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. Author manuscript; available in PMC: 2013 Oct 15.
Published in final edited form as: Neuroimage. 2012 Jun 27;63(1):148–156. doi: 10.1016/j.neuroimage.2012.06.037

Variants in the DYX2 locus are associated with altered brain activation in reading-related brain regions in subjects with reading disability

Natalie Cope a,b,*, John D Eicher a,c,*, Haiying Meng a,b, Christopher J Gibson a,b, Karl Hager d, Cheryl Lacadie e, Robert K Fulbright e, R Todd Constable e, Grier P Page f, Jeffrey R Gruen a,b,c,g
PMCID: PMC3518451  NIHMSID: NIHMS390052  PMID: 22750057

Abstract

Reading disability (RD) is a complex genetic disorder with unknown etiology. Genes on chromosome 6p22, including DCDC2, KIAA0319, and TTRAP, have been identified as RD associated genes. Imaging studies have shown both functional and structural differences between brains of individuals with and without RD. There are limited association studies performed between RD genes, specifically genes on 6p22, and regional brain activation during reading tasks. Using fourteen variants in DCDC2, KIAA0319, and TTRAP and exhaustive reading measures, we first tested for association with reading performance in 82 parent-offspring families (326 individuals). Next, we determined the association of these variants with activation of sixteen brain regions of interest during four functional magnetic resonance imaging-reading tasks. We nominally replicated associations between reading performance and variants of DCDC2 and KIAA0319. Furthermore, we observed a number of associations with brain activation patterns during imaging-reading tasks with all three genes. The strongest association occurred between activation of the left anterior inferior parietal lobe and complex tandem repeat BV677278 in DCDC2 (uncorrected p=0.00003, q=0.0442). Our results show that activation patterns across regions of interest in the brain are influenced by variants in the DYX2 locus. The combination of genetic and functional imaging data show a link between genes and brain functioning during reading tasks in subjects with RD. This study highlights the many advantages of imaging data as an endophenotype for discerning genetic risk factors for RD and other communication disorders and underscores the importance of integrating neurocognitive, imaging, and genetic data in future investigations.

Keywords: dyslexia, DCDC2, TTRAP, imaging-genetics, neuroimaging

INTRODUCTION

Reading disability (RD) or dyslexia is the most commonly observed learning disability with a prevalence in Western countries estimated to be between 5% and 17% (Interagency Committee on Learning Disabilities 1987; Shaywitz et al. 1994; Pennington and Bishop 2009). RD is marked by difficulty reading despite adequate education, motivation, and intelligence (Shaywitz et al. 1994; Pennington and Bishop 2009). Twin and family studies showed that RD has both environmental and genetic determinants with heritability estimates of 44-75% (DeFries et al. 1987). Our group and others have identified various RD associated genes in the DYX2 locus on chromosome 6p22 including replicated associations of DCDC2, KIAA0319, and a haplotype including KIAA0319/TTRAP (Francks et al. 2004; Cope et al. 2005; Meng et al. 2005; Harold et al. 2006; Schumacher et al. 2006; Luciano et al. 2007; Ludwig et al. 2008; Paracchini et al. 2008; Wilcke et al. 2009; Lind et al. 2010; Scerri et al. 2011; Marino et al. 2012). How these genes function in neuronal circuitry and mechanistically contribute to RD remains unknown, especially since most of the associated variants have not yet been shown to be functional. However, two possible functional variants on DYX2 have been identified. Our group has identified a microdeletion/complex tandem repeat in intron 2 of DCDC2 (GenBank ID: BV677278) that associates with RD, influences DCDC2 gene expression, and binds a putative transcriptional regulatory complex (Meng et al. 2005; Wilcke et al. 2009; Meng et al. 2011; Marino et al. 2012). Paracchini et al. showed that the KIAA0319/TTRAP risk haplotype yielded decreased expression of KIAA0319 but not TTRAP (Paracchini et al. 2006). Additionally, animal studies demonstrated that DCDC2 and KIAA0319 are involved in neuronal migration (Paracchini et al. 2006; Velayos-Baeza et al. 2008; Levecque et al. 2009). The role of DCDC2 and KIAA0319 in neuronal migration suggests variants of these genes may cause abnormalities in brain morphology and functioning.

Functional magnetic resonance imaging (fMRI) studies show that subjects with RD display different patterns of brain activation during reading tasks compared to non-impaired controls (Rumsey et al. 1992; Paulesu et al. 1996; Rumsey et al. 1997; Shaywitz et al. 1998; Horwitz et al. 1998; Brunswick et al. 1999; Paulesu et al. 2001). Subjects with RD also have structural brain differences compared to non-impaired controls, both in grey matter density and in white matter microstructure connectivity (Eliez et al. 2000; Klingberg et al. 2000; Brown et al. 2001; Paulesu et al. 2001; Brambati et al. 2004; Silani et al. 2005; Vickenbosch et al. 2005; Deutsch et al. 2005; Beaulieu et al. 2005; Niogi and McCandliss 2006; Hoeft et al. 2007). These functional and structural differences are most prominent in the superior temporal gyrus, inferior frontal gyrus, parietal-temporal gyrus, and left inferior parietal lobule. In general, subjects with RD utilize different brain regions and neuronal circuits during reading tasks compared to non-impaired controls, suggesting determinants of RD alter brain development and functioning.

