Genetic influences on depression and selection into adverse life experiences.

Genome-wide association studies find that a large number of genetic variants jointly influence the risk of depression, which is summarized by polygenic indices (PGIs) of depressive symptoms and major depression. But PGIs by design remain agnostic about the causal mechanisms linking genes to depression. Meanwhile, the role of adverse life experiences in shaping depression risk is well-documented, including via gene-environment correlation.

Differential sensitivity to adversity by income: Evidence from a study of Bereavement.

Adverse life events are often understood as having negative consequences for mental health via objective hardships, which are worse for persons with less income. But adversity can also affect mental health via more subjective mechanisms, and here, it is possible that persons with higher income will exhibit greater psychological sensitivity to negative events, for various reasons. Drawing on multiple sociological literatures, this article theorizes potential mechanisms of increasing sensitivity with income.

Advances in transparency and reproducibility in the social sciences.

Worries about a "credibility crisis" besieging science have ignited interest in research transparency and reproducibility as ways of restoring trust in published research. For quantitative social science, advances in transparency and reproducibility can be seen as a set of developments whose trajectory predates the recent alarm. We discuss several of these developments, including preregistration, data-sharing, formal infrastructure in the form of resources and policies, open access to research, and specificity regarding research contributions.

Measuring the predictability of life outcomes with a scientific mass collaboration.

How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. Within each outcome, prediction error was strongly associated with the family being predicted and weakly associated with the technique used to generate the prediction.