The role of common genetic variation in educational attainment and income: evidence from the National Child Development Study

Education plays a crucial role in the pathway from poverty to smoking: a Mendelian randomization study

BACKGROUND AND AIMS

Disproportionately high rates of smoking have been found in low-income communities, but the causal direction and role of education in this relationship remains less well understood. Here, we used bidirectional Mendelian randomization (MR) to measure the causal relationships between smoking, income and education.

DESIGN

Two-sample univariable and multivariable MR analyses were conducted to evaluate the total and direct effect of income and education on tobacco smoking. The effects of smoking on education and income were explored with reverse MR analysis.

SETTING

European ancestry.

PARTICIPANTS

The most recent large-scale genome-wide association study (GWAS) summary data on educational attainment, household income and smoking (n = 143 210-766 345).

MEASUREMENTS

Genetic variants for exposures including income, education and smoking.

FINDINGS

Both income and education had protective effects against smoking, especially for smoking initiation (education: β = -0.447, 95% CI = -0.508 to -0.387, P < 0.001; income: β = -0.290, 95% CI = -0.43 to -0.149, P < 0.001) and cessation (education: β = -0.364, 95% CI = -0.429 to -0.298, P < 0.001; income: β = -0.323, 95% CI = -0.448 to -0.197, P < 0.001). Here, higher scores in cessation indicated a lower likelihood of quitting according to the coding scheme. There was little evidence that income influenced smoking once education was controlled for, whereas education could significantly affect smoking behaviours independently of income (P = 3.40 × 10^-10 -0.0272). The reverse MR results suggested that smoking may result in a loss of years of schooling (β = -0.190, 95% CI = -0.297 to -0.083, P < 0.001) and reduced earnings (β = -0.204, 95% CI = -0.347 to -0.060, P = 0.006).

CONCLUSIONS

Education appears to play an important role in the relationship between income and smoking. There is a bidirectional association of smoking with socioeconomic status.

The Impact of Genes and Environment on Brain Ageing in Males Aged 51 to 72 Years

Magnetic resonance imaging data are being used in statistical models to predicted brain ageing (PBA) and as biomarkers for neurodegenerative diseases such as Alzheimer's Disease. Despite their increasing application, the genetic and environmental etiology of global PBA indices is unknown. Likewise, the degree to which genetic influences in PBA are longitudinally stable and how PBA changes over time are also unknown. We analyzed data from 734 men from the Vietnam Era Twin Study of Aging with repeated MRI assessments between the ages 51-72 years. Biometrical genetic analyses "twin models" revealed significant and highly correlated estimates of additive genetic heritability ranging from 59 to 75%. Multivariate longitudinal modeling revealed that covariation between PBA at different timepoints could be explained by a single latent factor with 73% heritability. Our results suggest that genetic influences on PBA are detectable in midlife or earlier, are longitudinally very stable, and are largely explained by common genetic influences.

The social and genetic inheritance of educational attainment: Genes, parental education, and educational expansion

Recently, several genome-wide association studies of educational attainment have found education-related genetic variants and enabled the integration of human inheritance into social research. This study incorporates the newest education polygenic score (Lee et al., 2018) into sociological research, and tests three gene-environment interaction hypotheses on status attainment. Using the Health and Retirement Study (N = 7599), I report three findings. First, a standard deviation increase in the education polygenic score is associated with a 58% increase in the likelihood of advancing to the next level of education, while a standard deviation increase in parental education results in a 53% increase. Second, supporting the Saunders hypothesis, the genetic effect becomes 11% smaller when parental education is one standard deviation higher, indicating that highly educated parents are more able to preserve their family's elite status in the next generation. Finally, the genetic effect is slightly greater for the younger cohort (1942-59) than the older cohort (1920-41). The findings strengthen the existing literature on the social influences in helping children achieve their innate talents.

Stable longitudinal associations of family income with children's hippocampal volume and memory persist after controlling for polygenic scores of educational attainment

Despite common notion that the correlation of socioeconomic status with child cognitive performance may be driven by both environmentally- and genetically-mediated transactional pathways, there is a lack of longitudinal and genetically informed research that examines these postulated associations. The present study addresses whether family income predicts associative memory growth and hippocampal development in middle childhood and tests whether these associations persist when controlling for DNA-based polygenic scores of educational attainment. Participants were 142 6-to-7-year-old children, of which 127 returned when they were 8-to-9 years old. Longitudinal analyses indicated that the association of family income with children's memory performance and hippocampal volume remained stable over this age range and did not predict change. On average, children from economically disadvantaged background showed lower memory performance and had a smaller hippocampal volume. There was no evidence to suggest that differences in memory performance were mediated by differences in hippocampal volume. Further exploratory results suggested that the relationship of income with hippocampal volume and memory in middle childhood is not primarily driven by genetic variance captured by polygenic scores of educational attainment, despite the fact that polygenic scores significantly predicted family income.

Genetically modulated educational attainment and coronary disease risk

AIMS

Genetic disposition and lifestyle factors are understood as independent components underlying the risk of multiple diseases. In this study, we aim to investigate the interplay between genetics, educational attainment-an important denominator of lifestyle-and coronary artery disease (CAD) risk.