Despite the evidence of functional and structural differences between individuals with RD and non-impaired controls, the use of imaging data as an endophenotype to explore mechanisms of neurobehavioral disorders remains under-utilized. Specifically, investigations of the interactions among RD associated genes, behavioral measures, and imaging data are few in number and limited in functional imaging data. One study found a relationship between grey matter volume in subjects with RD and the BV677278 microdeletion (Meda et al. 2008). Recent studies by Wilcke et al. in subjects with RD and Pinel et al. in non-impaired individuals found differences among variants in FOXP2 in regional brain activation during reading tasks including temporo-parietal, inferior frontal, and precentral brain regions (Wilcke et al. 2011, Pinel et al. 2012). Pinel et al. also observed an association between KIAA0319/TTRAP/THEM2 and lower asymmetric activation of the posterior superior temporal sulcus during reading and phonology tasks. These studies suggest that functional brain patterning can be an informative endophenotype for conditioning genetic associations.

The goals of this investigation are to replicate associations of DCDC2, KIAA0319, and TTRAP with behavioral reading measures and to use fMRI to interrogate brain regions involved in reading tasks in the same subjects. This investigation is one of the first comprehensive analyses of behavioral, functional imaging, and genetic data in the same RD cohort. By using both behavioral and imaging-reading tasks, we aim to strengthen associations of DYX2 variants with RD and to evaluate functional consequences of RD associated variants using fMRI data. We hypothesize that associations of DYX2 variants with RD using behavioral data will be replicated and that these variants will influence brain activation patterns during imaging-reading tasks in reading-related regions of interest (ROIs).

MATERIALS AND METHODS

Subjects

Eighty-two unrelated subjects of European American ancestry (50 RD cases / 25 non-impaired controls / 7 with unknown affectation status) from the Yale Center for the Study of Learning and Attention were used in this investigation (Supplementary Table S1). The 50 RD cases were identified by scores below the 25th percentile on either the Word Identification or Word Attack portions of the Woodcock Johnson III Achievement Battery or reading fluency on the Gray Oral Reading Test (GORT) (Woodcock 1987; Woodcock and Johnson 1989; Wiederholt and Bryant 1992). The 25 non-impaired controls were defined as subjects with scores above the 40th percentile on the same tasks. Among subjects, there were 51 males and 31 females, with a mean age of 8.8 years (range 7 – 12 years). Exclusion criteria for subjects included IQ < 85, left-handedness, hearing loss, severe articulation problems, severe emotional disturbance, autism, mental retardation, brain injury, neurologic disorders, and speaking English as a second language. Reading measures were performed on 75 subjects, imaging-reading data were collected on 82 subjects (51 males/31 females), and DNA was collected by buccal swab from subjects and all available family members (n=326). All subjects gave informed consent approved by the Human Investigation Committee of the Yale University School of Medicine, and all studies were performed in accordance with the Declaration of Helsinki.

Behavioral Measures of IQ and Reading Performance

Verbal, performance and full scale IQ were determined by the Wechsler Abbreviated Scale of Intelligence (WASI) (Psychological Corporation 1999). A range of quantitative neurobehavioral tasks measured reading performance of subjects. The Woodcock Johnson III Achievement Battery determined basic reading, letter-word identification, word attack, passage comprehension, and spelling (Woodcock 1987; Woodcock and Johnson 1989). The GORT assessed reading rate, accuracy, fluency, and comprehension (Wiederholt and Bryant 1992). The Test of Word Reading Efficiency (TOWRE) measured sight word efficiency, phonemic decoding efficiency and total word efficiency (Torgesen et al. 1999). The Comprehensive Test of Phonological Processing (CTOPP) measured phonological awareness ability, blending words, memory for digits, rapid digit naming, blending of non-words, and segmenting non-words (Wagner et al. 1999). Behavioral measures are compiled in Supplementary Table S1, and correlations between tests are shown in Supplementary Table S2.

Imaging-Reading Tasks

Subjects completed four reading tasks while being imaged: Word Rhyming (WR)/Non-Word Rhyming (NWR) and Print Categorization (PC)/Auditory Categorization (AC) (Shaywitz et al. 1998; Pugh et al. 2008; Landi et al. 2010). In the WR/NWR tasks, subjects were asked to match a printed target consisting of a word (WR) or pseudoword (NWR) to a pictorially presented cue. A small set of cue pictures was used for both tasks consisting of pictures of either living or nonliving things. Subjects observed the cue picture, while a target stimulus was printed just below. Subjects were instructed to press one button if the cue and target stimulus rhymed, another if they did not. The WR/NWR tasks measured brain activation patterns involved in phonological processing. Phonological processing refers to the detection and separation of specific phonemes or speech sounds within words. Phonological processing is essential to reading and impaired in individuals with RD (Shaywitz et al. 1999; Pugh et al. 2001).

