Accumulated common variants in the broader fragile X gene family modulate autistic phenotypes
2015; Springer Nature; Volume: 7; Issue: 12 Linguagem: Inglês
10.15252/emmm.201505696
ISSN1757-4684
AutoresBeata Stepniak, Anne Kästner, Giulia Poggi, Marina Mitjans, Martin Begemann, Annette M. Hartmann, Sandra Van der Auwera, Farahnaz Sananbenesi, Dilja Krueger‐Burg, Gabriela Matuszko, Cornelia Brosi, Georg Homuth, Henry Völzke, Fritz Benseler, Claudia Bagni, Utz Fischer, Alexander Dityatev, Hans J. Grabe, Dan Rujescu, André Fischer, Hannelore Ehrenreich,
Tópico(s)Congenital heart defects research
ResumoResearch Article26 November 2015Open Access Accumulated common variants in the broader fragile X gene family modulate autistic phenotypes Beata Stepniak Beata Stepniak Clinical Neuroscience, Max Planck Institute of Experimental Medicine, Göttingen, Germany Search for more papers by this author Anne Kästner Anne Kästner Clinical Neuroscience, Max Planck Institute of Experimental Medicine, Göttingen, Germany DFG Research Center for Nanoscale Microscopy and Molecular Physiology of the Brain (CNMPB), Göttingen, Germany Search for more papers by this author Giulia Poggi Giulia Poggi Clinical Neuroscience, Max Planck Institute of Experimental Medicine, Göttingen, Germany Search for more papers by this author Marina Mitjans Marina Mitjans Clinical Neuroscience, Max Planck Institute of Experimental Medicine, Göttingen, Germany Search for more papers by this author Martin Begemann Martin Begemann Clinical Neuroscience, Max Planck Institute of Experimental Medicine, Göttingen, Germany Search for more papers by this author Annette Hartmann Annette Hartmann Department of Psychiatry and Psychotherapy, University of Halle, Halle, Germany Search for more papers by this author Sandra Van der Auwera Sandra Van der Auwera Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany Search for more papers by this author Farahnaz Sananbenesi Farahnaz Sananbenesi Epigenetics in Neurodegenerative Diseases, German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany Search for more papers by this author Dilja Krueger-Burg Dilja Krueger-Burg Molecular Neurobiology, Max Planck Institute of Experimental Medicine, Göttingen, Germany Search for more papers by this author Gabriela Matuszko Gabriela Matuszko Molecular Neuroplasticity, German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany Search for more papers by this author Cornelia Brosi Cornelia Brosi Department of Biochemistry, University of Würzburg, Würzburg, Germany Search for more papers by this author Georg Homuth Georg Homuth Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany Search for more papers by this author Henry Völzke Henry Völzke Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany Search for more papers by this author Fritz Benseler Fritz Benseler Molecular Neurobiology, Max Planck Institute of Experimental Medicine, Göttingen, Germany Search for more papers by this author Claudia Bagni Claudia Bagni KU Leuven, Center for Human Genetics and Leuven Institute for Neurodegenerative Diseases, Leuven, Belgium Department of Biomedicine and Prevention, University of Rome "Tor Vergata", Rome, Italy Search for more papers by this author Utz Fischer Utz Fischer Department of Biochemistry, University of Würzburg, Würzburg, Germany Search for more papers by this author Alexander Dityatev Alexander Dityatev Molecular Neuroplasticity, German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany Search for more papers by this author Hans-Jörgen Grabe Hans-Jörgen Grabe Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany Search for more papers by this author Dan Rujescu Dan Rujescu Department of Psychiatry and Psychotherapy, University of Halle, Halle, Germany Search for more papers by this author Andre Fischer Andre Fischer Epigenetics in Neurodegenerative Diseases, German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany Department of Psychiatry & Psychotherapy, University of Göttingen, Göttingen, Germany Search for more papers by this author Hannelore Ehrenreich Corresponding Author Hannelore Ehrenreich Clinical Neuroscience, Max Planck Institute of Experimental