grin2b mediates susceptibility to iq and cognitive impairments in developmental dyslexia

8/9/2019 GRIN2B mediates susceptibility to IQ and cognitive impairments in Developmental Dyslexia

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GRIN2Bmediates susceptibility to IQ and cognitive impairments in Developmental Dyslexia

Sara Mascheretti1, Andrea Facoetti

1,2, Roberto Giorda

3, Silvana Beri

3, Valentina Riva

1, Vittoria Trezzi

1,

Maria R. Cellino4, Cecilia Marino

1,5,6

1Department of Child Psychiatry, Scientific Institute Eugenio Medea, Bosisio Parini (Lecco), Italy

2 Developmental and Cognitive Neuroscience Lab, Department of General Psychology, University of

Padua, Padua, Italy

3Molecular Biology Laboratory, Scientific Institute Eugenio Medea, Bosisio Parini (Lecco), Italy

4Centro Regionale di Riferimento per i Disturbi dellApprendimento CRRDA, ULSS 20, Verona, Italy

5Centre de recherche de lInstitut universitaire en sant mentale de Qubec, Qubec (Qubec), Canada

6 Dpartement de Psychiatrie et Neurosciences, Facult de Mdecine, Universit Laval, Qubec (Qubec),

Canada

Running Title:GRIN2Band susceptibility to Dyslexia

!Sara Mascheretti, Ph.D.

Child Psychopathology Unit, Scientific Institute Eugenio Medea

via don Luigi Monza, 20

23842 Bosisio Parini (Lecco), Italy

Phone: +39.031.877.813

Fax: +39.031.877.499

e-mail address: [email protected]

Conflicts of Interest and Source of Funding:Nothing to declare


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Abstract

Objective(s) Developmental dyslexia (DD) is a complex heritable condition associated with impairments

in multiple neurocognitive domains. Substantial heritability has been reported for DD and related

phenotypes, and candidate genes have been identified. Recently, a candidate gene for human cognitive

processes, i.e., GRIN2B, has been found significantly associated with working memory in a German DD

sample. In this study, we explored the contribution of six GRIN2Bmarkers to DD and key DD-related

phenotypes by association analyses in a sample of Italian nuclear families. Moreover, we assessed

potential gene-by-environment interactions on DD-related phenotypes.

Methods We performed a family-based association study to determine whether the GRIN2B gene

influence both DD as a categorical trait and its related cognitive traits, in a large cohort of 466 Italian

nuclear families ascertained through a proband affected by DD. Moreover, we tested the role of the

selected GRIN2Bmarkers and a set of commonly-described environmental moderators, by applying a test

for GxE interaction in sib pair-based association analysis of quantitative traits in 178 Italian nuclear

families.

Results Evidence for significant association were found with the categorical diagnosis of DD,

performance IQ, phonemic elision and auditory short-term memory. No significant gene-by-environment

effects were found.

ConclusionsOur results add further evidence in support of GRIN2Bcontributing to DD and deficits in

DD. More specifically, our data support the view that GRIN2Binfluences DD as a categorical trait and its

related quantitative phenotypes, thus shedding further light into the etiologic basis and the phenotypic

complexity of this disorder.

Key Words: Developmental Dyslexia; Developmental Dyslexia-related neuropsychological traits;

GRIN2B; N-methyl-D-aspartate receptors; association study; gene-by-environment interaction


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Riassunto

Obiettivo: La Dislessia Evolutiva (DD) una condizione complessa ed ereditabile associata a deficit in

differenti domini neurocognitivi. E stata riscontrata una significativa ereditabilit sia nella Dislessia

Evolutiva che nei fenotipi ad essa associati. Recentemente, un gene implicato nelle fasi del

neurosviluppo, e.i., GRIN2B, stato osservato essere significativamente associato alla memoria di lavoro

in un campione di dislessici tedeschi. In questo studio abbiamo esplorato il contributo di sei marcatori del

gene GRIN2Bsulla DD e dei principali fenotipi associati alla Dislessia Evolutiva per mezzo di analisi di

associazione genetica in un campione di famiglie nucleari italiane.

