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Published in final edited form as:
J Educ Psychol. 2006 February 1; 98(1): 112–121. doi:10.1037/0022-0663.98.1.112.
Genetic and Environmental Effects of Serial Naming and
Phonological Awareness on Early Reading Outcomes
Stephen A. Petrill1, Lee Anne Thompson3, Kirby Deater-Deckard2, Laura S. DeThorne1, and
Christopher Schatschneider4
1 Department of Biobehavioral Health, Pennsylvania State University
2 Department of Psychology, University of Oregon
3 Department of Psychology, Case Western Reserve University
4 Department of Psychology, Florida State University
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Abstract
The current study involved 281 early-school-age twin pairs (118 monozygotic, 163 same-sex
dizygotic) participating in the ongoing Western Reserve Reading Project (S. A. Petrill, K. DeaterDeckard, L. A. Thompson, & C. Schatschneider, 2006). Twins were tested in their homes by separate
examiners on a battery of reading-related skills including phonological awareness, rapid automatized
naming, word knowledge, and phonological decoding. Results suggested that a core genetic factor
accounted for a significant portion of the covariance between phonological awareness, rapid naming,
and reading outcomes. However, shared environmental influences related to phonological awareness
were also associated with reading skills.
Keywords
reading; genetics; phonological awareness; development
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For nearly 2 decades, researchers have argued for the primacy of phonological processing in
the acquisition of early literacy skills (Bradley & Bryant, 1983; Stanovich & Siegel, 1994;
Torgesen, Wagner, Rashotte, Burgess, & Hecht, 1997). More recently, others have suggested
that in addition to phonology, naming speed constitutes an independent and additive source of
variance in early reading skills (e.g., Wolf & Bowers, 1999). In a recent meta-analysis,
Swanson, Trainin, Necoechea, and Hammill (2003) examined the association between
phonological awareness, rapid automatized naming (RAN), reading, and related abilities in a
set of 49 independent samples. Although their results suggested that phonological awareness
and RAN were factorially distinct, particularly in their prediction of real-world reading
outcomes, phonological awareness and RAN were also significantly correlated with one
another (weighted r = .39). Thus, although phonological awareness and RAN contributed
independently to reading outcomes, there was also significant overlap between phonological
awareness and the skills that underlie RAN performance.
Correspondence concerning this article should be addressed to Stephen A. Petrill, Department of Biobehavioral Health, Center for
Developmental and Health Genetics, 101 Gardner House, Pennsylvania State University, University Park, PA 16802. E-mail: E-mail:
sap27@psu.edu.
Kirby Deater-Deckard is now at the Department of Psychology, Virginia Polytechnic Institute and State University. Laura Dethorne is
now at the Department of Speech & Hearing Science, University of Illinois at Urbana – Champaign.
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One important issue is the extent to which genes and environments influence the overlap and
independence among phonological awareness, serial naming, and reading outcomes. It is clear
that genetic and environmental influences are significant when examining not only reading
outcomes (e.g., Pennington & Smith, 1983; Stevenson, Graham, Fredman, & McLoughlin,
1987) but also reading-related measures such as phonological awareness (Gayan & Olson,
2001; Knopik, Alarcón, & DeFries, 1998; Olson, Gillis, Rack, DeFries, & Fulker, 1991) and
RAN (Compton, Davis, De-Fries, Gayan, & Olson, 2001). The importance of genetic
influences on reading and reading performance has been further supported by a series of
independent studies that have identified and replicated quantitative trait loci for reading on the
short arms of Chromosomes 2, 6, and 18 (Cardon et al., 1994; Gayan et al., 1999; Grigorenko,
2003; Grigorenko et al., 1997; Grigorenko, Wood, Meyer, & Pauls, 2000; Fagerheim et al.,
1999; Fisher et al., 1999, 2002). Furthermore, studies have shown that genes are primarily
responsible for the overlap among reading-related outcomes. For example, the comorbidity
among orthographic and phonological kinds of reading skills is due largely to overlapping
genetic influences (e.g., Gayan & Olson, 2001, 2003). Similar results have been found when
comparing RAN to reading outcomes, particularly in poor readers (Davis, Knopik, Olson,
Wads-worth, & DeFries, 2001).
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The purpose of the current study was to examine the genetic and environmental etiology of
covariance between phonological awareness, naming speed, and reading outcomes in the
ongoing Western Reserve Reading Project (WRRP; Petrill, Deater-Deckard, Thompson, &
Schatschneider, 2006). As suggested by Swanson et al. (2003), there is both overlap and
independence between phonological awareness and RAN as they predict reading outcomes. A
handful of studies have also examined the genetic and environmental etiology of the
relationship between phonological awareness, RAN, and reading outcomes. Compton et al.
