Laboratory

Max Planck – University of Helsinki Center for Social Inequalities in Population Health (MaxHel)

At a Glance Projects Publications Team

Project

Genetic Factors

Conducted by Mikko Myrskylä; Christian Dudel, Pekka Martikainen

Detailed Description

Poor health and social disadvantage run in families. However, the causal processes underlying this clustering may relate to genetic resemblance among family members, shared environmental exposures (both in the family and in the wider community), or a complex interplay between these factors. Comparing the resemblance of identical and nonidentical twins is an approach that has been widely used to tackle these issues in behavioral genetics, but it is less commonly employed in research on social inequalities in health. This may be due to data availability, disciplinary boundaries, or the reliance of twin designs on a set of assumptions that may, for example, underestimate the importance of shared environments.

More recently, however, polygenic scores (PGS) have been recognized as having considerable explanatory power in terms of disease outcomes (e.g., coronary heart disease) and social traits (e.g., education). PGS are linear combinations of the significant single nucleotide polymorphism variants that have been associated with a disease or a social trait in an independent genome-wide association study. Thus, PGS provide a measure of known genetic liability for a given phenotype. However, the application of methods exploiting PGS, such as direct adjustment for genetic influences and Mendelian randomization analyses, is still in its relative infancy in studies of social inequalities in health. These approaches offer new ways of overcoming some of the selection bias inherent in traditional observational studies. In addition, they enhance our understanding of the mechanisms though which genotypes affect phenotypes, and provide new opportunities for studying gene-environment interactions. Given the lack of clarity on how genetic differences manifest themselves, these analyses are in high demand as they help to bridge the gap between the social sciences and genetics.

We incorporate natural experiments such as policy changes into our research on how genetic factors produce social inequalities in health. In addition to triangulating causal evidence, exploiting exogenous policy changes in the environmental conditions alleviates a common limitation in previous analyses of gene-environment interactions: namely that the nonrandom distribution of genes across environments may create spurious interactions between them.

Publications

Lahtinen, H.; Martikainen, P.; Korhonen, K.; Morris, T. T.; Myrskylä, M.:

Educational tracking and the polygenic prediction of education

Sociological Science 11:8, 186–213. (2024)    

Lahtinen, H.; Korhonen, K.; Martikainen, P.; Morris, T. T.:

Polygenic prediction of education and its role in the intergenerational transmission of education: cohort changes among Finnish men and women born in 1925–1989

Demography 60:5, 1523–1547. (2023)    

Lahtinen, H.; Martikainen, P.; Korhonen, K.; Morris, T. T.; Myrskylä, M.:

Educational tracking and the polygenic prediction of education

MPIDR Working Paper WP-2023-015. (2023)