In the PC/AC tasks, subjects were asked to match either a printed (PC) or spoken (AC) target to a pictorially presented cue of living and non-living things. Subjects were instructed to press one button if the two stimuli were in the same category and another if they were not. For instance, “pig” and a picture of a fox are in the same category because both are living. The PC/AC tasks measure the brain activation patterns involved in semantic processing. Semantic processing refers to how one connects written and/or spoken words to actual language meaning. Individuals with RD can have deficits in semantic processing, yielding inaccurate comprehension of written language (Pugh et al. 2001).

Imaging Protocol

Head positioning in the magnet was standardized using the canthomeatal landmarks. In the scanner, cushions inside the head coil reduced head movement, and headphones (RTC technologies) were used to dampen scanner noise, to communicate with participants, and to deliver audio components of the task. Conventional T1-weighted spin-echo sagittal anatomical images were acquired for slice localization using a 1.5T whole body imaging system with a quadrature head coil (Sonata; Siemens AG, Erlangen, Germany). After a 3-plane localizer and a multiple-slice sagittal localizer, fourteen T-1 weighted axial slices (TR=420 ms; TE=11 ms; bandwidth=130 Hz/pixel; FA= 90°; slice thickness=7mm; FOV=200 × 200 mm; matrix=256 × 256) were obtained using flash spin-echo imaging parallel to the anterior and posterior commissure. Eight functional data series (four runs alternating between both category/rhyming tasks and baseline within each run) were acquired with a single-shot gradient-echo echo planar imaging (EPI) sequence (TR=1500 ms; TE=60 ms; bandwidth=1735 Hz/pixel; FA=60°; slice thickness=7mm; FOV=200 × 200 mm; matrix=64 × 64; images per slice=91). Stimuli were projected onto a semi-transparent screen at the head of the bore and viewed by the subject via a mirror mounted on the head coil. At the end of functional imaging, a high resolution 3D Spoiled Gradient Recalled Acquisition in the Steady State (SPGR) T1-weighted sequence (TR=24 ms; TE=4.73 ms; bandwidth=130 Hz/pixel; FA= 45°; slice thickness=1.5mm; FOV=240 × 240 mm; matrix=256 × 256) was used to acquire sagittal images for multi-subject registration.

Imaging Data Analysis

Imaging data were converted from Digital Imaging and Communication in Medicine (DICOM) format to analyze format using XMedCon (Nolf et al. 2003). The first six images at the beginning of each functional series were discarded to enable the signal to achieve steady-state equilibrium between radio frequency pulsing and relaxation, leaving 85 images per slice per trial for analysis. Motion-correction of images was performed with Statistical Parametric Mapping (SPM) (www.fil.ion.ucl.ac.uk/spm) for three translational directions (x, y or z) and three possible rotations (pitch, yaw or roll). Trials with linear motion that had a displacement in excess of 2 mm or rotation in excess of 3° were rejected. Individual subject data was analyzed using a General Linear Model (GLM) on each voxel in the entire brain volume with regressors specific for each task. The resulting functional images were spatially smoothed with a 6 mm Gaussian kernel to account for variations in the location of activation across subjects. Output maps were normalized beta-maps, which were in the acquired space (3.125mm × 3.125mm × 7mm).

Image Registrations

To take these data into a common reference space, three registrations were calculated within the Yale BioImage Suite software package (v2.6.1) (http://www.bioimagesuite.org/) (Evans et al. 1993; Nolf et al. 2003; Duncan et al. 2004). The first registration performs a linear registration between the individual subject raw functional image and that subject’s 2D anatomical image. The 2D anatomical image is then linearly registered to the individual’s 3D anatomical image. The 3D differs from the 2D in that it has a 1×1×1 mm resolution whereas the 2D z-dimension is set by slice-thickness and its x-y dimensions are set by voxel size. Finally, a non-linear registration is computed between the individual 3D anatomical image and a reference 3D image. The reference brain used was a single control child’s high resolution anatomical that was manually stripped to remove all skull and meninges. All three registrations were applied sequentially to the individual normalized beta-maps to bring all data into the common reference space. The 3D anatomical reference brain was non-linearly registered to the Colin27 Brain which is in Montreal Neurological Institute (MNI) space (Evans et al. 1993; Holmes et al. 1998). Talairach coordinates are based on a conversion from MNI space to Talairach coordinates (Talairach and Tournoux 1988; Lacadie et al. 2008).

Regions of Interest

Based on previous reports of altered activation in phonological processing tasks using fMRI, 16 ROIs were chosen for examination with genotype data (Table 1) (Shaywitz et al. 1998). ROIs were defined functionally in a separate sample of 25 control subjects by using activated language areas in the NWR and AC tasks (Shaywitz et al. 1998). Activation values were used as quantitative measures for testing genetic association.

Table 1.

