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DNA As Cristalline Sphere: The Role Of Genes In Academic Performance

Scientists have used a genetic test thought to predict academic achievements solely based on genes. They are already talking about a test for dyslexia.

Why is it that children perform so heterogeneously at school? While one is coping apparently easily with math exams and language tests, others suffer desperately from the demands of teachers and schools. The fact that children can differ so widely may have many reasons, one of them being their genes.

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Scientists at King’s College, London, wanted to know whether and how far one could predict academic achievements solely based on genetic grounds. By evaluating thousands of gene variants, researchers, under the guidance of Saskia Sekzam, could explain only about 9 percent of the differences in the school grades of 16-year-old scholars. For their study (1), the scientists used polygenetic tests. This is a method allowing to screen the effects of genes on quite different human characteristics.

For this purpose, they needed genome-wide association studies (GWAS) that analyze variants of single sections of genes called single nucleotide polymorphisms, or SNPs, a kind of “successful point mutations.” The totality of SNPs of a specific group with a certain characteristic, for instance, excellent academic achievements, is compared to another group that is missing this signature. This way the scientists can screen for those SNPs that are associated with this characteristic. Selzarm et al. based their study on a GWAS that recorded around 10 million SNPs in 329.000 individuals and identified 74 gene variants that were associated with the number of years spent in training and education.

Subsequently, Selzarm and colleagues weighted the effect of the individual SNPs and added them to a polygenetic score; people with more of the relevant SPNs would, therefore, score higher and present higher academic achievements, while individuals with a lower score would rank lower, statistically.

These computations were then tested with 5825 twins. They calculated a polygenetic score for each twin using data from the GWAS, and they correlated their score with their school grades for English and math at age 7, 12, and 16. As was expected, the genetic outfit did exhibit a strong influence on the results. At age 7, genes explained 3 percent of the difference between twins. At age 12 this value was 5 percent, and finally, at 16, it reached 9 percent.

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Transferred to school grades, this implies that twins with a high polygenetic score would grade, on average, A or B at school. Twins with a lower polygenetic scoring ended up on lower school grade level. Furthermore, in the group with high scores, 65 percent succeeded in maintaining their high grade of an A, while this was possible only for 35 percent of the other group.

Níne percent does not sound like a lot, at a first glance, but the polygenetic score explains more than other known factors. If you look, for instance, at the difference between boys and girls in math, sex explains only one percent of the difference between twins. Most likely, the influence of genes is even higher, but the current investigation was based only on SNPs with the most frequent occurrence.

Selzarm even believes in a commercial relevance of their findings, stating that polygenetic scorings will soon be used to identify individuals at risk of experience learning disabilities, according to a press release from the university. This way one may be able to offer support to those who need it. However, this may be unlikely in the near future. More research would be needed to avoid the implicit danger to stigmatize children on the one hand, and to deliver false messages to pupils with strong learning abilities. Until then, the test may stay as a valid research tool, however.

This is part 8 of a series covering twin health provided by Paul Enck from the T√ľbingen University Hospital and science writer Nicole Simon. Further studies on twin research can be found at the¬†TwinHealth website.

Reference:

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  • Selzam S, Krapohl E, von Stumm S, O’Reilly PF, Rimfeld K, Kovas Y, Dale PS, Lee JJ, Plomin R. Predicting educational achievement from DNA. Mol Psychiatry. 2017 Feb;22(2):267-272. doi: 10.1038/mp.2016.107. Epub 2016 Jul 19. Erratum in: Mol Psychiatry. 2018 Jan;23(1):161. doi: 10.1038/mp.2017.203

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