Why it is hard to find genes associated with social science traits: theoretical and empirical considerations

Abstract

Objectives: We explain why traits of interest to behavioral scientists may have a genetic architecture featuring hundreds or thousands of loci with tiny individual effects rather than a few with large effects and why such an architecture makes it difficult to find robust associations between traits and genes.

Methods: We conducted a genome-wide association study at 2 sites, Harvard University and Union College, measuring more than 100 physical and behavioral traits with a sample size typical of candidate gene studies. We evaluated predictions that alleles with large effect sizes would be rare and most traits of interest to social science are likely characterized by a lack of strong directional selection. We also carried out a theoretical analysis of the genetic architecture of traits based on R.A. Fisher's geometric model of natural selection and empirical analyses of the effects of selection bias and phenotype measurement stability on the results of genetic association studies.

Results: Although we replicated several known genetic associations with physical traits, we found only 2 associations with behavioral traits that met the nominal genome-wide significance threshold, indicating that physical and behavioral traits are mainly affected by numerous genes with small effects.

Conclusions: The challenge for social science genomics is the likelihood that genes are connected to behavioral variation by lengthy, nonlinear, interactive causal chains, and unraveling these chains requires allying with personal genomics to take advantage of the potential for large sample sizes as well as continuing with traditional epidemiological studies.

FIGURE 1—

R. A. Fisher’s geometric model of adaptation. Note. A is the current mean phenotype of the population, A′ is the mean phenotype that would result if the mutation denoted by the arrow were to be instantly fixed, and O is the new optimum favored by natural selection. The narrow distribution of trait 1 values around A is the situation that would prevail under strong stabilizing selection, whereas the broad distribution would prevail under weak stabilizing selection. Source. Fisher.

FIGURE 2—

Examples of directed acyclic graphs containing a “collider” (the common effect of 2 or more causes) for (a) 2 uncorrelated causes of a wet driveway that can incorrectly appear to be correlated and (b) situation in which a gene is associated with a trait that is itself a cause of participation in the study. Note. Conditioning on a collider alters the apparent covariation among the causes; for example, 2 independent causes that are uncorrelated when all observations are considered can appear to be negatively correlated when we consider only those observations where the collider assumes a certain value. Source. Pearl.

FIGURE 3—

Effect of the reliability (measurement error; Rho) of a phenotype on the relationship between effect size of a genetic association and the sample size required to achieve 50% statistical power to detect the effect at the genome-wide significance threshold of 5 × 10⁻⁸. Note. For example, if one expects a genotype to explain 0.4% of the variance in a trait (R² = 0.004), then a sample of about 10 000 participants is required to achieve 50% power when reliability is 0.80, but a sample of 20 000 participants is required if reliability is 0.40. That is, with a sample of 20 000 instead of 10 000, instruments that are only one quarter as reliable provide the same power to detect the effect.