dc.contributor.author | Rhemtulla, Mijke | |
dc.contributor.author | Tucker-Drob, Elliot M. | |
dc.date.accessioned | 2017-02-23T17:42:52Z | |
dc.date.available | 2017-02-23T17:42:52Z | |
dc.date.issued | 2012-02-15 | |
dc.identifier.citation | Rhemtulla, M. & Tucker-Drob, E.M. Behav Genet (2012) 42: 549. doi:10.1007/s10519-012-9527-0 | en_US |
dc.identifier.uri | http://hdl.handle.net/1808/23236 | |
dc.description.abstract | In previous work with a nationally representative sample of over 1,400 monozygotic and dizygotic twins born in the US, Tucker-Drob et al. (Psychological Science, 22, 125–133, 2011) uncovered a gene × environment interaction on scores on the Bayley Short Form test of mental ability (MA) at 2 years of age—higher socioeconomic status (SES) was associated not only with higher MA, but also with larger genetic contributions to individual differences in MA. The current study examined gene × SES interactions in mathematics skill and reading skill at 4 years of age (preschool age) in the same sample of twins, and further examined whether interactions detected at 4 years could be attributed to the persistence of the interaction previously observed at 2 years. For early mathematics skill but not early reading skill, genetic influences were more pronounced at higher levels of SES. This interaction was not accounted for by the interaction observed at 2 years. These findings indicate that SES moderates the etiological influences on certain cognitive functions at multiple stages of child development. | en_US |
dc.publisher | Springer Verlag | en_US |
dc.rights | © Springer Science+Business Media, LLC 2012 | en_US |
dc.subject | Gene-by-environment interaction | en_US |
dc.subject | Reading | en_US |
dc.subject | Mathematics | en_US |
dc.subject | School readiness | en_US |
dc.subject | Socioeconomic status | en_US |
dc.title | Gene-by-Socioeconomic Status Interaction on School Readiness | en_US |
dc.type | Article | en_US |
kusw.kuauthor | Rhemtulla, Mijke | |
kusw.kudepartment | Center for Research Methods and Data Analysis | en_US |
dc.identifier.doi | 10.1007/s10519-012-9527-0 | en_US |
dc.identifier.orcid | https://orcid.org/0000-0003-2572-2424 | |
kusw.oaversion | Scholarly/refereed, author accepted manuscript | en_US |
kusw.oapolicy | This item meets KU Open Access policy criteria. | en_US |
dc.rights.accessrights | openAccess | |