(2001) examined this issue in a sample of 800 affected twin pairs in which one or more of the
twin pairs had a history of reading problems and a related sample of 450 control twin pairs
with no history of reading problems. Both samples ranged in age from 8 to 18 years. The
affected group showed evidence of a core set of genes common to phonology, RAN, and
reading outcomes as well as a set of genes specific to the relationship between RAN and reading
outcomes. An examination of the control group revealed that although the core set of genes
common to phonology, RAN, and reading outcomes was significant, independent genetic
variance associated with RAN was not correlated with reading outcomes. Although there was
little evidence for shared environment in the affected group, there was some evidence for shared
environmental overlap between RAN and reading outcomes in the control group.
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Although Compton et al.’s (2001) study was important, its generalizability to specific age
groups, particularly to children who are just beginning to learn to read, is unclear. As reading
skills develop, they are characterized by an early emphasis on learning to decode single words
and a later emphasis on developing reading comprehension, which is the ultimate goal of
reading (Catts, Hogan, & Adlof, 2005; Chall, 1983; Dale & Crain-Thoreson, 1999; Lyon,
Shaywitz, & Shaywitz, 2003). Byrne et al. (2005) examined the relationship between
phonological awareness, RAN, and reading outcomes in a sample of 627 preschool- and
kindergarten-age twin pairs drawn from the United States (355 pairs), Australia (150 pairs),
and Scandinavia (122 pairs). This study found evidence of substantial genetic overlap between
phonological awareness, RAN, and word-reading efficiency. Moreover, although there was
evidence for significant independent genetic contributions from RAN, the effect size was about
half of the genetic variance common to phonology and rapid naming.
Thus, in the present study we explored four possibilities. First, genetics may be primarily
responsible for both the overlap as well as the independence between phonological awareness
and rapid naming as they predict reading outcomes, as suggested by the sample of individuals
with reading disabilities reported by Compton et al. (2001) and Byrne et al. (2005). In other
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words, whereas rapid naming and phonological processing may share common genes,
additional genes specific to rapid naming may also affect reading outcomes. A second
possibility is that genes contribute to a basic set of skills common to phonological awareness
and rapid naming, but environmental influences result in specificity, such as the unique effect
of phonological-based instruction on phonological awareness. In the typically developing
sample reported by Compton et al. (2001), shared genes influenced the overlap between rapid
naming and phonological awareness, but independent environments were found for rapid
naming and phonological awareness. Third, it is possible that in addition to shared genes,
environmental influences are also important to the correlation between rapid naming and
phonological awareness. In this case, variance in the early home literacy environment may
affect phonological awareness, rapid naming, and reading outcomes. Recent behavioral genetic
studies have suggested that the importance of environmental variance may be significant,
particularly in early readers (Byrne et al., 2002; Petrill et al., 2006). Finally, it is possible that
genes and environments are important to both overlap and independence between phonological
processing and rapid naming in their prediction of reading outcomes.
Method
Participants
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The WRRP (Petrill, Deater-Deckard, Thompson, & Schatschneider, 2006) is a 5-year
longitudinal cohort sequential study examining gene–environment factors in the etiology of
early reading and related cognitive skills. The families are located throughout Ohio and
Western Pennsylvania, with most living in the greater Cleveland, Columbus, and Cincinnati
metropolitan areas. Recruiting is conducted through school nominations (N = 273 schools),
Ohio birth records, mothers of twins clubs, and media advertisements. Families are recruited
if they have same-sex twins who have already entered kindergarten but have not yet finished
first grade.
As part of the ongoing study, twins are assessed four times over a 3-year period. In the current
study, we examined the 118 monozygotic (MZ) twin pairs (61% female, mean Stanford–Binet
standard age scores [SAS] = 101.5, SD = 13.7) and 163 dizygotic (DZ) same-sex twin pairs
(56% female, mean Stanford–Binet SAS = 99.9, SD = 12.7) who completed the first home
visit. Twins were in kindergarten or first grade (mean MZ age = 6.1 years, SD = .75, range =
5.0–7.9; mean DZ age = 6.1 year, SD = .65, range = 4.9–7.7). Most children lived in two-parent
households (96%), and nearly all were White (91%). Forty-three percent of mothers and 40%
of fathers had less than a 4-year college education. Eleven percent of mothers and 19% of
fathers had a high school education or less.
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Procedure and Measures
Children completed a 90-min battery of cognitive and reading-related outcome measures as
part of the first home visit. Separate testers assessed each child in separate rooms. The study
focused on two predictor skills associated with reading (phonological awareness and RAN)
and three outcome variables (letter knowledge, word knowledge, and phonological decoding).
