Copyright 1999 by the American Psychological Association, Inc.
0012-1649/99/S3.00
Developmental Psychology
1999, Vol. 35, No. 2, 590-603
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The Structure of Genetic Influences on General Cognitive, Language,
Phonological, and Reading Abilities
Bettina Hohnen
Jim Stevenson
Institute of Child Health, University College London
University of Southampton
The etiology of individual differences in literacy, phonological awareness, and language ability is
reported in 126 pairs of monozygotic and dizygotic twins. At age 6 and 7 years, more than 60% of the
variance in literacy was heritable. Heritabilities for 6- and 7-year-olds were .52 and .62, respectively, for
phonological awareness and .43 and .50, respectively, for language ability. After genetic effects on IQ
were controlled, a separate genetic influence was identified that acted on literacy, phonological awareness, and language. No genetic link between phonological awareness and literacy independent of general
language ability was found; such covariance was mediated through environmental influences. Individual
differences in literacy ability are substantially influenced by genetic factors, some of which also act on
phonological awareness and general language ability.
An earlier exchange of articles highlighted an important debate
regarding the relationship between the three abilities, which has
since been left unresolved. Bowey and Patel (1988) argued that the
majority of studies that have claimed to find a specific relationship
between phonological awareness and literacy have measured only
one specific aspect of language functioning when controlling for
general language ability. In their study, they tested the hypothesis
that when a more comprehensive assessment of general language
ability was obtained and then controlled, phonological awareness
would not be found to contribute independently to reading
achievement. What this hypothesis implies is that although both
phonological awareness and general language ability contribute to
reading achievement, they do not make developmentally independent contributions to reading. Their data were cross-sectional, and
assessments were made of phonological awareness (by using a
sound categorization task), syntactic awareness, and general language ability. Two measures of general language ability, the
Peabody Picture Vocabulary Test—Revised (Dunn, 1965) and the
Sentence Imitation subtest of the Test of Oral Language Development—Primary (Newcomer & Hammill, 1982), which measures
syntactic proficiency, were used to provide a comprehensive assessment of language ability. In a multiple regression procedure,
they found that phonological awareness did not make an independent contribution to reading after they had partialed out variance
due to the two linguistic tasks.
The link between phonological processing and literacy ability is
now well established. The two appear to be reciprocally related at
the very early stages of learning to read (Wagner, Torgesen, &
Rashotte, 1994). This relationship remains strong when general
cognitive ability is controlled. Studies have also demonstrated
significant improvements in literacy skills for children trained in
phonological awareness (Ball & Blackman, 1988; Bradley & Bryant, 1983; Lundberg, Frost, & Petersen, 1988). To demonstrate the
specificity of this relationship, it has been common to control for
general verbal ability. The finding that tests of phonological processing account for independent variation in early reading achievement above that shared with general language ability has led to the
general conclusion that phonological processing operates independently of general language ability in its influence on reading (e.g.,
Bowey, 1986; Bradley & Bryant, 1983; Lundberg, Olofsson, &
Wall, 1980; Stanovich, Cunningham, & Cramer, 1984; Wagner,
Torgesen, Laughon, Simmons, & Rashotte, 1993; Wagner et al.,
1994). As a consequence, the relationship between these three
abilities has tended to be overlooked.
Bettina Hohnen, Behavioural Sciences Unit, Institute of Child Health,
University College London, London, United Kingdom; Jim Stevenson,
Department of Psychology, University of Southampton, Highfield, United
Kingdom.
We thank Thalia Eley for her assistance with the demanding testing
progam for this study. We also thank Valerie Muter, Alison Gallagher, Uta
Frith, and Dorothy Bishop for their invaluable advice on the development
of the test battery. Vince Connolly and Edmund Sonuga-Barke provided
helpful critiques on an earlier version of this article. Finally, we express our
gratitude to the families and children who participated for their cooperation
with our work.
Correspondence concerning this article should be addressed to Jim
Stevenson, Centre for Research into Psychological Development, Department of Psychology, University of Southampton, Highfield, Southampton
SO17 1BJ, United Kingdom. Electronic mail may be sent to jsteven®
psy.soton.ac.uk.
Bryant, Maclean, and Bradley (1990) directly challenged this
interpretation. They criticized Bowey and Patel's (1988) study for
not measuring phonological awareness early enough and for not
having longitudinal data. They provided evidence from longitudinal data that found that rhyme ability, when measured at the age
of 4 years, did make a significant contribution to reading at the age
of 6 years even after stringent controls for general language ability
were made. Interestingly, when they controlled for mothers' educational level and IQ ability before entering the linguistic measures
and rhyme ability scores, the linguistic measures were no longer
significant predictors of reading. However, rhyme scores with590
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STRUCTURE OF GENETIC INFLUENCES
stood the effects of partialing out not only general intelligence and
background but also expressive and receptive language ability.
These results support the view that phonological awareness does
have a relationship with reading that is independent of general
language ability.
One possible explanation for the contradictory results for the
two studies is the difference in age when phonological awareness
was measured. Rhyme ability measured at age 4, Bryant et al.
(1990) argued, is a much purer measure of phonological awareness. The same construct measured at age 6 may be influenced by
phonological or spelling knowledge. However, Bowey (1990) believes the difference in results may have arisen for other reasons.
In particular, he criticized many of the language measures used in
Bryant et al.'s study for being poorly standardized and having no
evidence of reliability.
Surprisingly, this argument has received little further investigation. Bishop (1991) suggested that the overriding concern with
phonological ability and its influence on reading had led to the role
of language ability in relation to reading being underinvestigated.
When general language ability and reading have been studied
together, they are highly correlated (e.g., Bowey & Patel, 1988;
Stanovich et al., 1984; Tunmer, Herriman, & Nesdale, 1988;
Tunmer & Nesdale, 1985; Vellutino & Scanlon, 1987; Wagner et
al., 1993), and verbal ability has been shown to significantly
predict reading in longitudinal studies (e.g., Bryant et al., 1990;
Carlisle & Nomanbhoy, 1993; Share, Jorm, Maclean, & Matthews,
1984; Wagner et al., 1994). Moreover, even in studies that have
found phonological processing to make a significant independent
contribution to reading, the amount of variance explained is relatively small compared with the amount that it jointly contributes
with general language. These findings suggest that the relationship
requires further investigation.
This debate is mirrored in work on reading disability. It has been
postulated that there is a specific deficit in phonological skills in
those with a reading disability (Bryant & Bradley, 1985; Stanovich, 1988). According to this theory, although syntactic and
semantic problems often occur together with a reading disability,
they are not central to the disability (Catts, 1989a, 1989b). A more
recent view, however, proposes that dyslexia is a "developmental
language disorder" and children who suffer from reading problems
have an underlying language deficit that manifests itself differently
with age (Catts, 1991; Kamhi & Catts, 1989). During the preschool
years, these children have problems with spoken language, such as
syntactical and morphological problems (Aram & Nation, 1980;
Scarborough, 1990). They have phonological processing difficulties during the school years (Wagner & Torgesen, 1987), when
their problems in acquiring literacy skills are also manifest. They
also show continuing language problems during adulthood (D.
