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University of York, United Kingdom
University of New Mexico, Albuquerque
King's College London
Contact author: Nicole Harlaar, who is now at the Department of Human Development and Family Science, The Ohio State University, 135 Campbell Hall, 1787 Neil Avenue, Columbus, OH 43210. E-mail: nharlaar{at}ehe.osu.edu.
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Abstract |
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 Top Abstract Appendix AAppendix BReferences |
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Method: A sample of 7,179 twin pairs was assessed on parent–report measures of syntax and vocabulary at ages 2, 3, and 4 years and on teacher assessments of reading achievement (RA) at ages 7, 9, and 10 years. These measures were used to construct latent factors of early language ability (LA) and RA in structural equation model-fitting analyses.
Results: The phenotypic correlation between LA and RA (r = .40) was primarily due to shared environmental influences that contribute to familial resemblance. These environmental influences on LA and RA overlapped substantially (rC = .62). Genetic influences made a significant but smaller contribution to the phenotypic correlation between LA and RA, and showed moderate overlap (rA = .36). There was also evidence for a direct causal influence of LA on RA.
Conclusions: The association between early language and later reading is underpinned by common environmental and genetic influences. The effects of some risk factors on RA may be mediated by language. The results provide a foundation for more fine-grained studies that examine links between specific measures of language, reading, genes, and environments.
KEY WORDS: language, literacy, genetics
Most children speak their first words around 12 months of age, string words together by 2 years, and are able to talk in long and complex sentences by 4 years. These emerging linguistic competencies are important for children's reading development (Elbro & Scarborough, 2004; Scarborough, 2005). Children with specific language impairments—whose language acquisition is delayed in the absence of broad cognitive difficulties—are much more likely to develop reading difficulties than children meeting normal language milestones (e.g., Catts, Fey, Tomblin, & Zhang, 2002; Snowling, Bishop, & Stothard, 2000). Language development is also a good predictor of reading achievement (RA) among typically developing populations. The goal of the present study was to illuminate the mechanisms underlying this link and, specifically, to assess the genetic and environmental origins of the association between preschool language development and elementary-school RA.
Importance of Syntax and Vocabulary in Learning to Read
Fledgling readers require an array of language skills to develop good reading. Phonological skills such as phoneme segmentation and phonological decoding are the strongest linguistic predictors of early reading development, but it is also clear that as children acquire a degree of proficiency in word recognition and other word-level skills, other oral language processes become the primary sources of variability in reading performance (Vellutino, Tunmer, Jaccard, & Chen, 2007). These include knowledge of the structural features of language (syntax) and knowledge of the lexical meanings of words (vocabulary), hereafter referred to together as syntactic–semantic skills.
Support for the importance of syntax and vocabulary in learning to read has been provided by longitudinal studies showing that syntactic and vocabulary performance in kindergarten predicts reading performance (Catts, Fey, Zhang, & Tomblin, 1999; Muter, Hulme, Snowling, & Stevenson, 2004) and reliably discriminates between poor and normal readers (e.g., Catts et al., 1999; Hagtvet, 2003; Share & Leikin, 2004). In addition, studies that have sought to identify student characteristics that predict responsiveness to early literacy interventions have shown that children who do not benefit from interventions (known as treatment resisters or nonresponders) typically also have poor vocabulary and syntactic awareness (Al Otaiba & Fuchs, 2002, 2006). The relationship between syntactic–semantic skills and reading depends somewhat on the reading outcome: Syntactic skills and vocabulary typically predict reading comprehension better than they do word recognition (e.g., Muter et al., 2004; Share & Leikin, 2004)—that is, syntactic and semantic knowledge become particularly important as children attempt to comprehend units of text larger than individual words.
Just how early vocabulary and syntactic knowledge play a role in learning to read is less clear. Two general positions have been advanced. One is that early syntactic–semantic skills and later reading partly reflect some of the same underlying risk and/or facilitative factors. These may include factors or skills that mediate the effects of early syntactic–semantic skills on later reading. For example, it has been suggested that all language-related correlates of poor word reading, including syntactic and vocabulary knowledge, have their origins in phonological processing deficits (e.g., Shankweiler, Crain, Brady, & Macaruso, 1992). According to this view, children with poorer vocabulary are less likely to develop fine-grained and well-organized phonological representations, which in turn will inhibit word recognition in reading. In other words, phonological awareness mediates the effects of early syntactic–semantic skills on word recognition. The risk and/or facilitative factors underlying syntactic–semantic skills and later reading may also include factors that independently influence both skill domains. For example, it has been proposed that the ability to process complex verbal material more or less efficiently (Scarborough, 1991, 2005) or the effectiveness of the procedural learning system (e.g., Nicholson & Fawcett, 2007) may underlie the prediction from language development to both typical and atypical reading performance. Under both scenarios—some factors may mediate the effects of early syntactic–semantic skills on later reading, whereas other factors independently influence both skill domains—the relationship between syntactic–semantic and later reading is assumed to be indirect. A prediction that follows from this hypothesis is that risk factors that are correlated with early vocabulary and syntactic knowledge will also be correlated with later reading.
A second possibility, not mutually exclusive of the first, is that syntactic–semantic skills may have a direct causal effect on later reading. It is not the case that oral language will cause children to be able to read, but it may cause them to be better at learning to read. Several models have incorporated a direct role of semantic or syntactic skills in reading development. According to Share's (1995) self-teaching hypothesis, for example, partial word decoding in combination with top-down support from oral vocabulary provides children with a method to read new words. For example, an attempt to decode an exception word such as have based on regular grapheme–phoneme correspondences will result in a pronunciation such as /haev/ (rhyming with gave). A child with good vocabulary skills, however, is more likely to be able to draw on their vocabulary to arrive at the correct pronunciation (e.g., they may be reasonably confident that /haev/ is not a real word). Likewise, some models of reading comprehension posit a direct role for vocabulary in reading comprehension (Cromley & Azevedo, 2007). If too many words are unknown, reading for meaning will be disrupted. It is important to note that, in most studies that have examined a possible causal role of syntactic–semantic skills for reading development, "causality" can only be inferred in the theoretical sense because most of the data available has been correlational rather than experimental. Accepting this caveat, a prediction that follows from the hypothesis that syntactic–semantic skills have a direct causal effect on later reading is that risk factors that influence syntactic–semantic skills may have a distal effect on children's later reading development.