Phonological awareness was assessed with Robertson and Salter’s (1997) Phonological
Awareness Test. It includes six subtests that measure rhyming (discrimination and production),
phoneme isolation (initial), phonemic segmentation (whole word), and phonemic deletion
(syllabic deletion and phoneme deletion). Given that phonological awareness has been shown
to be a unitary construct (Schatschneider, Francis, Foorman, Fletcher, & Mehta, 1999), the six
subtests were summed to form a raw total score for phonological awareness that was then
residualized for age and gender with a regression procedure.
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Rapid automatized naming was assessed with the letter-naming and number-naming tasks from
the Comprehensive Test of Phonological Processing (Wagner, Torgesen, & Rashotte, 1999).
Letter and number naming were highly correlated (r = .73) and thus were residualized for child
age, gender, and, given the age of the sample, score on the Letter Identification subtest of the
Woodcock Reading Mastery Tests—Revised (WRMT–R; Woodcock, 1987). Residuals were
then z scored and averaged to form a RAN composite, which was then reverse scored so that
a high score corresponded to faster naming speed.
Reading outcomes were assessed with the WRMT–R (Woodcock, 1987). We used the Word
Identification (Word ID) subtest to assess word knowledge and the Word Attack subtest to
assess phonological decoding (PD) skills. Finally, given the rapid development of reading
skills from kindergarten to first grade, we also examined the data in the context of months of
school. Months of school correlated .88 with child age.
Results
Descriptive statistics are shown in Table 1. The average level and range of performance are
typical for community samples of this kind, with mean scores slightly above average. The
average months of school was 6.8 months for identical twins and 6.7 months for DZ twins.
The range for both groups was 0–22 months.
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Sibling intraclass correlations, also shown in Table 1, suggest that sibling similarity was
greatest among MZ twins (.74 to .89), followed by DZ twins (.37 to .57). Because MZ
correlations were generally less than 2 times the DZ correlation, these results suggest that
sibling similarity is accounted for by both additive genetic and shared environmental variance.
Correlations among the outcome variables were generally moderate to substantial, ranging
from .20 between phonological awareness and RAN to .70 between Word ID and PD (see Table
2). The months of school variable was not significantly correlated with phonological
awareness, RAN, or WRMT Letter ID but was significantly correlated with Word ID and PD.
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The major goals of the study were to test whether (a) phonological awareness and RAN
independently predicted reading-based outcomes and (b) the overlap and/or independence
between phonological awareness and RAN was influenced by genetic and/or environmental
influences. Prior to conducting multivariate genetic analyses, we performed a series of multiple
regressions to examine the independent prediction of reading outcomes. Phonological
awareness and RAN were simultaneously entered as the independent variables. Word ID and
PD were the dependent variables in separate analyses. Results suggested that phonological
awareness and RAN accounted for independent variance in reading outcomes. In particular,
the independent prediction of phonological awareness varied from β = .47 when predicting
Word ID to β = .56 when predicting PD. Descriptively, the independent prediction of RAN
was smaller in magnitude, ranging from β =.14 for Word ID to β =.15 for PD (see Table 3).
Given that phonological awareness and RAN accounted for independent sources of variance
in reading outcomes, the next step was to examine the genetic and environmental etiology of
these relationships. We conducted a series of Cholesky decomposition analyses (see Neale &
Cardon, 1992) similar to those used in Compton et al. (2001) and Byrne et al. (2005). As shown
in Figure 1, the covariance among phonological awareness, RAN, and outcomes (Word ID and
PD, in separate analyses) was parameterized using nine latent factors. A general genetic factor
(A1) was set to load on phonological awareness, RAN, and the outcome variable. If this factor
was significant, then the overlap among phonological awareness, RAN, and the outcome was
influenced by genetic covariance. A2 estimated the genetic relationship between RAN and
outcome, after controlling for phonological awareness. A3 estimated the genetic variance in
outcome not accounted for by phonological awareness and RAN. A core set of genetic variance
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common to phonology, RAN, and reading outcomes was supported if A1 was large and
significant and A2 was small and nonsignificant. The genetic independence of RAN was
supported if the pathway from A2 to RAN was large and significant. The independent genetic
relationship between RAN and reading outcomes, separate from genes associated with
phonological awareness, was supported to the extent that the pathways from A2 to RAN and
reading outcomes were both significant. Similar factors were estimated for shared environment
(C1, C2, C3) and non-shared environment (E1, E2, and E3). A core set of environmental
influences common to all measures was supported to the extent that C1 (shared environments
such as same home and same schools, etc.) and E1 (child-specific environmental experiences
that influence both phonological awareness and RAN) and C2/E2 were small and
nonsignificant. The role of independent shared and nonshared environmental influences
associated with RAN was supported to the extent that C2 and E2 were significant. All models
were estimated by means of Mx (Neale, Boker, Xie, & Maes, 1999), using raw data.
