摘要：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. People differ in their intelligence, personality, and behavior, and a century of research in behavioral genetics has left little doubt that some of this variation is caused by differences in their genomes. 1–3 Nonzero (and sometimes substantial) heritability of psychological traits has been consistently established in twin, adoption, and family studies that have often had very large sample sizes. Beyond establishing that genes matter, however, such studies have said little about the detailed genetic architecture of psychological traits, that is, how many genetic polymorphisms affect a trait, how the polymorphisms interact, what they are, and what they do. The recent advent of affordable genome-wide association studies (GWAS) offers the exciting opportunity to understand the genetic factors that influence psychological trait variation with far greater precision. GWAS have the potential to uncover some of a given trait’s genetic architecture, including the number, genomic locations, average effects, and allele frequencies of the DNA variants that affect the trait. Even an incomplete understanding of a trait’s genetic architecture could prove a boon to social scientists for at least 4 reasons. 4 First, the presence of genetic variants can be detected with high reliability. Thus, they may constitute direct measures of constructs that were previously regarded as only latent. For example, some evidence has shown that a person’s genotype for the single-nucleotide polymorphism (SNP) in FTO associated with body mass index (BMI; defined as weight in kilograms divided by the square of height in meters) may indicate a preference for certain kinds of high-calorie foods, 5 and one might speculate that other genes may affect how much body weight is produced from a person’s caloric intake. These genetic variants could then be used as variables of interest, or as controls, in testing models of the causation of obesity that formerly could only appeal loosely to genetic factors. Second, the discovery of genetic associations may identify or clarify the actual biological mechanisms that underlie social and health behaviors. For example, a mechanistic role for the hormone oxytocin in trust-related behavior has been suggested by findings that variants in the oxytocin receptor gene ( OXTR ) are associated with differences in performance in a behavioral–economic trust game (albeit with mixed results so far). 6,7 Also, just as in medicine, for which genetic discoveries have suggested new pathways for understanding and treating disease (e.g., Crohn’s disease 8 ), genetic discoveries may help social scientists decompose crude concepts such as risk aversion and time preference, both of which play roles in health behaviors, into biologically meaningful subcomponents. Third, under very special circumstances, genetic variants could be used as instrumental variables that would identify causal relationships from nonexperimental data. For such analysis to be valid, the allele must reliably and exclusively affect a specific biological trait (and no other biological traits). If these strong conditions are met, then one can use the random assignment (during meiosis) of each person’s genotype at that allele as a natural experiment to test the hypothesis that the biological trait, in turn, causes variation in some behavioral phenotype. For example, Chen et al. 9 showed that SNPs in ALDH2 that are known to increase alcohol metabolism are associated with decreased blood pressure, thus providing evidence that alcohol consumption in fact causes an increase in blood pressure—under the crucial, and perhaps implausible, condition that those SNPs are assumed not to also affect blood pressure through some other channel. Other studies of this type have been published, 10 but it seems likely that the circumstances in which the instrumental variable approach can work are rare. Fourth, knowledge of individuals’ genotypes could help in targeting social science interventions to those who stand to benefit from them the most—an application of concepts from personalized medicine to public health and policy. Such a benefit is particularly likely to help children because their abilities and preferences are less developed and harder to measure. For example, children with genotypes that confer a susceptibility to dyslexia might be offered personalized educational resources from a very early age. The leap in precision from GWAS compared with twin studies promises to help not just working social and behavioral scientists but anyone interested in the evolutionary history and adaptive pressures that shaped the human species and its variation. Not only does an individual’s genome provide a partial recipe for the development of his or her unique phenome (set of phenotypes) forward in time, but our species’ array of genomic data provides a trace of our collective evolutionary history backward in time. For example, once it was discovered that mutations in the gene FOXP2 could cause a severe developmental deficit in speech and language, comparative genomic analyses showed that this gene’s sequence had changed at least twice since the separation of humans and chimpanzees from their common ancestor and that these changes predate the separation of humans from Neanderthals—all relevant to venerable and hitherto nearly unresolvable debates on the evolution of language.
