Associations between common genetic variants and income provide insights about the socioeconomic health gradient

Philipp D. Koellinger; Hyeokmoon Kweon; Casper Burik; Yuchen Ning; Rafael Ahlskog; Charley Xia; Erik Abner; Yanchun Bao; Laxmi Bhatta; Tariq Faquih; Maud de Feijter; Paul Fisher; Andrea Gelemanović; Alexandros Giannelis; Jouke-Jan Hottenga; Bita Khalili; Yunsung Lee; Ruifang Li-Gao; Jaan Masso; Ronny Myhre; Teemu Palviainen; Cornelius Rietveld; Alexander Teumer; Resnke Verweij; Emily Willoughby; Esben Agerbo; Sven Bergmann; D Boomsma; Anders Borglum; Ben Brumpton; Neil Davies; Tonu Esko; Scott D Gordon; Georg Homuth; M Arfan Ikram; Magnus Johannesson; Jaakko Kaprio; Michael Kidd; Zoltán Kutalik; Alex Kwong; James J. Lee; Annemarie Luik; Per Magnus; Pedro Marques-Vidal; Nicholas G Martin; Dennis Mook-Kanamori; Preben Mortensen; Sven Oskarsson; Emil Pedersen; Ozren Polašek; Frits Rosendaal; Melissa C. Smart; Harold Snieder; Peter J van der Most; Peter Vollenweider; Henry Völzke; Gonneke Willemsen; Jonathan Beauchamp; Thomas DiPrete; Richard Karlsson Linnér; Qiongshi Lu; Tim Morris; Aysu Okbay; K Harden; Abdel Abdellaoui; William Hill; Ronald de Vlaming; Daniel Benjamin

doi:10.21203/rs.3.rs-3782300/v1

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Associations between common genetic variants and income provide insights about the socioeconomic health gradient

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Associations between common genetic variants and income provide insights about the socioeconomic health gradient

Philipp D. Koellinger, Hyeokmoon Kweon, Casper Burik, Yuchen Ning, Rafael Ahlskog, Charley Xia, Erik Abner, Yanchun Bao, Laxmi Bhatta, Tariq Faquih, …

Research Square

2024

DOI: https://doi.org/10.21203/rs.3.rs-3782300/v1

Abstract

Genetic association study

Genome-wide association studies

Biological Sciences

Economics

Genetics

Social Sciences

We conducted a genome-wide association study (GWAS) on income among individuals of European descent and leveraged the results to investigate the socio-economic health gradient (N=668,288). We found 162 genomic loci associated with a common genetic factor underlying various income measures, all with small effect sizes. Our GWAS-derived polygenic index captures 1 - 4% of income variance, with only one-fourth attributed to direct genetic effects. A phenome-wide association study using this polygenic index showed reduced risks for a broad spectrum of diseases, including hypertension, obesity, type 2 diabetes, coronary atherosclerosis, depression, asthma, and back pain. The income factor showed a substantial genetic correlation (0.92, s.e. = .006) with educational attainment (EA). Accounting for EA's genetic overlap with income revealed that the remaining genetic signal for higher income related to better mental health but reduced physical health benefits and increased participation in risky behaviours such as drinking and smoking.

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Title: Associations between common genetic variants and income provide insights about the socioeconomic health gradient
Creators: Philipp D. Koellinger - Vrije Universiteit Amsterdam
Hyeokmoon Kweon - Vrije Universiteit Amsterdam
Casper Burik - Vrije Universiteit Amsterdam
Yuchen Ning - Vrije Universiteit Amsterdam
Rafael Ahlskog - Uppsala University
Charley Xia - University of Edinburgh
Erik Abner - University of Tartu
Yanchun Bao - University of Essex
Laxmi Bhatta - Norwegian University of Science and Technology
Tariq Faquih - Brigham and Women's Hospital
Maud de Feijter - Erasmus MC
Paul Fisher - University of Essex
Andrea Gelemanović - University of Split
Alexandros Giannelis - University of Minnesota
Jouke-Jan Hottenga - Vrije Universiteit Amsterdam
Bita Khalili - University of Lausanne
Yunsung Lee - Norwegian Institute of Public Health
Ruifang Li-Gao - Leiden University Medical Center
Jaan Masso - University of Tartu
Ronny Myhre - Norwegian Institute of Public Health
Teemu Palviainen - University of Helsinki
Cornelius Rietveld - Erasmus University Rotterdam
Alexander Teumer - Universität Greifswald
Resnke Verweij - Erasmus University Rotterdam
Emily Willoughby - University of Minnesota
Esben Agerbo - Aarhus University
Sven Bergmann - University of Lausanne
D Boomsma - Vrije Universiteit Amsterdam
Anders Borglum - Aarhus University
Ben Brumpton - Norwegian University of Science and Technology
Neil Davies - University of Bristol
Tonu Esko - University of Tartu
Scott D Gordon - QIMR Berghofer Medical Research Institute
Georg Homuth - Universität Greifswald
M Arfan Ikram - Erasmus MC
Magnus Johannesson - Stockholm School of Economics, Department of Economics
Jaakko Kaprio - University of Helsinki
Michael Kidd - RMIT University
Zoltán Kutalik - University of Lausanne
Alex Kwong - University of Bristol
James J. Lee - University of Minnesota
Annemarie Luik - Erasmus University Medical Center
Per Magnus - Norwegian Institute of Public Health
Pedro Marques-Vidal - University of Lausanne
Nicholas G Martin - QIMR Berghofer Medical Research Institute
Dennis Mook-Kanamori - Leiden University Medical Center
Preben Mortensen - Aarhus University
Sven Oskarsson - Uppsala University
Emil Pedersen - Aarhus University
Ozren Polašek - University of Split
Frits Rosendaal - Leiden University Medical Center
Melissa C. Smart - University of Essex
Harold Snieder - University Medical Center Groningen
Peter J van der Most - University Medical Center Groningen
Peter Vollenweider - University Hospital of Lausanne
Henry Völzke - Universitätsmedizin Greifswald
Gonneke Willemsen - Vrije Universiteit Amsterdam
Jonathan Beauchamp - George Mason University
Thomas DiPrete - Columbia University
Richard Karlsson Linnér - Leiden University
Qiongshi Lu - University of Wisconsin–Madison
Tim Morris - University of Bristol
Aysu Okbay - Vrije Universiteit Amsterdam
K Harden - The University of Texas at Austin
Abdel Abdellaoui - Vrije Universiteit Amsterdam
William Hill - University of Edinburgh
Ronald de Vlaming - Vrije Universiteit Amsterdam
Daniel Benjamin - University of California Los Angeles
Publisher: Research Square
Number of pages: 31
Copyright: This work is licensed under a CC BY 4.0 License.
Academic Unit: Department of Economics
Language: English
Resource Type: Preprint

Associations between common genetic variants and income provide insights about the socioeconomic health gradient

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