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To further investigate the role of months of school, we used the model presented in Figure 2
to conduct a second set of analyses. This model was developed in our prior research (Petrill &
Deater-Deckard, 2004;Petrill, Pike, Price, & Plomin, 2004) and is based on Neale et al.
(1999). In this model, the months of school variable is set to load on phonology, RAN, as well
as on reading outcomes. Because months of schooling was invariant within families, this
variable constituted a measured shared environmental influence on early reading and related
skills. Thus, to the extent that months of schooling accounted for shared environmental
influences associated with phonology, rapid naming, and/or reading outcomes, the pathways
from months of school should be significant. Moreover, because these pathways would
describe a portion of the shared environment, the pathways from C1, C2, and/or C3 should be
diminished relative to the model described in Figure 1. Because all variables were either
corrected for age or involved age-standardized scores, significant effects for months of school
represent residual effects of schooling not associated with the age of the child.
First, these models were used to estimate univariate estimates of heritability (h2), shared
environment (c2), and nonshared environment (e2), as well as genetic and environmental
contributions to the correlations among phonological awareness, RAN, Word ID, and PD.
These results, presented in Tables 4 and 5, decompose the estimated phenotypic correlations
into genetic, shared environmental, and nonshared environmental components of variance/
covariance. The diagonals describe the proportion of variance accounted for by genetic, shared
environmental, and nonshared environmental influences. The off-diagonals describe the
genetic, shared environmental, and nonshared environmental contributions to the observed
correlations between phonological awareness, RAN, and reading outcomes.
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Table 4 presents results for phonological awareness, RAN, and Word ID. An examination of
the basic Cholesky decomposition (using Figure 1: months of school not included) reveals that
phonological awareness, RAN, and Word ID demonstrated significant genetic, shared
environmental, and nonshared environmental effects (phonological awareness: h2 = .61, c2 = .
27, e2 = .12; RAN: h2 = .46, c2 = .24 e2 = .30; Word ID: h2 = .59, c2 = .31, e2 = .10). The
correlation between phonological awareness and RAN was influenced significantly by genetic
influences (r = .53) but not by shared environment (r = .00) or nonshared environmental (r = .
08) overlap. The correlation between phonological awareness and Word ID was influenced by
both genetic (r = .24) and shared environmental (r = .27) overlap. The correlation between
RAN and Word ID was influenced significantly by genetic overlap (r = .21). An examination
of the model in which the months of school variable was included (see Figure 2) revealed that
months of school did not account for significant variance in phonological awareness or RAN,
nor did this variable account for significant covariance among phonological awareness, RAN,
and Word ID. However, months of school explained 15% of the variance in Word ID skills.
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When the basic Cholesky model (months of school not included; see Table 5) is used to examine
the relationship between phonological awareness, RAN, and PD, it is clear that phonological
decoding demonstrated significant genetic, shared environmental, and nonshared
environmental influences (h2 = .46, c2 = .35, e2 = .19). Similar to the pattern of results found
when examining the relationship between phonological awareness, RAN, and Word ID, the
correlation between phonological awareness and PD was influenced by both genetic (r = .37)
and shared environmental (r = .21) overlap, whereas the correlation between RAN and PD was
influenced by genetic overlap (r = .33). Estimates were nearly identical for phonological
awareness and RAN when the months-of-school-included model was examined. However,
similar to Word ID, months of school accounted for 11% of the total variance in PD. Taken
together, the results in Tables 4–5 suggest that (a) phonological awareness and RAN, (b) RAN
and Word ID, and (c) RAN and PD are associated primarily through genetic influences. In
contrast, phonological awareness appears to be correlated with Word ID and PD through both
genetic and shared environmental pathways.
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By fitting submodels to the Cholesky analyses described previously, we were able to test
whether the genetic and environmental covariance found in Tables 4–5 was due to independent
genetic and environmental effects related to phonological awareness and RAN. In particular,
eight submodels were fit to the basic Cholesky model presented in Figure 1: four testing genetic
effects and four testing shared environmental effects. First, the pathway from A1 to Word ID
was dropped (Drop GenWID) to test whether Word ID was influenced by genetic effects
common to both phonological awareness and RAN. Next, the pathways for A2 were dropped
(Drop RAN) to test the independent genetic effects related to RAN and the independent
relationship between RAN and Word ID. Next, the pathway from A2 to Word ID was dropped
(Drop RANWID) to test whether the independent genetic relationship between RAN and Word
ID was significant. Finally, A3 was dropped to test whether there was residual genetic variance
for Word ID not accounted for by phonological awareness and RAN. Similar models were
tested for shared environmental factors (C1, C2, and C2). This process was repeated for PD.
