Person:

Lee, James J.

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.

Background: Individual differences in human cognitive abilities show consistently positive correlations across diverse domains, providing the basis for the trait of “general intelligence” (g). At present, little is known about the evolution of g, in part because most comparative studies focus on rodents or on differences across higher-level taxa. What is needed, therefore, are experiments targeting nonhuman primates, focusing on individual differences within a single species, using a broad battery of tasks. To this end, we administered a large battery of tasks, representing a broad range of cognitive domains, to a population of captive cotton-top tamarin monkeys (Saguinus oedipus). Methodology and Results: Using a Bayesian latent variable model, we show that the pattern of correlations among tasks is consistent with the existence of a general factor accounting for a small but significant proportion of the variance in each task (the lower bounds of 95% Bayesian credibility intervals for correlations between g and task performance all exceed 0.12). Conclusion: Individual differences in cognitive abilities within at least one other primate species can be characterized by a general intelligence factor, supporting the hypothesis that important aspects of human cognitive function most likely evolved from ancient neural substrates.