Genetic influences on social network characteristics.

W ho becomes the most central individual in a society and why? What determines how many friends a given individual has? What determines how clustered or tightly-knit the friendships in a society are? In a set of important and original new findings reported in this issue of PNAS, Fowler, Dawes, and Christakis (1) provide evidence that network characteristics such as those mentioned above are heritable: that is, they show that an increase in the overlap in genetic material in twins corresponds to an increase in the covariation of some of their social network characteristics. The heritability of network characteristics is important because of its implications for how networks form. Given that social networks play important roles in determining a wide variety of things ranging from employment and wages to the spread of disease (2–4), it is important to understand why networks exhibit the patterns that they do. Although it is well established that personal characteristics and behaviors play critical roles in determining who interacts with whom (5, 6), Fowler et al.’s analysis (1) suggests that genetic traits may influence individuals in terms of their social behavior, for instance, by having genetic predispositions regarding things like the tendency of an individual to introduce his or her friends to each other. As a basis for a more detailed discussion, let me start with a brief overview of their analysis. Fowler et al. (1) examine the social network characteristics of 1,110 twins from the Adolescent Health Dataset (SI Text in ref. 1), which is based on interviews of high school students. They use standard techniques from twins studies that have been useful in identifying the heritability of a variety of traits and behaviors (7). The method is based on comparing covariation in outcomes for samesex twins who are monozygotic (identical twins from 1 egg who share all of their genes) with that for twins who are dizygotic (fraternal twins from 2 eggs who share on average half of their genes). Presuming that the social environment that twins share is not influenced by whether they are monozygotic or dizygotic, if network characteristics are significantly more correlated among monozygotic twins than dizygotic twins then there is evidence for a genetic role in network formation. The network characteristics that Fowler et al. (1) investigate are: in-degree (how many students name a given student as a friend), out-degree (how many students a given student names as friends), transitivity (if A and B are friends, and B and C are friends, what is the likelihood that A and C are friends), and betweenness centrality (the fraction of shortest paths between other pairs of students that a given student lies on). Their statistical analysis assumes that the variation in a network characteristic can be additively separated into a component that is genetic, a component caused by the environment that would be shared with a twin, and a component caused by the environment that would not be shared with a twin. The covariance between monozygotic twins is then the variance caused by the common environment plus the variance caused by genetic factors, whereas the covariance between dizygotic twins is the variance caused by the common environment plus half of the variance caused by genetic factors. This formulation allows one to solve for the percentage of variation in a given network characteristic that is caused by each of the genetic, common environment, and unshared environment components, as pictured in Fig. 1, which is based on Fowler et al.’s SI Text and Table S1. As we see in Fig. 1, Fowler et al. (1) find that almost half of the variation in transitivity and in-degree are genetically attributable, and more than a quarter of betweenness centrality is genetically attributable, but the genetic component of the out-degree variation is too small to be statistically significant. It is also worth noting that the common environment is statistically insignificant in all cases. Let me point out 2 issues here. First, we should be cautious in drawing conclusions from the observation that genetics show a statistically significant role in determining in-degree but not out-degree. The confidence interval on out-degree extends from 0 to 50%, which indicates that we have little idea of whether genetics really matter in the determination of out-degree. In fact, the 95% confidence intervals on the percentage of in-degree and out-degree variation attributable to genetics [(23%, 69%) and (0%, 47%), respectively] show substantial overlap and one could not reject the hypothesis that they are the same or even reversed. Second, in-degree and out-degree in the Adolescent Health Dataset are somewhat spe-