Johnson & Blalock, 1987). The basis of this developmental disorder is a deficit in language processing.
The implication of this formulation of reading disability is that
there may be an intrinsic limitation in some cognitive capacity that
is specific to verbal skills. This view was outlined by Scarborough
(1991), who suggested that rather than seeing early languagerelated problems as "precursors" to reading disability and later
problems as "outcomes," it is more helpful to look at the two as
symptoms of an underlying dimension of individual difference.
The cognitive limitation could conceivably affect both syntactical
and phonological proficiency through a problem with mastering
591
rule systems, a skill required for both. These tasks require the
ability to understand specific rule systems that underlie how abstract elements (phonemic or syntactic) are combined, but for
which only the surface features are perceived. A limitation in
dealing with such a system might also impede the acquisition of
letter-sound correspondence rules, which are required for the
development of literacy. Scarborough proposed a genetic basis to
this cognitive limitation.
The study reported here attempted to investigate the relationship
between general language, phonological ability, and reading ability in a general population sample. Previous studies have examined
these relationships on the basis of test scores (i.e., at the level of
the phenotype). In the present study, a genetically sensitive design
was used that allowed an analysis at the level of the genotype and
environmental influences. The measures related to reading ability
included single-word reading, prose reading, pseudohomophone
judgment, pseudoword reading, and spelling. A composite score
from these measures was obtained and is referred to as "literacy
ability" because it reflects a broader range of skills than just
reading.
Previous studies have established the strong genetic influences
on individual differences in literacy. Stevenson, Graham, Fredman, and McLoughlin (1987) found between 30% and 70% of the
phenotypic variance in reading and spelling to be due to genetic
factors in a general population sample of 13-year-old twins. Similarly, for poor readers, genetic factors have been shown to be
significant by using both quantitative genetic approaches (DeFries,
Fulker, & LaBuda, 1987) and molecular genetic approaches (Cardon et al., 1994). Recent estimates from a large-scale twin study in
Colorado found approximately 50% of the variance in literacy to
be due to genetic factors in a group of poor readers (Olson,
Forsberg, & Wise, 1994). Importantly, these analyses also identified a significant contribution from shared environmental factors
to deficits in word recognition (45%) and spelling (36%). These
findings suggest that the role of nonshared environmental influences in reading deficits is minor—perhaps less than 10%.
The findings of higher heritability for phonological coding as
compared with orthographic coding led to early suggestions that
the phonological coding in reading carries the genetic effect (Olson, Wise, Connors, Rack, & Fulker, 1989; Stevenson, 1991). If
this is the case, then phonological awareness would be expected to
be highly heritable. This has not yet been investigated when
normal variation is being examined. However, recent results from
the Colorado study do indeed show substantial genetic influences
on phonological awareness in an extreme group of disabled readers, with around 60% of the variance due to genetic factors. A
bivariate analysis from the same study found a high genetic correlation between phonological awareness and reading (Olson et al.,
1994). This finding suggests that the same genetic factors influence these two abilities.
The degree to which language ability is heritable is harder to
determine because of the small number of participants involved in
the published twin studies and the diverse set of language measures used. However, certain aspects of normal variation in language functioning do appear to be significantly heritable (Mather
& Black, 1984; Musinger & Douglass, 1976). More recently, the
genetic basis to specific language impairment has been identified
(Bishop, North, & Donlan, 1995).
Children in the present study were tested on a large battery of
HOHNEN AND STEVENSON
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592
cognitive tests at the beginning stages of literacy development. A
twin design was used that allowed genetic and environmental
influences to be estimated separately for each ability. In addition,
a multivariate genetic analysis was used to investigate the degree
to which the covariation between language, phonological awareness, and literacy ability is influenced by genetic and environmental factors. On the basis of previous research, we expected to find
strong genetic influences on all three abilities. In addition, we
expected the covariation between abilities to be accounted for to a
large degree by genetic factors. This would show that there is a
single underlying dimension of individual difference with a genetic basis that is specific to verbal skills. In addition, the analysis
was designed to identify whether individual differences in phonological awareness and literacy are linked independently of language and, if so, whether genetic or environmental influences in
common are producing this association.
The design was cross-sectional, and children were assessed at
the early stages of learning to read. Children in the present study
were chosen so that the younger group would have just been
exposed to literacy instruction. The older group was just over 1
year older than the younger group and had received at least 1 year
of schooling. This design allowed a comparison of the processes
acting at these two early stages of literacy development, with the
potential to identify age-related changes in genetic and environmental influences in this important transitional stage of development (Ehri, 1992; Frith, 1985).
Method
Participants
The sample consisted of 126 pairs of monozygotic (MZ) and dizygotic
(DZ) same-sex twins. The design was cross-sectional. Twin pairs were
identified by contacting all of the schools in the 12 education authorities
serving the London area. Schools supplied names and addresses of the
parents of twins who had consented to participate. The aim was to recruit
a sample of same-sex twin pairs from two age groups for whom English
was their first language and who were thereby representative of the general
population. There were two age groups: a younger group (mean age = 5
years 10 months, range = 5 years 8 months to 6 years 1 month) and an
older group (mean age = 7 years 0 months, range = 6 years 10 months to 7
years 4 months). The younger age group consisted of 32 MZ and 28 DZ
pairs, of which 59% were girls and 41% were boys. The older age group
consisted of 34 MZ and 32 DZ pairs, of which 62% were girls and 38%
were boys. Approximately 80% of the sample was Caucasian. The remainder were of African, Caribbean, Indian, or mixed-race origin. English was
the primary language of all participants.
Zygosity Determination
Zygosity was determined through the Twin Similarity Questionnaire
(Cohen, Dibble, Grawe, & Pollin, 1975), where parents rate the physical
similarity and confusability of the twins. This questionnaire technique is of
previously demonstrated validity in that it agrees with blood group typing
with 90%-95% accuracy. All univariate analyses were repeated with cases
of uncertain zygosity removed (n = 2), and this did not substantially alter
the estimates.
Tasks
The tasks included in the battery measured literacy, phonological awareness, general language ability, and performance IQ. The majority of mea-
sures were taken from standardized tests with established reliability and
validity. In addition, a small test-retest study (n = 16) was carried out to
establish the reliability of all the measures. Unreliable tests were dropped
from the battery. Intraclass correlations for the reliability of each measure
are reported below. The younger age group received only a subset of tests.
Literacy was measured with a number of tasks. A comprehensive assessment of phonological awareness was obtained. According to Yopp (1988),
there are two main aspects of phonological awareness as indicated by her
factor analytic study, both of which were represented in this battery. A
broad range of language skills was assessed with measures of both expressive and receptive language.
