| Title: | Exome sequencing identifies genes for socioeconomic status in 350,770 individuals |
| Journal: | Proceedings of the National Academy of Sciences of the United States of America |
| Published: | 7 Jan 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39772748/ |
| DOI: | https://doi.org/10.1073/pnas.2414018122 |
Publication 14554
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Abstract
Socioeconomic status (SES) is a critical factor in determining health outcomes and is influenced by genetic and environmental factors. However, our understanding of the genetic structure of SES remains incomplete. Here, we conducted a large-scale exome study of SES markers (household income, occupational status, educational attainment, and social deprivation) in 350,770 individuals. For rare coding variants, we identified 56 significant associations by gene-based collapsing tests, unveiling 7 additional SES-associated genes (NRN1, CCDC36, RHOB, EP400, NCAM1, TPTEP2-CSNK1E, and LINC02881). Exome-wide single common variant analysis revealed nine lead single-nucleotide polymorphisms (SNPs) associated with household income and 34 lead SNPs associated with EduYears, replicating previous GWAS findings. The gene-environment correlations had a substantial impact on the genetic associations with SES, as indicated by the significantly increased P values in several associations after controlling for geographic regions. Furthermore, we observed the pleiotropic effects of SES-associated genetic factors on a wide range of health outcomes, such as cognitive function, psychosocial status, and diabetes. This study highlights the contribution of coding variants to SES and their associations with health phenotypes.</p>
10 Keywords
- Adult
- Exome
- Exome Sequencing
- Female
- Genome-Wide Association Study
- Humans
- Male
- Middle Aged
- Polymorphism, Single Nucleotide
- Social Class
11 Authors
- Xin-Rui Wu
- Liu Yang
- Bang-Sheng Wu
- Wei-Shi Liu
- Yue-Ting Deng
- Ju-Jiao Kang
- Qiang Dong
- Barbara J. Sahakian
- Jian-Feng Feng
- Wei Cheng
- Jin-Tai Yu
1 Application
| Application ID | Title |
|---|---|
| 19542 | Identifying multi-level biomarkers and disease mechanisms for major mental disorders |
Enabling scientific discoveries that improve human health