Metodi: Abbiamo messo a punto uno studio di associazione family-based per determinare se il gene

GRIN2B influenza sia la DD come tratto categoriale sia i tratti cognitivi ad essa correlati, in un ampio

campione di 466 famiglie nucleari italiane accertate per mezzo di un probando affetto da DD. Inoltre

abbiamo testato il ruolo di specifici marcatori del gene GRIN2B e una serie di moderatori ambientali

comunemente descritti, applicando test per linterazione GxE utilizzando analisi di associazione con

coppie di fratelli in 178 famiglie nucleari italiane.

RisultatiSono state trovate evidenze di associazioni significative tra la diagnosi categoria di DD, QI di

Performance, elisione fonemica e memoria a breve termine uditiva. Non son stati riscontrati effetti di

interazione gene-ambiente.

ConclusioniI nostri risultati aggiungono una forte evidenza in supporto al contributo del gene GRIN2B

sulla DD e sui deficit associate alla DD. Pi specificatamente, i nostri dati supportano la teoria per cui il

gene GRIN2B influenza la DD come tratto categoriale e i fenotipi ad essa associati, tali risultati ci

permettono di gettar luce sulle basi eziologiche e sulla complessit fenotipica di questo disturbo.

Parole chiave: Dislessia Evolutiva; Dislessia Evolutiva tratti neuropsicologici correlati; GRIN2B;

recettori N-methyl-D-aspartate; studio di associazione; interazione gene-ambiente.


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Introduction

Developmental dyslexia (DD) is a complex heritable condition typically diagnosed in the first school

years characterized by an impaired reading acquisition in spite of normal intelligence and adequate

educational opportunities (American Psychiatric Association, 1994), and associated with impairments in

multiple neurocognitive domains (Gabrieli, 2009). Several data show that intelligence quotient (IQ) is

strongly associated with reading development and impairment (Stanovich, 1986; Shaywitz et al., 1995;

Berninger et al., 2001; Ferrer and McArdle, 2004; Ferrer et al., 2007, 2010), although the exact nature of

this relationship still remains unclear (Newman et al., 1991; Rispens, 1991; Jimnez Glez and Rodrigo

Lpez, 1994; Stuebing et al., 2002).

Following earlier descriptions (Hallgren, 1950) of high familial aggregation of the disorder, substantial

heritability has been reported for DD (Fisher and De Fries, 2002). Twin studies show wide variation of

heritability estimates (range: 0.18 - 0.72; Plomin and Kovas, 2005) across DD and DD-related

phenotypes, while shared environmental influences appear to play a less important role in explaining the

familial aggregation of the disorder (Pennington, 1995). The aetiology of DD involves multiple

interacting risk factors, which can be either genetic or environmental, and underlies a continuously

distributed liability. Since the early 1980s at least nine DD risk loci have been mapped to chromosomes 1,

2, 3, 6, 15, 18 and X (Scerri and Schulte-Krne, 2010) and DYX1C1, KIAA0319, DCDC2and ROBO1

have been suggested as DD susceptibility genes (Taipale et al., 2003; Francks et al., 2004; Meng et al.,

2005; Hannula-Jouppi et al., 2005), although negative findings have also been reported.

Recently, a candidate gene for human cognitive processes, i.e., GRIN2B, located on chromosome

12p13.1, has been found significantly associated with working memory in a German DD sample (Ludwig

et al., 2009). In this study, four markers within intron 3 were associated with short-term memory in DD,

by suggesting that variation within this gene may contribute to the genetic background of specific DD-

related neuropsychological phenotypes (Ludwig et al., 2009). More specifically, GRIN2Bgene codes for

a specific subunit composition of N-methyl-D-aspartate (NMDA) receptors - a class of ionotropic

glutamate receptors (i.e., GluN2B subunit). Over the past years, the involvement of N-methyl-D-aspartate

receptors (NMDARs) in learning and memory formation has been emphasized (Cull-Candy et al., 2001;