The difference between −2 log likelihood is distributed as a chi-square (χ2change). If a submodel
results in a significant decrease in model fit, it is assumed that the parameters dropped in those
submodels are statistically significant. In particular, if the correlations found in Tables 4–5 are
due to a single source of genetic or shared environmental variance, then dropping independent
effects related to RAN (e.g., DROP RANWID) should not result in a significant decrease in
model fit. If, however, RAN contributes independent variance to Word ID or PD, then dropping
independent genetic and environmental effects related to RAN should significantly decrease
model fit.
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Results presented in Table 6 suggest that general genetic effects were found when examining
Word ID, χ2change(1) = 18.37, p < .01, and PD, χ2change(1) = 29.81, p < .01. Independent genetic
effects related to RAN were not significant for Word ID, χ2change(2) = 0.00, p > .05, or PD,
χ2change(2) = .00, p > .05, suggesting that RAN does not possess genetic variance independent
from PA. Finally, residual genetic effects for Word ID and PD were not significant. Thus, the
genetic overlap between RAN and Word ID was influenced completely by genetic influences
shared with phonological awareness. Similar effects were found for PD.
Regarding the shared environment, dropping the pathway from the general shared
environmental factor led to a significant decrease in model fit for Word ID: χ2change(1) = 17.28,
p < .01, and PD, χ2change(1) = 8.95, p < .05. Dropping all pathways from factor C2 resulted in
a significant decrease in fit for Word ID and PD, χ2change(2) = 12.50 and 11.57, respectively,
ps < .01; dropping the pathway from factor C2 to WID, χ2change(1) = 0.40, p > .05, and PD,
χ2change(1) = 0.36, p > .05, did not result in a significant decrease in fit. Finally, in no case was
there residual shared environmental variance in Letter ID, Word ID, and PD.
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Regarding the months of school model (see Table 7), results were highly similar to the basic
Cholesky model when comparing the eight submodels described previously. Dropping the
parameters from months of school to phonological awareness and RAN did not result in a
significant decrease in model fit. Dropping the parameters from months of school to WID and
PD resulted in significant decreases in model fit: χ2change(1) = 42.32 and 31.48, respectively,
ps < .01.
Discussion
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The major goals of the study were to examine whether phonological processing and rapid
naming independently predicted reading-based outcomes and whether the overlap and/or
independence between phonological awareness and RAN was influenced by genetic and/or
environmental factors. Results suggested large overlap with some specificity between
phonological awareness and RAN in their prediction of reading outcomes. Pearson correlations
and multiple regression analyses suggested that phonological awareness and RAN were
correlated but that they also independently predicted word identification and phonological
decoding. Multivariate quantitative genetic analyses suggested that a general genetic factor
was responsible for the covariance between RAN, phonological processing, and reading
outcomes. In contrast to Byrne et al. (2005) and the reading disabled sample described by
Compton et al. (2001), there was no evidence for independent genetic effects on RAN.
Furthermore, independent shared environmental covariance between phonological awareness
and word identification as well as between phonological awareness and phonological decoding
was also significant. Independent shared environmental influences were found for RAN, but
they were not associated with word identification or phonological decoding.
These shared environmental effects are somewhat of a departure from previous studies of
reading, which generally show genetic overlap with little evidence for shared environmental
overlap (e.g., see Davis et al., 2001; Gayan & Olson, 2003). However, the importance of shared
environment in the overlap among early reading skills is consistent with our own univariate
examination of WRRP (Petrill et al., 2006) as well with univariate results reported by Byrne
et al. (2002, 2005). Moreover, these results are somewhat consistent with the typically
developing sample described by Compton et al. (2001).
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However, despite the similarity in the age of the twins who participated in the present study
and in Byrne et al.’s (2005) study, there are some discrepancies between the present results
and the multivariate results presented by Byrne et al. (2005). In particular, the current study
suggested shared environmental variance specific to RAN, whereas Byrne et al. (2005)
suggested that shared environmental variance in RAN was related to phonological awareness.
Moreover, Byrne et al. (2005) showed evidence for independent genetic effects for RAN,
whereas our data do not. This inconsistency could be due to sampling and measurement
differences between the two studies. In terms of sampling, Byrne et al. (2005) involved U.S.,
Australian, and Scandinavian samples, whereas our study examined a U.S. sample only. In
addition, Byrne et al. (2005) used a composite of the Comprehensive Test of Phonological
Processing’s (Wagner et al., 1999) colors and shapes, whereas the current study involved a
composite of letters and numbers, corrected for age, sex, and letter identification. Despite these
differences, both studies converge on the general idea that shared environment may be more
important in early literacy and that genetics are important for the overlap among phonological
awareness, RAN, and reading outcomes.
Furthermore, although the age range of the current study is narrow relative to most behavioral
genetic studies of reading, and we regressed out the effects of age prior to analysis, the children
were not identical in age and grade level. Thus, the children in the current study have different
levels of exposure to phonological-based instruction. Because twins are the same age and in
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the same grade, this constitutes a between-family effect that would be estimated as shared
environmental variance. Unfortunately, we do not have detailed information about the curricula
used by the twins’ schools. However, months of school did not affect the etiology of the
relationships among phonological awareness, RAN, Word ID, and PD skills.
