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Recent advances in molecular genetics enabled social scientists to significantly extend their understanding of social phenomena by jointly analyzing the effects and interactions of nature and nurture on outcomes of interest (Freese 2018; Mills and Tropf 2020), in particular by the means of polygenic scores (PGS). The importance of this innovation is especially relevant to the study of the intergenerational transmission of social and economic status: Here, parents not only provide their children with access to social, cultural, and economic resources but also with genetic endowments, which threaten to confound the effects of social factors if left uncontrolled (Diewald et al. 2016; Nielsen 2016). Although PGS often still only explain a fraction of the heritability of phenotypes, that is expected from twin studies (Eichler et al. 2010; Young 2019), they prove to be valuable in explaining mechanisms and pathways from genes to social stratification (i.e. Belsky et al. 2018; Barth, Papageorge, and Thom 2020; McGue et al. 2020). The actual number of polygenic scores available to be used in such studies is, however, quite limited: Most research is based on PGS measuring educational attainment (Lee et al. 2018) or income (Hill et al. 2019), that are often utilized as general proxies of genetic endowments associated with status. Socioeconomic status is, however, a multidimensional phenomenon, which can not be reduced to educational attainment or earnings (Clark 2015): People may trade off income for other types of status and educational attainment might not always translate into success later in life.
In that regard, the sociogenomic literature so far neglected an important status measure, often even considered to be "the single most important dimension in social interaction" (p. 206, Ganzeboom and Treiman 1996) by sociologists: Occupational status. This is especially surprising, in light of the arguments which can be made for its relevance to not just the social but also the behavioral sciences: It is easily recorded in detail using standardized and fine-grained taxonomies (Ganzeboom 2010), leading to its availability in many genotyped data sources. At the same time it is a significant driver of social inequality, i.e. with respect to earnings (Weeden et al. 2007). Various validated scales exist which allow the measurement of occupational status as a continuous variable with respect to income and education (i.e. ISEI), social interaction (i.e. CAMSIS), and prestige (i.e. SIOPS) (Connelly, Gayle, and Lambert 2016). Such measures are strongly associated with noncognitive (Judge et al. 1999) and cognitive traits (Schmidt and Hunter 2004), the latter association being significantly stronger than the one between cognitive factors and earnings (Strenze 2007) and correlate between .90 and .95 with average mental ability scores of people in occupations (p. 293, Jensen 1998).
In this study, we are therefore interested in investigating the following questions: 1. What is the variance of different measures of occupational status explained by common genetic variants and are they affected by similar influences in the genome? 2. What are potential mediators between the genome and occupational status? 3. How strong is the genetic overlap between occupational status, educational attainment, and income and is there evidence for a general genetic factor of socioeconomic status? In order to answer these questions, a GWAS on the three aforementioned measures of occupational status is performed and its results analyzed in conjunction with the findings of two additional GWAS on household income and educational attainment as well as various GWAS summary statistics found in the academic literature.