In summary, research has shown that individual differences in oral vocabulary and syntactic skills play a role in later reading development. Evidence on the underlying nature of this relationship between syntactic–semantic skills and reading is fragmentary: Both indirect and direct links have been proposed, but these positions have rarely been examined and contrasted empirically. A window on this issue may be gained by examining the association between language and reading from an etiological, rather than from a purely phenotypic, perspective.
Common Genes and/or Common Environments?
Family, twin, and adoption studies converge on the conclusion that language and reading abilities and disabilities are influenced by multiple genetic influences in concert with multiple environmental risk factors (Pennington & Olson, 2005; Stromswold, 2001). Where there is evidence that two co-occurring traits are influenced by the same type of etiological influences—genes, environments, or both—there is reason to consider the hypothesis that they share a common etiology. The hypothesis that has received most attention is that language and reading development covary because there is a significant correlation between the genetic risk factors that influence early language ability (LA) and those that influence RA—essentially, the genetic analogue of the position that early language skills and later reading partly reflect some of the same underlying risk factors. A parallel, and not mutually exclusive, hypothesis is that there is a correlation between the environmental risk factors that influence language and those that influence reading.
Children with a family history of reading difficulties (referred to hereafter as HRD) have provided one testing ground for these two versions of the common risk factor hypothesis. Children from HRD families have a four- to sixfold greater risk of developing reading difficulties over population rates (Gilger, Pennington, & DeFries, 1991) and are less responsive than children not at similar family risk to interventions designed to provide foundation skills for reading development (Hindson et al., 2005). If reading difficulties and language impairments share a common familial basis, it may be expected that children from HRD families are at elevated risk for early language impairments as well as reading difficulties. Prospective studies following children from HRD families and matched controls from preschool through the early school years support this hypothesis: Children from HRD families not only tend to develop reading difficulties at higher rates compared with controls but they also exhibit problems in early language development that are reliably associated with later reading performance (Elbro & Scarborough, 2004). There is also evidence, both from the language performance of children from HRD families (van Alphen et al., 2004) and from their reading performance (e.g., Elbro, Borstrøm, & Peterson, 1998; Pennington & Lefly, 2001; Snowling, Gallagher & Frith, 2003), that the familiality of the association (either genetic or shared environmental; i.e., contributing to family resemblance among children) between early language and later reading reflects a continuous risk dimension. These findings suggest a familial basis to the association between preschool language impairments and early reading difficulties that operates across the distribution of abilities.
More incisive information on the hypothesis of common risk factors comes from twin studies, which allow familial resemblance to be decomposed into genetic and environmental components. Genetically sensitive studies using twin samples have pointed to the importance of heritable influences on normal variation in both language and reading ability as well as on impairments of language and reading. Recent estimates suggest that up to two-thirds of the variance in early reading skills, as well as the likelihood of reading disorder, is attributable to heritable factors (Pennington & Olson, 2005; Plomin & Kovas, 2005; Stromswold, 2001). Genetic influences on individual differences in oral language skills appear to be somewhat lower: Between one quarter and one half of the variance in early language performance and language impairments is due to genetic factors (Plomin & Kovas, 2005; Stromswold, 2001). In all studies, nongenetic environmental influences have also accounted significantly for variance in reading and language performance.
Twin studies can also be used to go beyond these univariate analyses—which determine the relative role of genetic, shared environmental, and nonshared (child-specific) environmental influences on single traits—to multivariate analyses, which do the same analysis for the covariance between two or more traits (Plomin, DeFries, McClearn, & McGuffin, 2008). To date, two twin studies have examined the developmental relationship between language and reading abilities. In the International Longitudinal Twin Study, 225 twin pairs from the United States and Australia were assessed on measures of phonological awareness at age 4 and measures of literacy (word reading and spelling) in the first grade (Byrne et al., 2005). There was evidence for common genetic influences acting on phonological awareness and word reading, although the genetic overlap was not complete—there were significant residual (i.e., specific) sources of genetic influences on both measures. There was weaker but suggestive evidence that phonological awareness and word reading were linked by shared environmental influences. Nonshared environmental influences (which include measurement error) accounted for some of the variance in phonological awareness and reading, but these influences did not overlap. A parallel profile of results—genetic and shared environmental overlap but not overlap of nonshared environmental influences—was obtained for the relationship between preschool phonological awareness and first-grade spelling ability.
The second study was based on a sample of 564 twin pairs from the Twins Early Development Study (TEDS; Hayiou-Thomas, Harlaar, Dale, & Plomin, 2006), from which the present, much larger sample is also drawn. A subset of the TEDS sample was assessed in home on a battery of nine language measures at age 54 months and on a composite measure of reading, comprising teacher assessments and a test of word reading, at age 7 years (Hayiou-Thomas et al., 2006). The association between early language and later reading was examined in terms of two sets of parameters that are central to multivariate analysis—first, the extent to which genetic and environmental influences account for the phenotypic correlation between early language and later reading performance, and second, the extent to which genetic and environmental influences on early language and later reading performance overlap (these latter sets are referred to as genetic and environmental correlations).
