1 THE WORLD JOURNAL OF BIOLOGICAL PSYCHIATRY, 2015 55 http://dx.doi.org/10.3109/15622975.2015.1093658 2 56 3 RESEARCH ARTICLE 57 4 58 5 Involvement of NRN1 in schizophrenia-spectrum and bipolar disorders 59 6 and its impact on age at onset and cognitive functioning 60 7 61 8 62 Mar Fatjo´-Vilas1,2*, Claudia Prats1,2*, Edith Pomarol-Clotet2,3, Luisa La´zaro2,4,5, Carmen Moreno2,6, 9 63 Itxaso Gonza´lez-Ortega2,7, Sara Lera-Miguel4, Salvador Miret2,8, Ma Jose´ Mun˜oz9, Ignacio Iba´n˜ez2,10, 10 Sı´lvia Campanera8, Maria Giralt-Lo´pez9, Manuel J. Cuesta11, Victor Peralta11, Genero´s Ortet2,10, 64 11 Mara Parellada2,6, Ana Gonza´lez-Pinto2,7, Peter J. Mckenna2,3 and Lourdes Fan˜ana´s1,2 65 12 66 1 2 13 Departament de Biologia Animal, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain; Instituto De Salud Carlos III, Centro De 67 Investigacio´n Biome´dica En Red De Salud Mental (CIBERSAM), Madrid, Spain; 3FIDMAG Germanes Hospitala`ries, Research Foundation, 14 Barcelona, Spain; 4Servei de Psiquiatria i Psicologia Infantil i Juvenil, Hospital Clı´nic de Barcelona, Barcelona, Spain; 5Institut 68 15 d’investigacions Biome`diques August Pi Sunyer (IDIBAPS), Barcelona, Spain; Departament de Psiquiatria i Psicobiologia Clı´nica, Facultat De 69 16 Medicina, Universitat de Barcelona, Barcelona, Spain; 6Servicio de Psiquiatrı´a del Nin˜o y del Adolescente, Hospital General Universitario 70 17 Gregorio Maran˜o´n, Madrid, Spain; Instituto de Investigacio´n Sanitaria del Hospital Gregorio Maran˜o´n (IiSGM); Departamento de Psiquiatrı´a, 71 Facultad de Medicina, Universidad Complutense, Madrid, Spain; 7Psychiatry Service, University Hospital of Alava-Santiago, EMBREC, EHU/ 18 UPV University of the Basque Country, Kronikgune, Vitoria, Spain; 8Centre de Salut Mental d’Adults de Lleida, Servei de Psiquiatria, Salut 72 19 Mental i Addiccions, Hospital Universitari Santa Maria, de Lleida, Lleida, Spain; 9A`rea d’Adolescents, Complex Assistencial en Salut Mental 73 20 Benito Menni, Sant Boi De Llobregat, Spain; 10Departament de Psicologia Ba`sica, Clı´nica i Psicobiologia, Facultat de Cie`ncies de la Salut, 11 74 21 Universitat Jaume I, Castello´, Spain; and Servicio de Psiquiatrı´a, Complejo Hospitalario de Navarra, Pamplona Spain; Instituto de Investigacio´n Sanitaria de Navarra (IdiSNA), Pamplona, Spain 75 22 76 23 77 ABSTRACT ARTICLE HISTORY 24 Objectives: Neuritin 1 gene (NRN1) is involved in neurodevelopment processes and synaptic Received 14 April 2015 78 25 plasticity and its expression is regulated by brain-derived neurotrophic factor (BDNF). We aimed to Revised 21 July 2015 79 26 investigate the association of NRN1 with schizophrenia-spectrum disorders (SSD) and bipolar Accepted 9 September 2015 80 disorders (BPD), to explore its role in age at onset and cognitive functioning, and to test the 27 epistasis between NRN1 and BDNF. Methods: The study was developed in a sample of 954 SSD/BPD 81 KEYWORDS 28 patients and 668 healthy subjects. Genotyping analyses included 11 SNPs in NRN1 and one 82 Schizophrenia-spectrum and 29 functional SNP in BDNF. Results: The frequency of the haplotype C-C (rs645649–rs582262) was bipolar disorders; NRN1; age 83 30 significantly increased in patients compared to controls (P ¼ 0.0043), while the haplotype T-C-C-T- at onset; intelligence; BDNF C-A (rs3763180–rs10484320–rs4960155–rs9379002–rs9405890–rs1475157) was more frequent in 84 31 controls (P ¼ 3.1 105). The variability at NRN1 was nominally related to changes in age at onset 85 32 and to differences in intelligence quotient, in SSD patients. Epistasis between NRN1 and BDNF was 86 33 significantly associated with the risk for SSD/BPD (P ¼ 0.005). Conclusions: Results suggest that: (i) 87 NRN1 variability is a shared risk factor for both SSD and BPD, (ii) NRN1 may have a selective impact 34 on age at onset and intelligence in SSD, and (iii) the role of NRN1 seems to be not independent of 88 35 BDNF. 89 36 90 37 91 38 92 39 Introduction 93 40 important questions about the boundaries and distinct- 94 41 Schizophrenia and bipolar disorder are psychiatric iveness of these psychiatric disorders. 95 42 disorders characterised by a prevalence of 2–3%, On the one hand, they have a number of symptoms in 96 43 which increases up to 3.5% when other affective and common particularly in acute episodes, with regard to the 97 44 non-affective psychotic disorders such as schizoaffective presence of psychotic symptoms; their age at onset is 98 45 or schizophreniform disorders are also included (Perala quite similar; and, although there must be neurochemical 99 46 et al. 2007). A growing body of research suggests that differences, several findings emphasise the likelihood of 100 47 schizophrenia-spectrum disorders (SSD) and bipolar dopamine dysregulation in both (Murray et al. 2004). 101 48 disorders (BPD) share several epidemiological, clinical, Available evidence also supports that a generalised 102 49 neurobiological and genetic characteristics, raising deficit is present across SSD and BPD, even though 103 50 104 51 105 52 *Joint first authorship. 106 CONTACT Dr Fatjo´-Vilas [email protected] Unitat d’Antropologia, Departament de Biologia Animal, Facultat de Biologia, Universitat de Barcelona, 53 Av. Diagonal 643, 08028 Barcelona, Spain. Tel: +34-934-021-460 107 54 ß 2015 Taylor & Francis 108 2 M. FATJO´ -VILAS ET AL.

