FAQs for Nature Human Behaviour occupational status study

This study examines occupational status (see Glossary) through a multidisciplinary lens to uncover the complex interplay between genetics and social environment.

The primary method to measure genetic associations with occupational status is a Genome-Wide Association Study (GWAS (see Glossary) in which a polygenic score is derived. From this, we construct polygenic scores and implement sibling family and adoption models to separate genetic and social environment associations. Crucially, the use of a GWAS is not an agenda to reduce occupational status to solely a genetic basis, but rather to examine the complex interplay between genetic and social environmental predictors and intergenerational transmission. Further multiple methods were used to uncover different aspects of this topic (see FAQ Figure 1).

We analysed data from 273,157 individuals (130,952 males; 142,205 females) using the UK Biobank and identified 106 independent genetic variants, including 8 newly associated with the genetics of socioeconomic status. 

Key findings are:

• Polygenic scores explains around 5-10% of the differences in occupational status amongst individuals, more than three times as much as found in a previous study that used a cruder measure of occupation.
• Using a sibling research design – a technique that allows us to account for the shared family environment experience by siblings – the predictive power of these polygenic scores drops by over 50%. This suggests that the polygenic score is also picking up non-genetic family and socio-environmental factors, which we demonstrated previously for multiple complex behavioural phenotypes.
• As expected, family environment remains an important factor in predicting occupational status of adults. The family someone grows up in impacts an individual via what is termed gene-environment (GxE) correlation. We were able to empirically show this GxE correlation by examining parental occupational status from adoptees (i.e., children raised by non-biological parents) to those who were not adopted.  Whereas the polygenic score dropped by over 50% when we compared siblings from the same family, the polygenic score’s prediction of adoptees diminished by only roughly 25%.
• We show that this 54-57% reduction in predictability of polygenic scores within-families stems from strong socioeconomic status-based assortative mating (21-27%) over generations and indirect parental effects (22-27%). Indirect parental genetic effects are the influence of parental genotypes on their offspring over and above the transmission of genes, often described as genetic nurture. Parents transfer only around 50% of their own genetic material to their children and although parents may not have transmitted particular genetics to offspring, their own genetics may still impact their children indirectly through creating a family environment shaped by their own parental genetic makeup. 
• The intergenerational correlation of occupational status between parents and their children is only partly explained by genetic factors, with 62% of the intergenerational correlation due to non-genetic factors such as family environment and potentially rare genetic variants. The rest is neither family nor genes but unique circumstances and factors that are still not measured. Notably, this is not specific to occupations, but to most complex diseases, behavioral, and social outcomes.
• Factors such as cognitive skills, educational motivation, occupational aspiration, personality traits, and ADHD are the main drivers of the association between polygenic scores and occupational status. Moreover, the links between polygenic scores, career trajectories, and health are interrelated with parental occupational status. 
• We examined the polygenic scores across 30-year career trajectories to reveal that societal structures correlate with genotypes and jointly predict career trajectories. Individuals who started in lower occupational status percentiles but ranked high in the PGS quintile for occupational status, consistency advanced their careers over those 30 years. Those who held higher occupational status jobs but had lower PGSs, exhibited a steady decline in their professional trajectories.
• There is a remarkably strong genetic correlation between genetic measures of other socioeconomic factors (educational attainment and income). It is noteworthy that the genetic correlations observed among these socioeconomic indicators exceed the phenotypic correlations by a factor of two to three. Such a pattern is highly unusual in genomics and not observed for behavioral phenotypes or diseases.

Our findings illustrate the interdependence between genetic associations (measured by polygenic scores) and social environments (measured by familial/parental characteristics). Rather than nature versus nurture, we identify the importance of nature and nurture. Such a co-existence reiterates our previous claims and is further empirical demonstration that genetic results cannot and should not be used to in exclusion to predict an individual's occupational status or other complex behavioural outcomes. 

