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. Author manuscript; available in PMC: 2014 Oct 1.
Published in final edited form as: Psychiatr Genet. 2014 Oct;24(5):191–200. doi: 10.1097/YPG.0000000000000045

Association between AVPR1A, DRD2, and ASPM and Endophenotypes of Communication Disorders

Catherine M Stein 1,5, Barbara Truitt 1, Fenghua Deng 1, Allison Avrich Ciesla 1, Feiyou Qiu 1, Peronne Joseph 1, Rekha Raghavendra 1, Jeremy Fondran 1, Robert P Igo Jr 1, Jessica Tag 2, Lisa Freebairn 2, H Gerry Taylor 2,3, Barbara A Lewis 2, Sudha K Iyengar 1,4
PMCID: PMC4141900  NIHMSID: NIHMS599810  PMID: 24849541

Abstract

Objectives

Speech sound disorder (SSD) is one of the most common communication disorders, with prevalence rates of 16% at 3 years of age, and an estimated 3.8% of children still presenting speech difficulties at 6 years of age. Several studies have identified promising associations between communication disorders and genes in brain and neuronal pathways, but there have been few studies focusing on SSD and its associated endophenotypes. Based on the hypothesis that neuronal genes may influence endophenotypes common to communication disorders, we focused on three genes related to brain and central nervous system functioning: dopamine D2 receptor (DRD2), arginine-vassopressin receptor 1a (AVPR1A), and microcephaly gene ASPM.

Methods

We examined the association of these genes with key endophenotypes of SSD – phonological memory measured by multisyllabic and nonword repetition, vocabulary measured by Expressive One Word Picture Vocabulary Test (EOWPVT) and Peabody Picture Vocabulary Test (PPVT), and reading decoding measured by Woodcock Reading Mastery Tests Revised – as well as the clinical phenotype of SSD. We genotyped tag SNPs in these genes and examined 498 individuals from 180 families.

Results

These data show several SNPs in all three genes were associated with phonological memory, vocabulary, and reading decoding with p<0.05. Notably, associations in AVPR1A (rs11832266) were significant after multiple testing correction. Gene-level tests showed DRD2 was associated with vocabulary, ASPM with vocabulary and reading decoding, and AVPR1A with all three endophenotypes.

Conclusions

Endophenotypes common to SSD, language impairment, and reading disability are all associated with these neuronal pathway genes.

Keywords: neural genes, phonology, speech impairment, language development, genetics

INTRODUCTION

Speech sound disorder (SSD) is one of the most common types of communication disorders, with prevalence rates of 16% at 3 years of age, and an estimated 3.8% of children continuing to present speech delay at 6 years of age (Shriberg, Tomblin, & McSweeny 1999). SSD is characterized by difficulties in articulation and weaknesses in phonological representation. SSD is often co-morbid with language impairment (LI) (Shriberg, Tomblin, & McSweeny 1999) and may be a precursor to reading disability (RD) (Lewis, Freebairn, & Taylor 2000a). These three communication disorders are also associated with similar deficits of cognitive skills or “endophenotypes” such as phonological memory, phonological awareness, vocabulary, and appreciation of sound-symbol relationships as required for reading decoding (DeThorne et al. 2006;Lewis et al. 2011;Rvachew 2007).

Several studies have shown that SSD and other communication disorders are partially influenced by genetic factors (Lewis et al. 2006;Newbury & Monaco 2010). Because of the comorbidity among SSD, LI, and RD, and the cognitive domains they share, many studies have revealed common genetic effects among loci linked and/or associated with SSD, LI, and RD, as well as their shared endophenotypes (Bishop & Hayiou-Thomas 2008;Lewis, Shriberg, Freebairn, Hansen, Stein, Taylor, & Iyengar 2006;Pennington & Bishop 2009). Vocabulary skills are related to both reading decoding and comprehension. In addition, vocabulary is associated with the formation of phonological representations for words, as they may be stored and organized in memory according to their phonological features and articulatory gestures (Goswami 2001). Phonological Memory (PM) is the component of short term memory that holds a temporary store of phonological information, a process necessary for the formation of stable phonologic representations (Gathercole & Baddeley 1990). Deficits in PM, as measured by the repetition of multisyllabic real and nonwords, may impair an individual’s ability to learn both spoken and written new words and result in SSD, LI and/or RD (Bishop, Adams, & Norbury 2006;Shaywitz 1998). The endophenotype of reading decoding refers to the process of translating printed words into sounds. Reading decoding draws on skills such as phonological awareness that may be deficient in children with SSD, LI and/or RD (Catts et al. 1999;Nathan et al. 2004). Genetic association studies of biologically plausible candidate genes may help to elucidate how and why these phenotypes are correlated (Lewis, Shriberg, Freebairn, Hansen, Stein, Taylor, & Iyengar 2006).

