Genetic Overlap Between Education, SES, and Psychopathology

Post by Anna Cranston

What's the science?

Socioeconomic status (SES) and education are known to be associated with psychiatric disorders and behaviors. However, it’s not yet clear exactly how these associations are related to our genetic risk as individuals. One way to understand this is through the use of genome-wide association studies (GWAS), which are essentially large-scale observational studies that scan the genomes from a large human population to identify potential genetic differences that might be associated with a particular trait or disease. This week in Nature Human Behaviour, Wendt and colleagues used GWAS data to determine potential genetic links across numerous psychiatric and brain traits, from depression and schizophrenia to brain volumetric changes, and how these could be potentially influenced by social factors such as our education, income or social status.

How did they do it?

The authors selected four educational factors (educational attainment, highest math class, self-rated math ability, and cognitive performance), two SES factors (household income and Townsend deprivation index), and GWAS data for many psychopathology and psychosocial factors (available from previous studies). These educational and SES factors were used to identify potential genetic variance that might lead to an increased or decreased susceptibility to particular psychological traits, such as depression, bipolar disorder, or schizophrenia. The authors used the presence of single nucleotide polymorphisms (SNPs) to identify the incidence of pleiotropy (i.e. when one gene affects multiple traits) in the sample group, and this method was used to determine genetic correlations between their selected psychological phenotypes. Since there is undoubtedly a lot of overlap in the genetic factors underlying SES/education, psychopathology, and psychosocial factors, the authors chose to use a specific statistical model, known as multi-trait conditioning and joint analysis. This model applies Mendelian randomization to disentangle this genetic overlap between traits, revealing associations that control for the genetic variance attributed to SES and educational factors. They also used an algorithm known as the linkage disequilibrium score regression (LDSC) which they used to estimate the SNP heritability of a trait. The authors then used transcriptomic profile analysis against each of the social factors to determine if there were tissue or cell-specific genetic variances between these factors, in order to ultimately pinpoint the exact genetic variation that determines these particular psychological traits in individuals. 

What did they find?

The authors found that specific genetic variance in SNPs was associated with both psychological traits including alcohol dependence, schizophrenia, and neuroticism as well as social factors such as education, income, and deprivation. For instance, a lower income was found to be highly genetically correlated with a higher deprivation index and a higher incidence of disorders such as ADHD, depression, and alcohol dependence. The group also found that genetic liability to a lower deprivation index (a measure of material deprivation) was associated with a significant increase in cortical grey matter. Education and SES phenotypes were found to be genetically correlated with neuroticism. When these relationships were controlled for, the heritability of neuroticism (specifically, the number of heritable components) increased.

Transcriptomic profile analysis revealed that different psychological phenotypes resulted in differences in cortical and cerebellar tissue phenotypes. Their findings showed that better cognitive performance and higher education are genetically correlated with increased cortical and hippocampal, cerebellar, and frontal cortex enrichment. The authors also identified key genetic correlations with specific psychological traits. They found that conditioning for genetic effects associated with education and SES factors uncovered mechanisms related to excitatory neuronal cell types for bipolar disorder and schizophrenia. Further, inhibitory GABAergic cell types were correlated with an increased incidence of risky behaviors in individuals. These findings suggest that while individuals may be genetically predisposed to certain psychological disorders, their risk may be significantly moderated by social factors such as education and socioeconomic factors such as income and deprivation.

What's the impact?

This study identified specific genetic variation underpinning both psychopathological and psychosocial traits. The authors’ findings have identified the underlying genetic variation that is shared between these psychological disorders, as well as novel tissue and cell-specific variation within each of these psychological groups. These findings highlight the importance of specific brain regions and their shared transcriptional regulation in human mental health and disease, which may provide future insight into the biological basis of these complex psychological disorders.

Wendt et al. Multivariate genome-wide analysis of education, socioeconomic status and brain phenome (2020). Access to the original scientific publication here.