Single-word reading. The subtest from the British Ability Scales (Elliot, 1983) was administered to both age groups and requires participants to
name individually presented words from a graded list. Reliability was .99
for the younger group and .96 for the older group.
Spelling. The Schonell Spelling Test (Schonell & Schonell, 1960),
which is a graded list of single words, was administered to both age groups.
Reliability was found to be .89 and .98 for the younger group and the older
group, respectively.
Prose word reading. The Neale Analysis of Reading Ability (M. D.
Neale, 1967) was administered to assess ability to read a piece of prose.
Only the accuracy score was used in the present analysis (reliability = .92),
and this was administered only to the older group.
Phonological and orthographic reading. Seven-year-olds also attempted two additional single-word lists, one of irregularly spelled words
(e.g., pint, answer) that assess the orthographic route to reading and one of
nonwords (pseudowords; e.g., kule, tockens) that assess the phonological
route to reading as postulated in the dual-route theory (Coltheart, 1978).
The list of words was taken from that of Spring and Davis (1988).
Reliability was .97 for irregular words and .83 for pseudowords. A
pseudohomophone judgment task, which was adapted from Olson et al.
(1994), was also administered to the older group. Pairs of words—both of
which sounded the same but one of which was a real word (e.g., true-trew,
engine-enjine)—were shown to the children. The children had to indicate
which of the two words was the correct spelling by ticking it with a pencil.
This test showed marked practice effects, which lowered the intraclass
correlation. However, the test-retest reliability, as indexed by the Pearson
product—moment correlation, was good (r = .74).
Simple phoneme awareness. The phoneme blending task measures this
aspect of phonological awareness. The children listened to isolated parts of
a word and were asked to blend them together to pronounce the word. The
items were taken from Wagner et al. (1993, 1994) and consisted of 21
items increasing in difficulty (e.g., n-u-t, sk-ir-t). Reliability was good for
the younger group (r = .91) and satisfactory for the older group (r = .51).
Compound phoneme awareness. The phoneme deletion task was administered to only the 7-year-old group. The children were required to say
a word by removing one of the sounds. The items were taken from Wagner
et al. (1993, 1994) and were shortened for the present study (e.g., see[d],
dri[v]er). Reliability was .93.
Sound categorization. In Yopp's (1988) study, the sound categorization tasks did not load on either of the other factors, which suggests that
they measured a separate ability. Different tasks were administered to the
younger and older groups to ensure sufficient variability at both age
groups. Items were taken from Bowey and Patel (1988). Three or four
items were verbally presented to the children (with visual aid for the
younger group only), who were told that two (or three) of the items
sounded the same (rhyme) and one was the odd one out. The children were
asked to identify which two (or three) words sounded the same (e.g., dog,
man, log or hen, ten, pen, bed). Reliability was .84 for the younger group
and .66 for the older group.
Receptive language ability. The Test for Reception of Grammar
(Bishop, 1983) assesses receptive understanding of grammatical aspects of
the English language. The child is required to select from a choice of four
pictures the one that corresponds to the phrase spoken by the tester. No
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STRUCTURE OF GENETIC INFLUENCES
expressive language is required from participants. Reliability was .60 for
the younger group and .76 for the older group. The British Picture Vocabulary Scale (Dunn & Dunn, 1982) was used as a second measure of
receptive language, but this time, it was used to assess receptive understanding of vocabulary. Participants were asked to choose one of four
pictures on a page that was the best picture of the word spoken by the
examiner. The long form of the test was used in the present study.
Reliability was .89 for the younger group and .74 for the older group.
Expressive language ability. The Renfrew Bus Story (Renfrew, 1969)
was used as a measure of expressive language. This measure assesses the
child's ability to give a coherent description of a continuous series of
events. The child is told a story by the tester while looking at a picture
book. The child is told that at the end of the story, he or she will be asked
to tell the story back to the tester. The child is allowed to use the pictures
as prompts when retelling the story. The transcript is scored on the basis of
two factors: the amount of information the child recalls of the story line
(information score) and the average length of the child's sentence (sentence
length). There was a practice effect, which lowered the intraclass correlation. The Pearson product-moment correlations for information and length
were .87 and .84, respectively, for the younger group and .90 and .72,
respectively, for the older group.
General cognitive ability was assessed by using the Wechsler scales. The
Wechsler Preschool and Primary Scale of Intelligence—Revised UK
(Wechsler, 1990) was used in the 6-year-old group. The Wechsler Intelligence Scale for Children—Third Edition UK (Wechsler, 1992) was administered to the older group. British standardized norms were used to
score the test along with the standard coding instrument and manual. Four
subtests were selected to provide a short form of the assessment. The two
subtests Vocabulary and Similarities provided an estimate of verbal IQ.
The two subtests Block Design and Picture Completion provided an
estimate of performance IQ. The full-scale IQ score was derived from these
two scores. Reliability was found to be .80 or above for both age groups.
A number of indexes were derived from these test scores by averaging
the standardized (z-transformed) scores in the following clusters for the
younger and older groups:
Literacy: single-word reading, spelling plus at age 7 only irregular
word reading, prose reading, nonword reading, and pseudohomophones;
General language: Test for Reception of Grammar, British Picture
Vocabulary Scale, Bus Story (Information and Sentence Length),
Similarities, and Vocabulary;
Phonological awareness: phoneme blending, sound categorization,
and at age 7 only, phoneme deletion;
Performance IQ: Block Design and Picture Completion.
Procedure
All children were visited at home by trained testers. The tasks were
administered in two parts of no more than 1 hr each, with a break of at least
half an hour in between. To avoid tester bias, members of a twin pair were
tested independently by different testers, and children were seen in separate
rooms. A different random order of tasks was given to each child.
The power of this twin study was established by using the procedures
suggested by M. C. Neale and Cardon (1992, pp. 190-194). On the basis
of previous studies, the cognitive measures being analyzed were expected
to be moderately heritable (h2 = .60, approximately), and it was assumed
that the environmental influences would be equally divided between nonshared and environmental effects. This would result in expected correlations of .80 for MZ pairs and .50 for DZ pairs. With these correlations, it
would be necessary to have 37 pairs in each zygosity group to have 80%
power to detect the genetic effect with p < .05. For both age groups, there
593
were approximately 30 pairs in each zygosity group, resulting in a study
with power slightly below the conventional 80% level. With this sample
size, there is low power to detect shared environmental effects as small as
.20 (as hypothesized above). However, the sample size required to detect
a shared environmental effect (c2) of .60 with 80% power is 40. Therefore,
the study at each age level is capable of detecting moderate heritability and
shared environmentality (.60) with a power of 75%. The power to detect
effects in the multivariate analysis is lower than for the univariate analyses,
and the sample sizes in the present design indicate that the substantive
conclusions drawn from the multivariate analysis must be made with
caution.