Cognitive endophenotypes are more closely tied to underlying gene expression and thus have increased power to detect genetic association (Gottesman & Gould 2003;Rice, Saccone, & Rasmussen 2001). In addition, continuous traits as opposed to binary traits have increased statistical power for detecting genetic effects (Duggirala et al. 1997). As described above, the endophenotypes of phonological memory, vocabulary, and reading decoding are key components of multiple communication disorders, and regulation of these and related executive functions occurs in the same regions of the brain (Barbey et al. 2012). Thus, our focus on these endophenotypes reveals genetic effects that may be relevant to SSD, LI, and RD. Indeed, previous linkage analyses performed by our groups and others (Cardon et al. 1994;Gayan et al. 1999;Miscimarra et al. 2007;Smith SD et al. 2005;Stein et al. 2004;Stein et al. 2006;Willcutt et al. 2002) that utilized endophenotypes have been very successful in identifying significant linkage to the same chromosomal regions that were ascertained through different communication disorders.

Promising candidate gene associations with LI and RD have been found with genes in brain and neuronal pathways (Newbury & Monaco 2010). In our previous work, we conducted linkage analysis of regions that were previously linked to dyslexia, using endophenotypes common to RD and SSD, and found that these same chromosomal regions are linked to multiple correlated phenotypes (Lewis, Avrich, Freebairn, Hansen, Sucheston, Kuo, Taylor, Iyengar, & Stein 2011;Miscimarra, Stein, Millard, Kluge, Cartier, Freebairn, Hansen, Shriberg, Taylor, Lewis, & Iyengar 2007;Stein, Schick, Taylor, Shriberg, Millard, Kundtz-Kluge, Russo, Minich, Hansen, Freebairn, Elston, Lewis, & Iyengar 2004), it is also possible that similar phenotypes may have different underlying etiologies (Lewis, Shriberg, Freebairn, Hansen, Stein, Taylor, & Iyengar 2006). Previous studies of SSD have focused on dyslexia candidate genes, and have not explored new genes in neural pathways; thus, genetic association studies are needed in this understudied area.

Dopamine has a major role in fine motor movements (Comings, Wu, Chiu, Ring, Gade, Ahn, MacMurray, Dietz, & Muhleman 1996) due to its role in neural inhibition (Berman & Noble 1995). It is involved in language learning, procedural learning and working memory (Wong et al. 2012). In an fMRI study, the dopamine D2 receptor (DRD2) was associated with activity of the cortico-subcortical motor region (Fazio et al. 2011). DRD2 is a candidate gene for attention deficit hyperactivity disorder (ADHD) (Marino et al. 2004), which is often comorbid with various communication disorders (Lewis et al. 2012); because of this comorbidity, DRD2 has been considered a candidate gene for communication disorders. DRD2 has been associated with dysfluent speech and stuttering in a Han Chinese population (Lan et al. 2009), verbal skills as measured by the Peabody Picture Vocabulary Test (Beaver et al. 2010), and stuttering in children with Tourette syndrome (Comings, Wu, Chiu, Ring, Gade, Ahn, MacMurray, Dietz, & Muhleman 1996) but was not associated with dyslexia in an Italian population (Marino et al. 2003). Most recently, a SNP in DRD2 has been associated with overall language deficits captured by nonword repetition and verbal comprehension (Eicher et al. 2013).

The arginine-vasopressin (AVP) hormone affects social behavior and vocalization (Winslow & Insel 1991), and has been associated with autism and social behavior and cognition (Ebstein et al. 2012) as well as cognitive processes related to social, non-emotional information (Brunnlieb et al. 2013). AVP receptor 1a (AVPR1A) has been associated with LI in autistic children (Wassink et al. 2004). A small study of university students found that AVPR1A was associated with musical and phonological memory (Granot et al. 2007).

The Abnormal spindle-like microcephaly-associated protein (ASPM) gene is one of the genes that causes microcephaly, and it is involved in orientation of mitotic spindles during embryonic neurogenesis, adult neurogenesis, and decreased neural cell number (Mahmood, Ahmad, & Hassan 2011), through Wnt signaling pathways (Buchman, Durak, & Tsai 2011). Dediu and Ladd proposed that ASPM is associated with subtle differences in the organization of the cerebral cortex, resulting in cognitive consequences (Dediu & Ladd 2007). Dediu (Dediu 2011) hypothesized a relationship between ASPM and language development based on ecological analyses, and Kelsey et al. (Kelsey et al. 2007) reported an association between ASPM and single word reading and speech sound production proficiency. In addition, ASPM variants have been associated with lexical tone perception in Mandarin (Wong, Chandrasekaran, & Zheng 2012).