This interdisciplinary study looks at occupational status and draws from multiple scientific approaches from the social sciences, molecular genetics, biostatistics and medical sciences. Given the controversial nature of examining differences in socioeconomic status and class in the context of the genome (see Box 1 main article) and potential of misinterpretation of our research, the aim of this FAQ is to create an accessible document for a broader audience to clarify what we conclude, and importantly, cannot conclude with this study. It is aimed at those who are new to the scientific terminology and methods (see Glossary). Experts or those seeking more in-depth information and scientific references supporting our statements, should refer to our main article and the detailed Supplementary Material.

Assortative mating. Refers to a mating structure in which pairs of individuals that are (genetically) similar to each other mate with a higher probability than expected under random mating. Assortative mating is an important concept for statistical genetics; it biases heritability estimates.

Genetic ancestry. In the context of GWAS, genetic ancestry refers to the measure of genetic similarity among individuals to eliminate biases that are due to historical human migration. It should not be confused with race or ethnicity; it is also not a direct measure of genealogical ancestry.   

Genetic associations. Refers to the relationship between single nucleotide polymorphisms (SNPs) and a particular outcome of interest. GWAS methodology tests for these associations across the genome. Significant associations identified in GWAS can then be used to create polygenic scores, where the effect sizes of selected SNPs are combined to predict the outcome.

Genetic variant. Refers to a specific region of the genome that differs between two genomes.

Genome-wide association study (GWAS). A GWAS is designed to adopt an hypothesis-free approach to discover genetic variants are associated with a trait. They often combine data from multiple studies to gather the largest sample possible. An updated and searchable list of all GWAS discoveries to date can be found at www.gwasdiversity.com, with summary statistics available at the GWAS Catalog. 

Genotype. Describes part of an individual’s DNA that influences their phenotype.

GWAS-heritability. The fraction of phenotypic variance of a trait explained by genome-wide significant genetic variants—sometimes also by polygenic scores based on GWAS findings.

Heritability. A population measure defining the proportion of variance in a phenotype explained by genetic variance within a population. We can differentiate between broad-sense heritability, including both additive and non-additive genetic effects such as epistasis and dominance, and narrow-sense heritability focusing on additive genetic effects only.

Indirect genetic effects. Refers to situations when environmental influences which are important for complex outcomes and phenotypes are also associated with individual’s genotype. In such instances, environments referred as ‘mediators’ of the link between genetic variants and traits.

Occupational status. A measure developed primarily by sociologists to represent a stable indicator of an individual’s social position in society. It is mapped g on a continuous scale by three measures. (1) International Socioeconomic Index (ISEI) (status measure constructed from scaling weights that maximize the (indirect) influence of education on income through occupation), (2) Standard International Occupational Prestige Scale (SIOPS), (prestige-based measure using public opinion surveys where a representative population is tasked with ranking occupations by their relative social standing), (3) Cambridge Social Interaction and Stratification Scale (CAMSIS) (measures distance between occupations based on the frequency of social interactions between them (operationalized as husband-and-wife combinations)).

Phenotype or trait. The observable characteristic of an individual, ranging from physical traits (hair colour, height) to disease status (diabetic) to behavior (risk-taker, age at first sexual intercourse, educational attainment).

Polygenic score (PGS). A single quantitative variable that summarizes genetic association to a phenotype by combining multiple genetic variants and their associated weights, derived from a GWAS. Polygenic scores for social outcomes are not tools to derive individual-level predictions but rather a population-level analytic tools.

Single-nucleotide polymorphism (SNP). A common variation in a single nucleotide (i.e., A, C, G, or T) that occurs at a specific position in the genome. A SNP exists as two different forms (e.g., A vs. T). These different forms are called alleles. A SNP with two alleles has three different genotypes (e.g., AA, AT, and TT).

SNP-heritability. The fraction of phenotypic variance of a trait explained by all SNPs in the analysis. Usually less than the narrow-sense heritability as it does not take rare variants and structural variation into account.

The article by Akimova & Wolfram et al. (2024) examines occupational status, which is one of the core topics of social inequality and social stratification research, a field of research in the social sciences that studies and categorizes groups of people based on core socioeconomic (SES) factors like wealth, earnings, income, education, or occupation.