Results
The means and standard deviations for scores are shown in
Table 1. Significant differences were found between MZ and DZ
twins on the Bus Story (Information), f(118) = 2.28, p < .03, and
performance IQ, f( 118) = 2.94, p < .01, for the younger group.
For the older group, significant differences between zygosity
groups were found on spelling, r(130) = 2.00, p < .05; phoneme
blending, f(130) = 2.00, p < .05; both Bus-Story scores (Information), f(126) = 2.68, p < .01; (Sentence Length), t(l26) = 2.56,
p < .02; and full-scale IQ, r(126) = 2.06, p < .05. In each case,
DZ twins scored higher than MZ twins. However, such differences
were slight and were of little consequence for the behavioral
genetics analysis because they are based on the within-pair
covariances.
Univariate Model Fitting
Global ability scores were calculated for each ability by standardizing the variables and taking the average of those scores as
described above. Analyses were carried out separately for the two
age groups. Univariate genetic models were fitted to each variable
separately by using a structural equation modeling procedure
(M. C. Neale & Cardon, 1992). The phenotypic variance is assumed to result from three sources: additive genetic (A), shared
environmental (C), and nonshared environmental influences (E). A
and C are responsible for resemblance between family members
and E for differences between them. The expectation for resemblance between MZ and DZ pairs is different: A + C in MZ pairs
and 0.5A + C in DZ pairs. A structural equation modeling program (EQS; Bentler, 1995) was used to obtain parameter estimates
for the effects of A, C, and E that best reproduced the observed
variance- covariance matrices for MZ and DZ pairs simultaneously. The general practice in this type of genetic analysis is to
be parsimonious in model selection. That is, if a path can be set to
zero without a significant change in the overall fit of the model,
then that path is dropped and the simpler model is preferred.
However, because of the small number of participants in the
analysis and the resulting lack of power to detect significant
differences between models, the parameter estimates were interpreted from the full model to reduce the risk of a Type II error.
The results from the univariate analysis are shown in Table 2.
The two striking features of this table are, first, the remarkable
similarity between the results for the two age groups and, second,
the consistently high heritability estimates for all abilities. Approximately 60% of the variance in literacy was accounted for by
genetic factors, with 40%-50% of the variance in language and
50%-60% in phonological awareness. The estimates for perfor-
HOHNEN AND STEVENSON
594
Table 1
Mean Individual Test Scores (and Standard Deviations) Separated According to Zygosity
Younger group
Older group
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DZ pairs
MZ pairs
DZ pairs
MZ pairs
Test
n
M
SD
n
M
SD
n
M
SD
n
M
BAS Reading
Schonell Spelling
Neale (accuracy)
Nonword Reading
Irregular Word Reading
Pseudohomophone
Phoneme Blending
Phoneme Deletion
Sound Categorization
TROG
BPVS
Bus Story (Information)
Bus Story (Sentence Length)
Performance IQ
Verbal IQ
Full-scale IQ
68
68
68
68
68
33
68
68
62
68
68
64
64
66
66
66
34.31
17.06
21.37
12.68
15.78
50.09
12.16
7.32
6.45
12.79
59.41
26.25
10.33
89.05
96.55
91.32
20.78
10.47
15.18
9.67
12.37
9.14
6.78
4.09
3.00
3.48
13.34
7.50
2.34
17.72
21.74
18.96
64
64
64
64
64
41
64
64
62
64
64
64
64
62
62
62
38.73
20.70
23.56
15.39
18.00
48.70
14.23
7.81
7.27
13.61
62.16
29.60
11.46
93.89
98.77
97.69
19.83
10.47
14.21
11.22
11.19
9.22
4.92
4.36
2.85
3.04
12.21
6.56
2.64
21.22
17.46
15.82
64
64
—
—
—
—
64
—
64
64
64
64
64
64
64
64
12.06
7.11
—
—
—
—
8.38
—
8.16
10.44
50.47
23.08
9.41
96.50
98.52
97.23
14.95
7.66
—
—
—
—
6.77
—
2.87
3.30
10.89
7.27
2.64
12.64
16.45
14.55
56
56
—
—
—
—
56
—
56
56
56
56
56
56
56
56
14.95
8.25
—
—
—
—
8.54
—
8.46
11.23
52.02
25.95
10.06
103.79
101.46
102.80
SD
18.25
9.97
—
—
—
—
7.15
—
2.76
3.12
10.48
6.42
2.17
14.49
13.89
13.99
Note. Dashes indicate that data were not collected for this age group. MZ = monozygotic; DZ = dizygotic; BAS = British Ability Scales; TROG = Test
for Reception of Grammar; BPVS = British Picture Vocabulary Scale.
mance IQ (which was estimated from two subtests from the
Wechsler scales) were high, and for the older group, a model
including the C term would not converge without C being constrained to zero.
Multivariate Analysis
Multivariate genetic analyses enable estimations of the degree to
which covariance among traits is due to shared genetic or environmental influences by analyzing the cross-correlation of one test
score for a twin with the score on another test for the cotwin.
Genetic effects on one ability can overlap with genetic effects on
a second ability, as can environmental effects. There are a number
of different models that can be fitted to this type of data that allow
identification of genetic and environmental factors that have an
effect on more than one ability. The Cholesky decomposition was
chosen as the appropriate model to fit to the present data. The full
four-variable Cholesky decomposition was fitted to the data. In
such an analysis, four sets of ACE terms are specified. A 1; Ct, and
E! influence the variables of performance IQ, language, phonological awareness, and literacy; A2, C2, and E 2 influence only the
last three; A3, C 3 , and E 3 influence only phonological awareness
and literacy; A4, C4; and E 4 influence literacy alone. The appropriate ordering of the variables in a Cholesky decomposition is
essential. The first set of latent variables account for the variance
in performance IQ and the covariance between performance IQ
and the rest. The second set of latent variables (A2, C 2 , and E2)
explain variance in language ability that is independent of that
explained by the first set of latent variables (A lt C,, and E,). They
also account for the covariances between language, phonological
awareness, and literacy, again independent of the covariance in
common with performance IQ. This is repeated for the third and
fourth sets. A recent article by Loehlin (1996) warns against the
Table 2
Univariate Estimates of Genetic and Environmental Influences (Full Models Only)
Group and skill
Younger group
Literacy
Language
Phonological awareness
Performance IQ
Older group
Literacy
Language
Phonological awareness
Performance IQ
a2
c2
e2
.60
.43
.52
.62
.36
.35
.35
.03
.04
.23
.14
.36
.59
.32
.50
.39
.28
.62
.74 0a
.08
.11
.10
.26
df
P
AIC
CFI
rforMZ
r for DZ
5.57
1.39
0.06
0.21
3
3
3
3
.13
.71
.99
.97
-0.43
-4.61
-5.94
-5.79
.97
1.00
1.00
1.00
.95
.80
.87
.65
.71
.50
.59
.34
1.39
1.30
1.79
3.87
3
3
3
4
.70
.73
.62
.42
-4.60
-4.69
-4.20
-4.13
1.00
1.00
1.00
1.00
.92
.90
.92
.74
.61
.59
.53
.29
Note, a = genetics; c = shared environment; e = nonshared environment; AIC = Akaike's information
criteria; CFI = comparative fit index; MZ = monozygotic; DZ = dizygotic.
a
Parameter set to zero.