109 quantitative differences may exist (Hill et al. 2013). In view for SSD and also in their associated cognitive deficits 163 110 of these similarities, the integration of categorical and (Straub et al. 1995; Schwab et al. 1995; Hallmayer et al. 164 111 dimensional approaches has been suggested of particular 2005). The most studied gene included in this chromo- 165 112 interest to the complete understanding of psychotic somal region is Dysbindin-1 gene (DTNBP1, 6p22.3), 166 113 disorders (Peralta and Cuesta 2007). which has been consistently associated with SSD and 167 114 On the other hand, an important genetic overlap BPD (Schwab and Wildenauer 2009) as well as with age at 168 115 between SSD and BPD has been classically reported by onset and cognitive deficits (Wessman et al. 2009; Fatjo- 169 116 both epidemiological (Gottesman 1991; Lichtenstein Vilas et al. 2011). Also in this region, and far less explored, 170 117 et al. 2006) and molecular studies (Owen et al. 2007). there is the Neuritin 1 gene (NRN1, 6p25.1), also called 171 118 More recently, genome-wide approaches have evidenced candidate plasticity-related gene 15 (cpg15) (Nedivi et al. 172 119 a substantial shared polygenic contribution involving 1993). During early embryonic development, NRN1 is 173 120 thousands of common genetic variants of small effect to expressed in multiple brain regions and acts as a survival 174 121 the aetiology of these disorders (Lee et al. 2013). factor for neural progenitors and differentiated neurons 175 122 These shared genetic risk factors, along with the (Putz et al. 2005). Later in development, NRN1 promotes 176 123 clinical and cognitive similarities, have led to the notion growth and stabilisation of axonal and dendritic arbours 177 124 that these severe mental disorders can be placed in the along with synapse formation and maturation (Cantallops 178 125 same aetiopathological continuum, probably represent- et al. 2000; Javaherian and Cline 2005). NRN1 continues to 179 126 ing different phenotypic manifestations of common be expressed in the adult brain, where its expression is 180 127 underlying processes. correlated with activity-dependent functional synaptic 181 128 In the search for specific genetic factors related to plasticity (Corriveau et al. 1999; Harwell et al. 2005; Flavell 182 129 these disorders, studies face a number of challenges that and Greenberg 2008). Furthermore, the expression of 183 130 arise from the genetic and phenotypic complexity of NRN1 is regulated by neurotrophins such as brain-derived 184 131 these disorders. To this respect, it has been recently neurotrophic factor (BDNF, 11p13) (Naeve et al. 1997; 185 132 indicated that combining disorders with similar genetic Karamoysoyli et al. 2008). BDNF promotes the differen- 186 133 risk profiles improves power to detect shared risk loci tiation and growth of developing neurons in central and 187 134 (Ruderfer et al. 2014). Similarly, genotype–phenotype- peripheral nervous systems (Buckley et al. 2007) and its 188 135 based approaches and the use of features with strong intracellular distribution and activity-dependent secre- 189 136 aetiological significance have been suggested as a useful tion is altered by the Met variant of a functional 190 137 strategy to reduce heterogeneity and to identify specific polymorphism in the BDNF gene, which consists of a 191 138 genetic factors associated with such traits (Rasetti and valine (Val) substitution for methionine (Met) at codon 66 192 139 193 Weinberger 2011; Swerdlow et al. 2015). Then, the (Val66Met). Interestingly, BDNF gene polymorphisms 140 194 observed variability on traits such as cognitive impair- have been associated with clinical characteristics – such 141 195 ments and age at onset among patients may reflect as age at onset – and cognitive functioning in both SSD 142 196 differences in the distribution of aetiological factors and and BPD (Krebs et al. 2000; Rybakowski et al. 2006). 143 197 possibly also differences underlying vulnerability. To this According to all the above mentioned, NRN1 was 144 198 respect, heritability estimates indicate that genetic already defined as a candidate gene for neurodevelop- 145 199 factors contribute significantly to age at onset of ment disorders by Chandler et al. (2010), who reported 146 200 psychotic symptoms (Hare et al. 2010) and to general the effect of NRN1 polymorphic variation on general 147 201 cognitive functioning (Deary et al. 2009). Moreover, intelligence impairments in patients with schizophrenia. 148 202 cognitive impairments are present in 70% of the patients We considered the interest of investigating the implica- 149 203 with schizophrenia (Palmer et al. 1997) and twin studies tion of NRN1 in the aetiology not only of schizophrenia, 150 204 have shown that a large genetic overlap underlies the but also across the SSD and BD continuum. Moreover, 151 205 observed comorbidity between these two phenotypes we also aimed to extend the previous study on the 152 206 (Toulopoulou et al. 2007, 2010). Also, the earlier forms of relationship of NRN1 with cognitive impairments by 153 207 these disorders usually present severe clinical and testing the effect of this gene on age at onset, a 154 208 cognitive expression, high incidence of treatment refrac- characteristic that is related to cognitive performance. 155 209 tion and poor outcome (Rapoport et al. 2005; Joseph Since synaptic plasticity alterations have been sug- 156 210 et al. 2008). Accordingly, cognitive and clinical traits gested to be present both in SSD and BPD (Craddock 157 211 associated to age at onset may provide leads for et al. 2006), we hypothesised that sequence variability of 158 212 recognising and studying biological differences across the gene would be related to the risk for developing any 159 213 diagnostic boundaries (Ongur et al. 2009). of these disorders. Considering the described involve- 160 214 Linkage data have provided positional evidence ment of NRN1 in cognitive processes, we also hypoth- 161 215 implicating the short arm of 6 in the risk esised that NRN1 gene could exert its effect not only by 162 216 THE WORLD JOURNAL OF BIOLOGICAL PSYCHIATRY 3