Medicine, Göttingen, Germany DFG Research Center for Nanoscale Microscopy and Molecular Physiology of the Brain (CNMPB), Göttingen, Germany Search for more papers by this author Beata Stepniak Beata Stepniak Clinical Neuroscience, Max Planck Institute of Experimental Medicine, Göttingen, Germany Search for more papers by this author Anne Kästner Anne Kästner Clinical Neuroscience, Max Planck Institute of Experimental Medicine, Göttingen, Germany DFG Research Center for Nanoscale Microscopy and Molecular Physiology of the Brain (CNMPB), Göttingen, Germany Search for more papers by this author Giulia Poggi Giulia Poggi Clinical Neuroscience, Max Planck Institute of Experimental Medicine, Göttingen, Germany Search for more papers by this author Marina Mitjans Marina Mitjans Clinical Neuroscience, Max Planck Institute of Experimental Medicine, Göttingen, Germany Search for more papers by this author Martin Begemann Martin Begemann Clinical Neuroscience, Max Planck Institute of Experimental Medicine, Göttingen, Germany Search for more papers by this author Annette Hartmann Annette Hartmann Department of Psychiatry and Psychotherapy, University of Halle, Halle, Germany Search for more papers by this author Sandra Van der Auwera Sandra Van der Auwera Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany Search for more papers by this author Farahnaz Sananbenesi Farahnaz Sananbenesi Epigenetics in Neurodegenerative Diseases, German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany Search for more papers by this author Dilja Krueger-Burg Dilja Krueger-Burg Molecular Neurobiology, Max Planck Institute of Experimental Medicine, Göttingen, Germany Search for more papers by this author Gabriela Matuszko Gabriela Matuszko Molecular Neuroplasticity, German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany Search for more papers by this author Cornelia Brosi Cornelia Brosi Department of Biochemistry, University of Würzburg, Würzburg, Germany Search for more papers by this author Georg Homuth Georg Homuth Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany Search for more papers by this author Henry Völzke Henry Völzke Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany Search for more papers by this author Fritz Benseler Fritz Benseler Molecular Neurobiology, Max Planck Institute of Experimental Medicine, Göttingen, Germany Search for more papers by this author Claudia Bagni Claudia Bagni KU Leuven, Center for Human Genetics and Leuven Institute for Neurodegenerative Diseases, Leuven, Belgium Department of Biomedicine and Prevention, University of Rome "Tor Vergata", Rome, Italy Search for more papers by this author Utz Fischer Utz Fischer Department of Biochemistry, University of Würzburg, Würzburg, Germany Search for more papers by this author Alexander Dityatev Alexander Dityatev Molecular Neuroplasticity, German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany Search for more papers by this author Hans-Jörgen Grabe Hans-Jörgen Grabe Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany Search for more papers by this author Dan Rujescu Dan Rujescu Department of Psychiatry and Psychotherapy, University of Halle, Halle, Germany Search for more papers by this author Andre Fischer Andre Fischer Epigenetics in Neurodegenerative Diseases, German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany Department of Psychiatry & Psychotherapy, University of Göttingen, Göttingen, Germany Search for more papers by this author Hannelore Ehrenreich Corresponding Author Hannelore Ehrenreich Clinical Neuroscience, Max Planck Institute of Experimental Medicine, Göttingen, Germany DFG Research Center for Nanoscale Microscopy and Molecular Physiology of the Brain (CNMPB), Göttingen, Germany Search for more papers by this author Author Information Beata Stepniak1,‡, Anne Kästner1,2,‡, Giulia Poggi1,‡, Marina Mitjans1, Martin Begemann1, Annette Hartmann3, Sandra Van der Auwera4, Farahnaz Sananbenesi5, Dilja Krueger-Burg6, Gabriela Matuszko7, Cornelia Brosi8, Georg Homuth9, Henry Völzke10, Fritz Benseler6, Claudia Bagni11,12, Utz Fischer8, Alexander Dityatev7, Hans-Jörgen Grabe4, Dan Rujescu3, Andre Fischer5,13 and Hannelore Ehrenreich 