Lau and Zukin, 2007; Kalia et al., 2008). NMDAR are ionotropic, glutamatergic receptors involved in

excitatory synaptic transmission in the central nervous system (Cull-Candy et al., 2001). In particular, the


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NMDAR-GluN2B subunit plays a critical role in experience-dependent, synaptic plasticity associated

with learning and memory (Kutsuwada et al., 1996; Ito et al., 1997; Tang et al., 1999; Kim et al., 2005;

Akashi et al., 2009; Fetterolf and Foster, 2011). Animal studies show that the Glun2b subunit is required

for neuronal pattern formation in general, and for channel function and formation of dendritic spines in

hippocampal pyramidal cells in particular (Ito et al., 1997; Cull-Candy et al., 2001; Kim et al., 2005;

Akashi et al., 2009). Transgenic over-expression of Grin2bin the forebrain of mice, and in the cortex and

hippocampus of rats results in an increased activation of the NMDARs, with mice and rats displaying a

superior performance in various tests of learning and memory (Tang et al., 1999; Wang et al., 2009).

These findings point towards GRIN2Bas a susceptibility gene for complex traits/disorders in humans.

Nevertheless, while genetic contributions are clearly relevant to the aetiology of these phenotypic

expressions, it is widely recognized that additional factors whose nature cannot be immediately

identified as genetic also act as powerful predictors for human cognition and neurodevelopmental

disorders, such as DD. A large range of factors, i.e. pre/perinatal (Bowen et al., 2002; Samuelsson et al.,

2006; van Baar et al., 2009; Fried et al., 1997; Batstra et al., 2003; Gilger et al., 1992; Michaelsen et al.,

2009; Hoque et al., 2012), socio-demographic and familial (Fergusson and Lynskey, 1993; Fergusson and

Woodward, 1999; Melekian, 2001; Hoff and Tian, 2005), seems to impinge on human cognition and DD

(Davis et al., 2001; Gayn and Olson, 2001, 2003; Byrne et al., 2002; Petrill et al., 2006; Harlaar et al.,

2007), and may contribute to heighten the likelihood of developing this disorder. Given the above

evidence, it seems then appropriate to complement genomic investigation of such complex traits by taking

into account gene-by-environment interaction (GxE), which is a specific form of interplay, whereby

genetic susceptibility conferred by a specified allele is modulated by a measured environmental factor

(Rutter et al., 2006).

Here, we sought to resolve which aspects of the phenotypic profile of DD were unambiguously

attributable to GRIN2Beffects by exploring the contribution of four SNPs which have been previously

shown to contribute to the genetic background of DD (i.e., rs1012586G/C; rs2268119A/T; rs2216128T/C;

rs2192973C/T; Ludwig et al., 2009) and two markers (i.e., rs5796555-/A and rs11609779C/T) close

enough to markers rs1012586G/C and rs2216128T/C, respectively, to be amplified and sequenced with

them, to DD, defined as a categorical trait, and key DD-related phenotypes by association analyses in a

sizeable sample of Italian nuclear families. Finally, we tested the role of the selected GRIN2Bmarkers


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and a set of commonly-described environmental moderators, by applying a test for GxE interaction in sib

pair-based association analysis of quantitative traits (van der Sluis et al., 2008; Mascheretti et al., 2013a).

Methods

The protocol was approved by the Scientific Institute Eugenio Medea Ethics and Scientific Board.

Sample

This study is part of an ongoing project on the genetics of reading disabilities at the Department of Child

Psychiatry and Rehabilitation Centre at the Scientific Institute Eugenio Medea, Bosisio Parini, Italy, and

at the Centro Regionale di Riferimento per i Disturbi dellApprendimento CRRDA (Regional

Reference Center for the Specific Learning Disability), ULSS 20, Verona, Italy (Mascheretti et al.,

2013a,b, 2014). To date, 466 unrelated nuclear families of probands with DD (738 offspring) have been

recruited; except for 47 families that had only one parent available, all parents were represented, yielding

a total sample of 1623 individuals, all of Italian ancestry.

