Wednesday, December 23, 2009

Research Byte 12-23-09: Shared and unshared genetic factors in timed and untimed reading and math abilities

A factorial analysis of timed and untimed measures of mathematics and reading abilities in school aged twins (Sara A. Hart, Stephen A. Petrill and Lee A. Thompson)  Learning and Individual Differences, In Press, Corrected Proof, Available online 27 October 2009,

The present study examined the phenotypic and genetic relationship between fluency and non-fluency-based measures of reading and mathematics performance. Participants were drawn from the Western Reserve Reading and Math Project, an ongoing longitudinal twin project of same-sex MZ and DZ twins from Ohio. The present analyses are based on tester-administered measures available from 228 twin pairs (age M = 9.86 years). Measurement models suggested that four factors represent the data, namely Decoding, Fluency, Comprehension, and Math. Subsequent quantitative genetic analyses of these latent factors suggested that a single genetic factor accounted for the covariance among these four latent factors. However, there were also unique genetic effects on Fluency and Math, independent from the common genetic factor. Thus, although there is a significant genetic overlap among different reading and math skills, there may be independent genetic sources of variation related to measures of decoding fluency and mathematics.
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Comments extracted from article
Results suggested a four-factor model including reading Decoding, reading Fluency, reading Comprehension, and Math. Further quantitative genetic analysis suggested that a common genetic factor is important to the covariance among phenotypically distinct latent factors (e.g., Plomin & Kovas, 2005). However, Fluency and Math factors were also influenced by unique genetic influences, independent from the general genetic factor.
Interestingly, the two factors with unique genetic in fluences are the only ones to contain measures of timed performance, or fluency. Previous work has suggested that there are large and significant effects due to heritability on measures of reading fluency (h² = .65–.67; Harlaar, Spinath, Dale, & Plomin, 2005), and mathematics fluency (h² = .63; Hart et al., 2009). It is possible that the fluency components in each of these factors are important for explaining the unique genetic effects on both.
Notably, there is no genetic overlap between the factors which contain fluency-based measures, outside of the general genetic overlap among all the latent factors. That, as well as the comparison of phenotypic models 4 and 5 in Table 3, suggests that the genetic influences of reading fluency are not the same as the genetic influences of mathematics fluency, although both are strongly independently influenced by genes.
It is also interesting to note the shared environmental overlap among all the factors. Instruction in this age-group is typically for the skills represented by these factors (e.g., Chall, 1983). This would serve to influence these processes through the shared environment, especially given that for most students in the early elementary years, academic skill exposure and learning are a function of what is taught in school. Moreover, in the case of twins, they also share the same rearing environment. This overlap is of note as it is shared between all mathematics and reading factors, suggesting that whether it is school- and/or family-level influences, there is a common environmental etiology underlying academic difficulties. This can have ramifications in how academic skill-based interventions are conceptualized.
The math literature sometimes separates math into components of computation and problem solving. Our findings in the current study and others (Petrill & Hart, 2009) suggest that the data were best represented by one latent factor. However, all measures of math are based on the Woodcock–Johnson, which may be serving to make them more similar.
Related to this issue, although the shared environmental influences on math are higher than those on reading, this difference cannot be directly tested statistically.

Authors conclusion
In sum, the results suggest that there are some common genetic and environmental factors that connect reading and mathematics performance. At the same time, there also appear to be independent genetic effects for reading fluency and for mathematics. Although requiring further study, these findings may suggest that the overlap in reading and mathematics performance may be due to both genes and the shared environment whereas the discrepancy between math and reading may be genetically mediated. This has ramifications for our understanding of math and reading difficulties. Independent genetic effects may be serving to make math disability and reading disability distinct, and differentially prevalent. On the other hand, the extent to which they are comorbid in some children, common genes and environments may be affecting the outcomes.

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