Another shortcoming of the current study is that although the sample was heterogeneous in
terms of the mothers’ education levels and has been shown to be heterogeneous in terms of the
home literacy environment (Petrill & Deater-Deckard, 2005), the sample was not
heterogeneous in terms of ethnicity (91% White) or family composition (96% married or
cohabiting). Thus, although our sample is more representative in terms of the range of
environment than most behavioral genetic studies, the results of the current study must still be
interpreted in light of the restriction of range in ethnicity and family composition.
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Despite these issues, it is possible that the shared environmental influences found in the current
study reflect a true developmental effect. Research examining environmental predictors of
early literacy suggest that reading-related knowledge and skills that children acquire via the
home environment are associated with early reading success (McCardle, Scarborough, & Catts,
2001) but that the indices of the home environment that influence early reading are no longer
influential among older readers (Scarborough & Dobrich, 1994). Emerging longitudinal
genetic evidence pointing to the dissipation of shared environmental influences from preschool to kindergarten also supports this possibility (Bryne et al., 2005).
More generally, one long-standing axiom of reading development is that the main requirements
of successfully learning to read in young children are phonological awareness, orthography,
and visual–analytic ability (see Dale & Crain-Thoreson, 1999). As reading skills mature,
children use reading to learn new words and to integrate these words into developing semantic
knowledge (Chall, 1983). Therefore, it is sensible that shared environmental influences may
be greater for outcomes that are more likely to be influenced by direct instruction in the home,
such as expressive vocabulary, phonological awareness, or print knowledge. As longitudinal
data in the WRRP become available, we will be able to address two additional issues. First,
we will examine the extent to which genes and environments influence the stability and
instability of reading skills over time. Second, we will examine whether measured
environmental influences on early reading shift from a shared environmental to a genetic
etiology as children learn to read and as the environments associated with reading become
more a function of their own reading skills.
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Taken together, Compton et al. (2001), Byrne et al. (2005), and the current study all converge
on the importance of a core set of genes common to phonological awareness, RAN, and reading
outcomes. However, these three studies also provide support for the independent effects related
to RAN and phonology. In our own data, regression analyses suggested that RAN predicted
significant variance in Word ID and PD. Moreover, behavioral genetic analyses suggested that
phonological awareness was correlated with Word ID and PD via shared environmental
pathways independent of RAN. What remains to be established is whether the important skills
that are represented by naming speed performance lie outside of the phonological processing
domain.
Wagner, Torgesen, Laughon, Simmons, and Rashotte (1993) identified five correlated but
separable constructs that constitute the domain of phonological processing: analysis, synthesis,
memory, isolated naming, and serial naming. Subsequent work by Schatschneider et al.
(1999) demonstrated that the analysis and synthesis constructs should be combined into a singe
factor described previously in the literature as phonological awareness. On the one hand, the
significance of a core set of genes common to phonology, RAN, and reading outcomes provides
support for this unitary model of phonological processing. On the other hand, in our data,
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naming speed predicted variance in reading above and beyond phonological awareness, and
phonological awareness was correlated with reading outcomes through an additional shared
environmental factor. Although this does not necessarily mean that the skills being tapped by
naming speed that relate to reading lie outside the domain of phonological processing, that
serial naming is phenotypically separable from phonological awareness and could constitute
a second, etiologically distinct source of variance in reading skills.
Acknowledgements
The Western Reserve Reading Project was supported by National Institute of Child Health and Human Development
(NICHD) Grant HD38075 and NICHD/Office of Special Education and Rehabilitative Services (OSERS) Grant
HD46167.
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Figure 1.
General Cholesky model examining phonological awareness (PA), rapid automatized naming
(RAN), and reading outcomes. Outcomes were letter identification, word identification, and
phonological decoding, conducted in separate analyses. A1–A3, C1–C3, and E1–E3 refer to
genetic (A), shared environmental (C), and nonshared environmental (E) factors.
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Figure 2.
General Cholesky model examining months of school, phonological awareness (PA), rapid
automatized naming (RAN), and reading outcomes. Outcomes were letter identification, word
identification, and phonological decoding, conducted in separate analyses. A1–A3, C1–C3,
and E1–E3 refer to genetic (A), shared environmental (C), and nonshared environmental (E)
factors.
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Table 1
Descriptive Statistics and Sibling Intraclass Correlations Among Monozygotic (MZ) and Dizygotic (DZ) Twins
Variable
Phonological awareness
RAN
M
DZ
SD
M
SD
MZ
DZ
r
r
36.2
13.3
34.8
13.1
.85
.51
164.2
61.4
166.1
66.2
.55
.37
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Letter ID
102.9
9.0
102.2
8.7
.74
.57
Word ID
103.6
18.3
104.7
18.6
.89
.57
Phonological decoding
103.4
12.9
103.3
11.9
.82
.53
6.8
6.7
6.7
5.5
Months of school
Petrill et al.