Regions of Interest (ROIs), abbreviations, volumes, and Talairach coordinates

Region of Interest ROI Abbreviation Volume (mm3) Volume (voxels) Talairach Coordinates
x y z
Right inferior frontal gyrus, inferior aspect RIFGI 1899.51 112.66 32 20 0
Left inferior frontal gyrus, inferior aspect LIFGI 6203.05 367.92 -42 16 1
Right temporal lobe RT 2834.85 168.14 49 -27 2
Left temporal lobe LT 4832.07 286.60 -52 -35 5
Superior anterior cingulate gyrus SAC 2408.83 142.87 -4 22 41
Left paracentral lobule LPC 2580.20 153.03 0 -25 47
Posterior cingulate gyrus PC 10794.90 640.27 0 -46 32
Inferior anterior cingulate gyrus IAC 5431.07 322.12 -1 42 -1
Right posterior inferior parietal lobe (angular gyrus) RPIPL 2160.58 128.15 26 -64 34
Left posterior inferior parietal lobe (angular gyrus) LPIPL 1950.76 115.70 -34 -61 37
Right anterior inferior parietal lobe (supramarginal gyrus) RAIPL 4215.44 250.02 52 -28 33
Left anterior inferior parietal lobe (supramarginal gyrus) LAIPL 2396.01 142.11 -48 -28 39
Right lateral occipital temporal gyrus RLOTG 1370.98 81.32 51 -60 2
Left medial occipital temporal gyrus LMOTG 1905.92 113.04 -39 -51 -7
Right inferior frontal gyrus, superior aspect RIFGS 3220.84 191.03 38 19 20
Left inferior frontal gyrus, superior aspect LIFGS 4782.42 283.65 -45 17 23
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Genotyping

Fourteen variants spanning DCDC2, KIAA0319, and TTRAP were chosen based on previous association studies and are summarized in Supplementary Table S3 (Deffenbacher et al. 2004; Francks et al. 2004; Cope et al. 2005; Meng et al. 2005; Schumacher et al. 2006;). Genotyping methods by TaqMan Assay-on-Demand (Applied Biosystems, Foster City, CA), TaqMan Assay-by-Design, Pyrosequencing (Biotage, Uppsala), fluorescent dideoxynucleotide DNA sequencing with sequence trace analysis by Mutation Surveyor v2.6 (SoftGenetics, State College, PA), and allele specific PCR for the BV677278 microdeletion/complex tandem repeat (rsdel and rsSTR) are summarized in Supplementary Tables S3-S4 (Meng et al. 2005; Meng et al. 2011). Table 2 presents the multiple alleles (1-10) of the BV677278 complex tandem repeat alleles observed in this sample.

Table 2.

BV677278 complex tandem repeat in DCDC2

Allele Repeat Unit 1 Repeat unit 2 SNP1 Repeat Unit 3 Repeat Unit 4 Repeat Unit 5 Frequency in Sample
1 (GAGAGGAAGGAAA)2 (GGAA)7 (GGAA)2 (GGAA)4 (GGGA)2 0.650
2 (GAGAGGAAGGAAA)1 (GGAA)9 DelGAA A (GGAA)0 (GGAA)4 (GGGA)2 0.027
3 (GAGAGGAAGGAAA)1 (GGAA)6 (GGAA)2 (GGAA)4 (GGGA)2 0.012
4 (GAGAGGAAGGAAA)2 (GGAA)6 (GGAA)2 (GGAA)4 (GGGA)2 0.114
5 (GAGAGGAAGGAAA)2 (GGAA)8 (GGAA)2 (GGAA)4 (GGGA)2 0.034
6 (GAGAGGAAGGAAA)2 (GGAA)8 (GGAA)2 (GGAA)3 (GGGA)2 0.024
8 (GAGAGGAAGGAAA)2 (GGAA)7 DelGAA A (GGAA)0 (GGAA)4 (GGGA)2 0.005
9 (GAGAGGAAGGAAA)1 (GGAA)7 (GGAA)2 (GGAA)4 (GGGA)2 0.005
10 (GAGAGGAAGGAAA)2 (GGAA)4 (GGAA)2 (GGAA)4 (GGGA)2 0.051
deletion X X X X X X 0.078
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Statistical Analysis

Statistical Package for the Social Sciences (SPSS) (v18) was used to determine mean activation, standard error, correlations, and principle component analysis (PCA). PCA was performed on 11 highly correlated behavioral measures including subtests of the Woodcock Johnson (basic reading, letter-word identification, word attack, passage comprehension, spelling), GORT (measured reading test, accuracy, fluency), and TOWRE (sight word efficiency, phonemic decoding efficiency, total word efficiency). Mendelian transmission of alleles was assessed using the Genetic Analysis System (GAS; http://users.ox.ac.uk/~ayoung/gas.html). Identity-by-Descent probabilities were estimated using SIMWALK2 (v2.91) (Sobel et al. 2001). Quantitative transmission disequilibrium was assessed with QTDT (v2.6.1) using a total association, variance components model (-at -wega), with behavioral reading measures and brain activation data as quantitative measures. QTDT examines transmission of alleles from heterozygous parent to child in order to determine genetic associations with quantitative traits. QTDT correlates performance on a quantitative trait (in this case, brain activation in ROIs) with the observed number of an allele transmitted to offspring on quantitative measures from parents compared to the number expected following Mendelian transmission. The accumulation of an allele in subjects with similar levels of brain activation during tasks indicates association between the allele and trait analyzed. Haploview (v4.1) was used to determine linkage disequilibrium between markers. False discovery rate (FDR) was used to correct for multiple testing and to establish a threshold for statistical significance (Benjamini and Hochberg 1995).

