Genetics of environmental sensitivity to psychiatric and neurodevelopmental phenotypes: evidence from GWAS of monozygotic twins.

Individual sensitivity to environmental exposures may be genetically influenced. This genotype-by-environment interplay implies differences in phenotypic variance across genotypes. However, environmental sensitivity genetic variants have proven challenging to detect. GWAS of monozygotic twin differences is a family-based variance analysis method, which is more robust to systemic biases that impact population-based methods. We combined data from up to 21,792 monozygotic twins (10,896 pairs) from 11 studies to conduct the largest GWAS meta-analysis of monozygotic phenotypic differences in children and adolescents/adults for seven psychiatric and neurodevelopmental phenotypes: attention deficit hyperactivity disorder (ADHD) symptoms, autistic traits, anxiety and depression symptoms, psychotic-like experiences, neuroticism, and wellbeing. The SNP-heritability of variance in these phenotypes were estimated (h2: 0% to 18%), but were imprecise. We identified a total of 13 genome-wide significant associations (SNP, gene, and gene-set), including genes related to stress-reactivity for depression, growth factor-related genes for autistic traits and catecholamine uptake-related genes for psychotic-like experiences. Monozygotic twins are an important new source of evidence about the genetics of environmental sensitivity.

Uncovering the Source of Patrimonial Voting: Evidence from Swedish Twin Pairs.

The boom in wealth inequality seen in recent decades has generated a steep rise in scholarly interest in both the drivers and the consequences of the wealth gap. In political science, a pertinent question regards the political behavior across the wealth spectrum. A common argument is that the wealthy practice patrimonial voting, i.e. voting for right-wing parties to maximize returns on their assets. While this pattern is descriptively well documented, it is less certain to what extent this reflects an actual causal relationship between wealth and political preferences. In this study, we provide new evidence by exploiting wealth variation within identical twin pairs. Our findings suggest that while more wealth is descriptively connected to more support for right-wing parties, the causal impact of wealth on policy preferences is likely highly overstated. For several relevant policy areas these effects may not exist at all. Furthermore, the bias in naive observational estimates seems to be mainly driven by environmental familial confounders shared within twin pairs, rather than genetic confounding. Supplementary information: The online version of this article (10.1007/s11109-020-09669-4) contains supplementary material, which is available to authorized users.

Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals.

We conduct a genome-wide association study (GWAS) of educational attainment (EA) in a sample of ~3 million individuals and identify 3,952 approximately uncorrelated genome-wide-significant single-nucleotide polymorphisms (SNPs). A genome-wide polygenic predictor, or polygenic index (PGI), explains 12-16% of EA variance and contributes to risk prediction for ten diseases. Direct effects (i.e., controlling for parental PGIs) explain roughly half the PGI's magnitude of association with EA and other phenotypes. The correlation between mate-pair PGIs is far too large to be consistent with phenotypic assortment alone, implying additional assortment on PGI-associated factors. In an additional GWAS of dominance deviations from the additive model, we identify no genome-wide-significant SNPs, and a separate X-chromosome additive GWAS identifies 57.

Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects.

Estimates from genome-wide association studies (GWAS) of unrelated individuals capture effects of inherited variation (direct effects), demography (population stratification, assortative mating) and relatives (indirect genetic effects). Family-based GWAS designs can control for demographic and indirect genetic effects, but large-scale family datasets have been lacking. We combined data from 178,086 siblings from 19 cohorts to generate population (between-family) and within-sibship (within-family) GWAS estimates for 25 phenotypes. Within-sibship GWAS estimates were smaller than population estimates for height, educational attainment, age at first birth, number of children, cognitive ability, depressive symptoms and smoking. Some differences were observed in downstream SNP heritability, genetic correlations and Mendelian randomization analyses. For example, the within-sibship genetic correlation between educational attainment and body mass index attenuated towards zero. In contrast, analyses of most molecular phenotypes (for example, low-density lipoprotein-cholesterol) were generally consistent. We also found within-sibship evidence of polygenic adaptation on taller height. Here, we illustrate the importance of family-based GWAS data for phenotypes influenced by demographic and indirect genetic effects.

Resource profile and user guide of the Polygenic Index Repository.

Polygenic indexes (PGIs) are DNA-based predictors. Their value for research in many scientific disciplines is growing rapidly. As a resource for researchers, we used a consistent methodology to construct PGIs for 47 phenotypes in 11 datasets. To maximize the PGIs' prediction accuracies, we constructed them using genome-wide association studies-some not previously published-from multiple data sources, including 23andMe and UK Biobank. We present a theoretical framework to help interpret analyses involving PGIs. A key insight is that a PGI can be understood as an unbiased but noisy measure of a latent variable we call the 'additive SNP factor'. Regressions in which the true regressor is this factor but the PGI is used as its proxy therefore suffer from errors-in-variables bias. We derive an estimator that corrects for the bias, illustrate the correction, and make a Python tool for implementing it publicly available.

Moral Enhancement Should Target Self-Interest and Cognitive Capacity.

Current suggestions for capacities that should be targeted for moral enhancement has centered on traits like empathy, fairness or aggression. The literature, however, lacks a proper model for understanding the interplay and complexity of moral capacities, which limits the practicability of proposed interventions. In this paper, I integrate some existing knowledge on the nature of human moral behavior and present a formal model of prosocial motivation. The model provides two important results regarding the most friction-free route to moral enhancement. First, we should consider decreasing self-interested motivation rather than increasing prosociality directly. Second, this should be complemented with cognitive enhancement. These suggestions are tested against existing and emerging evidence on cognitive capacity, mindfulness meditation and the effects of psychedelic drugs and are found to have sufficient grounding for further theoretical and empirical exploration. Furthermore, moral effects of the latter two are hypothesized to result from a diminished sense of self with subsequent reductions in self-interest.