1,2 1Clinical Neuroscience, Max Planck Institute of Experimental Medicine, Göttingen, Germany 2DFG Research Center for Nanoscale Microscopy and Molecular Physiology of the Brain (CNMPB), Göttingen, Germany 3Department of Psychiatry and Psychotherapy, University of Halle, Halle, Germany 4Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany 5Epigenetics in Neurodegenerative Diseases, German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany 6Molecular Neurobiology, Max Planck Institute of Experimental Medicine, Göttingen, Germany 7Molecular Neuroplasticity, German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany 8Department of Biochemistry, University of Würzburg, Würzburg, Germany 9Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany 10Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany 11KU Leuven, Center for Human Genetics and Leuven Institute for Neurodegenerative Diseases, Leuven, Belgium 12Department of Biomedicine and Prevention, University of Rome "Tor Vergata", Rome, Italy 13Department of Psychiatry & Psychotherapy, University of Göttingen, Göttingen, Germany ‡These authors contributed equally to this work *Corresponding author. Tel: +49 551 3899 628; Fax: +49 551 3899 670; E-mail: [email protected] EMBO Mol Med (2015)7:1565-1579https://doi.org/10.15252/emmm.201505696 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Fragile X syndrome (FXS) is mostly caused by a CGG triplet expansion in the fragile X mental retardation 1 gene (FMR1). Up to 60% of affected males fulfill criteria for autism spectrum disorder (ASD), making FXS the most frequent monogenetic cause of syndromic ASD. It is unknown, however, whether normal variants (independent of mutations) in the fragile X gene family (FMR1, FXR1, FXR2) and in FMR2 modulate autistic features. Here, we report an accumulation model of 8 SNPs in these genes, associated with autistic traits in a discovery sample of male patients with schizophrenia (N = 692) and three independent replicate samples: patients with schizophrenia (N = 626), patients with other psychiatric diagnoses (N = 111) and a general population sample (N = 2005). For first mechanistic insight, we contrasted microRNA expression in peripheral blood mononuclear cells of selected extreme group subjects with high- versus low-risk constellation regarding the accumulation model. Thereby, the brain-expressed miR-181 species emerged as potential "umbrella regulator", with several seed matches across the fragile X gene family and FMR2. To conclude, normal variation in these genes contributes to the continuum of autistic phenotypes. Synopsis Based on a novel approach, phenotype-based genetic association study, first evidence is provided that a particular constellation of completely normal genotypes in the "broader fragile X gene family" contributes to autistic phenotypes. An accumulation model of 8 SNPs from the broader fragile X gene family (FMR1, FXR1, FXR2, and FMR2) is associated with autistic traits in a discovery sample of male schizophrenia patients as well as three independent replication samples of neuropsychiatric patients or general population. The underlying novel approach, named phenotype-based genetic association study (PGAS), may serve as universal guide in the exploration of genotype contributions to quantifiable phenotypes. Comparing peripheral blood mononuclear cells of extreme group subjects with high versus low genetic risk by small RNA sequencing revealed quantitative differences in the brain-expressed miR-181 family, which has several seed matches across the broader fragile X gene family. Future studies based on extensive in vivo work in animal models are needed to delineate how and when the miR-181 family may act as an overarching modulator of the fragile X genes. Introduction Fragile X syndrome (FXS) is associated with symptoms ranging from learning, motor and emotional deficiencies to mental retardation (IQ < 70) and autism (Garber et al, 2008). Up to 60% of males with FXS fulfill criteria for autism spectrum disorder (ASD) (Hagerman et al, 1986; Bailey et al, 1998; Clifford et al, 2007; Harris et al, 2008), making FXS the most common monogenetic cause of syndromic