Across a wide range of studies, researchers often conclude that the home environment and children’s outcomes are causally linked. In contrast, behavioral genetic studies show that parents influence their children by providing them with both environment and genes, meaning the environment that parents provide should not be considered in the absence of genetic influences, because that can lead to erroneous conclusions on causation. This article seeks to provide behavioral scientists with a synopsis of numerous methods to estimate the direct effect of the environment, controlling for the potential of genetic confounding. Ideally, using genetically sensitive designs can fully disentangle this genetic confound, but these require specialized samples. In the near future, researchers will likely have access to measured DNA variants (summarized in a polygenic scores), which could serve as a partial genetic control, but that is currently not an option that is ideal or widely available. We also propose a work around for when genetically sensitive data are not readily available: the Familial Control Method. In this method, one measures the same trait in the parents as the child, and the parents’ trait is then used as a covariate (e.g., a genetic proxy). When these options are all not possible, we plead with our colleagues to clearly mention genetic confound as a limitation, and to be cautious with any environmental causal statements which could lead to unnecessary parent blaming.

According to the Multiple Deficit Model, comorbidity results when the genetic and environmental risk factors that increase the liability for a disorder are domain-general. In order to explore the role of domain-general etiological risk factors in the co-occurrence of learning-related difficulties, the current meta-analysis compiled 38 studies of third through ninth-grade children to estimate the average genetic, shared environmental, and nonshared environmental correlations between reading and attention-deficit/hyperactivity disorder (ADHD) symptoms, and reading and math, as well as their potential moderators. Results revealed average genetic, shared and nonshared environmental correlations between reading and ADHD symptoms of .42, .64, and .20, and reading and math of .71, .90, and .56, suggesting that reading and math may have more domain-general risk factors than reading and ADHD symptoms. A number of significant sources of heterogeneity were also found and discussed. These results have important implications for both intervention and classification of learning disabilities.

This study investigated developmental trajectories of reading and math using latent-growth-curve analyses across multiple academic skills, measures, and multiple time periods within a single sample. Reading-related growth was marked by significant individual differences during the early elementaryschool period and nonsignificant individual differences during the late elementary-school period. For math-related skills, nonsignificant individual differences were present for early math growth and significant individual differences were present in late elementary-school. No clear pattern of cumulative, compensatory, or stable development emerged for either reading-related or math skills. These differing growth patterns highlight developmental complexities and suggest domain-specific differences in achievement growth that are potentially associated with contextual factors. Educational Impact and Implications StatementOur article addresses the importance of developmental processes in reading and math. We took advantage of a large longitudinal data set to compare the similarity of developmental patterns across several reading and math skills. We found that individual differences in growth rates were greater in the early elementary school years (K-2) for reading than for math, but in later elementary school (third-fifth grades) individual differences in growth were greater for math than for reading. Our results suggest that domain-specific contextual factors may influence these developmental patterns and these differences could have important implications for the manipulation of associated contextual factors and future intervention development.

Numerous twin studies have been published examining the genetic and environmental etiology of reading comprehension, though the etiological estimates may be influenced currently unidentified sample conditions (e.g., Tucker-Drob & Bates, 2015). The purpose of the current meta-analysis was to average the etiological influences of reading comprehension and to explore the potential moderators that may be influencing these estimates. Results revealed an average heritability estimate of h2 = .59, with significant variation in estimates across studies, suggesting potential moderation. Heritability was moderated by publication year, grade level, project, zygosity determination method, and response type. The average shared environmental estimate was c2 = .16, with publication year, grade and zygosity determination method acting as significant moderators. These findings support the large role of genetic influences on reading comprehension, and a small but significant role of shared environmental influences. The significant moderators of etiological influences within the current synthesis suggest our interpretation of how genes and environment influence reading comprehension should reflect aspects of study and sample.