RESULTS

Linkage Disequilibrium

Linkage disequilibrium (LD) among markers is shown in Figure 1. Two LD blocks were identified: one within DCDC2 and the second within KIAA0319. There was little evidence of LD among the three genes as the TTRAP variant rs2143340 did not fall within either block and there was no significant LD between DCDC2 and KIAA0319. Associations with multiple markers from the same gene with activation of a ROI reflected local LD. All markers were in Hardy-Weinberg equilibrium and followed Mendelian inheritance.

Figure 1.

Figure 1

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Linkage disequilibrium (LD) structure across DCDC2, KIAA0319, and TTRAP in the sample genotyped. The top portion of the figure depicts the chromosomal region spanning DCDC2 and KIAA0319 with known genes and transcripts. The bottom is a plot of LD calculated as D’ for all possible pairs of markers. rsdel refers to the BV677278 microdeletion. Darker shading indicates increased LD. Two haplotype blocks, block 1 (DCDC2) and block 2 (KIAA0319) were identified. There was no evidence for significant LD between DCDC2, KIAA0319, and TTRAP.

RD Genes and Behavioral Reading Phenotypes

Correlations between most reading measures were highly significant with p ≤ 0.01 (Supplementary Table S2). Due to the consistent high correlations among 11 of the behavioral measures (Woodcock Johnson, GORT, and TOWRE), we performed PCA to reduce them to a single vector that showed strong correlations with the other behavioral measures and accounted for 88.3% of the phenotypic variance (Supplementary Table S2). Nominal associations were observed between DCDC2 markers and reading measures, supporting previous observations that DCDC2 variants contribute to reading performance and RD (Table 3). One KIAA0319 variant (rs6935076) showed nominal association with the blending word subtest of the CTOPP. However, the majority of the associations were observed between reading measures and DCDC2. TTRAP (rs2143340) did not show any associations with reading measures. Although we observed nominal associations between DCDC2 and KIAA0319 variants and reading measures, none of these associations remained significant following FDR correction for multiple testing.

Table 3.

Nominally significant associations with cognitive and behavioral reading and cognitive measures

Task Variant Gene Direction of Effecta P-valuec
WASIVIQ rsSTR (allele 5) DCDC2 18.491 0.019
WASIVIQ rsSTR (allele 6) DCDC2 -21.092 0.0194
WASIPIQ rs807724 DCDC2 6.264 0.0359
WJBAR rsSTR (allele 1) DCDC2 6.4 0.0291
WJLW rsSTR (allele 1) DCDC2 7.63 0.0197
WJPC rsSTR (allele 1) DCDC2 6.981 0.0289
WJPC rsSTR (allele 10) DCDC2 -10.69 0.0328
WJSP rsSTR (allele 1) DCDC2 9.872 0.0099
GORTAC rs807701 DCDC2 1.417 0.035
GORTCP rs807701 DCDC2 1.195 0.0411
GORTCP rs807724 DCDC2 1.642 0.0221
GORTCP rsSTR (allele 1) DCDC2 1.985 0.0028
CTOPPBW rs6935076 KIAA0319 -0.881 0.049
CTOPPMD rsSTR (allele 3) DCDC2 3.399 0.0308
CTOPPRD rsdelb DCDC2 1.629 0.0263
CTOPPRD rsSTR (allele10) DCDC2 -2.021 0.011
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a

For SNPs, the direction of effect refers to allele 1 (see Supplementary Table S3), allele 2 has the opposite effect.

b

Biallelic association (presence/absence of the BV677278 deletion)

c

No p-value remained significant following correction for multiple testing (437 tests performed).

RD Genes and Region of Interest Activation

Before assessing genetic associations with our ROIs, we assessed the correlations between brain activation patterns and behavioral reading measures (Supplementary Tables S5a–S5d). As shown in previous imaging studies in RD, there were significant correlations between brain activation patterns during reading tasks and performance on reading measures. These correlations suggest that activation of ROIs was related to reading processes in the brain and that genetic associations with these ROIs would be informative as to the etiology of RD.

There were nominal associations between DCDC2 and TTRAP and reading-related ROIs during WR and NWR tasks (Figure 2, Table 4). As with behavioral measures, most associations were observed with DCDC2 and not with KIAA0319 or TTRAP. The most significant associations were seen with alleles of the DCDC2 BV677278 complex tandem repeat and the superior anterior cingulate gyrus (SAC), posterior cingulate gyrus (PC), left paracentral lobule (LPC), and left inferior frontal gyrus, inferior aspect (LIFGI) along with rs2143340 in TTRAP with the right and left anterior inferior parietal lobe (RAIPL, LAIPL). However, none of the associations seen with brain activation during WR and NWR tasks remained significant after correction for multiple testing by FDR.

Figure 2.