ASD (Hagerman et al, 2011). Almost all individuals with FXS show at least some autistic characteristics like social withdrawal (Hatton et al, 2006; Dahlhaus & El-Husseini, 2010; Heitzer et al, 2013). Since FXS is an X-linked disorder, males are generally more severely affected, with a suggested prevalence in Caucasians ranging from 1/3,717 to 1/8,918 (Crawford et al, 2001, 2002; Coffee et al, 2009). FXS is in most cases caused by a CGG triplet expansion in the 5′-untranslated (UTR) region of the fragile X mental retardation 1 gene (FMR1). More than 200 repeat copies are considered a full mutation, triggering hypermethylation of the CpG island in the promoter region. This hypermethylation leads to transcriptional silencing of FMR1 and loss of the associated protein, fragile X mental retardation protein (FMRP) (Oberle et al, 1991; Pieretti et al, 1991; Verkerk et al, 1991). FMRP is an RNA-binding protein, abundantly expressed in the mammalian brain, where it binds 4% of the whole transcriptome, including its own message (Ashley et al, 1993). Since FMRP interacts with many other proteins, its absence has manifold consequences—in sum affecting neural development, synapse formation, and plasticity (Bassell & Warren, 2008; Darnell et al, 2011; Pasciuto & Bagni, 2014a). A premutation syndrome (55–200 repeats) has also been reported with elevated FMR1 mRNA and reduced FMRP levels, where RNA toxicity is a possible underlying molecular mechanism (Garcia-Arocena & Hagerman, 2010; Bagni et al, 2012). Premutation carriers display only subtle symptoms which are, however, still reminiscent of FXS, including deficits in social cognition, executive functioning, working memory, or selective attention (Moore et al, 2004; Cornish et al, 2005, 2008; Jacquemont et al, 2007; Kogan et al, 2008). Many of the FMRP mRNA targets, for example CAMK2A, PSD-95, GABRB1, NLGN2, have been linked to schizophrenia or ASD (Pasciuto & Bagni, 2014b). The most recent genomewide association study (GWAS) for schizophrenia described an enrichment of FMRP targets among the genomewide significant hits (Schizophrenia Working Group of the PGC, 2014), and in the largest whole exome sequencing study for schizophrenia, enhanced de novo mutations in mRNA targets of FMRP were found (Fromer et al, 2014). Two autosomal homologues of FMR1 exist—fragile X mental retardation autosomal homolog 1 (FXR1) and 2 (FXR2)—together forming the fragile X family of genes (Zhang et al, 1995). Both of these homologues encode also for RNA-binding proteins, FXR1P and FXR2P, respectively, with functions similar and complementary to FMRP (Penagarikano et al, 2007; Ascano et al, 2012). For instance, FMRP and FXR2P co-regulate crucial synaptic proteins like PSD95 (Fernandez et al, 2015). Interestingly, genomewide significant hits for schizophrenia also encompass the FXR1 locus (Schizophrenia Working Group of the PGC, 2014). Besides FXS, there is a phenotypically related unstable triplet expansion disorder, associated with mild mental retardation, the so-called fragile XE syndrome (Gecz, 2000). The mutation—similar to FXS—is due to an expansion of a CCG repeat beyond 200 in the 5′UTR of the AF4/FMR2 family member 2 (AFF2, also called FMR2), which leads to hypermethylation of the CpG island upstream of FMR2 and transcriptional gene silencing (Knight et al, 1993; Gecz et al, 1996; Gu et al, 1996). FMR2 is a nuclear protein expressed in fetal and adult brain and belongs to a gene family of transcription activators (Gecz et al, 1997; Hillman & Gecz, 2001). Importantly, an increased number of missense mutations in FMR2 was found in male patients with ASD compared to controls (Mondal et al, 2012). In summary, there seems to be a considerable connection of both schizophrenia and ASD with the "broader fragile X family" of genes, in which we have included FMR2 based on the striking functional/phenotypical similarities and interactions. Along with the genetic relationship between these mental disorders, clinical overlaps have also been described, such as the shared impairment of specific cognitive domains like theory of mind (King & Lord, 2011; Owen et al, 2011). Surprisingly, all that is known about