Generalist genes and the Internet generation: etiology of learning abilities by web testing at age 10

Abstract

A key translational issue for neuroscience is to understand how genes affect individual differences in brain function. Although it is reasonable to suppose that genetic effects on specific learning abilities, such as reading and mathematics, as well as general cognitive ability (g), will overlap very little, the counterintuitive finding emerging from multivariate genetic studies is that the same genes affect these diverse learning abilities: a Generalist Genes hypothesis. To conclusively test this hypothesis, we exploited the widespread access to inexpensive and fast Internet connections in the UK to assess 2541 pairs of 10-year-old twins for reading, mathematics and g, using a web-based test battery. Heritabilities were 0.38 for reading, 0.49 for mathematics and 0.44 for g. Multivariate genetic analysis showed substantial genetic correlations between learning abilities: 0.57 between reading and mathematics, 0.61 between reading and g, and 0.75 between mathematics and g, providing strong support for the Generalist Genes hypothesis. If genetic effects on cognition are so general, the effects of these genes on the brain are also likely to be general. In this way, generalist genes may prove invaluable in integrating top-down and bottom-up approaches to the systems biology of the brain.

Figure 1

Path diagram for the full phenotypic Cholesky decomposition model.The Cholesky partitions variance into a universal factor influencing all three traits (latent factor 1), a factor influencing reading and mathematics (latent factor 2) and a factor influencing mathematics alone (latent factor 3).

Figure 2

Multivariate analysis.The top panel gives the estimates for the additive genetic (A) component of the variance, with the Cholesky solution on the left and the correlated factors solution on the right. The middle panel does the same for the shared environment (C) and the bottom panel gives the estimates for non-shared environmental effects (E). In the Cholesky diagrams, line weights and intensities represent strength of association. Dotted paths can be dropped individually without a significant (P > 0.05) decrement in model fit. In the correlated factors diagrams, the curved arrows represent correlations between the latent factors. R, reading; M, mathematics.