217 modulating general cognitive functioning, but also age WISC-IV (Wechsler 2004), in accordance with the method 271 218 at onset. Finally, given that NRN1 is a BDNF-regulated suggested by Sattler (2001). Cognitive assessment was 272 219 gene, we explored the statistical epistasis between NRN1 carried out by experienced neuropsychologists. In 273 220 and BDNF as a proxy analysis of their involvement in patients, the cognitive evaluation was conducted when 274 221 common biological pathways. stabilisation of symptoms and readiness for cognitive 275 222 evaluation was decided by the clinical team. 276 223 277 Materials and methods 224 Molecular analyses 278 225 Sample 279 226 Genomic DNA was extracted from peripheral blood cells The patients’ sample comprised 954 individuals of 280 227 or from buccal mucosa using standard methods: the Real 281 Spanish Caucasian origin. They were drawn from con- 228 Extraction DNA Kit (Durviz S.L.U., Valencia, Spain) or the 282 secutive admissions to three Child and Adolescent 229 BuccalAmp DNA Extraction Kit (EpicentreÕ 283 Psychiatry Units and four Adult Psychiatric Units, and 230 Biotechnologies, Madison, WI, USA). 284 were evaluated by experienced psychiatrists. All of them 231 Coverage of NRN1 genomic sequence and 10 kb 285 met the DSM-IV-TR diagnosis criteria: 73% SSD (49% 232 upstream and downstream was achieved by including 11 286 schizophrenia, 11% schizophreniform disorder, 8% schi- 233 tag SNPs (Table 1). The optimal set of SNPs that contained 287 zoaffective disorder, 5% psychotic disorder NOS) and 234 maximum information about surrounding variants was 288 27% bipolar disorder I or II. Exclusion criteria included: 235 selected by using SYSNPs (http://www.sysnps.org/) with a 289 age above 65 years, major medical illnesses that could 236 minor allele frequency (MAF)45%, using pairwise option 290 affect brain functions, substance-induced psychotic dis- 237 tagger (threshold of r2¼0.8). The SNPs included in the 291 order, neurological conditions, history of head trauma 238 study by Chandler et al. (2010) study were also con- 292 with loss of consciousness and having at least one 239 sidered. The SNP rs6265 (Val66Met) at BDNF gene was 293 parent not from Spanish Caucasian origin. Patients were 240 also genotyped. Genotyping was performed using a 294 diagnosed based on the following schedules: KSADS 241 fluorescence-based allelic discrimination procedure 295 (Kaufman et al. 1997) for patients up to 17 years of age, 242 0 296 and SCID (First et al. 1997) or CASH (Andreasen et al. (Applied Biosystems Taqman 5 -exonuclease assays). 243 297 1992) for adult patients. Age at onset of the first episode Standard conditions were used. The genotyping call 244 298 was determined by means of these clinical schedules rate for all SNPs was higher than 94.2% and all were in 245 299 and/or the SOS inventory (Perkins et al. 2000). Hardy–Weinberg equilibrium. 246 300 The control sample consisted of 668 Spanish 247 301 Caucasian unrelated adult healthy individuals. They Statistical analyses 248 302 met the same exclusion criteria as patients. They were 249 All data were processed using SPSS 21.0 software (SPSS 303 recruited from university students and staff, and their 250 IBM, New York, USA). Haploview v4.1 (Barrett et al. 2005) 304 acquaintances, plus independent sources in the com- 251 was used to estimate the Hardy–Weinberg equilibrium 305 munity. They were interviewed and excluded if they 252 and the linkage disequilibrium (LD) between NRN1 SNPs 306 reported a history of mental illness and/or treatment 253 (Supplementary Figure S1 available online). By means of 307 with psychotropic medication. 254 using the Solid Spine criteria three haplotype blocks 308 All participants provided written consent after being 255 were identified (Block 1: SNP1-SNP3, Block 2: SNP4-SNP5 309 informed about the study procedures and implications. 256 310 In the case of patients below the age of 18, written and Block 3: SNP6-SNP11) and a sliding window analysis 257 311 informed consent was also obtained from their parents was conducted within each block. 258 312 or legal guardians. The study was performed in accord- The genetic power was calculated using Epi-info-v3.5.1 259 313 ance with the guidelines of the institutions involved and (Dean et al. 1991) by assuming an additive model, a 260 314 was approved by the local ethics committee of each disease prevalence of 3% and considering the minor allele 261 315 participating centre. All procedures were carried out frequencies observed in our sample. All markers had an 262 316 according to the Declaration of Helsinki. 80% power to detect a genetic effect with an OR 1.2. 263 Case–control associations were analysed using the 317 264 Unphased-v3.1.4 (Dudbridge 2003), using a cut-off 318 265 Neurocognitive assessment threshold for rare haplotypes of 1%. A 10,000-permuta- 319 266 320 The general cognitive performance was evaluated in 607 tions procedure was applied to all tests to limit type II 267 321 patients and in 476 healthy subjects. Intellectual quotient error. The odds ratios (OR) were estimated from the 268 322 (IQ) was estimated using the Block Design and Vocabulary absolute number of alleles/haplotypes estimated in 269 323 or Information subtests of the WAIS-III (Wechsler 1997) or patients and controls (EpiInfo-v3.5.1). 270 324 4 M. FATJO´ -VILAS ET AL.