genotype–phenotype associations in this broader fragile X gene family is derived from mutations, but the contribution of common, frequent variations in these genes to the normal continuum of autism-related phenotypes, for example social interaction, communication, or stereotypies, has never been investigated. In the present study, we asked for the first time whether accumulated common genetic variants in genes of the broader fragile X gene family (FMR1, FXR1, FXR2, FMR2) modulate autistic features in males, independently of the described mutations, that is, the polymorphic repeats in FMR1 and FMR2. For quantification of autistic phenotypes, we used the PAUSS, an autism severity score composed of specific items of the Positive and Negative Syndrome Scale (PANSS autism severity score) (Kästner et al, 2015). We report here an accumulation model of 8 single nucleotide polymorphisms (SNPs) that yields significant association with autistic traits in a schizophrenic discovery and two independent replicate samples of mentally ill subjects, as well as one replicate sample from the general population. In a first and still preliminary approach toward mechanistic insight, we employed small RNA sequencing and found lower expression of miR-181 species in peripheral blood mononuclear cells (PBMC) of subjects with high- versus low-risk constellation in the 8-SNP model. The fact that this microRNA family has several seed matches across the broader fragile X gene family may suggest an overarching regulatory mechanism. Results Length distribution of FMR1 and FMR2 repeat polymorphisms in the male schizophrenic discovery sample is indistinguishable from healthy individuals As prerequisite for exploring the contribution of normal variation in genes of the broader fragile X gene family (FMR1, FXR1, FXR2, FMR2) to the overall continuum of autism-related phenotypes, we had to determine the polymorphic repeat lengths in FMR1 and FMR2 in the schizophrenic discovery and the healthy control sample to exclude mutations. As illustrated in Fig 1, repeats were similarly distributed in the Göttingen Research Association for Schizophrenia (GRAS) patients and healthy controls. All had < 50 CGG and < 40 CCG repeats in FMR1 and FMR2, respectively, that is, far away even from premutation carrier status (Tassone et al, 2014). We then checked whether the (normal) repeat length would still have any relevance for schizophrenia symptom severity in the discovery sample. As shown in Table 1, no associations were found with age at disease onset, positive, cognitive, neurological symptoms or PAUSS (Kästner et al, 2015). Even comparing the top and bottom 10% of GRAS individuals with smallest or largest repeat lengths did not result in any significant differences (Table 1). Figure 1. Positions of SNPs in FMR1, FXR1, FXR2, and FMR2, forming the 8-SNP model as well as FMR1 and FMR2 repeat polymorphism length distribution in the discovery sample Schematic overview of FMR1, FXR1, FXR2, FMR2, and position of the 8 selected single nucleotide polymorphisms (SNPs). Line represents introns, gray box at the beginning and end of each gene stands for UTR region, and red boxes represent exons. Gene structure plots generated using FancyGene (Rambaldi & Ciccarelli, 2009). Distribution of repeat polymorphism lengths in FMR1 of male GRAS schizophrenia patients and healthy controls. Distribution of repeat polymorphism lengths in FMR2 of male GRAS schizophrenia patients and healthy controls. Download figure Download PowerPoint Table 1. Correlation of repeat length polymorphisms with measures of schizophrenia disease severity and autistic features in the male schizophrenia GRAS sample and extreme group comparison of repeat length polymorphisms for the same measures FMR1 repeat polymorphism FMR2 repeat polymorphism Correlation coefficient P-value 10% with shortest repeats (mean ± SD) 10% with longest repeats (mean ± SD) Z, T value P-value Correlation coefficient P-value 10% with shortest repeats (mean ± SD) 10% with longest repeats (mean ± SD) Z, T value P-value n = 596–654a n = 68–70a n = 58–67a n = 595–654a n = 63–70a n = 64–70a Age at disease onset rs = 0.024 P = 0.540 24.75 ± 7.95 24.25 ± 