MZ
Note. Phonological awareness is expressed in number of correct items (total possible correct = six subtests, 10 items each = 60), whereas rapid automatized naming (RAN) is expressed in number of
seconds to complete both letter and number subtests of the Comprehensive Test of Phonological Processing. Phonological awareness was residualized for age and gender and RAN for age, gender, and
letter identification for all additional analyses. Letter ID and Word ID refer to the Letter Identification and Word Identification subtests of the Woodcock Reading Mastery Tests—Revised.
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Table 2
Intercorrelations Among Phonological Awareness (PA), Rapid Automatized Naming (RAN), Letter Identification (Letter ID), Word
Identification (Word ID), Phonological Decoding (PD), and Months of School (MSCHOOL)
1
1. PA
—
2
3
4
5
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2. RAN
.20*
—
3. Letter ID
.52*
.00
—
4. Word ID
.52*
.23*
.56*
—
5. PD
.59*
.26*
.42*
.70*
—
.06
−.07
.37*
.28*
6. MSCHOOL
.07
6
Petrill et al.
Variable
—
Note. The correlation between RAN and Letter ID was .47, p < .05, when RAN was residualized for age and gender only.
*
p < .01.
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Table 3
Multiple Regression Analyses
β
sr
t
.47
.46
11.70*
474
.14
.14
3.55*
474
Phonological awareness
.56
.55
15.11*
478
RAN
.15
.14
3.95*
478
Variable
df
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Word IDa
Phonological awareness
RAN
Phonological decodingb
Note. Word ID = the Word Identification subtest of the Woodcock Reading Mastery Tests—Revised; RAN = rapid automatized naming.
a
F(473) = 85.79, p < .01, R2 = .27.
b
F(477) = 139.08, p < .01, R2 = .37.
*
p < .01.
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Table 4
Phenotypically Standardized Genetic, Shared Environmental, and Nonshared Environmental Variance/Covariance Matrices:
Phonological Awareness (PA), Rapid Automatized Naming (RAN), and Word Identification (Word ID)
Variable
PA
RAN
Months of school includedb
Word ID
Genetic
PA
PA
2
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.53*
h = .46*
Word ID
.24*
.21*
h2 = .59*
Shared environment
RAN
Word ID
h2 = .59*
RAN
.52*
h2 = .46*
Word ID
.26*
.22*
h2 = .57*
Shared environment
c2 = .27*
PA
2
RAN
.00
c = .24*
Word ID
.27*
.04
c2 = .31*
Nonshared environment
PA
PA
Genetic
h2 = .61*
RAN
PA
Variable
Petrill et al.
Months of school not includeda
c2 = .27*
RAN
.00
c2 = .23*
Word ID
.22*
.04
c2 = .18*
Nonshared environment
e2 = .12*
PA
2
RAN
.08
e = .30*
Word ID
.02
.01
e2 = .10*
Months of school
e2 = .13*
RAN
.08
e2 = .30*
Word ID
.02
.01
e2 = .10*
Months of school
PA
—
RAN
—
—
Word ID
—
—
PA
—
sch2 = .01
RAN
.01
sch2 = .01
Word ID
.04
.03
sch2 = .15*
Note. Asterisks indicate statistically significant values (using 95% confidence intervals in Mx). h2 = heritability; c2 = shared environment; e2 = nonshared environment; sch = months of school.
a
See Figure 1.
b
See Figure 2.
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Table 5
Phenotypically Standardized Genetic, Shared Environmental, and Nonshared Environmental Variance/Covariance Matrices:
Phonological Awareness (PA), Rapid Automatized Naming (RAN), and Phonological Decoding (PD)
Variable
PA
RAN
Months of school includedb
PD
Genetic
PA
PA
2
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.53*
h = .47*
PD
.37*
.33*
h2 = .46*
Shared environment
RAN
PD
h2 = .59*
RAN
.52*
h2 = .46*
PD
.37*
.33*
h2 = .47*
Shared environment
c2 = .27*
PA
2
RAN
.00
c = .23*
PD
.21*
.04
c2 = .35*
Nonshared environment
PA
PA
Genetic
h2 = .60*
RAN
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Variable
Petrill et al.