325 Table 1. SNPs genotyped in Neuritin 1 gene (NRN1, chromosome 6p25.1, from 598233 to 6007633 bp). 379 326 Distance from Distance from 380 327 SNP Position Region SNP1 previous SNP Allelesa MAFb 381 328 SNP1 rs2208870 5992490 Intergenic A/G 0.333 382 SNP2 rs12333117 5994992 Downstream 2502 2502 C/T 0.402 329 SNP3 rs582186 6001381 Intronic 8891 6389 A/G 0.393 383 330 SNP4 rs645649 6004959 Intronic 12469 3578 C/G 0.356 384 331 SNP5 rs582262 6007991 Upstream 15501 3032 G/C 0.273 SNP6 rs3763180 6009848 Upstream 17358 1857 G/T 0.437 385 332 SNP7 rs10484320 6010437 Upstream 17947 589 C/T 0.236 386 333 SNP8 rs4960155 6010539 Upstream 18049 102 T/C 0.492 387 SNP9 rs9379002 6012391 Intergenic 19901 1852 T/G 0.27 334 SNP10 rs9405890 6012721 Intergenic 20231 330 T/C 0.309 388 335 SNP11 rs1475157 6017169 Intergenic 24679 4448 A/G 0.176 389 336 The table includes the dbSNP number, the genomic and gene position and the alleles of the 11 SNPs genotyped along the gene (UCSC Genome Browser on 390 337 Human Mar. 2006 Assembly (hg18), http://genome.ucsc.edu/cgi-bin/hgTracks). aThe less frequent allele (minor allele) is placed second. 391 338 bMAF refers to Minor Allele Frequency observed in the 1000 Genomes project (Abecasis et al. 2012). 392 339 393 340 394 341 Additive models as implemented in Plink 1.07 (Purcell Association analysis of NRN1 and schizophrenia- 395 342 et al. 2007) were used to conduct lineal regression spectrum and bipolar disorders 396 343 analyses to explore the relationship between NRN1 and 397 There were no differences between sampling groups as 344 age at onset and IQ. First, the relationship between the 398 regards the genotypic distribution of each polymorph- 345 NRN1 and age at onset was tested in the complete 399 ism (data not shown), and genotype frequencies showed 346 patients’ sample (including gender and diagnosis group 400 no gender differences within groups (patients and 347 as covariates) and also separately in each group 401 controls; data not shown). 348 (adjusted by gender). Second, the relationship between 402 SNP1 (G allele), SNP4 (C allele) and SNP5 (C allele) 349 the NRN1 and IQ was tested in the complete patient’s 403 were significantly more frequent among patients com- 350 sample (including age at onset, months of evolution and 2 2 404 pared to controls ( ¼4.81 P ¼ 0.028, ¼5.05 P ¼ 0.024 351 diagnosis group as covariates) and also separately in 2 405 and ¼8.04 P ¼ 0.004, respectively). After multiple 352 406 SSD, BPD (adjusted for age at onset and months of correction adjustment only the association of SNP5 353 evolution) and controls. PLINK’s max(T) permutation 407 remained significant (OR(95%CI) ¼ 1.27(1.07–1.49), 354 408 procedure with 10,000 iterations was performed. empirical P value ¼ 0.044)). 355 409 The effect of NRN1 and BDNF interaction was tested Haplotypes associated with SSD and BPD are given in 356 410 on: (i) the risk for developing SSD or BPD, (ii) age at onset Table 3. The frequency of the haplotype G-C (Block 1: 357 411 (adjusted for sex and diagnosis) and IQ (adjusted for age SNP1-SNP2) and haplotype C-C (Block 2: SNP4-SNP5) was 358 412 at onset and months of evolution), in patients. Epistasis significantly increased in patients than in controls. The 359 413 was explored using the model-based multifactor dimen- result in Block 2 remained significant after permutation 360 414 sionality reduction (MB-MDR) approach by applying procedure; then, this haplotype was considered a risk 361 ‘mbmdr’ R-package (Calle et al. 2010). This method 415 362 haplotype for SSD and BPD. On the contrary, the merges multi-locus genotypes in order to overcome the 416 363 haplotype T-C-C-T-C-A (Block 3: SNP6-SNP11) had higher dimensionality problem and to increase the power to 417 364 frequencies in controls. Results in Block 3 also remained detect gene interactions associated with disease or 418 365 significant after multiple testing and could be interpreted phenotype. It also allows adjusting for confounding 419 366 as reflecting a protective effect of this haplotype. Note 420 effects and correcting for multiple testing by 1000 367 that other haplotypes included in the haplotype in Block 421 permutations approach. In all analyses, the significance 368 3 were also detected (Supplementary Table S1 available 422 cut-off was established at P value of 0.05. 369 online). These results remained essentially unchanged 423 370 when only SSD patients and controls were included. 