Though these three genes have plausible roles in brain and nervous system functioning, few studies have examined their potential contributions to communication disorders. Furthermore, most of these studies have only focused on one or a few polymorphism(s) per gene, thus overlooking other polymorphisms with unknown function and the complex linkage disequilibrium (LD) structure within these genes, and these genes do not reside on chromosomal regions previously linked to SSD, LI, and/or RD. The objective of this study was to examine the association of DRD2, AVPR1A, and ASPM with endophenotypes related to SSD. We focused on phonological memory, vocabulary, and reading decoding, as deficits in these domains are associated with SSD as well as with LI and RD.

METHODS

Study Design and Patient Enrollment

Data were collected as part of an ongoing longitudinal study of SSD that has been described in detail elsewhere (Lewis, Avrich, Freebairn, Hansen, Sucheston, Kuo, Taylor, Iyengar, & Stein 2011;Lewis, Freebairn, & Taylor 2000b;Stein, Schick, Taylor, Shriberg, Millard, Kundtz-Kluge, Russo, Minich, Hansen, Freebairn, Elston, Lewis, & Iyengar 2004). To summarize, probands were referred to the study from clinical caseloads of speech-language pathologists in the Greater Cleveland area. Probands were identified between the ages of 4 and 6, were enrolled in therapy for moderate to severe SSD, and had normal hearing and intelligence. Siblings were diagnosed with SSD using the same criteria as the probands, based on their performance on the Goldman-Fristoe Test of Articulation (GFTA) (Goldman & Fristoe 1986). Parents were defined as having SSD if they reported a history of speech therapy. All children and parents providing informed consent were enrolled in the study. As part of the longitudinal design, children and adults were administered age-appropriate measures (described below) at each visit; for statistical analyses, the first available assessment for each measure was utilized. Both the age range of the specific test and the cooperativeness of the individual children or parents affected the sample size available for analyses, so available sample sizes for each measure are provided below. This study was approved by the institutional review board of the Case Medical Center and University Hospitals of Cleveland, Ohio.

Measures

Phonological memory

The Multisyllabic Word Test (MSW) (Catts 1986) and Nonsense Word Repetition Test (NSW) (Kamhi & Catts 1986) were used to assess phonological short-term memory skills. Participants 4 years of age through adult were asked to repeat 20 multisyllabic real (MSW test) and 15 nonsense words (NSW test) in response to audiorecorded presentations of the words. Responses were audiorecorded and transcriptions scored to obtain the percentage of words correctly repeated. In prior studies, MSW and NSW have discriminated individuals with histories of speech and language disorders who no longer demonstrate overt speech production errors in conversational speech from individuals without such histories, even into adulthood (Lewis et al. 2007;Lewis & Freebairn 1998). Data were available for 498 individuals for MSW and NSW.

Vocabulary

Peabody Picture Vocabulary Test–Third Edition (PPVT) (Dunn & Dunn 1997) and the Expressive One Word Picture Vocabulary Test–Revised (EOWPVT) (Gardner 1990) assess receptive and expressive vocabulary, respectively. PPVT is standardized for individuals 2–80 years of age, and EOWPVT is standardized for individuals 2–18 years of age. PPVT scores were available for 450 individuals, and EOWPVT scores for 405 children.

Reading measures

We administered the Woodcock Reading Mastery Tests–Revised (Woodcock 1987) Word Identification (WI) and Word Attack (WA) subtests to assess reading decoding. The WI task includes reading and identification of real words, and the WA test assesses reading decoding of nonwords. These reading tests are appropriate for children aged 7–18 years. WI scores were available for 467 children and WA scores for 466 children.

Because these individual measures may tap different cognitive domains (eg, expressive versus receptive language, identification of known words versus application of phonetic and structural analysis to read unknown words) (Bishop & Hayiou-Thomas 2008;Conti-Ramsden & Botting 1999;Woodcock 1987), we analyzed them individually rather than creating composite scores.

