Article: The genetics of specific cognitive abilities.
Intelligence
2023 Volume 95, Page(s) 101689
Abstract: Most research on individual differences in performance on tests of cognitive ability focuses on general cognitive ability (g), the highest level in the three-level Cattell-Horn-Carroll (CHC) hierarchical model of intelligence. About 50% of the variance ... ...
| Abstract | Most research on individual differences in performance on tests of cognitive ability focuses on general cognitive ability (g), the highest level in the three-level Cattell-Horn-Carroll (CHC) hierarchical model of intelligence. About 50% of the variance of g is due to inherited DNA differences (heritability) which increases across development. Much less is known about the genetics of the middle level of the CHC model, which includes 16 broad factors such as fluid reasoning, processing speed, and quantitative knowledge. We provide a meta-analytic review of 747,567 monozygotic-dizygotic twin comparisons from 77 publications for these middle-level factors, which we refer to as specific cognitive abilities (SCA), even though these factors are not independent of g. Twin comparisons were available for 11 of the 16 CHC domains. The average heritability across all SCA is 56%, similar to that of g. However, there is substantial differential heritability across SCA and SCA do not show the developmental increase in heritability seen for g. We also investigated SCA independent of g (SCA.g). A surprising finding is that SCA.g remain substantially heritable (53% on average), even though 25% of the variance of SCA that covaries with g has been removed. Our review highlights the need for more research on SCA and especially on SCA.g. Despite limitations of SCA research, our review frames expectations for genomic research that will use polygenic scores to predict SCA and SCA.g. Genome-wide association studies of SCA.g are needed to create polygenic scores that can predict SCA profiles of cognitive abilities and disabilities independent of g. |
|---|---|
| Language | English |
| Publishing date | 2023-05-17 |
| Publishing country | United States |
| Document type | Journal Article |
| ISSN | 0160-2896 |
| ISSN | 0160-2896 |
| DOI | 10.1016/j.intell.2022.101689 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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