Associations between executive functions assessed in different contexts in a genetically informative sample.

Executive functions (EFs) are cognitive functions that help direct goal-related behavior. EFs are usually measured via behavioral tasks assessed in highly controlled laboratory settings under the supervision of a research assistant. Online versions of EF tasks are an increasingly popular alternative to in-lab testing. However, researchers do not have the same control over the testing environment during online EF assessments. To assess the extent to which EFs assessed in-lab and online are related, we used data from the Colorado Online Twin Study (CoTwins; 887 individual twins aged 13.98-19.05) and constructed an Lab Common EF factor and an Online Common EF factor from four EF tasks assessed in-lab and online. The Lab Common and Online Common EF factors were genetically identical (rA = 1.00) but phenotypically separable (r = .77, 95% confidence interval [0.59, 0.94]) indicating that these EF factors have the same genetic underpinnings but may be differentially influenced by environmental factors. We examined phenotypic, genetic, and environmental correlations between the EF factors and a general cognitive ability factor (g) assessed in the lab and found similar relationships between Lab Common EF and g and Online Common EF and g. Overall, these results suggest that Common EF factors assessed in different contexts are highly related to each other and similarly related to other cognitive outcomes. These findings indicate that online task-based EF assessments could be a viable strategy for increasing sample sizes in large-scale studies, particularly genetically informed studies. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Associations between polygenic risk of substance use and use disorder and alcohol, cannabis, and nicotine use in adolescence and young adulthood in a longitudinal twin study.

BACKGROUND: Recent well-powered genome-wide association studies have enhanced prediction of substance use outcomes via polygenic scores (PGSs). Here, we test (1) whether these scores contribute to prediction over-and-above family history, (2) the extent to which PGS prediction reflects inherited genetic variation v. demography (population stratification and assortative mating) and indirect genetic effects of parents (genetic nurture), and (3) whether PGS prediction is mediated by behavioral disinhibition prior to substance use onset. METHODS: PGSs for alcohol, cannabis, and nicotine use/use disorder were calculated for Minnesota Twin Family Study participants (N = 2483, 1565 monozygotic/918 dizygotic). Twins' parents were assessed for histories of substance use disorder. Twins were assessed for behavioral disinhibition at age 11 and substance use from ages 14 to 24. PGS prediction of substance use was examined using linear mixed-effects, within-twin pair, and structural equation models. RESULTS: Nearly all PGS measures were associated with multiple types of substance use independently of family history. However, most within-pair PGS prediction estimates were substantially smaller than the corresponding between-pair estimates, suggesting that prediction is driven in part by demography and indirect genetic effects of parents. Path analyses indicated the effects of both PGSs and family history on substance use were mediated via disinhibition in preadolescence. CONCLUSIONS: PGSs capturing risk of substance use and use disorder can be combined with family history measures to augment prediction of substance use outcomes. Results highlight indirect sources of genetic associations and preadolescent elevations in behavioral disinhibition as two routes through which these scores may relate to substance use.

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.

Exploring Relationships Between Internalizing Problems and Risky Sexual Behavior: A Twin Study.

Previous research links risky sexual behavior (RSB) to externalizing problems and to substance use, but little research has been conducted on relationships between internalizing problems (INT) and RSB. The current study addresses that literature gap, using both a twin sample from Colorado (N = 2567) and a second twin sample from Minnesota (N = 1131) in attempt to replicate initial results. We explored the hypothesis that the latent variable INT would be more strongly associated with the latent variable RSB for females than for males, examining relationships between INT and RSB via phenotypic confirmatory factor analysis and multivariate twin analyses. We found a small but significant phenotypic association between the latent variables. However, despite using two large twin samples, limited power restricted our ability to identify the genetic and environmental mechanisms underlying this association. Our sex differences hypothesis was not fully supported in either sample and requires further investigation. Our findings illustrate the complexity of the relationship between internalizing problems and risky sexual behavior.

The association between saving disposition and financial distress: A genetically informed approach.

Saving disposition, the tendency to save rather than consume, has been found to be associated with economic outcomes. People lacking the disposition to save are more likely to experience financial distress. This association could be driven by other economic factors, behavioral traits, or even genetic effects. Using a sample of 3,920 American twins, we develop scales to measure saving disposition and financial distress. We find genetic influences on both traits, but also a large effect of the rearing family environment on saving disposition. We estimate that 44% of the covariance between the two traits is due to genetic effects. Saving disposition remains strongly associated with lower financial distress, even after controlling for family income, cognitive ability, and personality traits. The association persists within families and monozygotic twin pairs; the twin who saves more tends to be the twin who experiences less financial distress. This result suggest that there is a direct association between saving disposition and financial distress, although the direction of causation remains unclear.