The confidence intervals for parameter estimates were very wide, thus precluding conclusions about the precise magnitude of effect sizes or whether there were differences in the patterns of association for different language measures. Nevertheless, the general pattern of estimates and confidence intervals was broadly consistent with the results obtained in the International Longitudinal Twin Study. The phenotypic correlations between language skills at 54 months and reading at 7 years were due partly to genetic influences (point estimates of .37–1.00) and partly to shared environmental influences (.02–.61). The genetic correlations were consistently greater than zero (.23–.65), indicating that genetic influences, on average, showed some overlap on early language and later reading performance. The point estimates were also suggestive of shared environmental overlap, as indexed by the shared environmental correlations (.02–.84), although only estimates for story retelling and oral vocabulary were significantly greater than zero. Nonshared environmental influences, in contrast, made little contribution to the phenotypic relationship between early language and later reading, and the nonshared environmental influences acting on each measure were negligibly correlated.
The Current Study
The modest research literature to date indicates that the association between preschool language and early reading abilities is partially mediated both by genetic and shared environmental factors. These studies go some way to providing support for the view that early syntactic–semantic skills and later reading partly reflect some of the same underlying risk factors. However, there are a number of limitations in these studies. One is that neither study considered whether there might be a direct causal influence of early language on later reading performance—a possibility raised earlier. A second limitation is that the sample sizes of both studies were modest, which, as indicated by the TEDS results, leads to large confidence intervals surrounding parameter estimates. Multivariate analyses require very large samples for adequate statistical power; without that, the wide confidence intervals make it difficult or impossible to compare specific relationships. Finally, both studies focused only on language at a single age, approximately 4 years. Clearly, however, much language development occurs before this. Indeed, Scarborough (2005) points out that in some cases, earlier language measures actually predict reading better than later language measures. Assessing children's earlier language abilities as well as their language at age 4 may provide more complete and reliable prediction of their reading development.
In this study, we sought to replicate and extend the previous studies of Byrne et al. (2005) and Hayiou-Thomas et al. (2006). We examined language and reading in a sample of more than 7,000 twin pairs and over an extended period of time: syntactic knowledge and vocabulary at three ages in early childhood (2, 3, and 4 years) and teacher assessments of RA at three ages in middle childhood (7, 9, and 10 years). The very large sample size of TEDS was made possible by the use of parent report in the preschool years, as explained in the Method section. Because of this methodology, only vocabulary and syntax could be assessed with high validity, in contrast to the broader range of measures used by Hayiou-Thomas et al. (2006) in their in-home testing, which was necessarily limited in sample size. Previous studies have demonstrated that vocabulary and syntactic knowledge are substantially correlated in early language development, both phenotypically (Bates & Goodman, 1997) and genetically (Dionne, Dale, Boivin, & Plomin, 2003). Thus, we used these measures as indicators of latent variables of language that capture the stability of vocabulary and grammar from ages 2 to 4 years. We used teacher assessments at ages 7, 9, and 10 years as latent variables of RA. Latent variables are independent of measure-specific variance and measurement error among the measures used and thus enable us to obtain more precise estimates of the genetic and environmental relationships between language and reading than would be possible using the individual measures (they do, of course, include any measure-specific variance common to the measures and therefore are not measure independent in an absolute sense). We hypothesized that we would find both genetic and shared environmental overlap between early language and later reading, mirroring earlier studies. Our main goals were twofold: (a) to clarify the effect sizes of these sources of overlap and (b) to test the hypothesis that there may also be a direct influence of early syntactic–semantic skills on later reading performance.
Method
Participants
Participants were drawn from twins born in 1994 and 1995 who were participating in TEDS, a longitudinal study of twins ascertained from population records of live twin births in England and Wales (Kovas, Haworth, Dale, & Plomin, 2007; Oliver & Plomin, 2007). Zygosity was determined for 75% of same-sex twins using polymorphic DNA markers (Freeman et al., 2003). In the remaining families of same-sex twins for whom DNA was not available, zygosity assignments were based on responses to a well-validated questionnaire for twin children that was completed by parents at 2, 3, and 4 years (Price et al., 2000).
Twins were excluded if parental or teacher assessments (described below) were returned more than 90 days after they were originally sent. We also excluded pairs in which one or both twins had a neurological condition or specific medical syndrome (e.g., cystic fibrosis) and children whose first language in the home was not English. The final sample available for study consisted of 7,179 twin pairs: 2,496 monozygotic (MZ) twin pairs (1,175 male pairs; 1,321 female pairs), 2,346 same-sex dizygotic (DZ) pairs (1,190 male pairs; 1,156 female pairs), and 2,337 twins in opposite-sex dizygotic (DZO) pairs. All children were assessed at ages 4 and 7 years, but children born between September 1995 and December 1996 were not included in the 2-, 3-, 9-, and 10-year waves of assessment due to funding constraints. These families do not differ systematically from twins born before September 1995 in terms of demographic characteristics, LA at age 4 years, or reading ability at age 7 years. Rates of assessment completion varied across the six waves of study (59.0%–84.5%); not all families participated at each wave of assessment or completed the assessments, and not all teachers who were contacted when their children were at ages 7, 9, and 10 years agreed to provide data, even when families themselves provided data. Although consistency of participation among families has varied, twins with poorer language or reading abilities were not significantly more likely to drop out of assessment waves compared with twins with better language or reading abilities (Harlaar, Dale, & Plomin, 2007).
Measures
Language Assessments
LA was assessed by parents at ages 2, 3, and 4 years using measures of vocabulary and syntax from age-appropriate, anglicized versions of the MacArthur Communicative Development Inventories, U.K. Short Form (MCDI: UKSF; described in Dale, Price, Bishop, & Plomin, 2003).