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STRUCTURE OF GENETIC INFLUENCES
potential misinterpretation of results after fitting a Cholesky decomposition. Loehlin identified a common error of concluding that
the first set of latent variables describe the influences on all four
variables. Although this is partly true, this set of latent variables
also include all influences that are specific to the first variable. For
this reason, the ordering of variables in a Cholesky decomposition
must be justified by theory and should be used only if there is a
strict logical priority that can be justified for the model being
tested.
For the present analysis, there was a logical priority for their
order, and a Cholesky decomposition was appropriate. The variables were placed in the order of performance IQ, language,
phonological awareness, and then literacy. By placing a measure
of general intelligence as the first variable in the analysis, covariance is removed between the three abilities of interest (language,
literacy, and phonological awareness) that arise from performance
IQ (Aj, C[, and E^. The second set of latent variables describe
influences on general language ability that also influence phonological awareness and literacy (A2, C2, and E 2 ). The third set of
latent variables account for covariance between phonological
awareness and literacy that is independent of general language
ability (A3, C 3 , and E3). The final set (A4, C4, and E4) accounts for
the remaining variance in literacy that is not shared with the other
abilities. The crucial paths to test the hypothesis of a strong genetic
effect on verbal skills were the size of the effects produced by the
second set of latent variables (A2, C2, and E2). In addition, it was
important to estimate the effects of the third set of latent variables
(A3, C3, and E3), which describe covariance shared only between
phonological awareness and literacy. This tests the alternative
theoretical positions as to whether phonological awareness makes
a contribution to literacy beyond that of general language ability.
Younger Group
The full Cholesky model did not converge. This was not a
problem due to identification because it is known that the
Cholesky decomposition is overidentified. The problems with convergence arise from a number of the parameters being very small.
Accordingly, these paths were constrained to be zero, and the
model was reestimated. The resultant solution is presented in Table
3. This model produced only a moderately good fit to the data,
A^(55, N = 60) = 102.65, p < .001; comparative fit index (CFI)
= .88; Akaike's information criteria (AIC) = -7.35. The fit of the
model was improved when the correlation rather than the
variance-covariance matrix was analyzed, ^ ( 5 5 , N = 60)
= 94.07, p < .001; CFI = .90. This finding suggests that there
were differences in the variance for MZ and DZ pairs that were
accounting for the relative lack of fit.
The parameter values in Tables 3 and 4 need to be squared to
obtain an estimate of the percentage of variance in each ability
explained by each latent variable. The squared values sum to 100%
for each of the measures. The covariance between all four abilities
was accounted for mainly by genetic factors. The genetic influence
on performance IQ (A,) influenced language, phonological awareness, and literacy. This had its strongest influence on performance
IQ (59%) and language ability (44%), with a moderate influence
on phonological awareness (29%), and a small influence on literacy (10%). There was a separate genetic influence on language
(32%), which also influenced phonological awareness (23%) and
595
Table 3
Cholesky Decomposition of Performance IQ, Language,
Phonological Awareness, and Literacy for the
Younger Group
Path
A,
c,
E,
A2
c2
E2
A3
c3
E3
A4
cE4
4
Performance
IQ
Language
Phonological
awareness
Literacy
.77
0a
.64
—
—
—
—
—
—
—
—
—
.67
.00
.00
.56
.00
.50
—
—
—
—
—
—
.54
.00
.00
.48
.00
.00
.30
.50
.37
—
—
—
.33
.00
.00
.41
.00
.10
.00
.61
.08
.55
.00
.17
Note. ^ ( 5 5 , N = 60) = 102.64, p < .001; comparative fit index = .88;
Akaike's information criteria = -7.35. A, . . . E 4 represent the paths from
A[ .. . E4 to each of the measures. A, . . . E 4 were constrained to be equal
for Twin 1 and Twin 2. Dashes indicate that parameter was absent from the
model. a Parameter set to zero.
literacy (17%). Genetic influences were also identified on phonological awareness (9%) and literacy (30%), but these were not
shared between the two abilities.
To summarize, for the younger group, no genetic influence was
identified that linked phonological awareness and literacy that was
independent of language. However, literacy and phonological
awareness shared a common environmental term (C3) and a specific environmental influence (E3). The specific environmental
influence on language (E2) also had a very small influence on
literacy (1%).
Given the concern over power with this complex model, the
degree to which phonological awareness and literacy shared a
genetic influence independent of language ability was tested by
using a second method. Variance due to both general language
ability and performance IQ was regressed out of the literacy and
phonological awareness measures phenotypically to obtain scores
that were independent of these two abilities. Variance-covariance
matrices were then obtained for the residual scores, which were
entered into the genetic analysis. A bivariate independent pathway
model was fitted to these data (see Figure 1). This model specifies
a set of shared ACE terms that influence both abilities and an
additional set of independent ACE terms for each ability. With two
variables, this type of model is not identified (M. C. Neale &
Cardon, 1992). To overcome this, the shared paths (Aj, C1? and E,)
were constrained to be equal. An alternative way of ensuring the
model is identified is to remove either the shared C term or the
shared genetic term. This did not seem to be an appropriate method
for reducing the number of parameters to be estimated. We know
from the Cholesky decomposition that covariance between these
two abilities is probably mediated through a shared environmental
influence. The question of interest is whether there is a shared
genetic term.
For the younger group, the residual variables were correlated .47
at the phenotypic level. The independent pathway model fitted the
data well, ^ ( 1 2 , N = 60) = 16.84, p = .16; CFI = .96, and can
be seen in Figure 2. Covariance between the two abilities was
HOHNEN AND STEVENSON
596
1.0/0.5
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Phonological
awareness
Twin 1
Phonological
awareness
Twin 2
1.0/0.5
Figure 1. Path diagram for an independent pathway model for two measures. The paths labeled 1.0/0.5 are
values for monozygotic and dizygotic twin pairs, respectively. A = genetics; C = shared environment; E =
nonshared environment.
6 year olds
o
Figure 2. Final independent pathway model fitted to the literacy and phonological awareness data with
performance IQ and language phenotypically regressed out (younger group). C = shared environment; E =
nonshared environment; A = genetics.
STRUCTURE OF GENETIC INFLUENCES
accounted for by shared environmental (Cj) and specific environmental (E[) factors. Sixty-nine percent of the phenotypic covariance was accounted for by the C term and 31% by the E term.
This model confirms that there is no shared genetic influence
between phonological awareness and literacy that is not also
shared with general language ability at the age of 6 years. However, phonological awareness and literacy are jointly influenced by
environmental factors that are independent of general language
ability.