Smoking cessation, harm reduction, and biomarkers protocols in the PhenX Toolkit: Tools for standardized data collection.

The use of standard protocols in studies supports consistent data collection, improves data quality, and facilitates cross-study analyses. Funded by the National Institutes of Health, the PhenX (consensus measures for Phenotypes and eXposures) Toolkit is a catalog of recommended measurement protocols that address a wide range of research topics and are suitable for inclusion in a variety of study designs. In 2020, a PhenX Working Group of smoking cessation experts followed a well-established consensus process to identify and recommend measurement protocols suitable for inclusion in smoking cessation and smoking harm reduction studies. The broader scientific community was invited to review and provide feedback on the preliminary recommendation of the Working Group. Fourteen selected protocols for measuring smoking cessation, harm reduction, and biomarkers research associated with smoking cessation were released in the PhenX Toolkit ( https://www.phenxtoolkit.org) in February 2021. These protocols complement existing PhenX Toolkit content related to tobacco regulatory research, substance use and addiction research, and other measures of smoking-related health outcomes. Adopting well-established protocols enables consistent data collection and facilitates comparing and combining data across studies, potentially increasing the scientific impact of individual studies.

Individual differences in adolescent and young adult daily mobility patterns and their relationships to big five personality traits: a behavioral genetic analysis.

Youth behavior changes and their relationships to personality have generally been investigated using self-report studies, which are subject to reporting biases and confounding variables. Supplementing these with objective measures, like GPS location data, and twin-based research designs, which help control for confounding genetic and environmental influences, may allow for more rigorous, causally informative research on adolescent behavior patterns. To investigate this possibility, this study aimed to (1) investigate whether behavior changes during the transition from adolescence to emerging adulthood are evident in changing mobility patterns, (2) estimate the influence of adolescent personality on mobility patterns, and (3) estimate genetic and environmental influences on mobility, personality, and the relationship between them. Twins aged Fourteen to twenty-two (N=709, 55% female) provided a baseline personality measure, the Big Five Inventory, and multiple years of smartphone GPS data from June 2016 - December 2019. Mobility, as measured by daily locations visited and distance travelled, was found via mixed effects models to increase during adolescence before declining slightly in emerging adulthood. Mobility was positively associated with Extraversion and Conscientiousness (r of 0.17 - 0.25, r of 0.10 - 0.16) and negatively with Openness (r of -0.11 - -0.13). ACE models found large genetic (A = 0.56 - 0.81) and small-moderate environmental (C of 0.12 - 0.28, E of 0.07 - 0.15) influences on mobility. A and E influences were highly shared across mobility measures (rg = 0.70, re= 0.58). Associations between mobility and personality were partially explained by mutual genetic influences (rg of -0.27 - 0.53). Results show that as autonomy increases during adolescence and emerging adulthood, we see corresponding increases in youth mobility. Furthermore, the heritability of mobility patterns and their relationship to personality demonstrate that mobility patterns are informative, psychologically meaningful behaviors worthy of continued interest in psychology.

Bayesian Forecasting with a Regime-Switching Zero-Inflated Multilevel Poisson Regression Model: An Application to Adolescent Alcohol Use with Spatial Covariates.

In this paper, we present and evaluate a novel Bayesian regime-switching zero-inflated multilevel Poisson (RS-ZIMLP) regression model for forecasting alcohol use dynamics. The model partitions individuals' data into two phases, known as regimes, with: (1) a zero-inflation regime that is used to accommodate high instances of zeros (non-drinking) and (2) a multilevel Poisson regression regime in which variations in individuals' log-transformed average rates of alcohol use are captured by means of an autoregressive process with exogenous predictors and a person-specific intercept. The times at which individuals are in each regime are unknown, but may be estimated from the data. We assume that the regime indicator follows a first-order Markov process as related to exogenous predictors of interest. The forecast performance of the proposed model was evaluated using a Monte Carlo simulation study and further demonstrated using substance use and spatial covariate data from the Colorado Online Twin Study (CoTwins). Results showed that the proposed model yielded better forecast performance compared to a baseline model which predicted all cases as non-drinking and a reduced ZIMLP model without the RS structure, as indicated by higher AUC (the area under the receiver operating characteristic (ROC) curve) scores, and lower mean absolute errors (MAEs) and root-mean-square errors (RMSEs). The improvements in forecast performance were even more pronounced when we limited the comparisons to participants who showed at least one instance of transition to drinking.

