Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2023.03.31.534944

Genome-wide association study of occupational status and prestige identifies 106 genetic variants and defines their role for intergenerational status transmission and the life course

Akimova, E. T.·Wolfram, T.·Ding, X.·Tropf, F. C.·Mills, M. C.
View original source ↗Export metadata ↓

Abstract

Socioeconomic status (SES) impacts health and the life course. This GWAS on sociologically informed occupational status measures (ISEI, SIOPS, and CAMSIS) using the UKBiobank (N=273,157) identified 106 genetic variants of which 8 are novel to the study of SES. Genetic correlation analyses point to a common genetic factor for SES. Within-family prediction and its reduction was attributable in equal parts to genetic nurture and assortative mating. Using polygenic scores from population predictions of 5-8%, we, firstly, showed that cognitive and non-cognitive traits - including scholastic and occupational motivation and aspiration - link genetic scores to occupational status. Second, 62% of the intergenerational transmission of occupational status can be ascribed to non-genetic inheritance (e.g., family environment). Third, the link between genetics, occupation, and health are interrelated with parental occupational status confounding the genetic prediction of general health. Finally, across careers, genetic prediction compresses during mid-career with divergence in status at later stages.

Explore related subjects

geneticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Akimova, E. T., Wolfram, T., Ding, X., Tropf, F. C., Mills, M. C.. 2023-04-03. Genome-wide association study of occupational status and prestige identifies 106 genetic variants and defines their role for intergenerational status transmission and the life course. https://doi.org/10.1101/2023.03.31.534944

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Large language model-based bibliometric evaluation of population descriptors in human genetics

As the use of population descriptors such as race, ethnicity, and ancestry have become increasingly common in modern genetics research, there have been growing calls to critically examine their use. Most notably, in 2023, the National Academies of Science, Engineering, and Medicine (NASEM) published a report titled Using Population Descriptors in Genetics and Genomics Research: A New Framework for an Evolving Field, which included eight specific and actionable recommendations for researchers to implement the ethical and accurate use of population descriptors in genetic research. Here, we use the 2023 NASEM report as a benchmark to analyze the use of population descriptors in genome-wide association studies (GWAS). We develop a general toolkit for large language model-based bibliometrics, operationalize the report's recommendations into an evaluation framework, and apply this framework to evaluate all 4,007 papers from the GWAS Catalog published between 2007 and 2025 with full text available on PubMedCentral. We find significant improvements in adherence to NASEM report recommendations over time. However, most improvements predate the publication of the NASEM report itself, suggesting the report functioned primarily as a synthesis of existing best practices rather than a catalyst for change. We conclude by highlighting opportunities for growth in the field of human genetics.

genetics↗
bioRxiv2026

Mitigating biases of rescaling in forward-in-time population genetic simulations

Forward-in-time population genetic simulations are widely used in evolutionary analyses, but simulating large populations and long genomic regions remains computationally demanding. To reduce this cost, parameter rescaling is widely employed, in which the original evolutionary process is approximated by one with a smaller population size and fewer generations. Recently, several studies using the SLiM simulator have raised concerns about the accuracy of this rescaling approach. In this study, we show that many of the biases reported in these studies can be mitigated by using a different simulation algorithm. These results reveal that the accuracy of parameter rescaling depends on how well the simulation algorithm preserves diffusion-limit properties under rescaling.

genetics↗
bioRxiv2026

OPA1 controls mitochondrial dysfunction-driven liver fibrosis in MASLD

Progressive hepatic fibrosis is the principal determinant of morbidity and mortality in metabolic dysfunction-associated steatotic liver disease and steatohepatitis (MASLD/MASH). Mitochondrial dysfunction is a hallmark of MASH, and the release of mitochondrial damage-associated molecular patterns (mito-DAMPs) from injured hepatocytes can promote fibrosis. However, how mitochondrial dynamics and quality control shape the fibrotic response in MASLD/MASH remains unclear. Here, through large-scale genomic analyses of mitochondrial genes governing mitophagy, fusion and fission in human MASLD, with a power-equivalent sample size of approximately 700,000 individuals, we identify a strong association between hepatic fibrosis and the mitochondrial fusion factor dynamin-like GTPase optic atrophy 1 (OPA1). OPA1 transcripts and protein abundance in the liver epithelium were progressively dysregulated with advancing fibrosis. In mice, hepatocyte-specific OPA1 loss alone was sufficient to induce hepatic stellate cell activation and fibrosis in zone 3, promoted the release of mito-DAMPs into the circulation and exacerbated fibrosis in experimental MASH. These findings identify OPA1 as a central regulator of the hepatic fibrotic response and connect defective mitochondrial homeostasis to mito-DAMP release, hepatic stellate cell activation and fibrosis in MASLD.

genetics↗