Vocabulary. The vocabulary assessment at each age comprised a checklist of words (100 words at ages 2 and 3 years; 45 words at age 4 years). Parents were asked to check those words that they have heard their child say. They were explicitly instructed to focus on words that their children could produce, rather than words they could comprehend, and to disregard pronunciation errors. Checked words are summed to give a total score. The 100 words on the 2-year version were selected from the longer MCDI (Fenson et al., 1994, 2000) and then anglicized for appropriate spelling in a U.K. setting. The 3-year vocabulary measure was developed for TEDS (and later was distributed more widely as the CDI-III; Fenson et al., 2007) in accordance with similar design principles. To ensure that the difficulty level of the 3-year measure was appropriate, 45 words were selected from the original, full MCDI, and 55 new words were included based on literature review and pilot testing. The 4-year vocabulary measure, also developed for this project, included 48 words chosen on the basis of literature review and pilot testing. Scores on the 2- and 3-year vocabulary measures were normally distributed. Scores on the 4-year vocabulary measure were skewed (skewness: –1.01) and were therefore transformed (by reflecting and taking the square root of scores) prior to analyses (skewness after transformation: –0.01).
Syntactic knowledge. The 2- and 3-year versions of the MCDI: UKSF included a grammar scale, with different items being used at 2 and 3 years. The first question asked whether the child is combining words. For the 12 remaining items, the rater was asked to indicate which of two sentences is most like the way the child talks. Both sentences in each item expressed the same meaning, but the first was expressed in a developmentally simpler form. For instance, in the 2-year version, one item was baby want eat versus baby want to eat. The 12 items on the 2-year version were selected from the full MCDI on the basis of good prediction of the full set of 37 items. The 12 items on the 3-year version included some new and more advanced aspects of grammar, chosen on the basis of literature review and pilot testing. Thus, the grammar scale at 2 and 3 years ranged from 0 to 13. The parents of children at age 4 years were asked to judge which of six statements best described their child's language, ranging from 0 (not yet talking) through 6 (talking in long and complicated sentences), with examples provided for each of the five stages that included talking. Scores on the 4-year syntactic knowledge measure were skewed (skewness: –1.36) and were therefore transformed (by reflecting and taking the square root of scores) prior to analyses (skewness after transformation: –0.64).
For the purposes of the present analyses, we took the mean of the vocabulary and grammar scores to form a general language composite at each age. Previous studies have shown that the vocabulary and grammar measures correlate substantially both phenotypically and genetically (Dionne, Dale, Boivin, & Plomin, 2003).
Reading Assessments
Teachers assessed children's reading at ages 7, 9, and 10 years using a rating scale of general RA that referenced U.K. National Curriculum (NC) achievement goals for literacy. The assessment at age 7 years was based on Key Stage 1 criteria (shown in Appendix A). Children can be awarded achievement levels ranging from 0 through 4. The assessments at ages 9 and 10 were based on Key Stage 2 (shown in Appendix B). Children can be awarded achievement levels ranging from 1 to 5. Scores at each age approximated a normal distribution and are representative of the distribution of teacher NC assessments of reading in the U.K. population (Kovas et al., in press).
At each wave of assessment, teacher assessments were obtained by postal questionnaire during the spring semester. Twins in the same classroom were assessed by the same teacher, whereas twins in different classrooms were assessed by different teachers. Most primary schools in the United Kingdom have no formal policy about educating twins in the same classroom or in different classrooms (British Broadcasting Corporation, 2001). Twins in our sample were somewhat more likely to have the same teacher at each age of assessment (65.5% at age 7 years, 58.4% at age 9 years, and 53.5% at age 10 years). However, placement in same or different classrooms in our sample was not systematically related to zygosity at any age— for one twin selected at random from each pair: age 7 years, 
2(1, N = 6,479) = 0.70, p = .40; age 9 years, 2(1, N = 3,412) = 0.09, p = .76; age 10 years, 2(1, N = 3,244) = 1.91, p = .17. There was a significant positive correlation between age at time of assessment and NC scores: Older children had higher NC scores (7-year NC: r = .08, confidence interval [CI] = .03–.09; 9-year NC: r = .08, CI = .05–.11; 10-year NC: r = .12, CI = .09–.15). Consequently, all analyses were based on age-adjusted NC scores.
Two strands of evidence support the validity of the NC assessments. First, NC assessments made by teachers correlate highly with test assessments. For example, cross-tabulation analyses on a nationwide sample of 600,000 children have shown that agreement between NC teacher assessments of reading and scores on group-administered NC reading tests at age 7 years is good (
= .80; Dale, Harlaar, & Plomin, 2005). High levels of agreement have also been reported for teacher and test assessments at Key Stage 2 (Reeves, Boyle, & Christie, 2001). Second, there is evidence for the concurrent validity of the NC assessments relative to individual direct testing. For example, NC scores at age 7 years correlate substantially with the Test of Word Reading Efficiency (TOWRE; Torgesen, Wagner, & Rashotte, 1999) at age 7 years (r = .69). These findings are consistent with a large body of research which shows that accurate word recognition skill is strongly related to children's ability to accomplish more general or complex reading tasks (Perfetti, 1985).
Analyses
We report both phenotypic and genetic analyses. The aim of the phenotypic analyses was to establish the extent to which early language abilities were associated with later RA, both at the level of the individual measures and at the level of latent factors of language and reading. The aims of the genetic analyses were to establish (a) the extent to which genetic and environmental influences mediate the phenotypic correlation between latent phenotypic factors of language and reading and (b) the extent to which genetic and environmental influences on early language and later reading overlap, on average. Genetic overlap is operationalized as the genetic correlation, which refers to the extent to which genetic influences on one trait overlap with those influencing another trait. Similar interpretations apply to the shared and nonshared environmental overlap, which are operationalized in terms of the shared and nonshared environmental correlations, respectively.