A Century of Behavioral Genetics at the University of Minnesota.

The University of Minnesota has played an important role in the resurgence and eventual mainstreaming of human behavioral genetics in psychology and psychiatry. We describe this history in the context of three major movements in behavioral genetics: (1) radical eugenics in the early 20th century, (2) resurgence of human behavioral genetics in the 1960s, largely using twin and adoption designs to obtain more precise estimates of genetic and environmental influences on individual differences in behavior; and (3) use of measured genotypes to understand behavior. University of Minnesota scientists made significant contributions especially in (2) and (3) in the domains of cognitive ability, drug abuse and mental health, and endophenotypes. These contributions are illustrated through a historical perspective of major figures and events in behavioral genetics.

Polygenic scores for smoking and educational attainment have independent influences on academic success and adjustment in adolescence and educational attainment in adulthood.

Educational success is associated with greater quality of life and depends, in part, on heritable cognitive and non-cognitive traits. We used polygenic scores (PGS) for smoking and educational attainment to examine different genetic influences on facets of academic adjustment in adolescence and educational attainment in adulthood. PGSs were calculated for participants of the Minnesota Twin Family Study (N = 3225) and included as predictors of grades, academic motivation, and discipline problems at ages 11, 14, and 17 years-old, cigarettes per day from ages 14 to 24 years old, and educational attainment in adulthood (mean age 29.4 years). Smoking and educational attainment PGSs had significant incremental associations with each academic variable and cigarettes per day. About half of the adjusted effects of the smoking and education PGSs on educational attainment in adulthood were mediated by the academic variables in adolescence. Cigarettes per day from ages 14 to 24 years old did not account for the effect of the smoking PGS on educational attainment, suggesting the smoking PGS indexes genetic influences related to general behavioral disinhibition. In sum, distinct genetic influences measured by the smoking and educational attainment PGSs contribute to academic adjustment in adolescence and educational attainment in adulthood.

Using multivariate endophenotypes to identify psychophysiological mechanisms associated with polygenic scores for substance use, schizophrenia, and education attainment.

BACKGROUND: To better characterize brain-based mechanisms of polygenic liability for psychopathology and psychological traits, we extended our previous report (Liu et al. Psychophysiological endophenotypes to characterize mechanisms of known schizophrenia genetic loci. Psychological Medicine, 2017), focused solely on schizophrenia, to test the association between multivariate psychophysiological candidate endophenotypes (including novel measures of θ/δ oscillatory activity) and a range of polygenic scores (PGSs), namely alcohol/cannabis/nicotine use, an updated schizophrenia PGS (containing 52 more genome-wide significant loci than the PGS used in our previous report) and educational attainment. METHOD: A large community-based twin/family sample (N = 4893) was genome-wide genotyped and imputed. PGSs were constructed for alcohol use, regular smoking initiation, lifetime cannabis use, schizophrenia, and educational attainment. Eleven endophenotypes were assessed: visual oddball task event-related electroencephalogram (EEG) measures (target-related parietal P3 amplitude, frontal θ, and parietal δ energy/inter-trial phase clustering), band-limited resting-state EEG power, antisaccade error rate. Principal component analysis exploited covariation among endophenotypes to extract a smaller number of meaningful dimensions/components for statistical analysis. RESULTS: Endophenotypes were heritable. PGSs showed expected intercorrelations (e.g. schizophrenia PGS correlated positively with alcohol/nicotine/cannabis PGSs). Schizophrenia PGS was negatively associated with an event-related P3/δ component [ß = -0.032, nonparametric bootstrap 95% confidence interval (CI) -0.059 to -0.003]. A prefrontal control component (event-related θ/antisaccade errors) was negatively associated with alcohol (ß = -0.034, 95% CI -0.063 to -0.006) and regular smoking PGSs (ß = -0.032, 95% CI -0.061 to -0.005) and positively associated with educational attainment PGS (ß = 0.031, 95% CI 0.003-0.058). CONCLUSIONS: Evidence suggests that multivariate endophenotypes of decision-making (P3/δ) and cognitive/attentional control (θ/antisaccade error) relate to alcohol/nicotine, schizophrenia, and educational attainment PGSs and represent promising targets for future research.

