No abstract

Outcomes of interest to demographers—fertility; health; education—are the product of both an individual’s genetic makeup and his or her social environment. Yet Gene × Environment research (GxE) currently deploys a limited toolkit on the genetic side to study gene-environment interplay: polygenic scores (PGS, or what we call mPGS) that reflect the influence of genetics on levels of an outcome. The purpose of the present paper is to develop a genetic summary measure better suited for GxE research. We develop what we call variance polygenic scores (vPGS), or polygenic scores that reflect genetic contributions to plasticity in outcomes. The first part of the analysis uses the UK Biobank (N ∼ 326,000 in the training set) and the Health and Retirement Study (HRS) (N = 10,524) to compare four approaches for constructing polygenic scores for plasticity. The results show that two widely-used methods for discovering which genetic variants affect outcome variability fail to serve as distinctive new tools for GxE. Then, using the polygenic scores that do capture distinctive genetic contributions to plasticity, we analyze heterogeneous effects of a UK education reform on health and educational attainment. The results show the properties of a new tool useful for population scientists studying the interplay of nature and nurture and for population-based studies that are releasing polygenic scores to applied researchers.

Recent scholarship suggests that the genomes of those around us affect our own phenotypes. Much of the empirical evidence for such “metagenomic” effects comes from animal studies, where the socio-genetic environment can be easily manipulated. Among humans, it is more difficult to identify such effects given the nonrandom distribution of genes and environments. Here we leverage the as-if-random distribution of grade-mates’ genomes conditional on school-level variation in a nationally representative sample. Specifically, we evaluate whether one’s peers’ genetic propensity to smoke affects one’s own smoking behavior net of one’s own genotype. Results show that peer genetic propensity to smoke has a substantial effect on an individual’s smoking outcome. This is true not only when the peer group includes direct friends, and therefore where the individual plays an active role in shaping the metagenomic context but also when the peer group includes all grade-mates and thus in cases where the individual does not select the metagenomic environment. We explore these effects further and show that a small minority with high genetic risk to smoke (‘bad apples’) can greatly affect the smoking behavior of an entire grade. The methodology used in this paper offers a potential solution to many of the challenges inherent in estimating peer effects in nonexperimental settings and can be utilized to study a wide range of outcomes with a genetic basis. On a policy level, our results suggest that efforts to reduce adolescent smoking should take into account metagenomic effects, especially bad apples, within social networks.

Social media creates new virtual public spaces where young women and men living in socially conservative non-Western societies can communicate in order to meet and engage in forbidden intimacies. In this essay, using survey data on thousands of Facebook users from Muslim-majority countries, we look at the relationship between romance in public physical spaces and cyberspaces. To what extent do Facebook users make use of the Internet to pursue romance? And what are the attributes of individuals who use it in this way?

At least one co-author has disclosed a financial relationship of potential relevance for this research. Further information is available online at http://www.nber.org/papers/w24113.ack NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

No abstract

Sociologists increasingly face choices among competing algorithms that represent reasonable approaches to the same task, with little guidance in choosing among them. We develop a strategy that uses simulated data to identify the conditions under which different methods perform well and applies what is learned from the simulations to predict which method will perform best on never-before-seen empirical data sets. We apply this strategy to a class of methods that group respondents to attitude surveys according to whether they share construals of a given domain. This allows us to identify the relative strengths and weaknesses of the methods we consider, including relational class analysis, correlational class analysis, and eight other such variants. Results support the “no free lunch” view that researchers should abandon the quest for one best algorithm in favor of matching algorithms to kinds of data for which each is most appropriate and provide direction on how to do so.

Birth weight has long been known to be a proxy for prenatal nutrition as well as social stress during pregnancy and, in this vein, has important associations with life outcomes ranging from infant mortality to cognition to adult stature. However, since birth weight and such developmental outcomes may share a genetic etiology, here we compare estimates of the effect of birth weight on traits with and without controls for genotype. Moreover, a recent literature has suggested that the effects of birth weight on later outcomes may be moderated by genotype. Namely, a handful of studies have assessed whether alleles putatively associated with neural plasticity interact with birth weight in twin and sibling models and have come to conflicting results. This confusion may result from the fact that these researchers have used only candidate gene markers-that is single alleles or a combination of a few alleles. Candidate gene studies, in turn, have failed to replicate and the general consensus in the genetics of behavior literature is that most published associations are false positives. To improve on this prior work, the present study uses well-validated, replicated polygenic scores that combine information across the entire genome by summarizing the effects of over one million individual alleles. Specifically, we use data from the National Longitudinal Study of Adolescent to Adult Health and the UK Biobank to assess whether the effects birth weight on cognitive functioning, educational attainment, height, body mass index, smoking, and depression are biased by or moderated by each phenotype's respective polygenic score. Results do not show any robustly significant interactions in cross-sectional models or in sibling and/or twin fixed effects models. Implications for future research are discussed.