The ascertainment scheme has been reported in detail elsewhere (Marino et al., 2003). Briefly, nuclear

families were recruited if probands met the criteria for DD according to the DSM-IV (American

Psychiatric Association, 1994). After parental informed consent, offspring underwent an extensive

medical assessment and a battery of tests, which evaluate text, word, non-word reading (Cornoldi and

Colpo, 1995, 1998; Sartori et al., 1995), writing-under-dictation of word, non-word and sentences-

containing-homophones (Sartori et al., 1995), forward/backward digit spans (Reynolds and Bigler, 1994),

phonemic elision and blending (Cossu et al., 1988), mathematics abilities (Cornoldi et al., 2003; Cornoldi

and Lucangeli, 2004), and the Wechsler Intelligence Scale for Children, Revised WISC-R (Wechsler,

1981) or the Wechsler Intelligence Scale for Children, third edition WISC-III (Wechsler, 2006). For all

tests standardized scores on the Italian population are provided. Sibs were administered only two subtests

of the intelligence scale, i.e. vocabulary and block design, that show a high correlation (r) with,

respectively, verbal IQ (r= 0.82; Wechsler, 1981, 2006) and performance IQ (r= 0.73; Wechsler, 1981,

2006). The criteria used to define the probands affection status were: A) a performance on a timed text-

reading test at least 2 standard deviations below the expected grade level mean on either accuracy or

speed; or: B) an absolute score at least 2 standard deviations below the expected grade level mean on


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accuracy or speed in a reading list of unrelated words or non-words; and: C) IQ > 84. Siblings were

included if they were fully-biological, older than 6 and younger than 18 years, if they had no history of

neurological and sensorial disorders, and if the mean score of vocabulary and block design subtests was

above 7, regardless of their reading performance. Blood or mouthwash samples were obtained from all

offspring and their biological parents.

Phenotypes

We considered 8 phenotypes in the single-marker TDT analyses:

1. DD as a discrete trait;

2.

READING, as measured by averaging speed (seconds) and accuracy (expressed in number of

errors) grade-standardized scores in text, word and non-word reading tasks (Cornoldi and Colpo, 1995,

1998; Sartori et al., 1995), since mean bivariate correlation among the above-mentioned tasks was

substantial (r= 0.450);

3. SPELLING, as measured by averaging the accuracy (expressed in number of errors), grade-

standardized scores in writing-under-dictation word, non-word and sentences-containing-homophones

(bivariate correlation among the above-mentioned tests r= 0.563; Sartori et al., 1995);

4. Performance IQ, as measured by the WISC-R or the WISC-III. For siblings, the block design

subtest was used to provide a prorated, performance IQ score (Wechsler, 1981, 2006);

5. Auditory STM, as measured by averaging age-normed scores in the forward and backward digit

spans (bivariate correlation among the above-mentioned spans r= 0.701; Reynolds and Bigler, 1994);

6. Phonemic elision (ELISION; Cossu et al., 1988);

7. Phonemic blending (BLENDING; Cossu et al., 1988);

8.

Mathematical abilities (Cornoldi et al., 2003; Cornoldi and Lucangeli, 2004), as measured by a

principal-component-analysis factor which accounted for 53% of total test variance among mental and

written calculation, number dictation and numerical facts (mean factor loadings= 0.64 0.08;

MATHEMATICS).

Of the total sample (n= 466; see Sample paragraph): i) 324 families (538 offspring) had complete

measures of READING, SPELLING, performance IQ, auditory STM (group 1), and ii) 174 families (223


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offspring) had complete measures of ELISION, BLENDING and MATHEMATICS (group 2). Group 1

and group 2 overlapped with respect to the phenotypic data available.

Environmental data collection

Selection of the environmental factors has been extensively described elsewhere (Mascheretti et al.,

2013a,b). Briefly, by employing a large case-control sample, we removed all variables with at-risk

category frequency under 5% and collapsed those variables that were highly correlated to obtain more

concise and comprehensive variables (Mascheretti et al., 2013a,b). Environmental factors included in

further analyses were:

1.

maternal smoking hereafter smoke;

2.

risk of miscarriage during pregnancy hereafter miscarriage;

3. birth weight;

4. breast feeding;

5. parental age, as an average of father and mothers age at childs birth;

6. socio-economic status SES, defined by parental employment;

7. parental education, as an average of father and mothers educational qualifications.