Genetic architecture of four smoking behaviors using partitioned SNP heritability.

BACKGROUND AND AIMS: Although genome-wide association studies have identified many loci that influence smoking behaviors, much of the genetic variance remains unexplained. We characterized the genetic architecture of four smoking behaviors using single nucleotide polymorphism (SNP) heritability (h2SNP ). This is an estimate of narrow-sense heritability specifically estimating the proportion of phenotypic variation due to causal variants (CVs) tagged by SNPs. DESIGN: Partitioned h2SNP analysis of smoking behavior traits. SETTING: UK Biobank. PARTICIPANTS: UK Biobank participants of European ancestry. The number of participants varied depending on the trait, from 54 792 to 323 068. MEASUREMENTS: Smoking initiation, age of initiation, cigarettes per day (CPD; count, log-transformed, binned and dichotomized into heavy versus light) and smoking cessation with imputed genome-wide SNPs. FINDINGS: We estimated that, in aggregate, approximately 18% of the phenotypic variance in smoking initiation was captured by imputed SNPs [h2SNP = 0.18, standard error (SE) = 0.01] and 12% [SE = 0.02] for smoking cessation, both of which were more than twice the previously reported estimates. Estimated age of initiation (h2SNP  = 0.05, SE = 0.01) and binned CPD (h2SNP  = 0.1, SE = 0.01) were substantially below published twin-based h2 of 50%. CPD encoding influenced estimates, with dichotomized CPD h2SNP  = 0.28. There was no evidence of dominance genetic variance for any trait. CONCLUSION: A biobank study of smoking behavior traits suggested that the phenotypic variance explained by SNPs of smoking initiation, age of initiation, cigarettes per day and smoking cessation is modest overall.

GPS2space: An Open-source Python Library for Spatial Measure Extraction from GPS Data.

Global Positioning System (GPS) data have become one of the routine data streams collected by wearable devices, cell phones, and social media platforms in this digital age. Such data provide research opportunities in that they may provide contextual information to elucidate where, when, and why individuals engage in and sustain particular behavioral patterns. However, raw GPS data consisting of densely sampled time series of latitude and longitude coordinate pairs do not readily convey meaningful information concerning intra-individual dynamics and inter-individual differences; substantial data processing is required. Raw GPS data need to be integrated into a Geographic Information System (GIS) and analyzed, from which the mobility and activity patterns of individuals can be derived, a process that is unfamiliar to many behavioral scientists. In this tutorial article, we introduced GPS2space, a free and open-source Python library that we developed to facilitate the processing of GPS data, integration with GIS to derive distances from landmarks of interest, as well as extraction of two spatial features: activity space of individuals and shared space between individuals, such as members of the same family. We demonstrated functions available in the library using data from the Colorado Online Twin Study to explore seasonal and age-related changes in individuals' activity space and twin siblings' shared space, as well as gender, zygosity and baseline age-related differences in their initial levels and/or changes over time. We concluded with discussions of other potential usages, caveats, and future developments of GPS2space.

Polygenic Score for Smoking is associated with Externalizing Psychopathology and Disinhibited Personality Traits but not Internalizing Psychopathology in Adolescence.

We examined whether a polygenic score (PGS) for smoking measured genetic risk for general behavioral disinhibition by estimating its associations with externalizing and internalizing psychopathology and related personality traits at multiple time points in adolescence (ages 11, 14, and 17 years; N = 3225). The smoking PGS had strong associations with the stable variance across time for all the externalizing measures (mean standardized ß = .27), agreeableness (ß = -.22, 95% CI: -.28, -.16), and conscientiousness (ß = -.19, 95% CI: -.24, -.13), but was not significantly associated with internalizing measures (mean ß = .06) or extraversion (ß = .01, 95% CI: -.05, .07). After controlling for smoking at age 17, the associations with externalizing, low agreeableness, and low conscientiousness remained statistically significant. The smoking PGS measures genetic influences that contribute to a spectrum of phenotypes related to behavioral disinhibition including externalizing psychopathology and normal-range personality traits related to behavioral control, but not internalizing psychopathology.