METHODS AND RESULTS

Based on the effect sizes of 74 genetic variants associated with educational attainment, we calculated a 'genetic education score' in 13 080 cases and 14 471 controls and observed an inverse correlation between the score and risk of CAD [P = 1.52 × 10-8; odds ratio (OR) 0.79, 95% confidence interval (CI) 0.73-0.85 for the higher compared with the lowest score quintile]. We replicated in 146 514 individuals from UK Biobank (P = 1.85 × 10-6) and also found strong associations between the 'genetic education score' with 'modifiable' risk factors including smoking (P = 5.36 × 10-23), body mass index (BMI) (P = 1.66 × 10-30), and hypertension (P = 3.86 × 10-8). Interestingly, these associations were only modestly attenuated by adjustment for years spent in school. In contrast, a model adjusting for BMI and smoking abolished the association signal between the 'genetic education score' and CAD risk suggesting an intermediary role of these two risk factors. Mendelian randomization analyses performed with summary statistics from large genome-wide meta-analyses and sensitivity analysis using 1271 variants affecting educational attainment (OR 0.68 for the higher compared with the lowest score quintile; 95% CI 0.63-0.74; P = 3.99 × 10-21) further strengthened these findings.

CONCLUSION

Genetic variants known to affect educational attainment may have implications for a health-conscious lifestyle later in life and subsequently affect the risk of CAD.

Testing the validity of value-added measures of educational progress with genetic data

Value-added measures of educational progress have been used by education researchers and policy-makers to assess the performance of teachers and schools, contributing to performance-related pay and position in school league tables. They are designed to control for all underlying differences between pupils and should therefore provide unbiased measures of school and teacher influence on pupil progress, however, their effectiveness has been questioned. We exploit genetic data from a UK birth cohort to investigate how successfully value-added measures control for genetic differences between pupils. We use raw value-added, contextual value-added (which additionally controls for background characteristics) and teacher-reported value-added measures built from data at ages 11, 14 and 16. Sample sizes for analyses range from 4,600 to 6,518. Our findings demonstrate that genetic differences between pupils explain little variation in raw value-added measures but explain up to 20% of the variation in contextual value-added measures (95% CI = 6.06% to 35.71%). Value-added measures built from teacher-rated ability have a greater proportion of variance explained by genetic differences between pupils, with 36.3% of their cross-sectional variation being statistically accounted for by genetics (95% CI = 22.8% to 49.8%). By contrast, a far greater proportion of variance is explained by genetic differences for raw test scores at each age of at least 47.3% (95% CI: 35.9 to 58.7). These findings provide evidence that value-added measures of educational progress can be influenced by genetic differences between pupils, and therefore may provide a biased measure of school and teacher performance. We include a glossary of genetic terms for educational researchers interested in the use of genetic data in educational research.

Widespread covariation of early environmental exposures and trait-associated polygenic variation

Although gene-environment correlation is recognized and investigated by family studies and recently by SNP-heritability studies, the possibility that genetic effects on traits capture environmental risk factors or protective factors has been neglected by polygenic prediction models. We investigated covariation between trait-associated polygenic variation identified by genome-wide association studies (GWASs) and specific environmental exposures, controlling for overall genetic relatedness using a genomic relatedness matrix restricted maximum-likelihood model. In a UK-representative sample (n = 6,710), we find widespread covariation between offspring trait-associated polygenic variation and parental behavior and characteristics relevant to children's developmental outcomes-independently of population stratification. For instance, offspring genetic risk for schizophrenia was associated with paternal age (R² = 0.002; P = 1e-04), and offspring education-associated variation was associated with variance in breastfeeding (R² = 0.021; P = 7e-30), maternal smoking during pregnancy (R² = 0.008; P = 5e-13), parental smacking (R² = 0.01; P = 4e-15), household income (R² = 0.032; P = 1e-22), watching television (R² = 0.034; P = 5e-47), and maternal education (R² = 0.065; P = 3e-96). Education-associated polygenic variation also captured covariation between environmental exposures and children's inattention/hyperactivity, conduct problems, and educational achievement. The finding that genetic variation identified by trait GWASs partially captures environmental risk factors or protective factors has direct implications for risk prediction models and the interpretation of GWAS findings.

Predicting educational achievement from DNA

A genome-wide polygenic score (GPS), derived from a 2013 genome-wide association study (N=127,000), explained 2% of the variance in total years of education (EduYears). In a follow-up study (N=329,000), a new EduYears GPS explains up to 4%. Here, we tested the association between this latest EduYears GPS and educational achievement scores at ages 7, 12 and 16 in an independent sample of 5825 UK individuals. We found that EduYears GPS explained greater amounts of variance in educational achievement over time, up to 9% at age 16, accounting for 15% of the heritable variance. This is the strongest GPS prediction to date for quantitative behavioral traits. Individuals in the highest and lowest GPS septiles differed by a whole school grade at age 16. Furthermore, EduYears GPS was associated with general cognitive ability (~3.5%) and family socioeconomic status (~7%). There was no evidence of an interaction between EduYears GPS and family socioeconomic status on educational achievement or on general cognitive ability. These results are a harbinger of future widespread use of GPS to predict genetic risk and resilience in the social and behavioral sciences.

How well can we predict educational outcomes? Examining the roles of cognitive ability and social position in educational attainment

Social inequalities in UK educational outcomes continue to persist despite improvements in recent years. However, studies that examine these inequalities fail to account for differences in prior cognitive ability. We seek to determine the influence of cognitive ability on educational outcomes and the extent of socio-economic disparities in education across a wide range of indicators while accounting for cognitive ability. Social inequalities exist whereby children from disadvantaged backgrounds systematically underperform compared to their advantaged peers regardless of cognitive ability; high ability children from disadvantaged backgrounds are disproportionately less likely to attain good grades compared to children from advantaged backgrounds. In addition, school effects operate to add to this inequality as children in fee-paying secondary schools outperform their state secondary school counterparts regardless of ability. Future UK policies should focus on reducing social inequality in education to ensure that all children are offered the same life chances regardless of background.