Dichotomous responses (yes/no) were coded with 0 when answers were no, and 1 when answers

were yes, except for breastfeeding, which was coded as 0 if child had been breastfed. The employment

response was coded according to the Hollingshead 9-point scale (Hollingshead, 1975). A score (from 10

to 90) was assigned to each job; the higher of two scores was used when both parents were employed.

The educational qualification response was scored according to a 9-point ordinal scale based on the

Italian school system (range between 10, corresponding to fifth-grade elementary school, and 90,

equivalent to a post-doctoral degree). One-hundred and seventy-eight families (144 pairs, 31 triplets, 2

with four siblings and 1 with five siblings, 394 offspring) had at least one sibling and complete

environmental data (group 3); in this latter group, complete phenotypes were available for READING,

SPELLING, performance IQ, auditory STM, while only 60 families had complete data for ELISION,

BLENDING and MATHEMATICS, which were therefore excluded from GxE analyses, due to limited

power. As for group 2, Group 1 and group 3 overlapped with respect to the phenotypic data available. All


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environmental factors, except SMOKE, MISCARRIAGE, breast feeding, and parental marital status were

z-transformed based on the sample norms to avoid a scaling effect in further analyses.

Genotyping

Amplification and sequencing of four portions of the human GRIN2B gene allowed typing of the

following SNPs: rs5796555-/A; rs1012586G/C; rs2268119A/T; rs2216128T/C; rs11609779C/T;

rs2192973C/T. In particular, we chose to genotype the four SNPs that in a previous study were found

significantly associated with short-term memory in a German DD sample (i.e., rs1012586, rs2268119,

rs2216128, and rs2192973; Ludwig et al., 2009), suggesting that variations in GRIN2Bmay contribute to

the genetic background of specific cognitive processes which are correlated to DD. Markers rs5796555-

/A and rs11609779C/T are close enough to markers rs1012586G/C and rs2216128T/C, respectively, to be

amplified and sequenced with them. Amplifications were performed in 10-microliter reactions using

JumpStart Red ACCUTaq LA DNA polymerase (Sigma) and the following protocol: 30 s at 96C, 35

cycles of 15 s at 94C/20 s at 58C/30 sec at 68C, 5 min final elongation time. Sequencing reactions

were performed with a Big Dye Terminator Cycle Sequencing kit (Applied Biosystems) and run on an

ABI Prism 3130xl Genetic Analyzer. (Primers are available on request from the authors).

Genotype error checking was completed in PEDSTATS (Wigginton and Abecasis, 2005) and inconsistent

genotypes were zeroed-out and were not considered for further analysis. Families were excluded in the

case of multiple inconsistent genotypes. Allelic frequencies and Hardy-Weinberg equilibrium for the

markers under consideration were calculated in parents. No significant deviations from allele frequencies

reported in Ludwig et al. (2009) were found. For all the 6 genotyped SNPs, p-values for deviation from

the HardyWeinberg equilibrium were not lower than 0.008 (= 0.05/6), and thus no SNPs were excluded.

The linkage disequilibrium structure of GRIN2Bwas analyzed using the parental genotypes only; linkage

disequilibrium was extracted and plotted in Haploview 4.0). Even if markers rs5796555-/A and

rs11609779C/T were typed as a by-product of rs1012586G/C and rs2216128T/C, respectively, they were

not excluded from further analysis since they did not show correlation with any of other genotyped SNPs.