The majority of social stratification research has focused on the social determinants and social aspects of intergenerational transmission (i.e., from parent to offspring) of SES, often neglecting any role of biology or genetics. A growing number of studies found genetic variants linked to other SES indicators of education, income, and wealth. Yet to date, there was a lack of research examining occupational status, which is one of the most prominent measures of SES, particularly in disciplines such as sociology. We used three measures derived from decades of research in sociology, the: International Socioeconomic Index (ISEI), Standard International Occupational Prestige Scale (SIOPS), and Cambridge Social Interaction and Stratification Scale (CAMSIS).

We therefore performed a GWAS (Genome Wide Association Study) on measures of occupational status along with various follow-up statistical analyses in order to understand the nature of discovered correlations and the complex interplay between genes and environments.

Socioeconomic status (SES) is a complex phenomenon influenced by behavior, biology, and the social environment. Understanding SES requires a multidisciplinary approach that identifies common drivers and addresses the multidimensional nature of their relationships with biology, health, social environments, and other behaviors.

Traditionally, biological and social processes of disease or complex behavior inheritance have been studied separately, often attributing them to social or biological factors and rarely considering both.

However, the joint consideration of social and biological factors in a biosocial model, provides a more comprehensive scientific understanding. Advances in technology, such as genotyping and large datasets that incorporate genetic and environmental/behavioral information, have spurred a wide range of empirical inquiries, enabling improved modeling of complex behaviors and traits.

Social stratification is a central predictor across the social and health sciences. The study of intergenerational status transmission and reproduction within families has received considerable attention in the literature. The focus has often been on how different family origins are associated with educational and labor market outcomes and the degree of occupational, class or social mobility from generation to generation. Here researchers often look at inequalities opportunities across countries and over time. Social science research has extensively demonstrated that socioeconomic status is influenced by multiple factors including sex and gender, family, societal and historical contexts, and social norms. Occupational status and mobility are shaped by inequalities of opportunity and also differ for men and women, often related to the opportunity costs and constraints of childbearing and rearing, but also when they were born and the geographical and policy context of individuals. Next to the family environment, parenting behavior and other investments in children, genetic inheritance plays a role.

For socioeconomic status measures such as educational attainment, nearly 4,000 genetic variants have been associated with the outcomes in previous studies. Genetic variants associated with income have also been assessed. Previous research has also shown that the third measure of occupational status, is not only driven by socio-environmental factors - twin studies suggest a heritability of occupational status as between 0.30 to 0.40. The current study goes substantially beyond what we know about the genetics and biological factors associated with occupational status.

Knowledge about potential genetic effects has led to various interpretations, often in relation to a measure of merit. Thus, a quantitative exploration of factors through which social and biological predictors are linked enhances our understanding of the nature of the links between the genome and social stratification in general. This approach helps prevent potentially misleading interpretations of latent genetic measures in the context of questions regarding equality of opportunity.

To date, the majority of research on differences in socioeconomic outcomes has been studied using a socially determinist approach, focusing only the role of social and contextual factors on prediction. More recently, however, researchers have conducted genome-wide association studies (GWASs), which scans the entire genome to discover the genetics related to socioeconomic and other complex behavioral outcomes. Previous GWASs were conducted education, income, and wealth.

Why study occupation if we already know about education and income? Although they are related, having lower education does not always translate to a lower occupation or income. Conversely, someone might have high education, but not obtain a good job or high income. Others might have low income and rise high in the occupational prestige ranks. It is therefore interesting to understand whether there are genetic underpinnings of occupational status and how these genetics operate in relation to other socioeconomic outcomes, with health and across different environments, families and over time.

The aim of this study is to improve our understanding of occupational status attainment and transmission, specifically through the complex interplay between biological inheritance and social processes.

To achieve this we:

• Identified genetic variants associated with our measures of occupational status to utilize them to create polygenic scores, which we used to control for genetic associations when studying factors related to socioeconomic status, occupations, labor market, and occupational mobility.
• Examined the genetic correlates of occupation in relation to other socioeconomic indicators.
• Investigated the extent to which genetic associations of occupational status reflect the interplay between genetics, biology, family, social, and environmental factors.
• Explored the potential mechanisms linking the genome and occupational status.
• Scrutinized the underlying structure of the discovered associations.
• Explored the relationships between genes, occupational status, and (mental) health.
• Introduce a life course perspective to examine how the polygenic scores operate over time as individuals age and progress through their careers.

