Nature CommunicationsGeneticsPeer-reviewed journal article
Genetic analysis identifies molecular systems and biological pathways associated with household income
William David HilliD, Neil M DaviesiD, Stuart James RitchieiD, Nathan SkeneiD, Julien BryoisiD, Steven BelliD, Emanuele Di AngelantonioiD, David J. RobertsiD, Xueyi SheniD, Gail DaviesiD, David C M LiewaldiD, David John PorteousiD, Caroline HaywardiD, Adam S. ButterworthiD, Andrew Mark McIntoshiD, Catharine R. GaléiD, Ian J. DearyiD
Abstract
Socio-economic position (SEP) is a multi-dimensional construct reflecting (and influencing) multiple socio-cultural, physical, and environmental factors. Previous genome-wide association studies (GWAS) using household income as a marker of SEP have shown that common genetic variants account for 11% of its variation. Here, in a sample of 286,301 participants from UK Biobank, we identified 30 independent genome-wide significant loci, 29 novel, that are associated with household income. Using a recently-developed method to meta-analyze data that leverages power from genetically-correlated traits, we identified an additional 120 income-associated loci. These loci showed clear evidence of functional enrichment, with transcriptional differences identified across multiple cortical tissues, in addition to links with GABAergic and serotonergic neurotransmission. We identified neurogenesis and the components of the synapse as candidate biological systems that are linked with income. By combining our GWAS on income with data from eQTL studies and chromatin interactions, 24 genes were prioritized for follow up, 18 of which were previously associated with cognitive ability. Using Mendelian Randomization, we identified cognitive ability as one of the causal, partly-heritable phenotypes that bridges the gap between molecular genetic inheritance and phenotypic consequence in terms of income differences. Significant differences between genetic correlations indicated that, the genetic variants associated with income are related to better mental health than those linked to educational attainment (another commonly-used marker of SEP). Finally, we were able to predict 2.5% of income differences using genetic data alone in an independent sample. These results are important for understanding the observed socioeconomic inequalities in Great Britain today.
- organism: human
- assay: GWAS summary statistics
- research focus: GWAS / association mapping
- approach: statistical model
- approach: benchmark study
- Citations
- 185
- FWCI
- 11.59
- Field percentile
- top 1%
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Molecular genetic contributions to social deprivation and household income in UK Biobank (n = 112,151)
W David Hill, Saskia P Hagenaars, … Ian J Deary·bioRxiv·Mar 9, 2016
- Associations between common genetic variants and income provide insights about the socioeconomic health gradient
Hyeokmoon Kweon, Casper A.P. Burik, … Philipp Koellinger·bioRxiv·Jan 9, 2024
- A combined analysis of genetically correlated traits identifies 107 loci associated with intelligence
William David Hill, Riccardo Emilio Marioni, … Ian J. Deary·Molecular PsychiatryPeer-reviewed·Jan 11, 2018
- Polygenic prediction of occupational status GWAS elucidates genetic and environmental interplay for intergenerational status transmission, careers, and health
Evelina Tamerlanov Akimova, Tobias Wolfram, … Melinda C Mills·bioRxiv·Mar 31, 2023
- Investigating the Genetic Architecture of Non-Cognitive Skills Using GWAS-by-Subtraction
Perline Demange, Margherita Malanchini, … Michel Guillaume Nivard·Nature GeneticsPeer-reviewed·Jan 1, 2021
- Household income does not affect the pleiotropy of schizophrenia genetic liability with mental and physical health outcomes
Manuela R. Kouakou, Brenda Cabrera‐Mendoza, … Renato A Polimanti·medRxiv·Sep 25, 2023