Binary traits

In addition to examining history of SSD (as defined in the Patient Enrollment section above) as a binary trait, we also created a binary trait using the GFTA. Analysis of GFTA as a continuous variable is problematic because it has a steep trajectory; typical children do not have overt speech errors beyond 9 years of age (Shriberg 2010;Smit et al. 1990;Wren, Roulstone, & Miller 2012). Thus, we created a binary variable capturing scores above and below the 25th percentile; this represents a severe articulation phenotype. The first version of the GFTA is normed through 16 years of age and the second version is normed through age 21, so parents do not get tested on this measure. This variable was available for 355 individuals.

Genetic methods

DNA was collected from blood or saliva samples and prepared as previously reported (Stein, Schick, Taylor, Shriberg, Millard, Kundtz-Kluge, Russo, Minich, Hansen, Freebairn, Elston, Lewis, & Iyengar 2004). We genotyped single nucleotide polymorphisms (SNPs) within DRD2, AVPR1A, and ASPM using a KASP platform at LCG Genomics (Teddington, Middlesex, UK) and called via the KASP SNP Genotyping System. SNP functions were looked up on Genome Variation Server (http://gvs.gs.washington.edu/GVS137/) and SNPDOC (https://wakegen.phs.wakehealth.edu/public/snpdoc/index.cfm). We used a haplotype-tagging approach to capitalize on the correlation (LD) structure within the genes while minimizing genotyping efforts (Gabriel et al. 2002). TagSNPs were selected with an R-squared threshold of 80% and 5% minor allele frequency (MAF) in the CEPH reference population, which is most appropriate for a predominantly Caucasian study population, using Haploview (Barrett et al. 2005). SNPs were selected 20 kb up- and downstream of the gene. Both parents and offspring were genotyped. There were 4 SNPs in ASPM, 7 in AVPR1A, and 27 in DRD2, with a range of MAF from 3.3% to 49.9% in our study sample.

Statistical analysis

Prior to further analysis, z-scores were computed for each of the measures described above after adjusting for age (at assessment) and age2. The distribution of endophenotypes in cases vs. controls was assessed using a t-test in SAS version 9.3. Genotype distributions and MAF were estimated in founders and non-founders using PLINK v1.07 (http://pngu.mgh.harvard.edu/purcell/plink/) (Purcell et al. 2007). SNP genotypes were also tested for Hardy-Weinberg proportions in founders and non-founders, both affected and unaffected with SSD, using PLINK. Standard quality control (QC) filters were applied (Laurie et al. 2010), resulting in exclusions of SNPs with Hardy-Weinberg equilibrium (HWE) χ2 P-value <1.0×10−6, MAF <0.01, or genotype call rates <95%. Call rates and MAF in our data met these criteria. Only one SNP was in significant departure from Hardy-Weinberg proportions: rs4938014 in DRD2, with p= 3×10−10 in unaffected founders, and p=3.7 × 10−20 in all unaffected individuals, though this latter test is biased because it does not properly account for relationships. This SNP was discarded from further analysis. Analyses were conducted in the entire sample as a whole and repeated in the Caucasian subsample. In general, the results were consistent among these two analyses, though slightly less significantly (within the same order of magnitude) so in the Caucasian subsample, as would be expected in a smaller sample size. Thus, only the results from the entire sample are presented for brevity.

Genetic association analyses were conducted using a method that models familial correlations as a polygenic random effect and estimates polygenic variance, as implemented in GWAF version 1.2 (Chen & Yang 2010). Both parents and offspring are included in this analysis when their phenotypes are available. For binary traits, GWAF estimates the correlation structure among relatives via a generalized estimating equation (GEE) model. Sex was included as a covariate in the model. SNPs were coded according to an additive or dominant model with respect to the minor allele. If there were fewer than 10 homozygotes for the rare allele in either the affected or unaffected group (Supplemental Table 1), the results from the additive model were not considered. Similarly, if there were fewer than 10 rare homozygotes plus heterozygotes, results from the dominant model were not considered. Adjustment for multiple testing of SNPs was performed with a correction based on the effective number of independent tests accounting for LD structure among SNPs in the same gene, as determined by the program SNPSpDlite (http://gump.qimr.edu.au/general/daleN/SNPSpDlite/) using the estimate proposed by Li and Ji (Li & Ji 2005). This analysis estimated that there are 38 independent tests, resulting in a corrected significance level of α=0.0013.

