Periodic Reporting for period 1 - GENIO (Genetic contributions to inequality of opportunity)
Período documentado: 2021-09-16 hasta 2023-09-15
In a fully egalitarian society, access to education should not be restricted by social background. In many contemporary societies, this goal remains unmet: children from low income households with high standardized test or exam scores are less likely to attend college than children with equal test scores from high income households. However, eliminating family resources as a source of unequal access may still leave inequality of opportunity based on another family factor: genetics. In this project, I apply both traditional and novel econometric techniques to measure and visualise genetic contributions to inequality of opportunity, and to measure the equality of opportunity gap in the United States and the United Kingdom. To measure childhood deprivation as a contributor of inequality of opportunity in middle-aged adults currently living in the UK, I digitise historical records relating to unemployment and (infant) mortality to generate a novel deprivation index. Finally, I quantify the boundaries of predicting individual outcomes with genetic data by showing that the statistical choices made to create the genetic profiles greatly determines where an individual is placed on the genetic spectrum.
I have developed a new methodological framework for quantifying and visualising genetic contributions to inequality of opportunity, and use a dataset from the United States to show that inequality of opportunity is mainly affected by family socioeconomic status. If environmental factors did not play a role in educational outcomes, the average US individual in the dataset would have been 1.9 years more educated – quantifying the equality of opportunity gap. I also find that genetic information cannot be used to predict individuals’ life outcomes such as educational attainment, and show that this will also not be possible in the direct future. Furthermore, I show that how the lack of precision for predicting individuals’ outcomes with genetic data can affect future clinical studies that aim to corporate polygenic scores in medical decision making processes, using a concrete example from personalised drug treatment for cardiovascular disease.
I have developed a new methodological framework for quantifying and visualising genetic contributions to inequality of opportunity, and use a dataset from the United States to show that inequality of opportunity is mainly affected by family socioeconomic status. If environmental factors did not play a role in educational outcomes, the average US individual in the dataset would have been 1.9 years more educated – quantifying the equality of opportunity gap. I also find that genetic information cannot be used to predict individuals’ life outcomes such as educational attainment, and show that this will also not be possible in the direct future. Furthermore, I show that how the lack of precision for predicting individuals’ outcomes with genetic data can affect future clinical studies that aim to corporate polygenic scores in medical decision making processes, using a concrete example from personalised drug treatment for cardiovascular disease.
The project has produced two papers published on the preprint server bioRxiv. These papers are especially important to other researchers who use genetic data to study socioeconomic and health outcomes. In the first paper, we find that genetic information cannot be used to predict outcomes for individuals’ socioeconomic traits, such as educational attainment. Simulations show that this is not only impossible now, but also in the near future, when genetic discovery studies grow and lead to increasing precision in determining individuals’ genetic predispositions. While the former has been shown in other studies previously, we were the first to simulate the boundaries of individual prediction given future increases in statistical power. In the paper, we use genetic information to construct so-called “polygenic scores” (or “polygenic indices”, PGS or PGI), which present an index of genetic predisposition for an individual. PGS can be constructed with a wide range of methodological techniques. We were the first to show that the rank correlations between differently constructed PGS vary enormously, which can have profound implications for how individual genetic data can be used by researchers, and in the future by clinicians as well. We show a concrete example of such an implication for a medical decision-making setting: the prescription of cardiovascular drugs according to genetic profiles. We conclude the paper by making specific recommendations for researchers who use genetic data to make predictions for individuals.
The other published preprint paper uses a well-established econometric technique in an innovative way for removing statistical attenuation bias when studying the effects of polygenic scores (PGS). Statistical attenuation bias is one of the major methodological challenges in the study of polygenic scores, and we show that genetic ORIV outperforms other approaches. Moreover, genetic ORIV can be applied in a way that is convenient and easy to implement in common research designs.
Finally, an unpublished working paper studies the genetic contributions to social inequalities, and models its environmental influences. We show that Stochastic Frontier Analysis (SFA), a traditional econometric technique used to study production maximisation, provides novel insights in the study of social inequalities. Our first specific innovation is to show that SFA can estimate the extent of inequality of opportunity in a dataset, and to separate the effects of accountable effort (a “fair” contributor to inequality of outcomes) from luck (an “unfair” contributor to inequality of outcomes). In an application to data from the United States, we show that poor family socioeconomic conditions pose the biggest threat to equality of opportunity. Other factors, such as childhood health and parental education, are less important. In addition, we conclude that if every person in the dataset had reached their full “genetic potential” unhindered by environmental circumstances, the average person would have been 1.9 years more educated.
The other published preprint paper uses a well-established econometric technique in an innovative way for removing statistical attenuation bias when studying the effects of polygenic scores (PGS). Statistical attenuation bias is one of the major methodological challenges in the study of polygenic scores, and we show that genetic ORIV outperforms other approaches. Moreover, genetic ORIV can be applied in a way that is convenient and easy to implement in common research designs.
Finally, an unpublished working paper studies the genetic contributions to social inequalities, and models its environmental influences. We show that Stochastic Frontier Analysis (SFA), a traditional econometric technique used to study production maximisation, provides novel insights in the study of social inequalities. Our first specific innovation is to show that SFA can estimate the extent of inequality of opportunity in a dataset, and to separate the effects of accountable effort (a “fair” contributor to inequality of outcomes) from luck (an “unfair” contributor to inequality of outcomes). In an application to data from the United States, we show that poor family socioeconomic conditions pose the biggest threat to equality of opportunity. Other factors, such as childhood health and parental education, are less important. In addition, we conclude that if every person in the dataset had reached their full “genetic potential” unhindered by environmental circumstances, the average person would have been 1.9 years more educated.
The project has produced applied interdisciplinary methods in an unparalleled way to study inequality of opportunity from a novel angle, providing a new method to quantify exactly how far a society is removed from equality of opportunity. However, this new method also shows how skewed outcomes are still distributed when equality of opportunity is actually achieved. The results have the potential to contribute to the societal discussion on inequality of opportunity, and the extent to which genetic and environmental factors should play a role in this process. At the same time, I show that genetic data obtained from polygenic scores cannot be used to reliably predict individual’s life trajectories -- not now, and not in the near future. This is important, since genetic data are increasingly used for individual-level prediction, for instance in personalised medicine, and in genetically profiling embryos. The project shows that new genetic data can indeed be used, but that it provides an informatively weak prediction for the actual outcomes an individual will achieve in his or her life.