12th Annual IGSS Conference • October 28-29, 2021

Integrating Genetics and the Social Sciences 2021

First results from a multivariate GWAS on different measures of income among ~756,000 individuals

Hyeokmoon Kweon, Vrije Universiteit Amsterdam

Poverty and economic deprivation are known to be major risk factors for mental and physical diseases as well as for lower life expectancy. Thus, a precise understanding of how income inequalities are related to health inequalities is of fundamental importance for science, social policy, and public health. In 2018, the Social Science Genetic Association Consortium (SSGAC) launched a genome-wide association study (GWAS) meta-analysis on income with the purpose to generate well-powered, publicly available GWAS summary statistics that will provide researchers from various disciplines with new ways to study the causes and consequences of inequalities in wealth and health. Thirty one cohorts from Europe, the US, and Australia joined the project and provided GWAS results on personal income, household income, occupational wages, or parental income from ≈756,000 individuals of European ancestries. The SNP-based narrow-sense heritability of these income measures varies between 4 and 15 percent, partly driven by differences in measurement accuracy. After rigorous quality control, a multi-trait analysis of GWAS summary statistics (MTAG) of all four income measures identified 160 independent genetic loci. All income measures have a high genetic correlation with each other as well as with educational attainment (rg > 0.8). Using Genomic SEM, we show that a common genetic factor underlies the positive genetic correlations between various measures of socio-economic status (SES). We find 989 independent genetic loci that are associated with the SES factor. We also find distinct differences in the genetic architectures of educational attainment (EA) and income EA (residual income after controlling for education). For example, residual income has more positive genetic correlations with delay discounting, extraversion, risk tolerance, alcohol dependence, and cannabis use than EA, as well as a much weaker genetic correlations than EA with IQ and parental life span. Furthermore, the results of our analyses yield one of the most predictive polygenic indices to date.

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