Additionally, because of the multiple SNPs and multiple traits analyzed, we examined a gene-level statistic for each trait. Gene-level tests have two primary advantages. First, by providing a single test of association for a given gene and endophenotype, interpretation for the reader is simpler and reflects the gene’s role in that endophenotype and not necessarily that of a specific genetic variant. Second, a single genetic association test per gene reduces the multiple testing problem, and thus a traditional α=0.05 level may be used for declaring statistical significance. These tests are essentially burden tests for common variants. For this, we used VEGAS (http://gump.qimr.edu.au/VEGAS/), which implements the method of Liu et al. (Liu et al. 2010) to derive a summary statistic for all SNPs within a gene (or pathway) while accounting for LD among the SNPs. This method was used for DRD2 and ASPM. Because AVPR1A had a SNP with low numbers of individuals with the minor allele (see Supplemental Table 1), we used a burden test that is appropriate for rare and common alleles in family data (Feng, Elston, & Zhu 2011). This method computes a “genetic score” that can be used as a fixed effect within a regression model.

RESULTS

This analysis included 498 individuals from 180 families, including both children and parents (Table 1). Of the individuals in this study, 50.2% had SSD. Most of them were males (67.2%, data not shown), which is typical of SSD because it is more prevalent in males than females. Most of the endophenotypes had significantly different means in individuals with and without SSD with p < 0.05, with the exception of EOWPVT (p=0.0665) (Table 2). This reaffirms our previous work demonstrating these endophenotypes significantly differ in individuals with and without SSD. In addition, as we have shown in our previous analyses, these endophenotypes are highly correlated (Table 3); measures within the same domain (phonological memory, vocabulary, and reading) were highly correlated (r > 0.6), and measures across domains were also significantly correlated (r > 0.3).

Table 1.

Sample description

Number of individuals 498
Number of families 180
Males / females (includes individuals with and without SSD) 271 / 227
Number of parents / children 105 / 366
Number of individuals with SSD 250 (50.2%)
Ethnic distribution
Caucasian 427 (85.7%)
African American 40 (8.0%)
Hispanic 4 (0.8%)
Middle Eastern 1 (0.2%)
East Asian 5 (1.0%)
Native American 1 (0.2%)
Mixed 20 (4.0%)
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Table 2.

Descriptive statistics for measures for individuals with and without speech sound disorder (SSD)

Individuals with SSD Mean (SD) Individuals without SSD Mean (SD) P-Value*
EOWPVT 106.25 (18.10) 114.47 (16.18) 0.0665
PPVT 100.74 (16.51) 108.45 (14.58) 0.0427
NSW 36.47 (27.50) 64.62 (22.26) 0.0037
MSW 39.28 (30.81) 77.99 (23.84) <.0001
WI 95.52 (17.22) 102.69 (13.28) <.0001
WA 95.22 (18.44) 104.87 (13.69) <.0001
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*

T-test comparing individuals with and without SSD, after adjustment for age and age-squared.

EOWPVT = Expressive One Word Picture Vocabulary Test, PPVT = Peabody Picture VocabularyTest, NSW = nonsense word repetition task, MSW = multisyllabic word repetition task, WI = single word reading task (Word Identification), WA = nonsense word reading task (Word Attack). All scores are standard scores except NSW and MSW, which are percent correct.

Table 3.

Correlation between measures

EOWPVT PPVT NSW MSW WI WA
EOWPVT 1
PPVT 0.61** 1
NSW 0.35** 0.35** 1
MSW 0.38** 0.39** 0.75** 1
WI 0.37** 0.49** 0.47** 0.46** 1
WA 0.34** 0.43** 0.43** 0.46** 0.81** 1
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**

significant at p < 0.001.

EOWPVT = Expressive One Word Picture Vocabulary test, PPVT = Peabody Picture vocabulary test, NSW = nonsense word repetition task, MSW = multisyllabic word repetition task, WI = single word reading task (Word Identification), WA = nonsense word reading task (Word Attack).

As can be seen by the genotype counts in affected and unaffected individuals (Supplemental Table 1), many SNPs had fewer than 10 rare homozygotes. For this reason, only results from the dominant model were considered further. The LD patterns for each gene are shown in Figures 1a–1c. The SNPs in ASPM were in very high LD, and the SNPs in AVPR1A and DRD2 were in moderate to high LD.

Figure 1.

Figure 1

Figure 1

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LD plots for a) DRD2, b) AVPR1A, and c) ASPM

Results significant at the nominal α=0.05 level from the dominant model are summarized in Tables 4 (continuous traits), full results of the dominant model are provided in Supplemental Table 2, and results of the binary trait analyses significant at the α=0.05 level are provided in Supplemental Table 3. In AVPR1A, one SNP was significantly associated with multiple traits after multiple testing correction; rs1182266 was associated with PPVT (p=4.27×10−4), WI (p=4.46×10−4), and WA (p=4.81×10−4), and nominally associated with MSW (p=0.027) and EOWPVT (p=0.010). Other SNPs were associated with all traits at the α=0.05 level. Because one SNP (rs11174810) had low frequencies for both the rare homozygote and heterozygote, we used a gene burden test appropriate for rare alleles (Table 5). This analysis revealed associations between AVPR1A, including EOWPVT (p=0.015), PPVT (p=0.002), NSW (p=0.002), MSW (p=0.011), WI (p=0.006) and WA (p=0.001). Thus, AVPR1A appears to be associated with receptive and expressive vocabulary, phonological memory, and reading decoding.