Statistical analysis

Genetic association analysis


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Genetic association was investigated by using a family-based association test, i.e., the quantitative

transmission disequilibrium test (QTDT, version 2.5.1) as modeled by Abecasis et al. (2000), which

allows to control for population stratification bias and for the analyses of quantitative traits. Single-

marker TDT analyses were carried out for DD as a discrete trait using the -ad option which allows to

test using only affected individuals. Quantitative traits were analyzed using the -wega option which

allows to adopt commonly used variance components, i.e., the environmental variance (e), polygenic

variance (g) and additive major locus (a). Genetic findings are not presented for the whole set of families

because of missing data and because transmission disequilibrium tests require individuals to have

heterozygous ancestors in the pedigree so offspring of homozygous parents are uninformative (Abecasis

et al., 2000). We calculated the genetic association for the total sample. Moreover, since following-up

association signals over different severity groups may confirm a possible true contribution of the selected

SNPs to DD and DD-related performance (Ludwig et al., 2009), we tested the genetic association for a

subsample selected by severity, i.e., by selecting only the nuclear families in which at least one offspring

scored !2.50 SD below the general population mean on either accuracy or speed in either text-, or word-,

or non-word reading tasks (n= 292). Only empirical p-values are reported which are computed from

10.000 Monte-Carlo permutations by entering all the selected markers (see Methods section,

Genotyping paragraph) and the above-described phenotypes (see Methods section, Phenotypes

paragraph) at the same time. Bonferroni correction for multiple testing was not applied because it would

have been too conservative (Deffenbacher et al., 2004; Francks et al., 2004; Cope et al., 2005; Meng et

al., 2005; Schumacher et al., 2006; Brkanac et al., 2007; Marino et al., 2012). Thus far, we decided to

adjust the significance levels by the false discovery rate (FDR) method (Storey, 2002) applied to the 7

neuropsychological traits analyzed for each marker, separately for each SNP.

Gene-by-environment interaction analysis

In order to explore the conjoint role of candidate genes markers and measured environmental factors upon

performance IQ and DD-related neuropsychological skills (i.e., READING, SPELLING, and Auditory

STM), we analyzed GxE effects through a general test for GxE interaction in sib pair-based association

analysis of quantitative traits (van der Sluis et al., 2008).

Briefly, this statistical model, is an extension of the Fulker et al.(1999) maximum likelihood variance

components analysis of quantitative traits in sib-pairs data that incorporates environmental main effects


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plus GxE effects (van der Sluis et al., 2008). The association effect is orthogonally decomposed here into

between-family (b) and within-family (w) effects, that is the expectation of each sib genotype conditional

on family genotype data and the deviation from this expectation for each offspring, respectively. To

model the interaction effect, we used the sibling-specific value for each environmental risk variable; the

phenotypic score (i.e., the observed phenotypic score y for subject j from family i with genotype g) is then

modeled as:

yijg="i + abAbi + awAwij + eEi+ ibgEijAbi + iwgEijAwij + #ij

where"i is the family-specific intercept, aband aware the estimated between- and within-family additive

genetic effects of the marker, according to the orthogonal decomposition, e represents the effects of the

environmental risk factor, ibg and iwg represent the between- and within-family effects of the interaction of

genotype g and the environmental risk factor, and #ij is the residual term (van der Sluis et al., 2008). As

such, iwgbecomes an estimate of the GxE effect inasmuch as it indicates the change in the allelic variant

marker association with the phenotype across the different ecological niches, and it represents the

variation of SD units of the z-score on the neuropsychological composite. For instance, a positive value

represents the increase of the effect of each additional transmission of the minor allele on the

neuropsychological composite z-score when environmental variable increases. A negative value of iwg

represents a decrease of the same effect (for more information, see Mascheretti et al., 2013a).

The permutation procedure was repeated 1.000 times for each analysis. We decided to adjust the

significance levels by the false discovery rate (FDR) method (Storey, 2002) applied to the 28 tests

performed for each marker (7 environmental variable x 4 phenotypes), separately for each marker

(Mascheretti et al., 2013a). Gender was taken into account in the extended equation because probands

sex ratio (males:females) in our sample was nearly 3:1, and it may imply differences in mean scores

between males and females.

Moreover, since simulations studies revealed specific situations in which dichotomized variables

performed as well as or better than the original quantitative factors, we subsequently decided to

dichotomize raw scores of quantitative environmental variables (i.e., birth weight, parental age, SES, and


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parental education; see Methods, Environmental data collection section) according to well-defined

cut-off points.

