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11 April 2019

DNA predicts differences in intelligence and educational achievement

New research from King's College London finds individuals’ DNA can predict differences between intelligence and educational achievement

Classroom hands
Classroom hands

The study, published in Molecular Psychiatry, looked at differences in educational achievement (how well pupils do in English, maths, and science), how many years of education they complete, and their IQ at age 16.

Individual differences in cognitive abilities in children and adolescents are partly reflected in variations in their DNA sequence, and these tiny differences in the human genome can be used together to create ‘polygenic scores’; the sum of a number of genetic variants an individual carries reflecting the genetic predisposition to a particular trait.

Researchers at the Institute of Psychiatary, Psychology & Neuroscience (IoPPN) analysed genetic information from 7,026 UK children at ages 12 and 16 included in the Twins Early Development Study. Intelligence and educational achievement at ages 12 and 16, and their associated genetic variants, were analysed. Intelligence was assessed via verbal and non-verbal web-based IQ tests.

Lead author Andrea Allegrini, from the Social Genetic & Developmental Psychiatry Centre at the IoPPN said: ‘The effects of single variants on a given trait are often extremely small, and difficult to capture accurately. Multivariate (so-called multi-trait) genomic approaches can be used to increase the predictive power of polygenic scores. We compared several novel, state of the art, multi-trait genomic methods to maximise polygenic score prediction.’

The authors found that when analysing genetic variants associated with intelligence, they were able to predict 5.3% of the difference in intelligence between individuals at age 12 and 6.7% of the difference at age 16. For educational achievement, analysing genetic variants associated with educational attainment (years of schooling), they predicted a maximum of 6.6% of the difference at age 12 and 14.8% at age 16. The authors also showed that analysing variants associated with educational attainment allowed them to predict 7.2% of the variance in intelligence at age 12 and 9.9% at age 16, because of the genetic correlation between the two traits.

When taking a multivariate/multi-trait approach, and adding three other, genetically correlated traits and their associated genes to the analysis, prediction accuracy improved to 10% of the difference in intelligence at age 16 and 15.9% of the difference in educational achievement. The authors also tested three different genomic methods to show that their predictive accuracy was similar.

Andrea Allegrini said: ‘Our findings indicate that there are no notable differences between the multi-trait prediction methods we tested. Even though these methods employ different mathematical models, they arrive at similar conclusions. This is extremely encouraging as it indicates that our estimates are robust, in that they are generally stable across methods tested.’

‘However, it is also important to understand that these are average differences, which means that many people with a low genetic predisposition to educational attainment can still do very well in school, and vice versa. As such, these scores are probabilistic; they do not show that education or intelligence are determined by a person’s genes.’

The full paper is available online.