Both phenotypic and genetic analyses were completed using the freely available Mx package (for an overview of Mx, see Neale, Boker, Xie, & Maes, 2002). Variance–covariance matrices derived from raw age- and sex-corrected (McGue & Bouchard, 1984) data were used in these analyses. We estimated model parameters and 95% CIs by full-information maximum likelihood (FIML) estimation, which allows missing data to be taken into account. Goodness-of-fit was quantified using the deviance information criterion (DIC; Spiegelhalter, Best, Carlin, & van der Linde, 2002) and the Bayesian information criterion (BIC; Raftery, 1995). Lower (more negative) DIC and BIC values identify the model that reproduces the observed variances and covariances with as few unknown estimated parameters as possible. These fit indices take into account the sample size and thus were preferred over more commonly used indices such as the chi-square test, which is sensitive to even minor and substantively trivial differences in models when large samples are analyzed (Mulaik et al., 1989).
Results
Phenotypic Analyses
At the level of the individual measures, we found substantial correlations among the language measures at 2, 3, and 4 years (.45–.67) and among the NC assessments at 7, 9, and 10 years (.58–.62). We conceptualized the language and reading measures as two correlated first-order factors, where the language measures were used as indicators of an LA factor, and NC assessments were used as indicators of an RA factor. This model is represented by the path diagram shown in Figure 1. To ensure model identification, measurement error was assumed to be uncorrelated across measures. The phenotypic analyses were adjusted to take into account the nonindependence of twins, as described in van der Sluis et al. (2006). The hypothesized two-factor solution provided a good fit to the data (lower BIC and DIC values), compared with a saturated Cholesky decomposition model (two-factor solution: BIC = –105119.28, DIC = –139738.02; Cholesky decomposition: BIC = –105113.72; DIC = –139727.10). No measure could be dropped from the model without causing the BIC and DIC values to increase (BIC = –104606.95 to –101750.21, and DIC = –139226.36 to –136369.62, compared with BIC = –105119.28, and DIC = –139738.02, for the two-factor solution that included all six measures).
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On the basis of these model comparisons, our final phenotypic analysis constrained the factor and covariance structure to be equal across boys and girls but allowed the means to vary as a function of sex. The parameter estimates for this model are shown next to the path coefficients in Figure 1. All factor loadings were substantial and significantly greater than zero. Squaring the loadings indicates that the LA factor accounted for 40% of the variance in language performance at age 2 years, 87% at age 3, and 53% at age 4. The RA factor accounted for 58% of the variance in NC scores at age 7 years, 66% at age 9, and 62% at age 10. Correlations between the individual language measures at ages 2, 3, and 4 years and NC assessments at ages 7, 9, and 10 years, shown in bold in Table 1, were moderate (.23–.29). The phenotypic correlation between the latent language and RA factors was .40 (CI: .38–.42).
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Twin correlations. To obtain a first impression of the genetic results, we computed cross-trait, cross-twin intraclass correlations between the language measures and NC scores (e.g., the correlation between 2-year language scores in the firstborn twin [Twin 1] and 7-year NC scores in Twin 2). These correlations can be used to estimate the decomposition of the covariance between the individual language measures and NC assessments at each age into genetic and environmental influences. In particular, the degree to which MZ correlations exceed DZ correlations provides evidence for a genetic contribution to the covariance between language measures and NC scores.
As shown in Table 1, for each pair of measures, the correlations for MZ twins were similar or slightly lower than the phenotypic correlation. The phenotypic correlations set a ceiling for the correlations in MZ twins because MZ twins are assumed to share both identical genotypes and shared environments that contribute to familial resemblance. Thus, the small discrepancies between the phenotypic and MZ correlations suggest that nonshared environmental influences (which result in differences between family members) do not contribute importantly to the covariance between the language and reading measures. Correlations in MZ twins were only slightly greater that the correlations for DZ twins. This pattern of twin correlations suggests that the primary source of covariance between the language and reading measures is shared environmental factors with a lesser contribution from genetics. Results are similar for male and female same-sex pairs as well as for same-sex and opposite-sex DZ pairs, indicating that there are no significant quantitative or qualitative sex differences in etiology.
Model-fitting. We used genetic model-fitting to examine these patterns further. This model (the common etiology model), depicted in Figure 2 for one member of a twin pair, is an extension of the phenotypic model shown in Figure 2. Language scores at ages 2, 3, and 4 years were used as indicators of a latent LA factor, whereas NC scores at ages 7, 9, and 10 years were used as indicators of a latent RA factor. Phenotypic variance in both factors is assumed to reflect the sum of additive genetic, shared environmental, and nonshared environmental influences (indicated by A1, C1, E1 for LA and A2, C2, E2 for RA). Each measure is also influenced by a set of unique A, C, and E factors. The unique A and C factors account for measure-specific additive genetic and shared environmental influences. The unique E factors account for measure-specific nonshared environmental influences plus measurement error.
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This model was used to assess two issues. First, we estimated the relative roles of genetic and environmental influences contributing to the phenotypic variance in LA and RA individually, independent of measure-specific genetic and environmental influences and measurement error. The second and more central issue was to examine the contributions of genetic and environmental influences to the overlap between LA and RA.
Contributions of Genetic and Environmental Influences to the Variance in LA and RA
The path coefficients from the correlated factors model are shown in bold (with confidence intervals just below them) in Figure 2. Squaring the path coefficients indicates that variance in LA and RA was due to both additive genetic and shared environmental influences. Shared environmental influences accounted for most of the variance in LA (.842 = .71; CI = .68–.72), with additive genetic influences also being important but accounting for significantly less variance than shared environmental influences (.532 = .28; CI = .26–.30). Conversely, stronger genetic influences and weaker shared environmental influences were found for RA (.852 = .72; CI = .67–.76; .452 = .20; CI = .16–.24). Nonshared environmental influences on the latent factors, reflecting the effects of twin-specific and nongenetic factors that are independent of measurement error, were negligible.