Months of school not includeda
c2 = .27*
RAN
.00
c2 = .23*
PD
.18*
.00
c2 = .23*
Nonshared environment
e2 = .13*
PA
2
RAN
.08
e = .30*
PD
.03
.02
e2 = .19*
Months of school
e2 = .13*
RAN
.08
e2 = .30*
PD
.03
.02
e2 = .19*
Months of school
PA
—
RAN
—
—
PD
—
—
PA
—
sch2 = .01
RAN
.01
sch2 = .01
PD
.03
.03
sch 2 = .11*
Note. Asterisks indicate statistically significant values (using 95% confidence intervals in Mx). h2 = heritability; c2 = shared environment; e2 = nonshared environment; sch = months of school.
a
See Figure 1.
b
See Figure 2.
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Table 6
Model-Fitting Results: Cholesky Decomposition
−2 log likelihood
df
χ2 change
dfchange
pchange
PA/RAN/Word ID
Full
3,392.34
1450
Drop GenWID (A1 to WID)
3,410.71
1451
18.37
1
Drop RAN (all A2)
3,392.34
1452
0.00
2
Drop RANWID (A2 to WID)
3,392.34
1451
0.00
1
Drop WIDresid (A3)
3,392.34
1451
0.00
1
Drop GenWID (C1 to WID)
3,409.62
1451
17.28
1
<.01
Drop RAN (all C2)
3,404.84
1452
12.50
2
<.01
Drop RANWID (C2 to WID)
3,392.74
1451
0.40
1
Drop WIDresid (C3)
3,392.38
1451
0.04
1
Petrill et al.
Model
Genetic
<.01
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Shared environment
PA/RAN/PD
Full
3,397.41
1454
Drop GenPD (A1 to PD)
3,427.22
1455
29.81
1
Drop RAN (all A2)
3,397.41
1456
0.00
2
Drop RANPD (A2 to PD)
3,397.41
1455
0.00
1
Drop PDresid (A3)
3,397.41
1455
0.00
1
Drop GenPD (C1 to PD)
3,406.36
1455
8.95
1
<.01
Drop RAN (all C2)
3,408.98
1456
11.57
2
<.01
Drop RANPD (C2 to PD)
3,397.77
1455
0.36
1
Drop PDresid (C3)
3,399.86
1455
2.45
1
Genetic
<.01
Shared environment
Note. Submodels were not run for nonshared environment because none of the bivariate relationships presented in Tables 4 and 5 was significant. A1, A2, A3, C1, C2, C3 refer to factors displayed in
Figure 1. PA = phonological awareness; RAN = rapid automatized naming; Word ID (or WID) = word identification; Gen = genetic; PD = phonological decoding; resid = residual.
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Table 7
Model-Fitting Results: Cholesky Decomposition With Months of School
−2 log likelihood
df
χ2 change
dfchange
pchange
PA/RAN/Word ID
Full
4,022.65
1688
Drop GenWID (A1 to WID)
4,044.24
1689
21.59
1
Drop RAN (all A2)
4,022.65
1690
0.00
2
Drop RANWID (A2 to WID)
4,022.65
1689
0.00
1
Drop WIDresid (A3)
4,022.65
1689
0.00
1
Drop GenWID (C1 to WID)
4,038.99
1689
16.34
1
<.01
Drop RAN (all C2)
4,034.45
1690
11.80
2
<.01
Drop RANWID (C2 to WID)
4,022.65
1689
0.00
1
Drop WIDresid (C3)
4,022.65
1689
0.00
1
Drop PA
4,025.29
1689
2.64
1
Drop RAN
4,024.66
1689
2.01
1
Drop WID
4,064.97
1689
42.32
1
<.01
<.01
Petrill et al.
Model
Genetic
<.01
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Shared environment
Months of school
PA/RAN/PD
Full
4,040.61
1692
Drop GenPD (A1 to PD)
4,071.87
1694
31.26
1
Drop RAN (all A2)
4,040.61
1693
0.00
2
Drop RANPD (A2 to PD)
4,040.61
1693
0.00
1
Drop PDresid (A3)
4,040.61
1693
0.00
1
Drop GenPD (C1 to PD)
4,049.21
1694
8.60
1
<.01
Drop RAN (all C2)
4,052.19
1693
11.58
2
<.01
Drop RANPD (C2 to PD)
4,040.61
1693
0.00
1
Drop PDresid (C3)
4,042.18
1693
1.57
1
Genetic
Shared environment
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χ2 change
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df
Drop PA
4,042.91
1693
2.30
1
Drop RAN
4,043.00
1693
2.39
1
Drop WID
4,072.09
1693
31.48
1
dfchange
pchange
<.01
Petrill et al.
−2 log likelihood
Model
Note. Submodels were not run for nonshared environment because none of the bivariate relationships presented in Tables 4 and 5 was significant. A1, A2, A3, C1, C2, C3, and months of school refer to
factors/variables displayed in Figure 2. PA = phonological awareness; RAN = rapid automatized naming; Word ID (or WID) = word identification; Gen = genetic; PD = phonological decoding; resid =
residual.
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