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Older Group
Table 4 shows the parameter estimates and fit indexes for the
Cholesky decomposition for the older group. The model provided
a good fit to the data, ^ ( 5 5 , N = 66) = 38.07, p = .96; AIC =
-71.93; CFI = 1.00.
There was a substantial amount of genetic covariance between
all four measures. The genetic effect on general intelligence (A])
influenced all four measures. The influence was most strong for
performance IQ (75%) and language ability (42%), with smaller
influences on phonological awareness (18%) and literacy (11%).
In addition, there was a separate genetic influence (A2) on language that also influenced literacy and phonological awareness but
that was independent of the genetic influence on general intelligence. This genetic influence loaded most strongly on phonological awareness (68%) and literacy (31%) and less strongly on
language (17%). A third genetic influence was identified (A4) that
influenced only literacy, which had a small effect (19%). Covariance between these two variables that was independent of general
intelligence or language ability was accounted for by a shared
environmental term (C3) and a specific environmental term (E3).
To summarize, at the age of 7 years, there was a genetic influence
acting on language, phonological awareness, and literacy. Independent covariance between phonological awareness and literacy that
was independent of language was mediated through environmental
Table 4
Cholesky Decomposition of Performance IQ, Language,
Phonological Awareness, and Literacy for the Older Group
Path
A,
c,
E,
A2
cE2
2
A3
c3
E3
A4
c4
E4
Performance
IQ
Language
Phonological
awareness
Literacy
.87
0"
.50
—
—
—
—
—
—
—
—
—
.65
.00
.00
.42
.54
.34
—
—
—
—
—
—
.43
.00
.00
.82
.00
.00
.00
.16
.34
—
—
—
.34
.00
.00
.56
.00
.00
.00
.55
.05
.43
.00
.28
Note. ^ ( 5 5 , N = 66) = 38.07, ns; comparative fit index = 1.00;
Akaike's information criteria = —71.93. A! . . . E 4 represent the paths
from Aj .. . E 4 to each of the measures. A[ . .. E 4 were constrained to be
equal for Twin 1 and Twin 2. Dashes indicate that parameter was absent
from the model.
a
Parameter set to zero.
597
factors. In a similar way to the younger group, a further bivariate
analysis was run to test whether there was an identifiable genetic
pathway that linked phonological awareness and literacy.
At this age, the residual phonological awareness and literacy scores
after regression on performance IQ and language were correlated .50.
The independent pathway model fit for the 7-year-olds' data is shown
in Figure 3. No shared genetic influence was found between the
residual phonological awareness and reading scores. Covariation between the two abilities was accounted for by a shared environmental
factor (Ct) and a specific environmental factor (E,).
To establish that this was the best model to account for the
data, the analysis was rerun with a shared genetic term replacing the shared common environmental term. These models were
not nested, and therefore, it was not possible to determine the
statistical significance of the difference between them by using
differences in chi-square. An alternative method of model comparison for nonnested models uses the AIC index. Here, the
lowest possible negative value indicates superior fit. With a
shared common environmental term, the AIC value was low and
negative (AIC = -5.69). With a shared genetic term, the AIC
was higher (AIC = 1.17). Moreover, the model with a shared A
term did not produce a good fit to the data, x 2 (14, N = 66)
= 29.17, p = .009; CFI = .89. This analysis, therefore, provides additional support for the conclusion that there is no
genetic influence shared between phonological awareness and
reading that is independent of the genetic influence they share
with general language ability.
Comparison of the Younger and Older Groups
The Cholesky decomposition models for the two age groups
were remarkably similar. In both cases, the pattern of genetic
influences was the same, with the genetic influence on general
intelligence loading on all three abilities (A,). In both age groups,
the genetic influence on language also loaded on literacy and
phonological awareness (A2), and additional covariance between
phonological awareness and literacy was environmentally mediated. This pattern was confirmed in additional bivariate genetic
analyses. An independent genetic influence in the Cholesky decomposition was specific to literacy ability (A4) and was also
identified in both age groups. This was a stronger influence for the
younger group (30%) than for the older group (18%).
The model fitting highlighted two additional interesting findings. First, the genetic influence on general intelligence had only a
moderate influence on literacy and phonological awareness at both
ages. This genetic factor influenced language ability more
strongly. Second, the genetic influence specific to verbal ability
had a stronger influence on phonological awareness and literacy
with age.
Discussion
A general population sample of 126 pairs of twins were tested in
a cross-sectional study at the ages of 6 and 7 years. The general
aim of the study was to add to our understanding of literacy
development by examining the etiology of individual differences
in literacy, language, and related skills. A model-fitting approach
was used in the analysis to estimate the degree to which individual
differences are caused by genetic, shared environmental, and spe-
HOHNEN AND STEVENSON
598
7 year olds
0.477
Phonological
awareness
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Literacy
0.336,
0.370
0.398
0.379
Figure 3. Final independent pathway model fitted to the literacy and phonological awareness data with
performance IQ and language phenotypically regressed out (older group). C = shared environment; E =
nonshared environment; A = genetics.
cific environmental factors. The findings from this study fill a gap
in the literature regarding the causes of individual differences in
reading and related cognitive abilities at a time when children are
just beginning to learn to read and write. Literacy, phonological
awareness, and language were all found to be strongly genetically
influenced at this age. Few age-related changes were identified in
the influences on individual differences between the ages of 5
and 7 years. The results from the multivariate analysis support the
view that there is a single underlying dimension of individual
difference that is genetically mediated and is specific to verbal
skills. This ability is independent of general intelligence and influences general language ability, phonological awareness, and
literacy.
Limitations
Before we interpret the findings of this study in light of previous
research, there are a number of limitations to the study that should
be borne in mind. There has been concern that it may be inappropriate to extrapolate findings from twin studies on language and
reading to suggest causal influences on individual differences in
singletons because of evidence of increased language and reading
delays in twin populations (Hay, Prior, Collett, & Williams, 1987;
Johnston, Prior, & Hay, 1984; Levy, Hay, McLaughlin, Wood, &
Waldman, 1996). By school age, twins appear to have recovered
from any initial language delay (Wilson, 1975), or their delay is
explainable by other factors (Lytton, Watts, & Dunn, 1987). Two
additional findings increase confidence in the validity of using
twins to study reading development. First, if these methodological
concerns are correct and the postnatal rearing environment adversely influences the twins' language development, then individual differences in twins would be strongly influenced by common
environmental factors (by making both MZ and DZ twins similar
to each other). This was not found in the univariate analyses in the
present study. Second, if early language delays were caused by
environmental factors peculiar to twins, which were then to impact
on reading development at school, as suggested by Johnston et al.,
then this would be evident as a large shared environmental influence causing covariance between language and reading. Again,
this was not the case in this study.