424 371 425 372 Results NRN1 and age at onset of the disorders 426 373 Sample characteristics 427 Patients carrying two copies of the T allele at SNP2 374 428 Table 2 shows the main sociodemographic and clinical (15.33%) presented a lower age at onset than those not 375 429 data of the sample. Variables that showed differences carrying this allele (b ¼ –0.772 P ¼ 0.029). Patients homo- 376 430 between groups were used as covariates when appro- zygous for the C allele of SNP10 (7.80%) also showed later 377 431 priate (see Statistical analyses section). age at onset than those not carrying this allele (b ¼ 0.918 378 432 THE WORLD JOURNAL OF BIOLOGICAL PSYCHIATRY 5

433 Table 2. Sample description and statistical comparisons between patients and controls. 487 434 All Patients (n¼954) SSD (n¼697) BPD (n¼257) Controls (n¼668) 488 435 Male (%) 65.6% 71.2% 50.6%** 46.7%* 489 436 Age at interview 32.33 (13.10) 31.79 (12.83)** 33.9 (13.71)** 27.05 (9.99)* 490 Years of education 10.13 (4.06) 9.58 (3.82) 11.98 (4.29)** 13.87(2.87)* 437 Age at onset 21.54 (6.47)a,b 20.72 (5.33)a 23.88(8.53)a,** – 491 438 Months of evolution 146.24 (137.6) 140.35 (140.07) 162.93 (129.25) – 492 c c c, c, 439 Current IQ 89.80 (15.26) 89.02 (15.37) 92.86 (14.48) ** 99.48 (13.64) * 493 440 Proportion (%) or mean scores (standard deviation) are given. SSD, schizophrenia-spectrum disorders; BPD, bipolar disorders. 494 aInformation about age at onset was available for the 73.5% of patients (74.3% SSD and 71.2% BPD). 441 b35.29% were classified as early-onset (first psychotic episode occurred before 18 years of age). 495 442 cInformation about IQ was available for 63.6% of patients (69.4% SSD and 47.8% BPD) and 71.25% of healthy subjects. 496 *Controls differed significantly from patients (P50.001). 443 **BPD patients differed significantly from SSD patients (P50.03). 497 444 498 445 499 446 500 447 501 448 Table 3. NRN1 most significant haplotypes associated to the risk for schizophrenia-spectrum and 502 bipolar disorders. Frequency estimates in patients and controls, significance levels and OR of the 449 case-control comparison are given. 503 450 504 451 SNP1 rs2208870 G I 505 452 SNP2 rs12333117 C 506 453 SNP3 rs582186 507 454 SNP4 rs645649 C 508 455 I 509 SNP5 rs582262 C 456 SNP6 rs3763180 T 510 457 I 511 SNP7 rs1048432 C 458 I 512 459 SNP8 rs4960155 C 513 460 I SNP9 rs9379002 T 514 461 I 515 462 SNP10 rs9405890 C 516 I 463 SNP11 rs1475157 A 517 a 464 Ca- Freq 34.3 25.9 0.1 518 Co- Freqb 30.7 21.4 1.5 465 2 4.26 7.99 17.45 519 466 OR (CI 95%)c 1.18 (1.01–1.37) 1.28 (1.08–1.51) 0.09 (0.02–0.37) 520 Global P value 0.11 0.038 0.001 467 Individual haplotype P value 0.037y 0.0043* 0.000031** 521 468 522 aCa- Freq refers to each haplotype frequency within cases. 469 bCo- Freq refers to each haplotype frequency within controls. 523 c 470 Chi-squared tests and OR were estimated from the absolute number of observed haplotypes in cases and controls. 524 yNot significant after performing 10,000 permutations, adjusted P value from permutation test P ¼ 0.1748. 471 *Significant adjusted level based on 10,000 permutations, adjusted P value from permutation test P ¼ 0.0219. 525 472 **Significant adjusted level based on 10,000 permutations, adjusted P value from permutation test P ¼ 0.002. 526 473 527 474 528 475 P ¼ 0.016). The haplotype C-A (SNP10-11) was associated permutation procedure. No association was detected 529 476 with age at onset: (b ¼ 0.956 P ¼ 0.015) and also several within BPD patients’ group. 530 477 haplotypes within Block 3 (all including the C-A haplo- 531 478 type) (Supplementary Table S2 available online). 532 NRN1 and cognitive functioning 479 When the same analysis was conducted only includ- 533 480 ing SSD patients, the results for SNP10 and haplotype In SSD patients, the same haplotypes within Block 3 534 481 SNP10-11 remained significant while SNP2 did not contributed to IQ scores (Supplementary Table S4 535 482 (Supplementary Table S3 available online). In an additive available online). A linear trend was detected between 536 483 way, carrying two copies of the haplotype C-A was the number of copies of these haplotypes and higher IQ 537 484 associated with later SSD age at onset (Figure 1A). scores (Figure 1B), meaning that subjects carrying these 538 485 However, these results were not significant after haplotypes showed better general cognitive 539 486 540 6 M. FATJO´ -VILAS ET AL.

