There are well‐established links between impulsivity and alcohol use in humans and other model organisms; however, the etiological nature of these associations remains unclear. This is likely due, in part, to the heterogeneous nature of the construct of impulsivity. Many different measures of impulsivity have been employed in human studies, using both questionnaire and laboratory‐based tasks. Animal studies also use multiple tasks to assess the construct of impulsivity. In both human and animal studies, different measures of impulsivity often show little correlation and are differentially related to outcome, suggesting that the impulsivity construct may actually consist of a number of more homogeneous (and potentially more meaningful) subfacets. Here, we provide an overview of the different measures of impulsivity used across human and animal studies, evidence that the construct of impulsivity may be better studied in the context of more meaningful subfacets, and recommendations for how research in this direction may provide for better consilience between human and animal studies of the connection between impulsivity and alcohol use.

Studying how genetic predispositions come together with environmental factors to contribute to complex behavioral outcomes has great potential for advancing the understanding of the development of psychopathology. It represents a clear theoretical advance over studying these factors in isolation. However, research at the intersection of multiple fields creates many challenges. We review several reasons why the rapidly expanding candidate gene–environment interaction (cG×E) literature should be considered with a degree of caution. We discuss lessons learned about candidate gene main effects from the evolving genetics literature and how these inform the study of cG×E. We review the importance of the measurement of the gene and environment of interest in cG×E studies. We discuss statistical concerns with modeling cG×E that are frequently overlooked. Furthermore, we review other challenges that have likely contributed to the cG×E literature being difficult to interpret, including low power and publication bias. Many of these issues are similar to other concerns about research integrity (e.g., high false-positive rates) that have received increasing attention in the social sciences. We provide recommendations for rigorous research practices for cG×E studies that we believe will advance its potential to contribute more robustly to the understanding of complex behavioral phenotypes.

There has been an explosion of interest in studying gene-environment interactions (GxE) as they relate to the development of psychopathology. In this article, I review different methodologies to study gene-environment interaction, providing an overview of methods from animal and human studies and illustrations of gene-environment interactions detected using these various methodologies. Gene-environment interaction studies that examine genetic influences as modeled latently (e.g., from family, twin, and adoption studies) are covered, as well as studies of measured genotypes. Importantly, the explosion of interest in gene-environment interactions has raised a number of challenges, including difficulties with differentiating various types of interactions, power, and the scaling of environmental measures, which have profound implications for detecting gene-environment interactions. Taking research on gene-environment interactions to the next level will necessitate close collaborations between psychologists and geneticists so that each field can take advantage of the knowledge base of the other.

Considerable advances in genetics at the level of understanding the structure of the human genome and associated diversity as well as in genotyping and analysis methods, have ushered in an era where identifying genes underlying complex disorders is no longer a distant possibility.¹ The number of articles published regarding such associations is rapidly escalating, and associations are replicating across independent samples. The growth of genomewide association studies and large consortia of researchers who are pooling data to create more powerful gene-finding samples only promises to continue this trend.^2,3