As a further check, the average scores for the whole sample
were compared with the standardized norms (where available) to
determine if the present sample was performing within the normal
range expected for their age. The 5-year-old group performed at an
appropriate level for their age on all language and literacy tests and
had average general intelligence (full-scale IQ = 99.83). The
7-year-old group, however, performed 6 months to 1 year below
average for their age on standardized language and literacy tests.
Their level of general intelligence was also below average (fullscale IQ = 94.41), which was probably a result of general overall
relatively low ability in this group rather than an effect specific to
language. The somewhat low scores for the older twins were seen
to arise by chance rather than as a consequence of delayed language development in twins.
There is likely to be a large degree of assortative mating for the
cognitive skills being studied. Indeed, assortative mating has been
found to be most potent for verbal skills (R. C. Johnson et al.,
1976). This will act to decrease heritability because it will make
DZ twins more similar to one another.
Without longitudinal data it is not possible to identify whether
the genetic effects at two different ages represent continuity or
change in influences over time. As it stands, it is unclear whether
genetic and environmental influences that have been identified in
the present study acting on abilities at age 6 are the same set of
factors influencing the same or similar abilities at age 7. The
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STRUCTURE OF GENETIC INFLUENCES
identification of change in development is just as important as
continuity. It is a mistake to suggest that genetic influences are
fixed from the moment of conception. Rather, gene influences are
dynamic and are reflected in the regulation of developmental
change. This is particularly true of the age being studied here,
where developmental transitions are taking place that could be due
to genetic reorganization at this time (Plomin, 1986).
It is important to recognize that the balance of genetic and
environmental influences found in any one population is specific
to that population. The amount of genetic and environmental
variance in the sample will affect parameter estimates that will
change for different samples. Olson et al. (1994) suggested that a
sample drawn from a large city, such as London, may present a
broad range of different environments. This may act to produce
heritability estimates that are lower than those found in populations where the environment is more homogenous.
Individual Differences in Literacy, Phonological
Awareness, and Language
The first aim of this study was to obtain estimates of the degree
to which individual differences in literacy, language, and phonological awareness are caused by genetic, shared environmental,
and nonshared environmental factors at the initial stages of literacy
development. Previous twin studies have estimated that around
50% of the variance in literacy is attributable to genetic factors
(Brooks, Fulker, & DeFries, 1990; DeFries, Stevenson, Gillis, &
Wadsworth, 1991; Olson et al., 1994; Stevenson, 1991; Stevenson
et al., 1987). This study also found a substantial heritable influence
on individual differences in literacy achievement. Stage theories of
literacy development suggest that children alter their approach to
reading as they learn to read (Frith, 1985). In fact, no age-related
changes were identified in the heritable influences on literacy at
the two ages. At age 6, heritability was estimated at .60, and at
age 7, it was .59. Remarkably few age-related changes were found.
One possible reason for this is because children in the two groups
were divided according to age, not stage of reading development.
Future studies should consider grouping children according to
reading level for genetic analyses, where more important differences might be found corresponding to their stage of literacy
development.
However, it may be that the stage approach is inappropriate, and
as Share (1995) argued, it is the facilitation of self-teaching provided by phonological recoding skills that drives proficiency in
reading at this age. According to this view, there may be no
substantial reorganization of the components of reading skill, as
per the stage models. Instead, the item-based model predicts the
use of phonological recoding used for low-frequency words with a
rapidly learned direct visual recognition of high-frequency words.
This account of Share is consistent with the data from the present
study where the same pattern of influences on reading skill was
found across ages.
This is the first study to investigate the etiology of individual
differences in phonological awareness by using a general population sample of twins. Phonological awareness has been shown to
be an extremely stable ability from the age of 4 to 7 years (Wagner
et al., 1994). Estimates of extreme group heritability have been
obtained for phoneme segmentation of .60 and .70 for phoneme
deletion (Olson et al., 1994). Given these previous findings, these
599
abilities were expected to be influenced by genetic factors. Agerelated changes were expected in the processes causing individual
differences in phonological awareness over the period from age 5
to 7 years, when children are just being exposed to reading matter.
In the univariate analyses, phonological awareness showed evidence of heritability at both ages. General language ability did so
as well.
Covariation Between Abilities
Finally, we hypothesized that there would be a single underlying
dimension of individual difference with a genetic basis that was
specific to verbal skills. This is the view held by Scarborough
(1990) to explain the strong relationship found between general
language ability, phonological processing, and literacy in children
with a reading disability. At both ages, there was a shared genetic
influence on general language ability, phonological awareness, and
literacy that was independent of the genetic influence on performance IQ. At both ages, there was residual covariance between
phonological awareness and literacy that was independent of language, and this arose from shared common environmental and
specific environmental influences, not from a genetic pathway.
This result was confirmed with an independent pathways model
when variance due to both language and performance IQ had been
phenotypically regressed out of both measures.
By using the Cholesky decomposition, the influence of performance IQ could be modeled in this analysis rather than it being
controlled. First, this shows that genetic influences on general
intelligence have a small impact on literacy, accounting for between 20% and 30% of the genetic influences on literacy. Second,
it shows that the major part of the genetic influence on language
ability is shared with performance IQ. Approximately 50%-60%
of the genetic influence on general language ability was also
shared with general intelligence.
This analysis also informs an important debate in the field
regarding the relationship between the three abilities. The view
widely held in recent years has been that phonological awareness
and literacy are linked independently of general language ability.
This is based on the finding that tests of phonological awareness
account for independent variation in early reading achievement
after variance due to general language ability has been removed
(e.g., Bowey, 1986; Bradley & Bryant, 1983; Lundberg et al.,
1980; Stanovich et al., 1984; Wagner et al., 1993, 1994). This
evidence, however, was challenged by Bowey and Patel (1988),
who found that when a broad range of language functioning is
assessed, the independent relationship between phonological
awareness and literacy disappears. The results from the present
study provide an explanation for these two opposing findings.
First, the study explains why a strong relationship between language and literacy is consistently found in the literature; both
abilities are influenced by a shared genetic factor. Moreover, this
also explains why a substantial amount of the variance in literacy
is accounted for jointly by phonological awareness and language.
Again, they are influenced by the same set of genetic factors. The
results also explain why phonological awareness accounts for
independent variation in early reading achievement after variance
due to general language ability has been removed. This is because
the two have an independent, environmentally mediated relationship, possibly through the kind of mechanism suggested by Ehri
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600
HOHNEN AND STEVENSON
(1992), where exposure to reading by way of instruction impacts
fundamentally on phonemic knowledge.
These results are also consistent with the account put forward by
Bishop (1991). She suggested that in the majority of children,
phonological awareness, language, and literacy are all highly related. With a subset of children, however, she suggested that there
may be a specific link between phonological awareness and literacy. It could be that for most children, the three abilities develop
in concert by way of the genetic mechanism. However, the more
extreme environments produce independent covariance between
phonological awareness and literacy in a subset of children.