541 595 542 596 543 597 544 598 545 599 546 600 547 601 548 602 549 603 550 604 551 605 552 606 553 607 554 608 555 609 556 610 557 611 558 612 559 613 560 614 561 615 562 616 563 617 564 618 565 619 566 620 567 621 568 622 569 623 570 624 571 625 572 626 573 627 574 628 575 629 576 630 577 631 578 632 579 633 580 634 581 Figure 1. Relationship between NRN1 and age at onset and IQ in SSD patients. Linear regression graphs showing the relationship 635 between SSD patients’ NRN1 haplotypes and: (A) age at onset, (B) IQ. For illustration purposes, the haplotype dump option was used 582 636 to estimate individual haplotype phases. Considering only those haplotypes estimated with a probability 95%, each subject was 583 defined according to its haplotype dose. (A) The haplotype C-A (SNP10-11) was selected to represent graphically the described 637 584 association between NRN1 and age at onset (b ¼ 0.89 P¼0.019). SSD patients were classified as: 47.01% non-carriers (0), 45.41% one- 638 585 copy carriers (1) and 7.58% two-copy carriers (2). (B) The haplotype C-T-G-C-A (SNP7-11) was selected to represent graphically the 639 586 detected association between NRN1 variability and IQ within SSD patients (b ¼ 4.02 P¼0.022). SSD patients were classified as: 82.2% 640 non-carriers (0), 16.9% one-copy carriers (1) and 0.9% two-copy carriers (2). 587 641 588 642 589 Epistasis between NRN1 and BDNF 643 performance than non-carrier subjects. However, after 590 644 permutation analyses these results did not remain Two order gene–gene interaction models were devel- 591 645 significant. No significant association with IQ was oped and revealed that the combination of the BDNF 592 646 detected between these polymorphisms either in the Val/Val genotype with different NRN1 variants (SNP1 (GG: 593 647 whole patients’ sample, in BPD or in healthy subjects. b ¼ 0.654 P ¼ 0.001), SNP3 (AA: b ¼ 0.514 P ¼ 0.003) and 594 648 THE WORLD JOURNAL OF BIOLOGICAL PSYCHIATRY 7

649 SNP9 (TG: b ¼ 0.457 P ¼ 0.0004)) was related to an described to be more quantitatively impaired in SSD 703 650 increased risk for developing both SSD and BPD. In (Hill et al. 2013). Again, although results did not reach 704 651 contrast, BDNF Met/Met was associated with a lower risk significance after permutation, it is of interest that the 705 652 in combination with NRN1 SNP2 (TT: b ¼ –2.185 haplotypes identified in the present study contain the 706 653 P ¼ 0.0052). After permutation analysis, the interaction same haplotype that Chandler et al. (2010) described to 707 654 BDNFxNRN1SNP9 remained significant (P ¼ 0.005). No be associated with better fluid intelligence in schizo- 708 655 significant epistatic effect was detected on age at phrenia patients and not in healthy subjects (SNP10- 709 656 onset and IQ after permutation. SNP11: C-A). 710 657 In all, our results suggest in a convergent manner that 711 658 allelic variants in Block 3 of NRN1 could represent a 712 659 Discussion protective factor, not only due to their association to a 713 660 reduction of the risk for SSD and BPD, but also because 714 661 This case–control based approach adds to the only one within SSD patients, these variants are related to a later 715 662 previous Neuritin 1 gene association study developed by of age at onset and a better cognitive performance. This 716 663 Chandler et al. (2010) in a sample of 336 patients with lends support to the notion that specific genetic 717 664 schizophrenia and 172 controls. Unlike Chandler and variability could play a role in defining illness subgroups 718 665 collaborators, in our sample of 954 patients and 668 and points towards the interest of understanding the 719 666 healthy subjects we report that NRN1 sequence variabil- pathways from genotype to clinical phenotype, which 720 667 ity accounts for a modest proportion of the risk for these will be crucial for new classification systems and to for 721 668 disorders. On the one hand, we have identified a two the development of novel therapeutic strategies. 722 669 SNP haplotype (SNP4-SNP5: C-C) that is associated with In further interpreting these results, it is necessary to 723 670 the risk for these disorders. As expected, due to the consider the results obtained by whole genome 724 671 polygenic architecture of the studied disorders, the approaches. To our knowledge, NRN1 has not appeared 725 672 effect of this haplotype is small although significant as a significant locus in the published GWAS for 726 673 (OR ¼ 1.28). On the other hand, we have observed schizophrenia and bipolar disorders. However, these 727 674 haplotypes in the 5 upstream region that have a negative results could be influenced, for example, by the 728 675 protective effect. Although significance for these asso- small effect attributable to common variants or by the 729 676 ciations persisted after permutation procedure, the low heterogeneity of the samples. It should also be con- 730 677 frequency of the protective haplotypes in the population sidered that NRN1 could be exerting its effect by means 731 678 has to be considered when evaluating the attributable of modifying more specific traits associated with psych- 732 679 risk associated to these genetic variants. otic disorders. In this regard, a genome-wide scan for 733 680 The present study also provides new evidence of intelligence conducted in a general population sample 734 681 interest as regards understanding the heterogeneity in revealed suggestive linkage for IQ on 6p25.3–21.31 and 735 682 age at onset and cognitive performance of SSD and BPD. already highlighted NRN1 as a positional candidate gene 736 683 Our results suggest that NRN1 variability has a role in (Posthuma et al. 2005). Moreover, a subtype of schizo- 737 684 SSD age at onset, pointing towards a specific effect on phrenia characterised by pervasive cognitive deficit was 738 685 modifying neurodevelopment processes related to the also linked to 6p25–p22 region (Hallmayer et al. 2005). 