Figure 2

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Significant P-values for association projected onto brain slices. A) DCDC2 from AC and PC tasks. P-values for DCDC2 (Tables 3a-3b) mapped onto LAIPL and RLOTG (P<0.0001, yellow); LPC, RAIPL, LMOTG (P<0.01, red); LIFGI, RIFGI, LIPL, LIFGS (P<0.02, green); PC, RT, SAC (P<0.05, blue). B) KIAA0319 from AC and PC tasks. P-values for KIAA0319 (Tables 3a-3b) mapped onto LMOTG and LPIPL (P<0.02, green); RAIPL, LIFGI, IAC (P<0.05, blue). C) DCDC2 from WR and NWR tasks. P-values for DCDC2 (Tables 3c and 3d) mapped onto PC, LIFGI, LPC, SAC (P<0.01, red); RLOTG, LAIPL, LIFGS, LPIPL, RPIPL, LMOTG, RT (P<0.02, green); LT (P<0.05, blue). D) KIAA0319 and TTRAP from WR and NWR tasks. P-values for KIAA0319 and TTRAP (Table 3c and 3d) mapped onto LAIPL and RAIPL (P<0.01, red); LT and LPC (P<0.05, blue).

Table 4.

Genetic associations (p<0.01) with auditory categorization (AC), print categorization (PC), word rhyming (WR), and nonword rhyming (NWR) imaging-reading tasks.

Task ROI Variant Gene Direction of Effecta p-value q-valued
AC RLOTG rsSTR (allele 8) DCDC2 -0.319 0.0001 0.0490
AC LMOTG rs1087266 DCDC2 0.033 0.0080 0.8337
PC LPC rsSTR (allele 1) DCDC2 -0.026 0.0032 0.7824
PC LAIPL rs793862 DCDC2 0.027 0.0065 0.9509
PC LAIPL rsSTR (allele 4) DCDC2 0.08 0.00003 0.0442
PC RLOTG rsSTR (allele 8) DCDC2 -0.263 0.00008 0.0588
PC LMOTG rs793862 DCDC2 0.023 0.0074 0.9835
PC LMOTG rsSTR (allele 4) DCDC2 0.046 0.0076 0.9253
WR SAC rsSTR (allele 5) DCDC2 0.096 0.0083 0.8068
WR RAIPL rs2143340 TTRAP -0.029 0.0038 0.7425
WR LAIPL rs2143340 TTRAP -0.024 0.0029 0.8514
NWR LIFGI rs1087266 DCDC2 0.02 0.0038 0.7425
NWR LPC rsSTR (deletion)b DCDC2 0.042 0.0050 0.8133
NWR LPC rsdelc DCDC2 0.039 0.0079 0.8872
NWR PC rsSTR (allele 8) DCDC2 -0.02 0.0016 0.5876
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a

For SNPs, the direction of effect refers to allele 1 (see Supplementary Table S3), allele 2 has the opposite effect.

b

multiallelic association (all STR alleles)

c

biallelic association (presence/absence of the BV677278 deletion)

d

q-values were calculated by the Benjamini and Hochberg method of False Discovery Rate (FDR) correction for multiple testing with 1472 hypotheses of imaging-genetic associations performed. Those in bold reached or trended toward statistical significance after FDR correction for multiple testing.

Associations were observed between DCDC2 and brain activation in ROIs during AC and PC tasks (Figure 2, Table 4). No nominal associations were seen for TTRAP. The strongest associations occurred between ROIs and DCDC2, specifically with alleles of the BV677278 complex tandem repeat. During the PC task, alleles 4 and 8 of the DCDC2 complex tandem repeat were associated with activation with the LAIPL (uncorrected p=0.00003, q=0.0442) and right lateral occipital temporal gyrus (RLOTG) (uncorrected p=0.00008, q=0.0588), respectively. During the AC task, allele 8 of the complex tandem repeat displayed association with activation of the RLOTG (uncorrected p=0.0001, q=0.0490).

DISCUSSION

In this investigation, we nominally replicated the association of DCDC2 and KIAA0319 with behavioral reading measures and associated markers in the DYX2 locus to brain activation patterns during imaging-reading tasks. Instead of only using diagnostic status and behavioral measures, genetic associations were further conditioned on brain regions activated during reading tasks. These associations not only implicate genes in RD but give functional insight into their effects on brain function. More importantly, our work demonstrates the utility of imaging-genetics in the study of RD and other neurobehavioral disorders and shows the need for future studies to analyze behavioral, imaging, and genetics data while examining these disorders.

Association of DYX2 to RD

DYX2, containing DCDC2, KIAA0319, and TTRAP, is the most replicated RD risk locus (Deffenbacher et al. 2004; Francks et al. 2004; Cope et al. 2005; Meng et al. 2005; Harold et al. 2006; Luciano et al. 2007; Schumacher et al. 2006; Paracchini et al. 2006; Ludwig et al. 2008; Paracchini et al. 2008; Wilcke et al. 2009; Lind et al. 2010; Scerri et al. 2011). In the current investigation, we observed nominal association primarily between DCDC2 and neurocognitive reading measures. There was association between one reading measure (CTOPP-blending words) and KIAA0319 (rs6935076). The lack of substantial associations between reading performance and TTRAP and other variants in KIAA0319 may reflect a lack of statistical power due to sparse SNP coverage of the gene and relative small sample size. Regardless, this study further strengthens the implication of DCDC2 and KIAA0319 in RD and reading performance.