Table 4.

Continuous trait association results significant at p < 0.05

EOWPVT PPVT NSW MSW WI WA
SNP beta p-value beta p-value beta p-value beta p-value beta p-value beta p-value
ASPM rs6700180 0.173 0.179 0.248 0.038 0.233 0.061 0.288 0.040 0.200 0.110 0.170 0.186
rs10754215 −0.189 0.170 −0.358 0.005 −0.209 0.121 −0.220 0.145 −0.322 0.016 −0.328 0.018
DRD2 rs4938013 0.249 0.024 0.312 0.002 0.180 0.097 0.265 0.031 0.254 0.019 0.294 0.009
rs2734849 0.192 0.127 0.160 0.175 0.366 0.003 0.283 0.043 0.343 0.006 0.283 0.026
rs1800497 0.175 0.140 0.244 0.027 0.257 0.026 0.244 0.062 0.226 0.053 0.279 0.020
rs10891549 0.106 0.370 0.127 0.253 0.241 0.038 0.254 0.054 0.342 0.003 0.269 0.024
rs2440390 0.269 0.049 0.262 0.043 0.034 0.799 0.221 0.138 0.199 0.130 0.199 0.142
rs2734831 0.097 0.400 0.100 0.352 0.166 0.141 0.192 0.134 0.292 0.010 0.230 0.048
rs4581480 −0.145 0.293 −0.301 0.019 −0.137 0.296 −0.198 0.181 0.003 0.980 −0.006 0.961
rs4483623 0.059 0.753 0.365 0.039 0.011 0.954 0.146 0.489 0.167 0.372 0.003 0.986
AVPR1A rs1587097 0.107 0.475 0.113 0.422 0.337 0.022 0.190 0.247 0.347 0.019 0.286 0.060
rs11174811 −0.047 0.712 0.039 0.748 0.223 0.073 0.038 0.786 0.204 0.105 0.152 0.238
rs1042615 0.183 0.118 0.277 0.011 0.188 0.100 0.322 0.013 0.220 0.055 0.206 0.081
rs10784342 −0.272 0.025 −0.272 0.020 −0.011 0.929 −0.128 0.351 −0.153 0.216 −0.265 0.037
4.272×10 −4* 4.46×10−5* 4.81×10−6*
rs11832266 −0.448 0.010 −0.568 −0.309 0.076 −0.428 0.027 −0.691 −0.787
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Uncorrected p-values < 0.05 are indicated in boldface font. Results significant after multiple testing correction (38 independent tests based on SNPSpDlite) are represented with *. EOWPVT = Expressive One Word Picture Vocabulary test, PPVT = Peabody Picture vocabulary test, NSW = nonsense word repetition task, MSW = multisyllabic word repetition task, WI = single word reading task (Word Identification), WA = nonsense word reading task (Word Attack). SNP location and function can be found in Supplemental Table 1.

Table 5.

Gene-level association results

Gene EOWPVT PPVT NSW MSW WI WA SSD
ASPMa 0.332 0.024 0.086 0.131 0.055 0.040 0.148
DRD2a 0.207 0.043 0.270 0.198 0.118 0.116 0.157
AVPR1Ab 0.015 0.002 0.002 0.011 0.006 0.001 0.649
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a

Using VEGAS method by Liu et al. (2010)

b

Using rare variant method by Feng et al. (2011)

Uncorrected p-values shown.

EOWPVT = Expressive One Word Picture Vocabulary test, PPVT = Peabody Picture vocabulary test, NSW = nonsense word repetition task, MSW = multisyllabic word repetition task, WI = single word reading task (Word Identification), WA = nonsense word reading task (Word Attack). SSD=binary trait for speech sound disorder.

