Genes for Good: Engaging the Public in Genetics Research via Social Media.

The Genes for Good study uses social media to engage a large, diverse participant pool in genetics research and education. Health history and daily tracking surveys are administered through a Facebook application, and participants who complete a minimum number of surveys are mailed a saliva sample kit ("spit kit") to collect DNA for genotyping. As of March 2019, we engaged >80,000 individuals, sent spit kits to >32,000 individuals who met minimum participation requirements, and collected >27,000 spit kits. Participants come from all 50 states and include a diversity of ancestral backgrounds. Rates of important chronic health indicators are consistent with those estimated for the general U.S. population using more traditional study designs. However, our sample is younger and contains a greater percentage of females than the general population. As one means of verifying data quality, we have replicated genome-wide association studies (GWASs) for exemplar traits, such as asthma, diabetes, body mass index (BMI), and pigmentation. The flexible framework of the web application makes it relatively simple to add new questionnaires and for other researchers to collaborate. We anticipate that the study sample will continue to grow and that future analyses may further capitalize on the strengths of the longitudinal data in combination with genetic information.

Evaluating longitudinal relationships between parental monitoring and substance use in a multi-year, intensive longitudinal study of 670 adolescent twins.

Introduction: Parental monitoring is a key intervention target for adolescent substance use, however this practice is largely supported by causally uninformative cross-sectional or sparse-longitudinal observational research designs. Methods: We therefore evaluated relationships between adolescent substance use (assessed weekly) and parental monitoring (assessed every two months) in 670 adolescent twins for two years. This allowed us to assess how individual-level parental monitoring and substance use trajectories were related and, via the twin design, to quantify genetic and environmental contributions to these relationships. Furthermore, we attempted to devise additional measures of parental monitoring by collecting quasi-continuous GPS locations and calculating a) time spent at home between midnight and 5am and b) time spent at school between 8am-3pm. Results: ACE-decomposed latent growth models found alcohol and cannabis use increased with age while parental monitoring, time at home, and time at school decreased. Baseline alcohol and cannabis use were correlated (r = .65) and associated with baseline parental monitoring (r = -.24 to -.29) but not with baseline GPS measures (r = -.06 to -.16). Longitudinally, changes in substance use and parental monitoring were not significantly correlated. Geospatial measures were largely unrelated to parental monitoring, though changes in cannabis use and time at home were highly correlated (r = -.53 to -.90), with genetic correlations suggesting their relationship was substantially genetically mediated. Due to power constraints, ACE estimates and biometric correlations were imprecisely estimated. Most of the substance use and parental monitoring phenotypes were substantially heritable, but genetic correlations between them were not significantly different from 0. Discussion: Overall, we found developmental changes in each phenotype, baseline correlations between substance use and parental monitoring, co-occurring changes and mutual genetic influences for time at home and cannabis use, and substantial genetic influences on many substance use and parental monitoring phenotypes. However, our geospatial variables were mostly unrelated to parental monitoring, suggesting they poorly measured this construct. Furthermore, though we did not detect evidence of genetic confounding, changes in parental monitoring and substance use were not significantly correlated, suggesting that, at least in community samples of mid-to-late adolescents, the two may not be causally related.

The effects of cannabis use on physical health: A co-twin control study.

BACKGROUND: Research on the influence of cannabis use on anthropometrics, cardiovascular and pulmonary function, and other indicators of physical health has reported mixed results. We examined whether cannabis frequency is associated with physical health outcomes phenotypically and after controlling for shared genetic and environmental factors via a longitudinal co-twin control design. METHODS: We tested the phenotypic associations of adolescent, young adult, and adult cannabis frequency with adult physical health. Next, we ran multilevel models to test if significant phenotypic associations remained at the between-family and within-twin pair levels. Participants include 677 individual twins (308 twin pairs) aged 25-35. RESULTS: At the phenotypic level, adolescent cannabis use was associated with less adult exercise engagement (b = - 0.846 min, p = .000). Adult cannabis use was associated with a lower resting heart rate (HR; b = - 0.170 bpm, p = .001) and more frequent appetite loss (b = 0.018, p = .000). Only between-family effects were significant for adolescent cannabis use and exercise engagement (b = - 1.147 min, p = .000) and adult cannabis use and appetite loss frequency (b = 0.041, p = .002). The total within-twin (b = - 0.184, p = .014), MZ only (b = - 0.304, p = .003), and between-family effects (b = - 0.164, p = .025) were significant between adult cannabis use and a lower resting HR, which persisted after controlling for familial confounds and other substance use. CONCLUSIONS: The associations between cannabis use with exercise engagement and frequency of appetite loss are explained by familial confounding while the association between cannabis use and resting HR was not. These results do not support a causal association between cannabis use once a week and poorer physical health effects among adults aged 25-35.

A characterization of cis- and trans-heritability of RNA-Seq-based gene expression.

Insights into individual differences in gene expression and its heritability (h2) can help in understanding pathways from DNA to phenotype. We estimated the heritability of gene expression of 52,844 genes measured in whole blood in the largest twin RNA-Seq sample to date (1497 individuals including 459 monozygotic twin pairs and 150 dizygotic twin pairs) from classical twin modeling and identity-by-state-based approaches. We estimated for each gene h2total, composed of cis-heritability (h2cis, the variance explained by single nucleotide polymorphisms in the cis-window of the gene), and trans-heritability (h2res, the residual variance explained by all other genome-wide variants). Mean h2total was 0.26, which was significantly higher than heritability estimates earlier found in a microarray-based study using largely overlapping (>60%) RNA samples (mean h2 = 0.14, p = 6.15 × 10-258). Mean h2cis was 0.06 and strongly correlated with beta of the top cis expression quantitative loci (eQTL, ρ = 0.76, p < 10-308) and with estimates from earlier RNA-Seq-based studies. Mean h2res was 0.20 and correlated with the beta of the corresponding trans-eQTL (ρ = 0.04, p < 1.89 × 10-3) and was significantly higher for genes involved in cytokine-cytokine interactions (p = 4.22 × 10-15), many other immune system pathways, and genes identified in genome-wide association studies for various traits including behavioral disorders and cancer. This study provides a thorough characterization of cis- and trans-h2 estimates of gene expression, which is of value for interpretation of GWAS and gene expression studies.

Behavioral impact of return of genetic test results for complex disease: Systematic review and meta-analysis.

OBJECTIVE: Advances in genomewide association studies have made possible the return of genetic risk results for complex diseases. Two concerns about these results are (a) negative psychological consequences and (b) viewing probabilistic results as deterministic, leading to misinterpretation and inappropriate decisions. The present study evaluates these concerns through a meta-analytic review of existing literature. METHOD: Seventeen genetic testing studies of complex disease, including 1,171 participants and reporting 195 effects, 104 of which were unadjusted for covariates, were meta-analyzed under a random effects model. Diseases included Alzheimer's, cardiovascular and coronary heart disease, lung cancer, melanoma, thrombophilia, and type II diabetes. Six domains of behavioral-psychological reactions were examined. RESULTS: Carriers showed significantly increased self-reported behavior change compared to noncarriers when assessed 6 months or later after results return (Hedges's g = .36, p = .019). CONCLUSIONS: Return of genetic testing results for complex disease does not strongly impact self-reported negative behavior or psychological function of at-risk individuals. Return of results does appear to moderately increase self-reported healthy behavior in carriers, although research on objectively observed behavior change is needed. This is a growing area of research, with preliminary results suggesting potential positive implications of genetic testing for complex disease on behavior change. (PsycINFO Database Record (c) 2018 APA, all rights reserved).