Association of DYX2 to Reading-Related ROIs

Genetic associations were observed between variants in DYX2 and brain activation levels in reading-related ROIs during imaging-reading tasks. In this study, the strongest genetic associations were between the BV677278 complex tandem repeat in DCDC2 and LAIPL and RLOTG (uncorrected p≤0.0001, q≤0.0588). The LAIPL and RLOTG regions of the brain have been implicated in dyslexia in past investigations and are a part of the temporo-parietal and occipito-temporal reading circuits, respectively. (Richlan et al 2009). The LAIPL contains Weirnicke’s area, which is vital for the accurate integration of language stimuli. A recent meta-analysis of functional imaging studies revealed that non-impaired readers have increased activation of the left inferior parietal lobe compared to subjects with dyslexia (Richlan et al. 2009). Our investigation found that having allele 4 of BV677278 within DCDC2 positively influences activation of the LAIPL during imaging-reading tasks. This suggests that allele 4 may improve the integration and activation of reading circuits and have a protective effect against dyslexia. Studies have shown a negative correlation between right occipito-temporal activation and reading ability (Shaywitz et al. 2002, Shaywitz and Shaywitz 2005). Our analyses showed that both allele 4 and 8 of BV677278 were associated with decreased activation of RLOTG, perhaps because they preserve function in temporo-parietal and occipito-temporal circuits and are therefore protective. The BV677278 repeat displays strong associations with RD in multiple studies (Meng et al. 2005; Wilcke et al. 2009; Meng et al. 2011; Marino et al. 2012). Meng et al. investigated possible regulatory functions by showing that different alleles altered DCDC2 gene expression (Meng et al. 2005; Meng et al. 2011). Electrophoretic mobility shift assays showed that BV677278 is a specific binding site for as-yet unidentified transcription regulatory complex(es) found in human brain nuclear lysate (Meng et al. 2011). Considered together, these data suggest that differential, allele-dependent expression of DCDC2 due to BV677278 variants during brain development may influence brain activation patterns in reading-related ROIs.

Pinel et al. recently reported an association between a locus containing KIAA0319, TTRAP, and THEM2 and lower asymmetry of brain activation during imaging-reading tasks in the posterior superior temporal sulcus (Pinel et al. 2012). This locus had previously been identified as part of a risk haplotype for RD in KIAA0319 and TTRAP and includes the rs2143340 variant in TTRAP examined in our study (Francks et al. 2004, Cope et al. 2005, Luciano et al. 2007, Paracchini et al. 2008). In this investigation, we observed an association between rs2143340 and activation in the RAIPL and LAIPL (supramarginal gyri) during the WR task. Although our association did not meet significance following correction for multiple testing, we did observe a nominal association in a brain region anatomically proximal to the posterior superior temporal sulcus. These brain regions have been implicated in reading and language processing and deficits in these regions are associated with RD (Paulesu et al. 2001). Our results, in combination with those of Pinel et al., suggest that TTRAP plays an undefined role in brain functioning in the supramarginal gyri and the posterior superior temporal sulcus that contributes to deficits in reading ability.

Our study was able to detect multiple genetic associations between DYX2 variants and fMRI data during reading tasks. While fMRI studies are state-dependent and influenced by many factors including effort, attention, and motivation, our methodologies were standardized and performed in order to minimize inter-subject variability. Additionally, performance on imaging-reading stimuli and the neurological process being interrogated (e.g. phonology and semantics) will influence the brain activation levels measured by fMRI (Pugh et al. 2008; Landi et al. 2010). The current study cannot distinguish whether activation levels reflect that of disorder state (i.e. RD or non-impaired) as opposed to deficits in performance on each task. However, the correlations between fMRI tasks (data not shown) and between fMRI tasks and behavioral assessments (Supplemental Tables S5a-S5d) support our findings of genotype associations with true physiologic differences in brain activity. These results, however, are limited by statistical power for genetic association due to sparse marker coverage of DYX2 and small sample size. Using data from this analysis, a sample size of 250 trios with severe RD would be needed for imaging-reading tasks to achieve 80% power for detecting associations (alpha = 0.05, total QTL variance = 0.01, cutoff = 2 SDs, additive genetic variance) (Purcell et al. 2003, http://pngu.mgh.harvard.edu/~purcell/gpc/qtdt.html). Despite these limitations, the observation of significant and suggestive genetic associations following correction for multiple testing demonstrates that imaging-genetics data is more sensitive than behavioral data, and the utility of imaging-genetics in further studies with larger sample size and increased coverage of RD associated genes.