739 686 time of emergence of these disorders. Although these More recently, a GWAS has established that common 740 687 results should be interpreted cautiously because they variants (SNPs) may account for 40–50% of intelligence 741 688 are only significant at an uncorrected level, it is variance (Davies et al. 2011) and a GWAS-based pathway 742 689 743 interesting to note that the C allele of SNP10, which is analysis has reported that general fluid intelligence 690 744 included in the above described protective haplotype, is appears to be characterised by affecting quantity 691 745 associated with a later age at onset of SDD. Then, taking and quality of neurons and therefore neuronal efficiency 692 746 into account that the 51% of SSD patients are carriers of (Christoforou et al. 2014). Among the genes included in 693 747 this allele (358 C carriers vs 339 TT), together with the the top pathways identified in this study, there was the 694 748 particularly poor prognosis associated to schizophrenia BDNF, a regulator of NRN1 expression. According to all 695 749 in childhood and adolescence in contrast to the adult these data and given the described gradual increase in 696 750 manifestation (Clemmensen et al. 2012), this modulatory heritability of IQ from childhood to late adolescence 697 751 effect is of non-dismissible potential clinical interest. (Deary et al. 2009; Bouchard 2013) and the reported early 698 752 Our study also shows the association between this occurrence of intellectual impairment even years before 699 753 gene and intelligence in SSD. This selective impact of the onset of the psychotic symptoms (Cannon et al. 700 754 NRN1 on intelligence may suggest its involvement in 2002), it is plausible that those genes that influence 701 755 processes underlying cognitive functioning, which are brain development, as NRN1, may be modulating illness 702 756 8 M. FATJO´ -VILAS ET AL.

757 traits, as IQ and age at onset, and ultimately influencing is of note that it is based on a previously described 811 758 the risk for these disorders. effects of BDNF on NRN1 regulation (Naeve et al. 1997). 812 759 Although the connection between the NRN1 sequence Then, it could be hypothesised that the reported 813 760 variability and the risk for SSD and BPD is still unclear, the functional effects of the BDNF Val66Met polymorphism 814 761 consideration of the putative effects of the analysed could impact on NRN1 availability or function, explaining 815 762 polymorphic sites on gene expression regulatory mech- therefore the gene–gene interaction on the risk for 816 763 anisms represents a valuable resource to provide add- developing SSD and BPD and contributing to under- 817 764 itional meaning and importance to our association data. stand the controversial results associated to single gene 818 765 Recent data has revealed the importance of intronic and analyses. To this respect, some studies have implicated 819 766 intergenic variants as regulatory elements of gene the BDNF Val allele in these disorders and, as the Val 820 767 expression (Dunham et al. 2012). The impact of non- allele is associated with increased synaptic plasticity and 821 768 coding variants of the NRN1 SNPs can be examined using growth (Egan et al. 2003), it has been suggested that this 822 769 HaploReg (Ward and Kellis 2012), which is a tool that uses allele could promote increased synaptic connections 823 770 LD information from the 1000 Genomes Project to between certain brain regions that underpin common 824 771 provide data on the predicted chromatin state of the symptoms. However, recent meta-analyses have failed to 825 772 queried SNPs, their sequence conservation mammals, confirm the direct association of Val66Met polymorph- 826 773 and their effect on regulatory motifs. As an example, SNP2 ism with the risk for schizophrenia (Zhao et al. 2015) or 827 774 (rs12333117), associated with age at onset in the present bipolar disorder (Gonzalez-Castro et al. 2014). On the 828 775 study, is located in a downstream region, in a DNAse other hand, taking into account that BDNF exerts a direct 829 776 region (T-47D) and it is predicted to alter several motifs impact on neuronal growth and plasticity in the limbic 830 777 that overlap the recognition sequences of transcription system (Conner et al. 1997; Rattiner et al. 2004), it should 831 778 factors such as AP-1/Jun, suggesting possible factor– be contemplated that G allele carriers of rs9379002 832 779 factor interactions. There is also evidence that this SNP (SNP9, NRN1) show higher NRN1 expression than TT 833 780 could modify the promoter histone mark H1, which plays homozygotes in the hypothalamus (GTEx Project). Then, 834 781 an active role in the formation of epigenetic silencing we could speculate that higher expression of both BDNF 835 782 marks (Yang et al. 2013). Another example refers to the and NRN1 could be underlying the detected epistatic risk 836 783 SNP4 (rs645649), included in the identified risk haplotype effect. To this respect, it is remarkable that a case-report 837 784 and that is located in an intronic region where two study suggested the