Path coefficients for the contributions of measure-specific A, C, and E factors to the variance in the measured variables are shown at the bottom of Figure 2. There is evidence for significant measure-specific genetic and nonshared influences on all measures, as well as significant measure-specific shared environmental influences on language assessments but not on NC assessments. Squaring the measure-specific A, C, and E factors that load on the LA factor indicates that between 3% and 28% of the total variance in these measures is due to measure-specific genetic, shared environmental influences, nonshared environmental influences, and measurement error. Language measures at ages 2 and 4 years show greater measure-specific genetic and shared environmental variance compared with language at age 3 years, which accords with the finding that the 3-year measure accounts for more variance (and, therefore, more genetic and environmental variance) in the latent language factors. Squaring the measure-specific A and E factors that load on NC scores indicates that between 14% and 25% of the variance in NC scores is due to measure-specific genetic and nonshared environmental influences. In contrast, measure-specific shared environmental influences on NC scores are negligible.
Contributions of Genetic and Environmental Influences to the Overlap Between LA and RA
The second and more central issue being studied concerns this question of how genes and environments influence the association between early language and later reading. We examined the contributions of genetic and environmental influences to the overlap between LA and RA in terms of two sets of parameters: (a) the extent to which genetic and environmental influences account for the phenotypic correlation between early language and later reading performance and (b) the extent to which genetic and environmental influences on early language and later reading performance overlap (the genetic and environmental correlations).
The phenotypic correlation between early language and later reading performance is a function of (a) the degree of genetic and environmental influence on early language and later reading performance individually and (b) the correlations between these genetic and environmental influences across the two domains. Of the phenotypic correlation of .40 between LA and RA, 40% (CI = 35%–46%) was mediated genetically. This value can be derived from the path coefficients in Figure 2. The product of the unsquared A paths and the genetic correlation connecting the latent LA and RA factors (.53 x .36 x .85) represents the genetic contribution to the phenotypic correlation. Dividing this genetic contribution (.16) by the phenotypic correlation (.40) indicates that 40% of the phenotypic correlation was mediated genetically. The contribution of shared environmental influences to the phenotypic correlations was also substantial (58%; CI = 53%–63%), whereas the contribution of nonshared environmental influences was significant but negligible (2%; CI = 1%–2%).
The extent to which genetic and environmental influences on early language and later reading performance overlap is indexed directly by the magnitude of the genetic correlation (rA), shared environmental correlation (rC), and nonshared environmental correlation (rE). The genetic correlation indicates the extent to which individual differences on NC scores at two ages reflect the same genetic influences, on average in the population, whereas the shared and nonshared environmental correlations indicate the extent to which individual differences reflect the same environmental influences. Genetic and environmental correlations may take any value between –1 and +1 and are independent of the extent to which two traits are each influenced by genetic and environmental influences.
As shown in Figure 2, there was a moderate degree of genetic overlap between LA and RA: The genetic correlation of .36 (CIs = .32, .37) indicates that about one third of genetic effects on LA correlate with genetic effects on RA. The shared environmental correlation (.62; 95% CIs = .55–.64) was substantial and significantly greater than the genetic correlation. The nonshared environmental correlation was modest but significantly greater than zero (.19; CI = .10–.27).
In a supplementary analysis, we tested for possible sex differences in the genetic and environmental results, of either a quantitative (different relative balance of genetic and environmental influences) or qualitative (different genetic and/or environmental influences operating for boys and girls) nature. In the standard genetic model, the genetic correlation between DZO twins for each variable is assumed to be 0.5, the same as for same-sex DZ twins. The genetic correlation between DZO twins would be attenuated if there are significant sex-specific genetic influences on language or NC scores because sex-specific influences would reduce within-pair similarity in DZO pairs. The results from these analyses essentially mirrored those based on the full-sample twin correlations. A model constraining the genetic correlations between DZO twins to 0.5 for each variable did not fit significantly worse (BIC = –97218.96; DIC = –131801.54) than a fully parameterized model, in which the genetic correlations were allowed to fall below 0.5 (BIC = – 97214.51; DIC = –131791.73). Consequently, we may assume that there are no significant sex-specific genetic influences on language or NC scores. Similarly, we found that a model constraining genetic and environmental parameters to be equal for girls and boys also provided a better fit to the fully parameterized model, indicating that we may assume that there are no significant sex differences in the magnitude of genetic and environmental influences on language and NC scores (BIC = –97229.06; DIC = –131806.95). Thus, the patterns of genetic and environmental covariance—such as the phenotypic factor loadings and covariance—were similar across sexes.
Direct Causal Effects of LA on RA?
Could the association between LA and later reading partly reflect a direct causal influence of language on reading and not just common influences on them? We examined this question using a series of models that included a direct causal influence of LA on RA. The basic model, the causal pathway model, is shown in Figure 3. Similar to the common etiology model (shown in Figure 2), this model posits that LA and RA are influenced by independent genetic, shared environmental, and nonshared environmental factors. However, it is assumed that the association LA and RA is also due, in part, to a direct influence of LA and RA (denoted by the path coefficient i) rather than by just correlations between the latent A, C, and E factors—that is, some of the genetic and environmental influences on RA are assumed to be mediated through children's language abilities. In addition, variation unique to each of the language and reading measures is partitioned into measure-specific latent A, C, and E factors.
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Genetic and Shared Environmental Overlap
We found that approximately 58% of the phenotypic correlation between latent factors of early LA and RA (r = .40) was due to shared environmental factors. Furthermore, the shared environmental correlation (.62) was of a large effect size, indicating that approximately two-thirds of the shared environmental influences that affect syntactic–semantic skills also influence RA. Taken together, these findings suggest that the reason why children with better language abilities typically demonstrate better reading performance at school is that there are substantial common (across domain) shared (within family) environmental influences, and in addition, there is a large degree of overlap in the shared environmental influences affecting both preschool language abilities and RA. These findings are consistent with previous TEDS findings, which showed that genetic and shared environmental influences made a greater contribution to the relationship between language skills at 54 months and 7-year reading compared with nonshared environmental influences (Hayiou-Thomas et al., 2006).