Results

Genetic association analyses with DD and DD-related quantitative traits

Significant associations were found between DD as a discrete trait and the minor alleles A and G of

the markers rs5796555-/A and rs1012586G/C, respectively, in the total sample (informative families=

331, T= -2.04, standard deviation= 8.63, nominal p-value= 0.043, and informative families= 347, T= -

1.99, standard deviation= 8.65, nominal p-value= 0.047, respectively). In the selected-by-severity

subsample DD as a discrete trait was significantly associated with the minor allele A of the marker

rs5796555-/A (informative families= 227, T= -1.99, standard deviation= 6.65, nominal p-value= 0.048),

while only a trend towards significance was observed for rs1012586G/C marker (informative families=

234, T= -1.79, standard deviation= 6.84, nominal p-value= 0.076). Table 1 and 2 shows both the

empirical p-values obtained after implementing 10.000 Monte-Carlo permutations and the q-values after

FDR correction.Market-trait associations for READING and SPELLING were non-significant in both

group 1 and in the selected-by-severity subsample (Table 1). Performance IQ was significantly associated

to the minor alleles T, C and T of markers rs2268119A/T ( $2= 6.44;nominal p-value= 0.011;

empirical p-value= 0.009; q-value= 0.004; 224 informative families; genetic effect= -4.714; Table 1),

rs2216128T/C ($2= 12.38;nominal p-value


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empirical p-value= 0.037; q-value= 0.041; 118 informative families; genetic effect= -0.427; Table 2).

BLENDING and MATHEMATICS did not yield any significant association with any markers (Table 2)

in both group 2 and the selected-by-severity subsample.

Gene-by-environment interaction analysis

We analyzed GxE interaction effects between 6 markers and 7 environmental factors through the general

test for GxE interaction upon READING, SPELLING, performance IQ, and auditory STM composites in

group 3. Although we found G-E combinations whose empirical p-values of the iwg term was significant,

none survived after FDR correction. With regard to the investigation of the gender effect, no empirical p-

values were significant.

Finally, we analyzed GxE interaction effects between 6 markers and the 4 dichotomized quantitative

environmental factors (see Methods, Statistical Analysis section, Gene-by-environment interaction

analysis paragraph) through the general test for GxE interaction upon the same neurosychological

phenotypes (i.e., READING, SPELLING, performance IQ, and auditory STM) in group 3. We found G-E

combinations whose empirical p-values of the iwgterm was significant and which showed 33% overlap

with results obtained by implementing quantitative variables, but none survived after FDR correction. No

significant gender effect was found.

Discussion

The ability to read relies on a complex, highly integrated, large-scale network of different cognitive

processes. Recently, research applied quantitative cognitive measures to dissect the heterogeneous DD

phenotype with respect to the genetic contribution (Marlow et al., 2003; Paracchini et al., 2007; Schulte-

Krne et al., 2007).

This study was primarily designed to replicate previous findings of the involvement of the GRIN2Bgene

in DD and its related neuropsychological phenotypes, in a sizable sample of Italian nuclear families

ascertainedfor DD. We therefore investigated both linear and GxE interplay effects upon DD and DD-

related quantitative phenotypes. QTDT analyses yielded significant associations between markers

spanning within GRIN2B and DD as a categorical trait and its related quantitative phenotypes, i.e.,

performance IQ, auditory STM and phonemic elision. These findings suggest that GRIN2Bcan account

for not only part of the disabilities proper of DD, defined as a categorical trait, but also for part of the


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reduced skills in key DD-related cognitive domains that are observable in these children. Indeed, DD is a

heterogeneous disorder in which various phenotypic dimensions are involved, including phonological

processing, and memory processes (Gabrieli, 2009). As such, our data support the view that GRIN2Bmay

influence inter-individual variation in multiple DD-related phenotypes thus shedding further light into the

etiologic basis and the phenotypic complexity of DD.