Though there were no SNPs in DRD2 or ASPM that were associated with these traits after multiple testing correction, there were several SNPs associated with multiple traits at the nominal α=0.05 level, suggesting these associations are robust. Shared associations at the uncorrected α=0.05 level were evident at rs4938013 with EOWPVT, PPVT , MSW,, WI and WA, as well as rs2734948 with NSW , MSW, WI and WA, and rs10891549 with NSW, WI and WA. In DRD2, 4 SNPs with PPVT, 3 SNPs with NSW, 2 SNPs with MSW, 4 SNPs with WI, and 4 SNPs with WA at the nominal α=0.05 level. Only PPVT was associated with DRD2 in the gene-level test (p=0.043) (Table 5). Thus, receptive vocabulary was associated with DRD2 and there was suggestive evidence of association with phonological memory and reading.

Fewer SNPs within ASPM were associated with traits at the α=0.05 level; 1 SNP was associated with PPVT, 1 SNP with MSW, 1 SNP with WI, and 1 SNPs with WA. Shared associations were again evident; rs10754215 was associated with PPVT, WI, and WA at the uncorrected α=0.05 level. In the gene-level test, both PPVT (p=0.024) and WA (p=0.04) were associated with ASPM. Thus, ASPM appears to be associated with both receptive language and reading decoding.

DISCUSSION

This is the first study to examine associations of polymorphisms in DRD2, AVPR1A, and ASPM with endophenotypes of communication disorders and SSD and is to our knowledge the most rigorous study of these genes with respect to LD coverage. Previous studies of the association of these genes with speech and language phenotypes generally only focused on one polymorphism per gene. We found that all three genes were associated with measures of vocabulary, phonological memory, and reading, though all three genes were most significantly associated with vocabulary. These common association effects are similar to genetic linkage effects observed between other dyslexia and SSD loci (Lewis, Shriberg, Freebairn, Hansen, Stein, Taylor, & Iyengar 2006).

The endophenotypes of phonological memory, phonological awareness, vocabulary and processing speed underlie both early SSD and LI and later reading disorder (RD), spelling disorders and written expression disorders, thus accounting for the high co-morbidity of these disorders (Pennington BF 1997;Pennington & Bishop 2009) Genes act on specific biologic process which can span a range of domains and brain regions, therefore association signals can dissolve distinctions between seemingly different communication and learning disorders (Haworth & Plomin 2010). These endophenotypes are highly correlated, so shared association effects are not surprising and do not represent true pleiotropy, as occurs when common associations are observed between the same SNP and phenotypes that are not known to be related (Pendergrass et al. 2011;Sivakumaran et al. 2011).

AVP is involved in social behavior and vocalization (Winslow & Insel 1991) and AVPR1A has been associated with autism (Ebstein, Knafo, Mankuta, Chew, & Lai 2012), which in turn has been related to decreased language ability in autistic children (Wassink, Piven, Vieland, Pietila, Goedken, Folstein, & Sheffield 2004). We found gene-level and significant SNP-specific association between AVPR1A and both expressive and receptive vocabulary, which reflects this gene’s role in language. Granot et al. (Granot, Frankel, Gritsenko, Lerer, Gritsenko, Bachner-Melman, Israel, & Ebstein 2007) provisionally reported an association between AVPR1A and phonological memory as measured by auditory discrimination and nonword repetition, and we replicated the finding for nonword repetition in a much larger cohort. Lastly, we observed association between AVPR1A and reading, which reflects this gene’s role in language and memory abilities that contribute to reading skills. Our most significant finding was at rs11832266,(p < 10−3); this SNP is in a methylated region (http://www.genome.ucsc.edu/cgi-bin/hgTracks?hgsid=356406641) and is a clinical variant associated with unexplained developmental and intellectual disability (http://www.ncbi.nlm.nih.gov/clinvar/RCV000051959/)

The dopaminergic system is involved in motor control, endocrine function, cognition, language learning, procedural learning, and working memory (Frank & Fossella 2011;Hoenicka et al. 2007;Wong, Morgan-Short, Ettlinger, & Zheng 2012). Berman and Noble found that variants of DRD2 have been associated with both visuospatial performance and vocabulary (Beaver, Delisi, Vaughn, & Wright 2010;Berman & Noble 1995). Recently, a variant in DRD2 (rs6278) has been associated with a language variable that combines nonword repetition and verbal comprehension (Eicher, Powers, Cho, Miller, Mueller, Ring, Tomblin, & Gruen 2013); this SNP is tagged by Block #2 (Figure 1). In our study, we found that variants in DRD2 were associated primarily with receptive vocabulary but additionally with measures of expressive vocabulary, phonological memory and reading. This suggests that DRD2’s role in cognition, learning, visuospatial skills, and especially working memory are also important for phonological memory. A study of Italian dyslexics did not detect a statistically significant association with DRD2 (Marino, Giorda, Vanzin, Molteni, Lorusso, Nobile, Baschirotto, Alda, & Battaglia 2003), possibly because they only analyzed one polymorphism, while our SNP coverage was extensive. Alternatively, we may have had increased power because we analyzed quantitative traits.