The primary analysis we conducted is called a Genome-Wide Association Study or GWAS  (pronounced gee-was), which is a search across the entire human genome, examining each genetic locus (or region) one by one to see if there is a relationship (or what we call an association) between our outcomes and a particular genetic variant. Variants refer to a specific region of the genome, which differs between two genomes. Different versions of the same variants are termed alleles and a SNP (pronounced SNiP; single-nucleotide polymorphism) can have two alternative bases or alleles (C and T).

We study DNA variants that distinguish us from each other. Humans are 99.9% identical to each other, and it is the 0.1% by which we differ that makes us all genetically unique. A small subset of the 0.1% by which we differ genetically is anticipated to be associated with occupational status scores.

A comprehensive interdisciplinary study such as this one demanded multiple analytical approaches, we:

• investigated the functional implications of genetic variants associated with occupational status through gene-based and gene-set analyses using MAGMA technique.
• employed multi-trait analysis (MTAG) to meta-analyze occupational status measures with household income and educational attainment.
• utilized genomic structural equation models (GSEM) to analyze the joint factor of occupational status, cognitive performance, ADHD, openness to experience, risk tolerance, and neuroticism.
• engaged in polygenic score (PGS) construction and prediction involved producing various scores, testing out-of-sample prediction, and assessing population stratification using LD score regression.
• applied sibling and adoption models to disentangle direct, indirect, and demographic effects. To study indirect ‘social transmission’ effects we used two approaches: first, adjusting our polygenic scores for parental SES (measured by an individuals’ parent’s occupational status when they were aged 11. Second, an adoption prediction study to see if children raised by non-biological parents had different results.
• conducted mediation and confounding analyses.

Are people biologically predetermined to have an occupational status?

No, that is not what we find. Our polygenic scores explain around 5-10% of the differences in occupational status amongst individuals but we also showed that when we account for the family environment (by looking at siblings), the predictive power of our polygenic scores drops by 50%, emphasizing the importance of family environment. But our results are still relevant since they show that the polygenic scores are picking up non-genetic family and social environment, which is relevant not only for our research but many complex outcomes. Importantly, we also provide empirical evidence that in addition to polygenic scores, family environment, socioeconomic based assortative mating over generations and the environment parents create (which is not only passing on genetics) are important. We also demonstrate that factors such as cognitive skills, educational motivation, occupational aspiration, personality traits, and ADHD are the main drivers of the association between polygenic scores and occupational status.

Could genetic results alone be used at the individual level to predict someone’s occupational status?

No. That would be technically incorrect and a very bad idea. With the exception of some diseases, to date, polygenic scores alone are usually not useful to predict complex individual disease and behavioral outcomes. When we examine complex behavioral outcomes each individual SNP or genetic variant has a small effect, so prediction of using genetic results alone is not possible. Even if we combine the information contained in the more than 10 million genetic variants that we studied together into a genetic predictor, we predict 5-10% of the variance across individuals. With larger samples we see that the ceiling of prediction is likely more in the range of 11-15% (depending on the measure of occupational status used). Extrapolating findings from other traits, more granular and detailed genetic data (on structural variation, insertions, deletions and rare variants) might further increase this ceiling.  For this reason, increasing standards are offered for using polygenic scores.​

But it isn’t a problem exclusive to genetics. Even the ‘gold standard’ social science predictors of occupational status, such as father’s or mother’s occupation or education when entered alone as a single variable in a regression equation would also low predictive power, generally under 10%. It is therefore unhelpfully reductive to think that it is useful or possible to enter one single variable as a predictor without considering additional factors. In reality, complex outcomes are a culmination of multiple factors such as genetics, parental background, lifestyle, level of education and national institutional configurations that constrain or enable behavior. We have even shown that the explanation of genetics can vary across country and time.

Should public officials, policy makers, insurers, or health care professionals use the polygenic score from this study to make decisions?

