Biology subjects

Tucker-Drob, E. M.

Publications and source records attributed to Tucker-Drob, E. M..

4 recordsLinked to original sources

bioRxiv2018

Genomic SEM Provides Insights into the Multivariate Genetic Architecture of Complex Traits

Methods for using GWAS to estimate genetic correlations between pairwise combinations of traits have produced \"atlases\" of genetic architecture. Genetic atlases reveal pervasive pleiotropy, and genome-wide significant loci are often shared across different phenotypes. We introduce genomic structural equation modeling (Genomic SEM), a multivariate method for analyzing the joint genetic architectures of complex traits. Using formal methods for modeling covariance structure, Genomic SEM synthesizes genetic correlations and SNP-heritabilities inferred from GWAS summary statistics of individual traits from samples with varying and unknown degrees of overlap. Genomic SEM can be used to identify variants with effects on general dimensions of cross-trait liability, boost power for discovery, and calculate more predictive polygenic scores. Finally, Genomic SEM can be used to identify loci that cause divergence between traits, aiding the search for what uniquely differentiates highly correlated phenotypes. We demonstrate several applications of Genomic SEM, including a joint analysis of GWAS summary statistics from five genetically correlated psychiatric traits. We identify 27 independent SNPs not previously identified in the univariate GWASs, 5 of which have been reported in other published GWASs of the included traits. Polygenic scores derived from Genomic SEM consistently outperform polygenic scores derived from GWASs of the individual traits. Genomic SEM is flexible, open ended, and allows for continuous innovations in how multivariate genetic architecture is modeled.

genetics↗

bioRxiv2018

The Neural Architecture of Executive Functions Is Established by Middle Childhood

Executive functions (EFs) are regulatory cognitive processes that support goal-directed thoughts and behaviors and that involve two primary networks of functional brain activity in adulthood. The current study assessed whether the same networks identified in adulthood underlie child EFs. Using task-based fMRI data from a diverse sample of N = 117 children and early adolescents (M age = 10.17 years), we assessed the extent to which neural activity was shared across three EF domains and whether these patterns reflected quantitative or qualitative differences relative to previously reported adult findings. Brain regions that were consistently engaged across switching, updating, and inhibition tasks closely corresponded to the cingulo-opercular and fronto-parietal networks identified in studies of adults. Isolating brain activity during more demanding task periods highlighted contributions of the dACC and anterior insular regions of the cingulo-opercular network. Results were independent of age and time-on-task effects. These results indicate that the two core brain networks that support EFs are in place by middle childhood. Improvement in EFs from middle childhood to adulthood, therefore, are likely due to quantitative changes in activity within these networks, rather than qualitative changes in the organization of the networks themselves. Improved knowledge of how the brains functional organization supports EF in childhood has critical implications for understanding the maturation of cognitive abilities.

neuroscience↗

bioRxiv2017

Measurement Error Correction of Genome-Wide Polygenic Scores in Prediction Samples

Abstract/IntroductionDiPrete, Burik, & Koellinger (2017; http://dx.doi.org/10.1101/134197) propose using an instrumental variable (IV) framework to correct genome-wide polygenic scores (GPSs) for error, thereby producing disattenuated estimates of SNP heritability in predictions samples. They demonstrate their approach by producing two independent GPSs for Educational Attainment (\"multiple indicators\") in a prediction sample (Health and Retirement Study; HRS) from independent sets of SNP regression weights, each computed from a different half of the discovery sample (EA2; Okbay et al. 2016), i.e. \"by randomly splitting the GWAS sample that was used for [the GPS] construction.\"\n\nHere, I elucidate how a structural equation modeling (SEM) framework that specifies true score variance in GPSs as a latent variable can be used to derive an equivalent correction to the IV approach proposed by DiPrete et al. (2017). This approach, which is rooted in a psychometric modeling tradition, has a number of advantages: (1) it formalizes the assumed data-generating model, (2) it estimates all parameters of interest in a single step, (3) is can be flexibly incorporated into a larger multivariate analysis (such as the \"Genetic Instrumental Variable\" approach proposed by DiPrete et al., 2017), (4) it can easily be adapted to relax assumptions (e.g. that the GPS indicators equally represent the true genetic factor score), and (5) it can easily be extended to include more than two GPS indicators. After describing how the multiple indicator approach to GPS correction can specified as a structural equation model, I demonstrate how a structural equation modeling approach can be used to correct GPSs for error using SNP heritability obtained using GREML or LD score regression to produce a correction that is equivalent to an approach recently proposed by Daniel Benjamin and colleagues. Finally, I briefly discuss what I view as some conceptual limitations surrounding the error correction approaches described, regardless of the estimation method implemented.

genetics↗

bioRxiv2017

Genetic and Environmental Influences on Pubertal Hormones in Human Hair across Development

Background: Puberty is a complex biopsychosocial process that is linked to an array of psychiatric and medical disorders that emerge in adolescence and persist across the lifespan. Although the pubertal process is driven by neuroendocrine changes, few quantitative genetic studies have directly measured puberty-relevant hormones. Hair samples can now be assayed for accumulation of hormones over several months. In contrast to more conventional salivary measures, hair measures are not confounded by diurnal variation or hormonal reactivity.\n\nMethods: In an ethnically and socioeconomically diverse sample of 1,286 child and adolescent twins and multiples from 672 unique families, we estimated genetic and environmental influences on concentrations of testosterone, DHEA, and progesterone in hair across the period of 8 to 18 years of age.\n\nResults: We identified sex-specific developmental windows of maximal heritability in each hormone. Peak heritability for DHEA occurred at age 9.8 years for males and 10.0 years for females. Peak heritability for testosterone occurred at age 12.5 and 15.2 years for males and females, respectively. Peak heritability for male progesterone occurred at 11.2 years, while the heritability of female progesterone remained uniformly low.\n\nConclusion: This is the first study of genetic influences on pubertal hormone concentrations in human hair and is the largest of the heritability of pubertal hormones in any form. The identification of specific developmental windows when genetic signals for hormones are maximized has critical implications for well-informed models of hormone-behavior associations in childhood and adolescence.

genetics↗