A View from Susan Young Rojahn
Are Your Grades Written in Your Genes?
A large genetic study finds gene variants with a subtle effect on scholastic achievement.
A study published on Thursday in Science reports that certain gene variants can affect how long someone stays in school.
In the first study from the Social Science Genetic Association Consortium, the investigators searched for correlations between more than two million of the genetic variants known to occur in humans and how much schooling an individual completed. They searched for the associations in more than 125,000 people from the United States, Australia and Western Europe, the vast majority of which were at least 30 years old and so were likely finished with their schooling.
Three particular DNA variants (each associated with a different gene) were found to associate with how many years of school a person had completed and whether or not they completed college. But each particular DNA variant could only explain 0.02 percent of the difference in the number of years a person stayed in school, so educational achievement will be more greatly influenced by other factors in life.
“Our study shows that the effects of every single genetic variant on educational attainment are much smaller than many scientists expected, but that they are present,” said Nicholas Timpson, a genetic epidemiologist at the University of Bristol, in a released statement. “From work such as this we are starting to understand in greater detail the delicate relationship between our genes and the environment and how they go on to shape complex outcomes such as educational attainment,” he said.
The study could guide the field of social-science genetics, said co-author David Cesarini, a New York University social- and neuro-economist in another release: “We used 125,000 individuals to conduct this study. Previous studies used far smaller samples, sometimes as small as 100 individuals and rarely more than 10,000,” said Cesarini. “If genes have small effects, as our study shows, then sample sizes need to be very large to produce robust findings that will reliably replicate in other samples.”
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