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We contend that social science variables are the product of multiple partly heritable traits. Genetic associations with socioeconomic status (SES) may differ across populations, but this is a consequence of the intermediary traits associated with SES differences also varying. Furthermore, genetic data allow social scientists to make causal statements regarding the aetiology and consequences of SES. [ABSTRACT FROM AUTHOR]

Individuals from less advantaged socioeconomic backgrounds will typically have more instances of poor physical and mental health compared to those from more advantaged backgrounds. Understanding the causes of such differences has the potential to decrease health inequalities and improve our understanding of the working of societal risk factors of illnesses. In the current study we examine the role that socioeconomic status (SES) plays on brain structure by performing a multivariate genome-wide association study (GWAS) to capture sources of SES differences that effect the individual, the household, and the area in which one lives. Our GWAS on SES was then used to derive instrumental variables for Mendelian randomisation to examine the potentially causal effect differences in SES have on frank indictors of brain ageing to identify potentially modifiable risk factors causal in brain ageing. First, we perform a common-factor model multi-variate genome-wide association study of four indicators of SES: occupational prestige (OP, n = 279,644), household income (HI, n = 488,233), educational attainment (EA, n = 753,152), and social deprivation (SD, n = 440,350) for an effective size of 893,604 participants. The use of these four indicators of SES in a multivariate framework allows for the assessment of heterogeneous effects across indicators of SES in conjunction with an investigation of common genetic effects that act on the individual, as well as the household, and geographical area in which one resides. Second, to examine the bidirectional causal effects of SES on brain structure we use two-sample MR on 13 brain imaging phenotypes sourced from an independent sample of ∼36,000 UK Biobank participants. Finally, we examine the role of cognitive ability on the links between SES and brain morphology as one of the heritable traits that is captured by GWAS conducted on the general factor of SES and these four indicators. First, we show that our multivariate general genetic factor of SES accounted for on average 76% (range =49%-93%) of the genetic variation found across occupational prestige, household income, educational attainment, and social deprivation. Furthermore, our common factor model showed that the same heritable traits underlie the bulk of the heritable variation in SES across each of the indicators where, of the 469 independent genomic loci identified, only two showed evidence of a heterogenous effect indicated by a significant Q value. Second, using two-sample MR we show that lower levels of the general factor of SES are a risk factor for white matter hyperintensities (β = -0.218, SE = 0.056, P = 8.63 × 10˗5). This effect was also found for each indicator separately. Finally, we show that using multivariable MR to control for the effect of cognitive ability the direct effect of SES on white matter hyperintensities remains (β = -0.182, SE = 0.079, P = 0.022). This study offers new insights into the complex interactions between SES, brain development and the risk factors underlying cognitive decline. Employing modern analytical methods on extensive datasets, the findings contribute to our comprehension of factors that influence physical and mental health. Ultimately, these results could highlight potential modifiable risk factors for maintaining cognitive ability in older-age. [ABSTRACT FROM AUTHOR]

Genome-wide association studies have successfully identified thousands of loci for a range of human complex traits and diseases. The proportion of phenotypic variance explained by significant associations is, however, limited. Given the same dense SNP panels, mixed model analyses capture a greater proportion of phenotypic variance than single SNP analyses but the total is generally still less than the genetic variance estimated from pedigree studies. Combining information from pedigree relationships and SNPs, we examined 16 complex anthropometric and cardiometabolic traits in a Scottish family-based cohort comprising up to 20,000 individuals genotyped for ~520,000 common autosomal SNPs. The inclusion of related individuals provides the opportunity to also estimate the genetic variance associated with pedigree as well as the effects of common family environment. Trait variation was partitioned into SNP-associated and pedigree-associated genetic variation, shared nuclear family environment, shared couple (partner) environment and shared full-sibling environment. Results demonstrate that trait heritabilities vary widely but, on average across traits, SNP-associated and pedigree-associated genetic effects each explain around half the genetic variance. For most traits the recently-shared environment of couples is also significant, accounting for ~11% of the phenotypic variance on average. On the other hand, the environment shared largely in the past by members of a nuclear family or by full-siblings, has a more limited impact. Our findings point to appropriate models to use in future studies as pedigree-associated genetic effects and couple environmental effects have seldom been taken into account in genotype-based analyses. Appropriate description of the trait variation could help understand causes of intra-individual variation and in the detection of contributing loci and environmental factors. [ABSTRACT FROM AUTHOR]

Background: Uncovering the functional relevance underlying verbal declarative memory (VDM) genome-wide association study (GWAS) results may facilitate the development of interventions to reduce age-related memory decline and dementia. Methods: We performed multi-omics and pathway enrichment analyses of paragraph (PAR-dr) and word list (WL-dr) delayed recall GWAS from 29,076 older non-demented individuals of European descent. We assessed the relationship between single-variant associations and expression quantitative trait loci (eQTLs) in 44 tissues and methylation quantitative trait loci (meQTLs) in the hippocampus. We determined the relationship between gene associations and transcript levels in 53 tissues, annotation as immune genes, and regulation by transcription factors (TFs) and microRNAs. To identify significant pathways, gene set enrichment was tested in each cohort and meta-analyzed across cohorts. Analyses of differential expression in brain tissues were conducted for pathway component genes. Results: The single-variant associations of VDM showed significant linkage disequilibrium (LD) with eQTLs across all tissues and meQTLs within the hippocampus. Stronger WL-dr gene associations correlated with reduced expression in four brain tissues, including the hippocampus. More robust PAR-dr and/or WL-dr gene associations were intricately linked with immunity and were influenced by 31 TFs and 2 microRNAs. Six pathways, including type I diabetes, exhibited significant associations with both PAR-dr and WL-dr. These pathways included fifteen MHC genes intricately linked to VDM performance, showing diverse expression patterns based on cognitive status in brain tissues. Conclusions: VDM genetic associations influence expression regulation via eQTLs and meQTLs. The involvement of TFs, microRNAs, MHC genes, and immune-related pathways contributes to VDM performance in older individuals. [ABSTRACT FROM AUTHOR]

A correction to the article "Pedigree- and SNP-Associated Genetics and Recent Environment are the Major Contributors to Anthropometric and Cardiometabolic Trait Variation" that was published in the previous issue of the periodical.