ASPM is involved in mitotic spindle formation, neurogenesis, cognition, and is associated with intracranial volume (Mahmood, Ahmad, & Hassan 2011;Rimol et al. 2010). In addition, examination of adaptive changes occurring with signatures of natural selection in ASPM and their co-evolution with language have lead to the hypothesis that ASPM may be related to language development (Dediu 2011). We found that SNPs in ASPM were associated with receptive vocabulary and, reading. This replicates a report of association between a SNP in ASPM and single word reading and speech sound production proficiency (Kelsey, Tomblin, Bjork, Iyengar, Sucheston, Samelson, & Murrary 2007); though their association was with a different SNP than we genotyped, we likely captured it via tagging because of the strong LD in this gene. Wong et al. (2012) found that ASPM was associated with lexical tone perception (recognition of Mandarin vowel syllable and pitch) but not with the mental manipulation of phonemes as measured by sound blending, suggesting ASPM has a role in the memory component of human communication. Together, these findings indicate that neuronal processes regulated by ASPM influence speech, language, and reading abilities.

The results must be considered in light of several study limitations. Though parental phenotypes were available for SSD, MSW, and NSW, they were missing for the other endophenotypes, as some measures are only age-appropriate through adolescence and some parents refused testing. Although to our knowledge this is the largest ongoing longitudinal study of genetic influences on SSD and our sample was of moderate size (N=498), these factors limited the sample sizes available for these genetic association analyses. A further limitation was reliance on a history of therapy as the basis for the clinical diagnosis of SSD in parents and older siblings. As some individuals may have been undiagnosed, the binary SSD trait may have misclassified in some instances. Because we do not have genome-wide data available, we are unable to conduct analyses to evaluate the potential impact of population substructure. Finally, many of our findings did not remain significant after correction for multiple testing. However, we believe the findings are robust given associations across multiple SNPs and multiple phenotypes, and we draw our strongest conclusions from results of gene-level tests.

In conclusion, we observed significant association between a SNP in AVRR1A and receptive vocabulary and reading, and gene-level associations between AVPR1A and all endophenotypes, ASPM and receptive vocabulary and reading, and DRD2 and receptive vocabulary. Additional studies of other neuronal pathway genes are warranted to see if they may also be related to communication disorders. The comorbidity between SSD and LI and RD make these strong candidate genes for those communication disorders and justify independent replication. Multivariate analyses will also be needed to disentangle these correlated genetic effects in order to construct a model of how these genes work collectively.

Supplementary Material

Supplemental Table 1

Supplemental Table 1. Genotype counts for affected and unaffected individuals

Footnote: Analysis does not adjust for relationship between individuals

n0 the number of individuals with 0 copy of minor alleles

n1 the number of individuals with 1 copy of minor alleles

n2 the number of individuals with 2 copies of minor alleles

Supplemental Table 2

Supplemental Table 2. Complete results for all SNPs analyzed for the dominant model

Supplemental Table 3

Supplemental Table 3. Binary trait association results significant at p < 0.05

Acknowledgments

FUNDING: This research was supported by the National Institutes of Health, National Institute on Deafness and Other Communication Disorders, Grant DC00528 awarded to Barbara A. Lewis, and grant DC012380 awarded to Sudha Iyengar.

We would like to thank the families who have so generously participated in this study for many years. Also, we would like to thank Drs. Fung and Zhu with assistance in using their genetic burden test.

Footnotes

CONFLICT OF INTEREST: The authors have no conflicts of interest to declare.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplemental Table 1

Supplemental Table 1. Genotype counts for affected and unaffected individuals

Footnote: Analysis does not adjust for relationship between individuals

n0 the number of individuals with 0 copy of minor alleles

n1 the number of individuals with 1 copy of minor alleles

n2 the number of individuals with 2 copies of minor alleles

NIHMS599810-supplement-Supplemental_Table_1.doc (139.5KB, doc)
Supplemental Table 2

Supplemental Table 2. Complete results for all SNPs analyzed for the dominant model

NIHMS599810-supplement-Supplemental_Table_2.doc (175.5KB, doc)
Supplemental Table 3

Supplemental Table 3. Binary trait association results significant at p < 0.05

NIHMS599810-supplement-Supplemental_Table_3.doc (33.5KB, doc)

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