No. As noted above, genetics only predicts 5-10% of the variance in occupational status amongst individuals and is highly polygenic, meaning that it contains multiple genetic variants where we do not fully understand the biological causal function nor their interaction. As noted throughout, we empirically demonstrate the importance of family environment and socioeconomic factors and the interplay with genetics. The polygenic score alone should therefore not be used to make decisions.

Are there societal or medical implications of this study?

Analytical implications, most certainly. Societal maybe, but medical applications, extremely unlikely. In the longer term, this study offers a better understanding of the genetic architecture and responsible observable traits for occupational status. It equips scientists to take genetic effects into account in their study of SES and reduce bias due to genetic effects in their study of interest. But it also alerts medical and health researchers that polygenic scores for complex phenotypes are also picking up considerable social environmental and family effects and that polygenic score prediction vary by age.

We reveal changing genetic effects across the occupational career which opens an interesting puzzle for life course researchers with the potential for discoveries of lifestyle factors interacting with genes. Our analyses of the relationship between occupational status, genes and, (mental) health does have some analytical ramifications for public health as we can demonstrate that ignoring one of the dimensions produces a biased view on the other one. Furthermore, it is important to understand whether and which proportion of these traits are driven by genetic, behavioral and environmental factors. The fact that we also found evidence that genetic influences are much more shared than it’s observed for different status measures suggests that continued research in this area is warranted to aid a better understanding of what makes the difference between income, education, and occupational status.

What are the potential risks of studying occupational status and genetics?

As outlined previously, the risks of introducing genomics in the study of occupation status for individuals are self-fatalism or self-stigmatization (i.e., believing their occupational status is fixed or inevitable or they are less capable). For society, the potential risks are discrimination against individuals (e.g., in employment, insurance, criminal justice), stigmatization of others or against entire groups, with potential for harmful or inequitably distributed policy applications. Another risk is that genetics distracts from the real problem and channels resources away from more effective ways of addressing social stratification. Despite clear messaging, this research could also be misunderstood under the lens of genetic determinism and used to justify and reinforce existing inequalities as inevitable, hence seeing any interventions as futile. We recognize this apprehension and explicitly distance our research from studies that were (or are) overtly classist and/or racist and reinforce inequalities, confuse structural inequality with biology or draw overly-simplistic policy implications. Our endeavor, rather, is rooted in the pursuit of a biosocial understanding of occupational stratification, intergenerational transmission and the role that socio-economic status plays in genetic estimates, firmly guided by a well-established ethical, theoretical and analytical framework. Another risk is that superficial critics simply do not read the article and caveats we overtly present in the main article (Box 1) and here in the FAQ and incorrectly miscategorise our intentions and research.

We are an interdisciplinary group of researchers working in the area of complex trait genomics, sociogenomics, statistical genetics and the social sciences. Although not exhaustive, we have published a broad variety of related work such as an MIT textbook on introducing quantitative statistical genetic data analysis (2021), identified statistical problems in genetic analyses (2021), the importance of country and historical time in genetic outcomes (2017) and problems with genetic essentialism narratives (2021).  We have conducted previous GWASs on reproductive behavior, led by Mills (reproductive onset, age at first birth, number of children), published previously in Nature Genetics (2016), and Nature Human Behaviour (2021, 2023). We have also highlighted the importance of including family-data in GWAS research (Nature Genetics, 2022), lack of the data diversity and outcomes in GWAS discoveries (2019) and provide a daily update showing the lack of diversity in a GWAS Diversity Monitor (Nature Genetics, 2020). Mills has also published extensive non-genetic work in the social sciences on the topic of this outcome of occupational status and mobility in men and women and in relation to fertility (2012, 2021). 

This project took multiple years and we are grateful for funding from various sources. Funding for this project for MCM and ETA is from the European Research Council ERC Advanced Grant CHRONO (835079), Leverhulme Trust (RC-2018-003) Leverhulme Centre for Demographic Science, and for MCM Economic and Social Research Council, United Kingdom Science and Innovation (UKRI) Connecting Generations Grant (ES/W002116/1), MapIneq Project, European Union’s Horizon Europe research and innovation programme (No. 101061645) and for FCT UKRI FINDME (EP/Y023080/1).

