributions deviated from normality. Data on OCD symptom severity, as assessed using the YBOCS, was analysed as mean and standard errors (SE). Age at interview was compared between the diagnostic groups (OCD or control) using Kruskal-Wallis test, implemented using base R functions in R Studio (version 0.98.501). Pearson’s Chi square test was used to compare gender and home language (Afrikaans, English or both) between the diagnostic groups. No statistically significant differences between diagnostic groups were observed for age at interview or for gender; however, statistically significant differences with regard to home language were observed (p = 0.006). Therefore, all genetic analyses were adjusted for home language, which served as a proxy for possible population structure. Hardy Weinberg Equilibrium (HWE) and association of polymorphisms with OCD were assessed using the R Genetics package SNPassoc (González et al. 2013), implemented in RStudio statistical software (Version 0.98.501). Statistical association was assessed using the additive, dominant and recessive genetics models, and the most parsimonious model was selected using Akaike’s Information Criteria (AIC). Haplotype frequencies were only inferred in the blocks constructed using the solid spine of linkage disequilibrium (LD) method, implemented in Haploview software (Version 4.2) (Gabriel et al. 2002). Linkage disequilibrium values were represented as D’ values. Haplotype freq ue n ci es w er e i n f er r e d u s i ng th e ex p ec t a t i on maximization (EM) algorithm implemented in the R package, haplo.stats (Schaid et al. 2005). Haplotype analysis was also performed using this package, and the haplo.score function was used to derive score statistics for each haplotype that occurred at a frequency of >0.05. The haplotype score tests are derived from generalised linear models, and allow for global and haplotype-specific tests of association. The global (overall) test analyses all haplotypes above a userdefined threshold simultaneously, while the haplotypespecific test interrogates the association phenotype and haplotype, compared to the remaining haplotypes. Haplotype All haplotype analyses were adjusted for home language (population), age and sex. For all haplotype analyses, we assumed additive effects. Haplotype analyses were performed using Haploview 4.2 (Gabriel et al. 2002). Haplotype blocks were designated based on 95 % confidence bounds on D’ indicating
strong linkage disequilibrium (LD) (Gabriel et al. 2002). Markers with MAF < 0.05 were excluded.
Results Demographic characteristics Two hundred and thirty-four OCD patient (234 [49.1 %] male) and 180 control (71 [39.4 %] male) participants were investigated in the study. The median age was 32.5 years (interquartile range [IQR] = 22 years). No statistically significant differences were observed between OCD and control groups for gender (p = 0.423) or age at interview (p = 0.133). The frequency of home language (which can be used as a proxy for population group) was, however, found to be significantly different between OCD and control groups (p = 0.006). All subsequent genetic analyses were thus adjusted for home language. Clinical characteristics YBOCS scores were recorded in 222 (94.8 %) OCD patients, whilst information pertaining to age at onset of OCD was recorded in 224 (95.7 %). Patients presented with mean YBOCS scores of 19.93 (SD = 7.4) and median age at onset of OCD was 15 years (IQR = 14.25 years). Neither YBOCS nor age at onset of OCD was found to differ significantly by gender (p = 0.423 and p = 0.149, respectively). One hundred and fifty-four (65.8 %) of the OCD patients presented with lifetime comorbid major depression, which was subsequently adjusted for in case-only genetic association analyses. Genetics All SNPs were in HWE (p > 0.05). Seven CDH2 polymorphisms were investigated for possible association with OCD in the current sample (rs1041985, rs7240351, rs1220154, rs1662731, rs8087457, rs1148374, rs12605662). Single nucleotide polymorphism rs12605662 was found to be associated with OCD under the additive allelic model (p < 0.001). Here, the addition of each G-allele significantly reduced the probability that the individual would have OCD (OR = 0.46; 95 % CI: 0.30–0.71). In addition, the rs1220154 polymorphism was also found to be associated with OCD under the additive allelic model of inheritance. Here, C-allele carriers were found be at significantly lower risk of having OCD compared to those individuals who were TT-homozygotes (OR = 0.49, 95 % CI: 0.32–0.75; p < 0.001) (Table 1). When OCD symptom severity was investigated, we found a negative association between rs8087457 and the YBOCS score. Individuals possessing the rs8087457 AT genotype
Metab Brain Dis Table 1
YBOCS, age and onset and case–control association analyses for CDH2 polymorphisms
CDH2 SNP
rs1041985 rs7240351 rs1220154 rs1662731 rs8087457 rs1148374 rs12605662
Alleles
G/A G/A T/C C/T A/T T/A A/G
Major allele freq.
HWE pvalue
Missing (%)
68.8 54.0 60.6 72.4 55.1 55.0 57.9
0.47 0.15 0.83 0.89 0.38 1.00 0.06
10.6 18.8 13.0 15.2 17.9 22.0 14.7
p-value: case–control
p-value: YBOCS
p-value: onset
Additive
Codominant
Additive
Codominant
Additive
Codominant
0.653 0.258