Our results also demonstrate a role for genetic influences in the relationship between early language and later reading. Specifically, genetic influences accounted for one third of the phenotypic correlation—less than the contribution made by shared environmental influences but nonetheless significant. The genetic correlation (.36) indicates that approximately one third of the genetic effects influencing LA also influence reading performance. That is, part of the reason why children with better language abilities typically demonstrate better reading performance at school is that their genotype facilitates language and reading development—syntactic–semantic skills and later reading performance are influenced, to some extent, by common or "generalist" genes (Plomin & Kovas, 2005).
It is interesting to compare these findings on the etiology of the covariation between syntactic–semantic skills and later RA with the etiology of the variance in the latent language and RA factors individually. In fact, the two showed quite different etiological profiles. Variance in the latent LA factor was attributable mainly to shared environmental influences and, to a lesser extent, additive genetic influences, whereas the inverse pattern was seen for the RA factor. That is, shared environmental influences primarily contribute to the covariation between LA and later reading, but shared environmental influences account for relatively little variance in RA itself. In part, this likely reflects the moderate phenotypic correlation between LA and later reading. What we are seeing is that RA is influenced by many of the same environmental influences that impinge on early vocabulary and syntactic awareness, but the residual variance—the variance not explained by vocabulary and syntactic awareness—is primarily due to genetic influences. The source of this residual genetic variance is unknown but may include genetic variance associated with other early linguistic predictors of reading, such as phonological awareness (Petrill, Deater-Deckard, Thompson, DeThorne, & Schatschneider, 2006) and language comprehension (Keenan et al., 2006). This finding suggests that certain shared environmental factors may place a child at risk for both language and reading difficulties. Interventions that target these factors could facilitate reading development, although they may not be sufficient for children who have additional genetic vulnerabilities for reading difficulties—for these children, it is likely that other types of support will be necessary to help them gain competence in reading.
A final point of interest is that nonshared environmental influences showed little overlap, in contrast to genetic and shared environmental influences. Only 2% of the phenotypic correlation was due to nonshared environmental influences, and the correlation across domains between nonshared environmental influences, estimated as .19, could be described as being of small effect size. This dissociation provides some perspective on discontinuities in the relationship between early language and later reading—that is, children who read better or worse than might be expected, on the basis of their language scores in early childhood, to do so partly as a result of nonshared environmental influences.
A Causal Influence of LA on RA?
The second goal of the present study was to evaluate the possibility that there is direct influence of early syntactic–semantic skills on later reading performance. Our model-fitting results provided some support for this possibility. The model with the lowest BIC and DIC values included a direct causal influence of the latent LA factor on RA in addition to correlated genetic factors between these factors. The direct causal influence of LA on RA suggests that the effects of some risk factors on RA will be mediated by syntactic–semantic skills.
What are the factors that are mediated by LA? Speculatively, shared environmental factors are the most likely candidates. Our model-fitting comparisons indicated that the shared environmental correlation (estimated as .62 in the common etiology model) could be dropped without a significant decrease in model fit when a direct causal influence of the latent LA factor on RA was estimated. This finding suggests that at least some of the shared environmental overlap between LA and RA observed in the common etiology model is mediated by LA itself. That is, some shared environmental influences promote children's vocabulary and syntactic knowledge, which in turn facilitate reading development. Our analyses do not reveal which specific shared environmental factors are involved. There is preliminary evidence that children's language and reading outcomes are predicted by maternal education attitudes and parental education involvement independent of shared genes between parents and children (Petrill, Deater-Deckard, Schatschneider, & Davis, 2005), and these factors may be worth investigating further in future research on environmental factors that may influence RA via language abilities. This research should also consider the role of gene–environment correlations: Putative environmental factors may partly reflect genetic influences of the child (through the reactions they elicit from others or through the environments that they themselves select) or the parent (through the environments that they provide for their children; Plomin, DeFries, & Loehlin, 1977). Within the TEDS sample, Oliver, Dale, and Plomin (2005) have shown that almost one-quarter of the variance in amount of early literacy experience (e.g., being read to) is genetic in the sense that it is correlated with the child's genotype.
The evidence that genetic factors on the LA and RA factors remained significantly correlated when a causal path was introduced between these factors buttresses the interpretation that syntactic–semantic skills and later reading performance are influenced, to some extent, by common genes. Such effects could occur because some genes that contribute to processes that are important for both language and reading development—for example, auditory processing, working memory—are expressed continuously throughout development. Other genes may show temporal-specific patterns of expression but have effects that result in genetic stability. For example, the relevant genes in early childhood might no longer be actively transcribed, but their structural legacy (e.g., differences in neural networks) could produce the genetic correlation between two ages (DeFries, Plomin, & LaBuda, 1987). Finally, although some of the genetic risk factors may overlap between the evidence for the causal pathway between syntactic–semantic skills and RA, it is possible that some of the genetic risk factors will be mediated by syntactic–semantic skills.
The keenest tests of these predictions are likely to depend on the identification of specific environments and specific genes associated with early syntactic–semantic skills and later reading. At present, these are flourishing but largely independent research areas. Much is likely to be gained by studying environmentally informative research into genetically sensitive designs in order to study more precisely how the common links between early language and reading are established.