7.25 Z = −0.36 P = 0.721 −0.040 P = 0.313 24.16 ± 8.46 24.18 ± 7.57 Z = −0.27 P = 0.787 PANSS positive rs = −0.057 P = 0.149 14.24 ± 5.70 13.20 ± 5.62 Z = −1.11 P = 0.269 −0.016 P = 0.678 14.37 ± 6.37 13.45 ± 5.80 Z = −0.94 P = 0.350 Cognitive composite scoreb rs = 0.021 P = 0.595 0.07 ± 1.12 −0.07 ± 0.97 t = 0.70 P = 0.484 0.035 P = 0.378 0.02 ± 0.99 0.04 ± 0.96 t = −0.08 P = 0.935 CNIb rs = −0.037 P = 0.362 −0.02 ± 0.89 0.03 ± 0.95 Z = −0.34 P = 0.736 0.035 P = 0.400 −0.05 ± 0.84 0.04 ± 0.91 Z = −0.69 P = 0.494 PAUSSc rs = 0.006 P = 0.870 −0.03 ± 0.67 0.05 ± 0.75 Z = −0.53 P = 0.598 −0.027 P = 0.499 0.06 ± 0.64 −0.03 ± 0.61 Z = −0.67 P = 0.504 Uncorrected mean ± SD presented. Spearman's correlation coefficient (rs) was calculated for FMR1/FMR2 repeat polymorphisms and respective disease measures. For further statistical analysis of extreme groups (10% with longest and 10% with shortest repeats), Mann–Whitney U-test or t-test for normally distributed variables was used. PANSS, positive and negative syndrome scale; CNI, Cambridge neurological inventory. a Because of missing data, sample sizes vary. b Corrected for age and chlorpromazine equivalents (standardized residual after linear regression). c Z-standardized PANSS autism severity score. An accumulation model of 8 proautistic genotypes of the broader fragile X family predicts autistic phenotypes in the schizophrenia discovery sample Having a comparable basis of repeat polymorphism distribution in the schizophrenia discovery sample with no obvious influence on disease readouts, we first selected SNPs in the broader fragile X gene family according to our standard operating procedure (SOP) as meticulously described in Figs 2 and 3 and in the materials and methods section. The high internal consistency of all individual PAUSS items (Kästner et al, 2015) allowed their aggregation to form a single dimensional measure of the severity of autistic symptoms (Fig 4A) and to explore the preselected 13 SNPs (Fig 2) individually with respect to potential proautistic genotypes (SOP: Fig 3). According to this SOP, the following 8 proautistic genotypes out of the 13 SNPs were chosen that revealed a tendency of a higher PAUSS (i.e. higher severity of autistic symptoms): T in FMR1 rs25699, TT in FXR1 rs6763069, AA in FXR1 rs2601, GG in FXR2 rs34416693, CC in FXR2 rs62059833, A in FMR2 rs241084, G in FMR2 rs17318323, and G in FMR2 rs6641482. These genotypes were used for generation of the 8-SNP accumulation model. Figure 2. SNP overview and unbiased selection according to the standard operating procedure (SOP) developed for phenotype-based genetic association study (PGAS) approaches Genes from the "broader fragile X family" and their chromosomal position. SNP numbers available through direct genotyping in the here used semicustom genotyping array (Affymetrix, Santa Clara, CA, USA). SNPs fulfilling some of the first round of selection criteria ("functional" = SNPs, i.e. located in promoter region, 3′UTR or coding sequence; MAF = MAF ≥ 0.2; LD = SNPs that "survive" after linkage disequilibrium pruning: r² < 0.8). Underlined are the 13 SNPs selected for the PGAS approach using the PAUSS (selection requirements: fulfilled 2 of the above criteria or were functional). Not more than 3 SNPs per gene are selected to avoid overrepresentation of one gene. SNPs with a tendency in PGAS (see Fig 3A) at single SNP basis went into the final accumulation model. Download figure Download PowerPoint Figure 3. Criteria of final SNP selection in the phenotype-based genetic association study (PGAS) approach—Standard operating procedure (SOP) A, B. A total of 13 SNPs preselected according to the SOP presented in Fig 2 underwent PGAS screening as exemplified here: (A) PAUSS association pattern of an exemplary fictitious autosomal (upper panel) and a sex-chromosomal (lower panel) SNP which are eligible for the accumulation model. The genotype associated with the highest average PAUSS (in this example CC) is the "proautistic genotype" (indicated by the black arrow) and is assigned a score of 1. The heterozygous genotype is assigned a score of 0.5 and the homozygous genotype associated with the lowest PAUSS receives