relationship between a duplication 838 785 bound: SUZ12 (involved in methylation pro- of NRN1 gene (i.e. increased gene dosage) and the white 839 786 cesses leading to transcriptional repression of the matter and neurocognitive abnormalities observed in 840 787 affected target genes) and ZNF263 (implicated in basic one patient (Linhares et al. 2015). Accordingly, we would 841 788 cellular processes as a transcriptional repressor). have expected to detect the association not only with 842 789 Furthermore, several resources provide information the heterozygous TG genotype but also with the GG. 843 790 about the correlation between genotype and tissue- This lack of significant interaction could be explained by 844 791 845 specific gene expression levels, which may help in the the low frequency of GG genotype (7%) and the 792 846 interpretation of molecular genetics association studies corresponding low frequency of the combination of 793 847 (GTEx Project, www.gtexportal.org (Lonsdale et al. 2013); Val/Val x GG (BDNFxNRN1SNP9). Therefore, although 794 848 BrainCloud, http://braincloud.jhmi.edu/ (Colantuoni et al. further studies are needed, these results are in line 795 849 2011)). In this regard, variations in NRN1 expression have with recent trends in the field of molecular genetics, 796 850 been associated with SNPs along the gene. Therefore, which consider the importance of testing gene networks 797 851 although functional studies are needed, the association rather than isolated gene effects for better understand- 798 852 of NRN1 sequence variants with SSD and BPD phenotypes ing the gene–phenotype relationship in complex dis- 799 853 could be linked to the final availability or functionality of orders (Gilman et al. 2012). Nonetheless, the fact that the 800 854 the which, in turn, could dysregulate NRN1 role SNP9 is included in the protective haplotype while it is 801 855 on neurite outgrowth and arborisation and/or on neur- detected to exert a risk effect when interacts with Val/Val 802 856 onal processes associated with plasticity. genotype could suggest that the effect of this SNP may 803 857 Finally, based on the analyses of epistasis between differ depending on the genetic background in which 804 858 NRN1 and BDNF, our data suggest that the interaction the alleles are present (Moore 2003). Moreover, beyond 805 859 between the Val/Val genotype (BDNF) and the TG gene–gene interactions, the effect of environmental 806 860 genotype (NRN1, SNP9: rs9379002) could modulate the factors should also be studied. In this regard, the fact 807 861 risk for SSD and BPD. Despite the fact that evidence of a that NRN1 is classified as an immediate early gene 808 862 statistical interaction as we report here does not (Loebrich and Nedivi 2009), meaning that it can be 809 863 necessarily map directly onto biological interaction, it rapidly induced by extracellular stimuli and act as a 810 864 THE WORLD JOURNAL OF BIOLOGICAL PSYCHIATRY 9

865 transcription factor on downstream targets, highlights Investigacio´n en Red de Salud Mental (CIBERSAM), ii) 919 866 the interest of analysing the combined effect of NRN1 Fundacio´n Alicia Koplowitz; iii) Ministry of Science and 920 Innovation (PIM2010ERN-00642)-ERA-NET NEURON; iv) 867 and BDNF in gene-environment studies. 921 P11B2010-40 and P11B2011-47 from the Fundacio´ Bancaixa- 868 Some limitations of this study must be acknowledged. UJI; v) Miguel Servet Research Contracts (CP10/00596) from the 922 869 First, the controls’ age range is partially overlapped with Plan Nacional de I + D+i and co-funded by the Instituto de 923 870 the age range of incidence of SSD and BPD. However, due Salud Carlos III-Subdireccio´n General de Evaluacio´n y Fomento 924 871 to the fact that personal psychiatric history and treatment de la Investigacio´n and the European Regional Development 925 872 was discarded, the percentage of false negatives would Fund (FEDER); vi) Health research funds from the Spanish 926 Government (PI11/01977); vii) METSY project of the 7th 873 927 be very low and should not interfere with the obtained Framework Programme of the European Commission (FP7- 874 results. Second, the polygenic nature of mental disorders HEALTH-602478) and Ministry of Economy and 928 875 and the minor effect of the common genetic variants limit Competitiveness (PI10/01920, PI14/02096). Thanks to the 929 876 the power of our sample size, especially in the case of the Comissionat per a Universitats i Recerca del DIUE 930 877 analyses split by diagnosis. In line with this, although the (2014SGR1636, 2014SGR1573, 2014SGR489). 931 878 use of features with strong aetiological significance has 932 879 been suggested as a useful strategy to increase the power Statement of interest 933 880 to detect genetic effects, the power of the analyses 934 None to declare. 881 targeting age at onset and neurocognition is reduced due 935 882 to the non-availability of data in all subjects. This 936 883 statistical power reduction could be related with the References 937 884 loss of significant effects after permutation procedures. 938 Abecasis GR, Auton A, Brooks LD, DePristo MA, Durbin RM, 885 Third, the antipsychotic treatment was not specified and, Handsaker RE, Kang HM, Marth GT, McVean GA. 2012. An 939 886 therefore, cognitive analyses, although covaried by age at integrated map of genetic variation from 1,092 human 940 887 onset and months of evolution, were not adjusted by genomes. Nature. 491:56–65. 941 888 treatment type or duration. 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