This set of analyses revealed that although the majority of
genetic influences on literacy are shared with phonological awareness and language, additional genetic influences were identified at
both ages that acted on literacy. This was also independent of
performance IQ and was most influential at the age of 6 years and
much less so at the age of 7 years. It is unclear what these
additional genetic factors might be. The fact that the influence was
so much stronger at the age of 6 years and that it was independent
of phonological processing skills might indicate that it underlies a
cognitive skill used in logographic approaches to literacy.
Possible Models of Genetic Influences on Verbal Abilities
It is worth considering what the underlying nature of the genetically mediated influence on the three verbal skills might be. The
present data are cross-sectional; thus, it is not possible to say
exactly how the genetic influence might be having its effect. In this
context, only with longitudinal data would it be possible to disentangle the causal relationships. The data are consistent with a
number of possibilities. The models presented in Figure 4 are an
attempt to formulate the various mechanisms whereby genetic
influences in common between language ability, phonological
ability, and literacy development could have their effect. The
results from this study also suggest the presence of an environmentally mediated association between phonological awareness
and literacy ability. In Figure 4, this is represented by a dashed line
connecting these two abilities.
Model A illustrates the possibility that the genetic influence
identified affects all three abilities. This mechanism could be due
to pleiotropy, where a gene has multiple effects, or it could be
consistent with Scarborough's (1991) view that there is a verbal
cognitive capacity that constrains all aspects of language processing. Scarborough suggested that this skill may be the ability to
combine abstract elements (phonemes or syntax) and to understand
the specific rule systems that underlie how they are combined. A
genetic effect acting on this kind of ability would explain the
present data well. It should be noted that this factor is not general
intelligence, because in our data, its impact was in addition to that
associated with performance IQ.
Another possibility, illustrated in Model B, is that there is a
genetically determined cognitive processing ability that influences
children's capacity to represent words at the phonological level.
This basic cognitive deficit would then have implications for both
language acquisition and literacy development. Three different
theories are consistent with this view. The first draws on the
growing evidence that young children do not use individual phonemes to represent words early on. They represent words holistically (Walley, 1993). As children need to expand their vocabulary
and develop more efficient means for storing lexical items, they
need to differentiate between words. It is only then that they begin
to divide words according to syllables, then subsyllables, and only
later by phonemes (also see Fowler, 1991). A genetically influenced ability to divide words according to different levels of
representation would, by this theory, have implications for early
language competence by impacting on children's ability to organize the lexicon segmentally. This same ability would also affect
phonemic segmentation ability, which would have implications for
learning to read and write.
A similar theory, but one that has a different emphasis, was put
forward by Bird and Bishop (1992). They found substantial deficits in children with phonological problems in tasks requiring the
recognition of phoneme constancy. They suggested that a deficit in
being able to identify whether two phonemes are the same would
lead to substantial problems learning novel words. If a child fails
to recognize that bat and bag share a phonemic segment, then each
new word requires a separate representation. As a consequence,
this would make the task of learning a new word harder and may
result in a language delay. The same underlying ability would also
have implications for phonological processing, and this, in turn,
would affect literacy development.
Aguiar and Brady (1991) showed that less skilled readers had
difficulty maintaining an accurate phonological representation
when learning new words. They had no problems learning the
semantic or conceptual information relating to the words. Aguiar
and Brady's study provided some preliminary evidence that establishing an accurate phonological representation for new words may
be linked to poor reading ability. This could be interpreted within
the framework outlined above and concords well with the present
findings.
A more biologically based explanation of Model B is that the
underlying ability that is genetically influenced is a more fundamental deficit in brain processes. Tallal and her colleagues (summarized in Tallal, Miller, & Fitch, 1995) suggested that children
with specific language impairment are deficient on tasks requiring
the ability to process brief acoustic components of information.
They proposed that the deficit is pervasive in affecting not only
speech perception but also perception within other modalities (e.g.,
tactile, motor). They suggested that the demonstration of a high
correlation between degree of impairment on temporal processing
and receptive language delay supports their hypothesis that this
deficit leads to delayed development of receptive language. These
same sets of experiments were conducted with a group of children
with reading problems. The children were divided into those with
an oral language disability and those without. They suggested that
the results were clear. Those with an oral language problem in
addition to their reading problems showed a significant deficit in
temporal processing. Those with normal oral language scores had
normal temporal processing skills. Tallal et al. suggested that the
temporal deficit they identified would have a specific impact on
speech perception and production because of the phonological
processing required for this task. This processing deficit is believed to affect reading as well as language because such children
are "unable to establish stable and invariant phonemic representations" (Tallal et al., 1995, p. 207). In other words, poor reading in
these children at least is the result of poor phonemic representations. It should be emphasized that this account explaining poor
reading as a function of a wider deficit in temporal processing has
601
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This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
STRUCTURE OF GENETIC INFLUENCES
B
Language
Phonological
awareness
Literacy
Language
Phonological
awareness
. t
^^^
Literacy
-
'
*
D
Language
Phonological,
awareness
Literacy
Figure 4. Four possible models to explain the genetic influences on language, phonological awareness, and
literacy. Dashed lines represent a correlation arising from shared environment influence.
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This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
602
HOHNEN AND STEVENSON
been stringently criticized. For example, Mody, StuddertKennedy, and Brady (1997) provided evidence in favor of a
speech-specific deficit rather than a general auditory deficit in poor
readers, and Studdert-Kennedy and Mody (1995) provided a wideranging critique of the methodological and conceptual inadequacies of the auditory temporal processing deficit hypothesis.
Model C illustrates the possibility that the genetic factor has a
direct impact on phonological awareness only, and this influences
literacy, which in turn affects language development. Language
functioning here is a secondary consequence of more basic cognitive processing effects. Reading has been shown to be one of the
main ways to acquire vocabulary; therefore, reading experience
would be expected to affect vocabulary development (Nagy &
Anderson, 1984). Moreover, Stanovich (1986) outlined how poor
reading can have a string of consequences that may impact other
aspects of cognitive ability through what he termed the "Matthew
effect." This possibility cannot be ruled out. However, at this very
early stage of reading development, exposure to literature should
not have impacted greatly on language skills.
The final model, Model D, illustrates the possibility that the
genetic influence again influences only phonological awareness
abilities. This has an impact on language learning, possibly by
affecting children's ability to perceive and store language as discussed in Model B. Literacy ability is a secondary consequence of
language functioning.
The data from the present study could not test the alternative
models in Figure 4. However, the findings do suggest that a logical
next step would be to conduct longitudinal and genetically informative studies of children making the transition into literacy. The
findings suggest that the mechanisms involved in this transition
include genetic influences on a broad range of language skills,
genetic influences that are specific to literacy development, and a
less marked environmentally mediated association between phonological awareness and literacy.
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Received July 30, 1997
Revision received July 28, 1998
Accepted July 28, 1998