Future of Imaging-Genetics

Our data suggest there are complex interactions between genes in DYX2, reading ability, and brain activation of reading-related ROIs. This is one of the first investigations to utilize both behavioral and functional imaging phenotypes of reading in genetic associations in the same sample. The examination of such a multidimensional dataset implies a functional role of these RD associated variants in brain activation. However, the current study cannot distinguish whether DYX2 variants directly influence the activation of reading-related ROIs or whether these variants affect an intermediate process which then alters ROI activation. For instance, variants of DCDC2 have been associated with brain morphological differences in schizophrenic and in control groups (Meda et al. 2008). It is plausible that the genetic associations with brain activation levels are actually a result of brain morphology differences that then alter ROI activation during reading tasks. DCDC2 and KIAA0319 function in neuronal migration during neurodevelopment, which may alter brain structure. Future studies are necessary to further discern the true molecular and gross neurological roles genes in the DYX2 locus play and to determine how these changes in gene and brain function contribute to RD.

Previous association studies conditioned on behavioral reading measures or a categorical RD diagnosis could not reliably distinguish single-gene effects. Our data preliminarily suggest that imaging-genetics is able to make this distinction. This conclusion is supported by the reported low false-positive rate of imaging-genetics studies, and that regardless of the relatively small sample size in this study, there were genetic associations that maintained and approached statistical significance following correction for multiple testing (Meyer-Lindenberg et al. 2008). Additionally, we employed a conservative correction for multiple testing for genetic associations with behavioral and imaging measures. Our corrections ignored the lack of independence among genetic markers due to LD and intra- and inter- correlations amongst behavioral and imaging measures (Figure 1, Supplemental Table S2). Some of the associations that were nominally significant but did not reach our stringent FDR threshold may reflect true positive associations. The lack of imaging-genetics studies reflect the inherent difficulties, technological capabilities, and costs of performing such studies and underscore the need for large, widely available consortia examining neurobehavioral disorders. If the present work can be replicated in a larger sample, our findings make an effective case for using functional imaging data to define informative endophenotypes for genetic studies of RD. The combination of functional imaging and genetic studies of RD could serve as a template for the investigation of other neurobehavioral disorders with unknown etiologies such as specific language impairment, attention deficit hyperactivity disorder, and speech sound disorder.

Conclusions

This study addresses the interaction between reading, regional brain activation, and genetics. We detected associations between a putative functional variant in DCDC2, previously associated with RD, and reading-related ROIs and nominally supported findings of the role of TTRAP in brain activation during imaging-reading tasks. Imaging data provides a viable, valuable endophenotype for RD and other neurobehavioral disorders due to the inherent complexity of brain functioning and the heterogeneous, intricate clinical presentations of these disorders. Further work is needed to validate our results in larger samples and with expanded networks of reading-related genes and ROIs. Additionally, future investigations of RD should examine the influence of various environmental determinants on brain activation of reading related regions.

Supplementary Material

01

Highlights.

Functional imaging data are an informative endophenotype for reading disability. A functional variant in DCDC2 associated with brain activation during reading tasks. We nominally replicated association of TTRAP with imaging endophenotypes. Future studies should examine cognitive, imaging, and genetic data in studying language deficits.

Acknowledgments

The authors would like to thank all subjects and families in the Yale Reading Disability Sample for their participation in this investigation. The authors also thank Professor Bennett Shaywitz, Professor Sally Shaywitz, Karen Marchione, and Dr. John Holahan for recruiting and testing all the subjects at the Yale Center for the Study of Learning and Attention. Support for JRG is from the National Institute of Health (NIH) R01 NS43530, and support for JDE is from NIH F31 DC012270 and T32 GM007223. Further support for RKF, and CL, is from R01 HD046171. Support for CJG is from a Yale-Rosenberg Genetics Fellowship. Support for RTC is from R01 NS038467 and NS051622. The authors wish to thank Dr. Xenophon Papademetris (XP) for image analysis guidance, Stefan Halvorsen for extracting the ROIs, and to acknowledge the NIH/National Institute of Biomedical Imaging and Bioengineering (NIBIB) under grant 1 R01 EB006494-01 for support of XP and CL.

Abbreviations

RD

Reading disability

fMRI

functional magnetic imaging resonance

ROIs

regions of interest

GORT

Gray Oral Reading Test

WASI

Wechsler Abbreviated Scale of Intelligence

TOWRE

Test of Word Reading Efficiency

CTOPP

Comprehensive Test of Phonological Processing

WR

Word Rhyming

NWR

Non-Word Rhyming

PC

Print Categorization

AC

Auditory Categorization

PCA

principle component analysis

FDR

false discovery rate

LD

linkage disequilibrium

SAC

superior anterior cingulated gyrus

PC

posterior cingulated gyrus

LPC

left paracentral lobule

LIFGI

left inferior frontal gyrus, inferior aspect

RAIPL

right anterior inferior parietal lobe

LAIPL

left anterior inferior parietal lobe

RLOTG

right lateral occipital termporal gyrus

Footnotes

FINANCIAL DISCLOSURES

Yale University has applied for a patent covering the complex tandem repeat and deletion in BV677278 (inventors: JRG and HM), and sublicensed it to JS Genetics, Inc. JRG is a founder and equity holder of JS Genetics, Inc. KH is currently employed by JS Genetics, Inc. NC, JDE, CJG, CL, RKF, RTC, and GPP each reported no biomedical financial interests or potential conflicts of interest.

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