Using genetics for social science

Abstract

Social science genetics is concerned with understanding whether, how and why genetic differences between human beings are linked to differences in behaviours and socioeconomic outcomes. Our review discusses the goals, methods, challenges and implications of this research endeavour. We survey how the recent developments in genetics are beginning to provide social scientists with a powerful new toolbox they can use to better understand environmental effects, and we illustrate this with several substantive examples. Furthermore, we examine how medical research can benefit from genetic insights into social-scientific outcomes and vice versa. Finally, we discuss the ethical challenges of this work and clarify several common misunderstandings and misinterpretations of genetic research on individual differences.

Figure 1.. Genetic correlations of educational attainment with traits across the entire lifespan

Note: Genetic correlations of educational attainment (EA) with 196 traits were computed using bivariate LD-score regression and GWAS summary statistics with varying sample sizes obtained from LDHub (http://ldsc.broadinstitute.org/ldhub/). The Figure only shows a subset of all results for EA that are significant after Bonferroni-correction (p < 0.05/196 = 2.5×10⁻⁴, two-sided tests). Error bars represent 95% confidence intervals. Green and red colors represent positive and negative genetic relationships, respectively, between EA and health.

Figure 2:. The influence of GWAS sample size on the accuracy of polygenic scores for two genetically complex traits with assumed SNP heritability of 20% and 40%

Note: The two solid lines show theoretical expectations assuming 200,000 causal genetic markers for both traits. De Vlaming et al. estimate the SNP heritability of height and years of schooling with 43.3% (SE = 1.8%) and 16.4% (SE = 1.7%), respectively. In practice, the accuracy of polygenic scores depends on many technical parameters such as the number of SNPs in the score and which prediction sample is used. The empirical results for years of schooling in this figure are for PGS based on all HapMap 3 SNPs, using the Swedish Twin Registry as a prediction sample for EA1 and the Health and Retirement Study (HRS) as a prediction sample for EA2 and EA3. The empirical results for height are based on the HRS prediction sample with SNP p-value thresholds of <5×10⁻⁵ for Height1 and <0.001 for Height2. All statistical tests referred to here were 2-sided.

Figure 3:. The relationship between a polygenic score for educational attainment and actual years of schooling in the Health and Retirement Study

Note: The polygenic score for educational attainment (EA) was constructed using GWAS estimates from a sample of N = 1,123,243 individuals. The prediction sample was restricted to individuals of European ancestries (N = 8,638, Health and Retirement Study). EA was measured in typical years of schooling required to obtain the highest academic degree of an individual. EA was residualized for birth year, sex, and the first 10 principal components from the genetic data and then regressed on the polygenic score.

Figure 4.

How medical science can benefit from social science genetics.