Dissecting the genetic overlap of smoking behaviors, lung cancer, and chronic obstructive pulmonary disease: A focus on nicotinic receptors and nicotine metabolizing enzyme.

Smoking is a major contributor to lung cancer and chronic obstructive pulmonary disease (COPD). Two of the strongest genetic associations of smoking-related phenotypes are the chromosomal regions 15q25.1, encompassing the nicotinic acetylcholine receptor subunit genes CHRNA5-CHRNA3-CHRNB4, and 19q13.2, encompassing the nicotine metabolizing gene CYP2A6. In this study, we examined genetic relations between cigarettes smoked per day, smoking cessation, lung cancer, and COPD. Data consisted of genome-wide association study summary results. Genetic correlations were estimated using linkage disequilibrium score regression software. For each pair of outcomes, z-score-z-score (ZZ) plots were generated. Overall, heavier smoking and decreased smoking cessation showed positive genetic associations with increased lung cancer and COPD risk. The chromosomal region 19q13.2, however, showed a different correlational pattern. For example, the effect allele-C of the sentinel SNP (rs56113850) within CYP2A6 was associated with an increased risk of heavier smoking (z-score = 19.2; p = 1.10 × 10-81 ), lung cancer (z-score = 8.91; p = 5.02 × 10-19 ), and COPD (z-score = 4.04; p = 5.40 × 10-5 ). Surprisingly, this allele-C (rs56113850) was associated with increased smoking cessation (z-score = -8.17; p = 2.52 × 10-26 ). This inverse relationship highlights the need for additional investigation to determine how CYP2A6 variation could increase smoking cessation while also increasing the risk of lung cancer and COPD likely through increased cigarettes smoked per day.

Association Analysis and Meta-Analysis of Multi-Allelic Variants for Large-Scale Sequence Data.

There is great interest in understanding the impact of rare variants in human diseases using large sequence datasets. In deep sequence datasets of >10,000 samples, ~10% of the variant sites are observed to be multi-allelic. Many of the multi-allelic variants have been shown to be functional and disease-relevant. Proper analysis of multi-allelic variants is critical to the success of a sequencing study, but existing methods do not properly handle multi-allelic variants and can produce highly misleading association results. We discuss practical issues and methods to encode multi-allelic sites, conduct single-variant and gene-level association analyses, and perform meta-analysis for multi-allelic variants. We evaluated these methods through extensive simulations and the study of a large meta-analysis of ~18,000 samples on the cigarettes-per-day phenotype. We showed that our joint modeling approach provided an unbiased estimate of genetic effects, greatly improved the power of single-variant association tests among methods that can properly estimate allele effects, and enhanced gene-level tests over existing approaches. Software packages implementing these methods are available online.

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.

Proper conditional analysis in the presence of missing data: Application to large scale meta-analysis of tobacco use phenotypes.

Meta-analysis of genetic association studies increases sample size and the power for mapping complex traits. Existing methods are mostly developed for datasets without missing values, i.e. the summary association statistics are measured for all variants in contributing studies. In practice, genotype imputation is not always effective. This may be the case when targeted genotyping/sequencing assays are used or when the un-typed genetic variant is rare. Therefore, contributed summary statistics often contain missing values. Existing methods for imputing missing summary association statistics and using imputed values in meta-analysis, approximate conditional analysis, or simple strategies such as complete case analysis all have theoretical limitations. Applying these approaches can bias genetic effect estimates and lead to seriously inflated type-I or type-II errors in conditional analysis, which is a critical tool for identifying independently associated variants. To address this challenge and complement imputation methods, we developed a method to combine summary statistics across participating studies and consistently estimate joint effects, even when the contributed summary statistics contain large amounts of missing values. Based on this estimator, we proposed a score statistic called PCBS (partial correlation based score statistic) for conditional analysis of single-variant and gene-level associations. Through extensive analysis of simulated and real data, we showed that the new method produces well-calibrated type-I errors and is substantially more powerful than existing approaches. We applied the proposed approach to one of the largest meta-analyses to date for the cigarettes-per-day phenotype. Using the new method, we identified multiple novel independently associated variants at known loci for tobacco use, which were otherwise missed by alternative methods. Together, the phenotypic variance explained by these variants was 1.1%, improving that of previously reported associations by 71%. These findings illustrate the extent of locus allelic heterogeneity and can help pinpoint causal variants.