Limitations and Conclusions
The findings from the present study should be weighed against at least three limitations. First, the language abilities of twins were rated by the same parent (typically, the mother), and RA of twins in the same classroom (65% at age 7 years, 59% at age 9 years, and 21% at age 10 years) were rated by the same teacher. Using the same rater to assess each twin in the pair is likely to lead to rater response tendencies (e.g., stereotyping, idiosyncratic response styles) being shared across co-twins, which will artificially inflate shared environmental influences. Nevertheless, our estimates of the contribution of shared environmental influences are similar to those reported in previous twin studies of LA and RA in which different testers have tested twins (e.g., Samuellsson et al., 2007). Moreover, because mode of assessment differed for language and reading—parent reports for language, teacher assessments for reading—such biases cannot account for the finding of shared environmental covariance between the two.
A second concern is that our assessments were relatively limited and treated language and reading as single, omnibus phenotypes. Beyond vocabulary and syntax, language includes other components such as phonology and pragmatics. Vocabulary and syntax themselves are multiform. Vocabulary, for example, can be seen to encompass both the number of words known (vocabulary breadth) and vocabulary depth (Ouellette, 2006); similarly, syntactic abilities include syntactic awareness as well as syntactic knowledge (Gombert, 1992), and reading skills include, at the broadest level, word recognition and reading comprehension (Share & Leikin, 2004). Although our very large sample had several advantages, the necessary restriction in measures may mean that we have missed more subtle etiological relationships between specific language and reading skills. For example, when word- and text-level components of reading are disaggregated, it has been found that broad language skills such as vocabulary and syntactic awareness make a stronger contribution to the prediction of reading comprehension than to the prediction of skills in reading isolated words or nonwords (Scarborough, 2005). It would be interesting to see whether this differentiation also occurs genetically. Evidence from the TEDS project suggests that links between general language skills, including vocabulary and syntactic awareness, and later reading are mediated by both genetic and shared environmental factors. However, the links between speech at age 4
years and later reading seem to be mediated largely by common genetic factors (Hayiou-Thomas, Harlaar, Dale, & Plomin, in press). This is a good example of the way different research designs, each with specific strengths and limitations, can complement each other.
A third limitation specifically concerns the nature of the reading assessment. In the NC criteria, RA is largely defined in terms of oral behavior in the classroom (e.g., "In responding to fiction and nonfiction, they show understanding of the main points and express preferences"; "They express opinions about major events or ideas in stories, poems, and nonfiction"). To some degree, this type of measure may confound oral language proficiency and reading: Children with better vocabulary and syntactic skills may be more likely to perform better on measures of oral behavior in the classroom. Accordingly, environmental (and genetic) overlap may primarily reflect environmental effects on oral language skills at both ages rather than oral language at preschool and RA in the school years. In the absence of additional measures of children's reading and language, it is difficult to evaluate this possibility.
Finally, we cannot yet determine whether our findings are generalizable to children with language or reading difficulties. As noted in the introduction, studies of children from HRD families suggest that the familiality of the association between preschool language and later reading varies on a continuum. Specifically, children from HRD families who develop reading difficulties show the poorest language and reading skills, followed by children from HRD families whose reading abilities are still in the normal range. Control children without family risk for reading difficulties show the highest levels of language and reading skill. On the basis of this observation, we predict that we would find correlated genetic and shared environmental influences on the comorbidity between early language impairments and later reading difficulties, mirroring our findings for the covariation between language and reading in the general ability range; however, empirical evidence is needed to test this hypothesis.
The underlying mechanisms through which early language abilities predict later reading have been a focus of much theorizing and empirical research, but we suggest that it has been stymied by limited consideration of the role of genes and environments. Although the current study requires replication and further refinements, the results add to previous evidence documenting the dual influence of genetics and shared environmental factors on the covariance between early language abilities and later RA. We can infer that a simple view that this association reflects either genetic influences or environmental influences is untenable—both are important. One implication of the genetic and shared environmental overlap implies that medical and communication disorders specialists treating children with language or reading problems can expect to encounter language or reading problems in the child's family. Therefore, we may need to develop strategies for not only working with children with language or reading problems but also for helping their parents to help their children (McCauley & Fey, 2006).
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Appendix A |
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TopAbstractAppendix AAppendix BReferences |
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1 Pupils recognize familiar words in simple texts. They use their knowledge of letters and sound–symbol relationships in order to read words and to establish meaning when reading aloud. In these activities they sometimes require support. They express their response to poems, stories, and nonfiction by identifying aspects they like.
2 Pupils' reading of simple texts shows understanding and is generally accurate. They express opinions about major events or ideas in stories, poems, and nonfiction. They use more than one strategy, such as phonic, graphic, syntactic, and contextual, in reading unfamiliar words and establishing meaning.
3 Pupils read a range of texts fluently and accurately. They read independently, using strategies appropriately to establish meaning. In responding to fiction and nonfiction, they show understanding of the main points and express preferences. They use their knowledge of the alphabet to locate books and find information.
4 Reading is substantially more advanced than most pupils at Level 3.
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Appendix B |
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TopAbstractAppendix AAppendix BReferences |
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2 Pupils' reading of simple texts shows understanding and is generally accurate. They express opinions about major evens or ideas in stories, poems, and nonfiction. They use more than one strategy, such as phonic, graphic, syntactic, and contextual, in reading unfamiliar words and establishing meaning.
3 Pupils read a range of texts fluently and accurately. They read independently, using strategies appropriately to establish meaning. In responding to fiction and nonfiction, they show understanding of the main points and express preferences. They use their knowledge of the alphabet to locate books and find information.
4 In responding to a range of texts, pupils show understanding of significant ideas, themes, events, and characters, beginning to use inference and deduction. They refer to the text when explaining their views. They locate and use ideas and information.
5 Reading is substantially more advanced than most pupils at Level 4.
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Acknowledgments |
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Received June 23, 2006
Revision received November 30, 2006
Accepted September 4, 2007
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References |
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