Until now, it was believed that different gene variants were spread in a population almost randomly. This assumption is based on the Mendelian randomization method. Its essence is as follows: if certain variants of genes are associated with a certain feature, we can assume that the feature in the population is spread randomly as well. And then we can check whether it is true that people with this symptom are more likely to have this or that disease. And if so, then we can assume that we have established a causal relationship. For example, a recent study was held to check how smoking is associated with the risk of developing depression and schizophrenia.
Abdel Abdellaoui from the University of Amsterdam and his colleagues discovered a pitfall in this method. They showed that genetic variants can be spread in a population not randomly. This could be influenced, for example, by the migration routes of people of various social groups between countries or even within the same country.
Scientists have used the UK Biobank’s genetic database data. They chose 455,426 people from different regions of the UK. For each of them, a polygenic risk indicator was evaluated – this is a relative chance that a person will have one or another feature. To calculate it, scientists took a set of variants of genes that can be associated with this feature and summarized the values for each gene (is there a variant they were looking for, or not), multiplied by coefficients that reflect the significance of a particular gene.
Scientists calculated polygenic risk indicators for a number of features: there were both physiological ones – for example, body mass index, addiction to drugs, and social factors – for example, level of education. At the same time, the researchers took information about which gene variants affect the feature from articles whose authors did not work with UK Biobank – to be sure that the set of these gene variants is not unique to the British.
Researchers have found that polygenic risk indicators for a variety of reasons are unevenly distributed across the UK. In other words, people in the north of the country are predisposed to other conditions and diseases than in the south or in Wales. But even when scientists included the genealogies of people in their model as one of the factors, this effect did not disappear. Therefore, it means that in different regions of the UK live people with a similar level of risk of developing certain symptoms, even if they are not relatives.
This sequence was especially significant for the level of education. Scientists drew attention to the fact that the polygenic risk indicator for the level of education was the lowest (approximately -0.2 compared to 0.2 on average for the country) in the “mining” regions with a high index of economic distress (Townsend index). When the researchers checked how risk indicators for other features are spread across England, they found that they are very different for the inhabitants of these regions.
The risk indicators for people who left mining cities differed the most. For example, the polygenic risk score for body mass index was about 0.1 for people in a poor region, -0.5 for people in other areas of the country, and -0.1 for those who left a poor region for a rich one.
Scientists believe that in this way they managed to trace the stratification of society at the genetic level. The stronger a particular feature was correlated with the socio-economic situation, the higher the degree of clustering of risk indicators for this feature.
These are probably shades of industrialization: the representatives of the poor started working in the mines and settled around them. Now poor migrants continue to arrive in these areas. On the other hand, people who are interested in education and career, leave these areas.
The authors of the article note that such a manifestation of social inequality can distort the results of statistical studies – it may turn out that genetic variants are not evenly spread in the population. At the same time, inequality of one feature distorts data of another. For example, you can imagine that if people have a lower polygenic risk indicator for education, then they will often live in poor regions, eat junk food and be obese.
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