We are grateful for the contribution of all UK Biobank and NCDS participants to this scientific study. This research was conducted using the UK Biobank under application 32696 and NCDS under application GDAC_2021_16_TROPF, with ethical approval from the University of Oxford under application SOC_R2_001_C1A_21_60. Both the UK Biobank and NCDS applications were specific to the scope of this paper. For the UK Biobank approval, we received approval for a scope extension to ensure transparency, allowing us to expand from our focus on non-standard occupations to also occupational status. Here was specified that our plan was: “to perform GWAS analysis using employment histories from the UK Biobank to construct sociologically informed measures of occupational status.” We specified that we would construct sociologically informed measures of occupational status (CAMSIS, SIOPS, and ISEA) for our GWAS and noted that the analysis would be accompanied by NCDS genetic and phenotypic data. For the NCDS application, we specified not only the information mentioned above but also the set of polygenic prediction analyses. We also preregistered our analysis plan (https://osf.io/djbr2/) which was updated for replication (https://osf.io/x6va5).

Are the genetic associations small or large?

It is not really about size. Occupational status, similar to other socioeconomic measures, is a complex outcome that is not only genetically based is largely predicted by social and family background factors and a complex interplay with individual and socio-environmental contextual factors. As with any study that examines a complex behavioral outcome, genetics is only one piece of this larger puzzle. In this study we only examine common genetic variants (SNPs) and consider only one of the many possible biological and genetic ways in which individuals may vary. This does not impact the importance of the findings, since one single factor or variable ever fully explains complex outcomes. In earlier research, media and some scientists focused on the overall ‘predictive power’, which refers to the out of sample prediction of how much the genetic polygenic score alone predicts. In this study it is between 5-10%, depending on the occupational status and up to 9% depending on the career stage. But as noted throughout, scientists rarely ever use one predictor to explain an outcome, particularly a complex behavioral one like occupational status, BMI or Type 2 diabetes. It is always an interaction with multiple predictive factors.

Is it nature or nurture?

That is a false dichotonomy and it is neither nature or nature but rather nature and nurture. Occupational status – similar to other socioeconomic status measures or complex diseases - is a combination of both. Just as complex diseases such as obesity or Type 2 diabetes are neither purely genetically or socially determined, occupational status relates not only to biological factors, for example, influencing abilities or behavior, but also have a strong social and environmental component in that they are driven by one’s family, partner, job, and simultaneously shaped by the social, cultural, economic and historical environment. Genetic factors partly influence the first two factors of biological ability and behavior, complemented by social and environmental influences which also filter the types of behavior that are possible in the historical environment (e.g., via legislation, labor market structure, social norms).

What are the limitations of this study?

Although we open up new avenues of research, there are limitations that are not exhaustive or exclusive to this type of study, with the central ones are listed here. First, we focus on British-European genetic ancestry individuals only, a problem we have highlighted elsewhere. We conducted a scientometric review of all GWAS and found that 72% of genetic discoveries come from 3 countries, and therefore set up the GWASDiversityMonitor described in our Nature Genetics article. Second, we draw most of our results from the UK Biobank, which is a selective population that has fewer health problems and a higher SES. Such a participation bias limits the generalizability and introduces the potential that observed genetic associations may be influenced by the characteristics of the subset of individuals who chose to participate in the UK Biobank. A recent Nature Genetics (2023) study by colleagues working in our own Leverhulme Centre for Demographic Science (LCDS) explain why this is an issue for genetic research, also described here. Third, due to data limitations, we recognize that we were unable to also include parent’s polygenic scores to estimate genetic confounding effects, which we have shown can be problematic (Nature Genetics, 2022), as have our own LCDS researchers in our centre in other publications (Science, 2018).

Data availability

The GWAS summary statistics generated in this study are available on the GWAS Catalog website under accession codes GCST90446160, GCST90446162, GCST90446163. Access to the UK Biobank is available through: http://www.ukbiobank.ac.uk). Access to The National Child Development Study (NCDS) is available through: https://cls.ucl.ac.uk/data-access-training/. 

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