The association between GRIN2Band forward/backward digit spans, which measure memory formation,

storage and retrieval with an additional component of manipulation of items for the backward recall, can

be considered a replication of a previous study in which this task had been found associated with GRIN2B

variation in a sample ascertained for DD (Ludwig et al., 2009). This finding, together with the reported

association with phonemic elision task, is in harmony with several studies showing that individuals with

DD perform poorly in memory-related task (Swanson et al., 2009), and with genetic studies reporting that

variation in reading performance is explained by specific genes and by a set of genes in common with

STM and working memory. These evidence suggest a common genetic factor that could explain the

storage and manipulation of phonological relevant information, which are important aspects of STM and

working memory and fundamental steps in the reading acquisition process (van Leeuwen et al., 2009).

Turning to the association with performance IQ, it points towards GRIN2B as a susceptibility gene

explaining the observed phenotypic relationship between IQ and reading. Several studies show that IQ is

one factor typically associated with differences in reading development and reading disabilities

(Stanovich, 1986; Shaywitz et al., 1995; Berninger et al., 2001; Ferrer and McArdle, 2004; Ferrer et al.,

2007, 2010), suggesting an underlying common genetic etiology between IQ and DD. Actually, twin

studies show that shared genetic influences accounted for more than half of the phenotypic covariance

between the reading tests and IQ in both reading-disabled (Light et al., 1998) and control twin samples

(Wadsworth et al., 1995; Light et al., 1998; Luo etal., 2003; Wainwright et al., 2004). In particular, there

was evidence that genetic influences contribute a larger proportion of the covariance between

Performance IQ tests and reading measures than for Verbal IQ tests and reading measures (Wainwright et

al., 2004). This is due to the common environment factor having a somewhat stronger influence on the

correlations among Verbal IQ tests and reading measures, possibly through formal education or the

intellectual environment within the home, while having less effect on the correlations among Performance

tests (Wainwright et al., 2004). As such, these findings likely represent a first evidence in favor of the


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GRIN2Bgene as one of the multiple genes implicated in the reported genetic covariance between reading

(dis)ability and IQ.

Finally, although our results were not significant, the investigation of GxE interplay effects provides

preliminary evidence that GRIN2Bcan exert detrimental effects upon DD-related cognitive phenotypes in

response to environmental disadvantage, contributing to the intense debate about unraveling the intimate

mechanisms of response of this gene to environment. To date, only three studies investigated the

interactions effects between GRIN2B and environment in humans, both showing that environment

moderates the association between this gene and human behavioral and cognitive variation (Demontis et

al., 2011; Sokolowski et al., 2012; Riva et al., under review). In the light of these data, a larger sample is

needed in order to replicate previous studies and provide further evidence about the moderating effects

exerted by the GRIN2B gene upon the development of complex traits in interaction with putative

environmental risk variables.

In summary, our data add further evidence indicating shared biological or cognitive processes that

underlie DD and DD-related cognitive phenotypes. Since the N-methyl D-aspartate 2B receptor is found

at high concentrations in the hippocampus (Charton et al., 1999), contributes to experience-dependent

synaptic plasticity at structural and functional levels (Cull-Candy et al., 2001), and is implicated in both

basic neuronal functions and central nervous system processes and in behavioural tasks (Lau and Zukin,

2007; Kalia et al., 2008), it is plausible that GRIN2B might influence development of etiologically

heterogeneous disorders in which learning and memory functions represent core features, such as DD.

Nevertheless, these results should be viewed with some limitations in mind. First, the results of these

univariate association analyses should be considered exploratory, and in the future, the relationship

between potential risk loci and the various aspects of these traits may be better explored using newly

developed methods for multivariate association analyses of multiple related traits. Second, as with all

analyses of complex traits, the significance of the current association findings should be interpreted

cautiously until they can be replicated in independent samples of DD.

Acknowledgments


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We thank all the parents and children who took part in this study. We express our gratitude to Ermanno

Quadrelli, Lara Francesca Emilia Lanzoni, Lisa Meneghello and Elisabetta Furioni for helping in data

collection. Finally, we thank Giorgia Menozzi for helping in statistical analyses.

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