a score of 0. Please note that the difference between genotypes does not have to be statistically significant. (B) PAUSS association pattern of an exemplary fictitious autosomal (upper panel) and a sex-chromosomal (lower panel) SNP which would not be selected for the accumulation model because of unclear phenotypical/biological relevance. C, D. The specificity of the association of the 8-SNP accumulation model with an autistic phenotype (as determined using PAUSS; compare Fig 4B) is controlled by applying an unrelated (or "non-sense") phenotype, for example, delusional-depression: (C) Intercorrelation pattern of single items included in the delusional-depression composite score, used here as example control phenotype. Cronbach's alpha is presented as measure of internal consistency. (D) The delusional-depression composite score is not associated with the number of proautistic genotypes of the 8-SNP risk model in the discovery sample. Data information: Error bars represent SEM. Download figure Download PowerPoint Figure 4. Association of autism severity readouts in the discovery and 3 independent replication samples with the number of proautistic genotypes in the 8-SNP risk model derived from the broader fragile X gene family PAUSS (PANSS autism severity score) composition and item intercorrelation pattern in the GRAS sample of male schizophrenic individuals (discovery sample). Cronbach's alpha is presented as measure of internal consistency and also provided for the male replication samples I and II. Association of PAUSS with the number of proautistic genotypes of the 8-SNP risk model in the discovery sample; mean ± SEM. PAUSS comparison of extreme groups with high and low numbers of accumulated proautistic genotypes in the discovery sample; binary logistic regression analysis with non-z-standardized PAUSS as dependent variable; mean ± SEM. PAUSS comparison of extreme groups with high and low numbers of accumulated proautistic genotypes in replication sample I of male schizophrenia patients; binary logistic regression analysis; mean ± SEM. PAUSS comparison of extreme groups with high and low numbers of accumulated proautistic genotypes in replication sample II of male disease control patients; Mann–Whitney U-test; mean ± SEM. The highly significant correlation of PAUSS and social support underlines the validity of social support as an autism proxy phenotype; mean ± SEM. Comparison of the extreme groups with high and low numbers of accumulated proautistic genotypes for the autism proxy phenotype social support in the discovery sample; binary logistic regression analysis; mean ± SEM. Comparison of the extreme groups with high and low numbers of accumulated proautistic genotypes for the autism proxy phenotype social support in the male replication sample III from general population; binary logistic regression analysis; mean ± SEM. For all replications, P-values for one-sided tests are shown. Download figure Download PowerPoint To exclude that any of the so-selected 8 SNPs would be associated with the diagnosis of schizophrenia, we first conducted a case–control analysis of the male GRAS schizophrenic and healthy control subjects, yielding no statistically significant results. All markers fulfill Hardy–Weinberg equilibrium after significance level was corrected for multiple testing (P < 0.013) (Table 2). Hence, all individuals of the discovery sample could now be ranked according to their number of proautistic genotypes. In this sense, for the autosomal genes (FXR1, FXR2), homozygous proautistic genotypes were always counted as 1; heterozygous proautistic genotypes as 0.5 and the non-proautistic genotypes as 0. For the X-chromosomal genes (FMR1, FMR2), the proautistic genotypes were counted as 1 and the non-proautistic genotypes as 0 (Fig 3). Figure 4B displays the average PAUSS of all individuals in the discovery sample dependent on the number of accumulated proautistic genotypes. Higher numbers correlate with higher PAUSS (rs = 0.103, P = 0.008). In contrast, these numbers do not correlate with unrelated control phenotypes, for example, our delusional depression composi
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