领域：学术论文 · 自然科学

Genome-wide analysis identifies molecular systems and 149 genetic loci associated with income
Abstract
Socioeconomic position (SEP) is a multi-dimensional construct reflecting (and influencing) multiple socio-cultural, physical, and environmental factors. In a sample of 286,301 participants from UK Biobank, we identify 30 (29 previously unreported) independent-loci associated with income. Using a method to meta-analyze data from genetically-correlated traits, we identify an additional 120 income-associated loci. These loci show clear evidence of functionality, with transcriptional differences identified across multiple cortical tissues, and links to GABAergic and serotonergic neurotransmission. By combining our genome wide association study on income with data from eQTL studies and chromatin interactions, 24 genes are prioritized for follow up, 18 of which were previously associated with intelligence. We identify intelligence as one of the likely causal, partly-heritable phenotypes that might bridge the gap between molecular genetic inheritance and phenotypicconsequence in terms of income differences. These results indicate that, in modern era Great Britain, genetic effects contribute towards some of the observed socioeconomic inequalities.
Introduction
People with advantaged socioeconomic backgrounds, on average, live longer, and have better mental and physical health than those from more deprived environments. An understanding of the causes underlying the association between socioeconomic position (SEP) and health is likely to be helpful to minimize social disparities in health and well-being.
The link between SEP and health is typically thought to be due to environmental factors, including, but not limited to access to resources, exposure to harmful or stressful environments, adverse health behaviours, such as smoking, poor diet and excessive alcohol consumption, and a lack of physical exercise5. However, genetic factors (most likely via mediated pleiotropy, Fig. 1) have been discussed as a partial explanation for the SEP–health gradient; for example, genetic predispositions towards certain diseases and/or genetic influences on what foods people like, could lead to poor diet, which in turn could lead to both lower SEP and poorer health6. It has recently been demonstrated that genome-wide association studies (GWASs) can capture shared genetic associations between measures of health and SEP7. Potential pleiotropic effects are highlighted in the observed genetic correlations between SEP variables, such as years of education completed, household income and social deprivation, and physical and mental health traits including longevity.
Loci associated with two SEP phenotypes, education and household income, have been identified via GWASs7,9,10,11, but—consistent with other complex traits, such as height—these loci collectively account for only a small fraction of the total heritability of the traits in question. For household income, an analysis of a sample of 96,900 individuals from Great Britain found that additive genetic effects tagged by common single-nucleotide polymorphisms (SNPs) accounted for ~11% (SE = 0.7%) of differences in household income7. Two loci attained genome-wide significance in that study, but they collectively accounted for <0.005% of the total SNP heritability.
Here, we use the UK Biobank data set12 to examine genetic associations with household income (n = 286,301) in a contemporary British sample. We identify 30 independent genome-wide significant loci, 29 of which are unreported in previous work. Using a method that leverages power from genetically correlated traits, multi-trait analysis of GWAS (MTAG), an additional 120 loci are found to be associated with income. We identify neurogenesis and the components of the synapse as being associated with income. Furthermore, we link transcription differences across multiple cortical tissue types, as well as both GABAergic and serotonergic neurotransmission, to income differences. We also show that the genes linked to differences in income are predominantly those that have been previously linked with intelligence8, and that intelligence is one of the likely causal factors leading to differences in income. We compare the genetic correlations derived using income with those derived using another measure of SEP, educational attainment, to show that the genetic variants associated with income are related to better mental health than those related to education. Finally, we predict 2.5% of income differences using genetic data alone in an independent sample.
Results
Graphical representation of statistical analysis
A flow chart summarizing all statistical analyses conducted is displayed in Fig. 2.
SNP-based analysis of income
For household income, 3712 SNPs attained genome-wide significance (P < 5 × 10−8), across 30 independent loci (Fig. 3a and Supplementary Data 1), which contained 68 independent significant SNPs and 31 lead SNPs. A total of 29 of these 30 loci were not identified in the previous UK Biobank analysis of income (Supplementary Data 2). The 30 loci predominantly contained SNPs found within intronic regions (47%) as well as non-coding RNA introns (29%). A total of 17% of the SNPs within the independent loci were found in intergenic regions, and only 1.2% were found in exons (Fig. 3b). Many of the loci contained SNPs showing evidence of influencing gene regulation with 33% having a Regulome DB score of <2 (Fig. 3c) and 86% having evidence of being in an open-chromatin state (indicated by a minimum chromatin state of <8, in Fig. 3d). Additionally, these loci were linked to intelligence (11 loci), mental health (schizophrenia, 1 locus; bipolar disorder, 2 loci; neuroticism, 4 loci) and neurological variables (corticobasal degeneration, 1 locus; subcortical brain volumes, 1 locus; and Parkinson’s disease, 1 locus) (Supplementary Data 3).
Linkage disequilibrium score (LDSC) regression showed that the mean χ2 statistic was 1.45 and the intercept of the LDSC regression was 1.04. These statistics indicate that around 92% of the inflation in the GWAS test statistics was due to a polygenic signal rather than residual stratification or confounding. The LDSC regression estimate of the heritability of household income was 7.39% (SE = 0.33%).
Gene prioritization
Three methods of mapping allelic variation to genes were used to better understand the functional consequences of the 30 independent loci linked to income (positional mapping, expression quantitative trait loci (eQTL) analysis and chromatin mapping). Using positional mapping, SNPs from the GWAS were aligned to 117 genes. eQTL mapping was used to match cis-eQTL SNPs to 186 genes, and chromatin interaction mapping linked the SNPs to 277 genes (Fig. 3e and Supplementary Data 4 and Supplementary Fig. 1). These mapping strategies identified a total of 400 unique genes, of which 133 (Fig. 3e cells 14 + 23 + 26 + 3 + 24 + 11 + 2 + 30) were implicated by at least two mapping strategies and 47 (Fig. 3e cells 23 + 24) were implicated by all three. Of the 133 implicated by two mapping strategies, two showed evidence of a chromatin interactions with two independent genomic risk loci (Supplementary Data 5). Both HOXB2 and HOXB7 showed interactions with loci 24 and loci 25. HOXB2 showed interactions in mesendoderm (an embryonic tissue layer) tissue and IMR90 (foetal lung fibroblasts)tissue, whereas HOXB7 showed associations in the tissues of hESC (human embryonic stem cell), mesenchymal (multipotent stromal cells which differentiate into a variety of different cell types) stem cell, IMR90, left ventricle, GM12878, and trophoblast-like cells.

翻译考点：
考点1： "genetic loci"应该译为“基因座”。
考点2： "meta-analyze"应译为“统合分析”。
考点3： "GABAergic '应译为“迦瑪胺基丁酸”。
考点4：" eQTL" 应该译为“表达数量性状位点”。
考点5：" pleiotropy'应译为“基因多效性”。
考点6： "synapse'应译为“突触”。
考点7："exons'应译为“外显子”。
考点8："RNA'应译为“核糖核酸”。
考点9："HOXB2"应译为“同源核基因B2”。
考点10："Linkage disequilibrium score"应译为“连锁不平衡分数”。 
考点11：'foetal lung fibroblasts"应译为“胎儿肺成纤维细胞 ”，其中foetal应该是拼写错误。