bioRxivGeneticsPreprint
Molecular genetic contributions to social deprivation and household income in UK Biobank (n = 112,151)
W David Hill, Saskia P Hagenaars, Riccardo E Marioni, Sarah E Harris, David CM Liewald, Gail DaviesiD, International Consortium for Blood Pressure, Andrew M McIntoshiD, Catharine R Gale, Ian J Deary
Abstract
Individuals with lower socio-economic status (SES) are at increased risk of physical and mental illnesses and tend to die at an earlier age [1-3]. Explanations for the association between SES and health typically focus on factors that are environmental in origin [4]. However, common single nucleotide polymorphisms (SNPs) have been found collectively to explain around 18% (SE = 5%) of the phenotypic variance of an area-based social deprivation measure of SES [5]. Molecular genetic studies have also shown that physical and psychiatric diseases are at least partly heritable [6]. It is possible, therefore, that phenotypic associations between SES and health arise partly due to a shared genetic etiology. We conducted a genome-wide association study (GWAS) on social deprivation and on household income using the 112,151 participants of UK Biobank. We find that common SNPs explain 21% (SE = 0.5%) of the variation in social deprivation and 11% (SE = 0.7%) in household income. Two independent SNPs attained genome-wide significance for household income, rs187848990 on chromosome 2, and rs8100891 on chromosome 19. Genes in the regions of these SNPs have been associated with intellectual disabilities, schizophrenia, and synaptic plasticity. Extensive genetic correlations were found between both measures of socioeconomic status and illnesses, anthropometric variables, psychiatric disorders, and cognitive ability. These findings show that some SNPs associated with SES are involved in the brain and central nervous system. The genetic associations with SES are probably mediated via other partly-heritable variables, including cognitive ability, education, personality, and health.
- organism: human
- assay: GWAS summary statistics
- research focus: GWAS / association mapping
- research focus: fine-mapping
- research focus: polygenic risk
- research focus: eQTL / molecular QTL mapping
- approach: statistical model
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