Efficient matrix profile computation with Euclidean distance using Eigen transformation: Performance evaluation based on beat-to-beat interval (BBI) data.

The matrix profile serves as a fundamental tool to provide insights into similar patterns within time series. Existing matrix profile algorithms have been primarily developed for the normalized Euclidean distance, which may not be a proper distance measure in many settings. The methodology work of this paper was motivated by statistical analysis of beat-to-beat interval (BBI) data collected from smartwatches to monitor e-cigarette users' heart rate change patterns for which the original Euclidean distance ( L 2 $$ {L}_2 $$ -norm) would be a more suitable choice. Yet, incorporating the Euclidean distance into existing matrix profile algorithms turned out to be computationally challenging, especially when the time series is long with extended query sequences. We propose a novel methodology to efficiently compute matrix profile for long time series data based on the Euclidean distance. This methodology involves four key steps including (1) projection of the time series onto eigenspace; (2) enhancing singular value decomposition (SVD) computation; (3) early abandon strategy; and (4) determining lower bounds based on the first left singular vector. Simulation studies based on BBI data from the motivating example have demonstrated remarkable reductions in computational time, ranging from one-fourth to one-twentieth of the time required by the conventional method. Unlike the conventional method of which the performance deteriorates sharply as the time series length or the query sequence length increases, the proposed method consistently performs well across a wide range of the time series length or the query sequence length.

Statistical methods for predicting e-cigarette use events based on beat-to-beat interval (BBI) data collected from wearable devices.

The prevalence of e-cigarette use among young adults in the USA is high (14%). Although the majority of users plan to quit vaping, the motivation to make a quit attempt is low and available support during a quit attempt is limited. Using wearable sensors to collect physiological data (eg, heart rate) holds promise for capturing the right timing to deliver intervention messages. This study aims to fill the current knowledge gap by proposing statistical methods to (1) de-noise beat-to-beat interval (BBI) data from smartwatches worn by 12 young adult regular e-cigarette users for 7 days; and (2) summarize the de-noised data by event and control segments. We also conducted a comprehensive review of conventional methods for summarizing heart rate variability (HRV) and compared their performance with the proposed method. The results show that the proposed singular spectrum analysis (SSA) can effectively de-noise the highly variable BBI data, as well as quantify the proportion of total variation extracted. Compared to existing HRV methods, the proposed second order polynomial model yields the highest area under the curve (AUC) value of 0.76 and offers better interpretability. The findings also indicate that the average heart rate before vaping is higher and there is an increasing trend in the heart rate before the vaping event. Importantly, the development of increasing heart rate observed in this study implies that there may be time to intervene as this physiological signal emerges. This finding, if replicated in a larger scale study, may inform optimal timings for delivering messages in future intervention.

Subtypes of Dual Users of Combustible and Electronic Cigarettes: Longitudinal Changes in Product Use and Dependence Symptomatology.

INTRODUCTION: Cross-sectional surveys found behavioral heterogeneity among dual users of combustible and electronic cigarettes. Yet, prior classification did not reflect dynamic interactions between cigarette and e-cigarette consumption, which may reveal changes in product-specific dependence. The contexts of dual use that could inform intervention were also understudied. METHODS: This study conducted secondary analysis on 13 waves of data from 227 dual users who participated in a 2-year observational study. The k-means method for joint trajectories of cigarette and e-cigarette consumption was adopted to identify the subtypes of dual users. The time-varying effect model was used to characterize the subtype-specific trajectories of cigarette and e-cigarette dependence. The subtypes were also compared in terms of use contexts. RESULTS: The four clusters were identified: light dual users, predominant vapers, heavy dual users, and predominant smokers. Although heavy dual users and predominant smokers both smoked heavily at baseline, by maintaining vaping at the weekly to daily level the heavy dual users were able to considerably reduce cigarette use. Yet, the heavy dual users' drop in cigarette dependence was not as dramatic as their drop in cigarette consumption. Predominant vapers appeared to engage in substitution, as they decreased their smoking and increased their e-cigarette dependence. They were also more likely to live in environments with smoking restrictions and report that their use of e-cigarettes reduced cigarette craving and smoking frequency. CONCLUSIONS: Environmental constraints can drive substitution behavior and the substitution behavior is able to be sustained if people find the substitute to be effective. IMPLICATIONS: This study characterizes subtypes of dual users based on the dynamic interactions between cigarette use and e-cigarette use as well as product-specific trajectories of dependence. The subtypes differ in not only sociodemographic characteristics but also contexts of cigarette and e-cigarette use. Higher motivation to use e-cigarettes to quit smoking and less permissive environment for smoking may promote substitution of cigarettes by e-cigarettes.

Retrospective and Real-time Measures of the Quantity of E-cigarette Use: An Ecological Momentary Assessment Study.

INTRODUCTION: Quantifying e-cigarette use is challenging because of the wide variety of products and the lack of a clear, objective demarcation of a use event. This study aimed to characterize the difference between retrospective and real-time measures of the quantity of e-cigarette use and identify the covariates that may account for discrepancies between the two types of measures. METHODS: This study analyzed data from 401 college student e-cigarette users in Indiana and Texas who responded to a web survey (retrospective) and 7-day ecological momentary assessments (EMA) (real-time) on their e-cigarette use behavior, dependence symptomatology, e-cigarette product characteristics, and use contexts from Fall 2019 to Fall 2021. Generalized linear mixed models were used to model the real-time measures of quantity offset by the retrospective average quantity. RESULTS: Although the number of times using e-cigarettes per day seems to be applicable to both retrospective and real-time measures, the number reported via EMA was 8.5 times the retrospective report. E-cigarette users with higher e-cigarette primary dependence motives tended to report more daily nicotine consumption via EMA than their retrospective reports (ie, perceived average consumption). Other covariates that were associated with discrepancies between real-time and retrospective reports included gender, nicotine concentration, using a menthol- or fruit-flavored product, co-use with alcohol, and being with others when vaping. CONCLUSIONS: The study found extreme under-reporting of e-cigarette consumption on retrospective surveys. Important covariates identified to be associated with higher than average consumption may be considered as potential targets for future vaping interventions. IMPLICATIONS: This is the first study that characterizes the direction and magnitude of the difference between retrospective and real-time measures of the quantity of e-cigarette use among young adults-the population most likely to use e-cigarettes. An average retrospective account of vaping events per day may significantly underestimate e-cigarette use frequency among young adults. The lack of insight into the degree of consumption among users with heavy primary dependence motives illustrates the importance of incorporating self-monitoring into cessation interventions.

Polygenic risk prediction based on singular value decomposition with applications to alcohol use disorder.

BACKGROUND/AIM: The polygenic risk score (PRS) shows promise as a potentially effective approach to summarize genetic risk for complex diseases such as alcohol use disorder that is influenced by a combination of multiple variants, each of which has a very small effect. Yet, conventional PRS methods tend to over-adjust confounding factors in the discovery sample and thus have low power to predict the phenotype in the target sample. This study aims to address this important methodological issue. METHODS: This study proposed a new method to construct PRS by (1) approximating the polygenic model using a few principal components selected based on eigen-correlation in the discovery data; and (2) conducting principal component projection on the target data. Secondary data analysis was conducted on two large scale databases: the Study of Addiction: Genetics and Environment (SAGE; discovery data) and the National Longitudinal Study of Adolescent to Adult Health (Add Health; target data) to compare performance of the conventional and proposed methods. RESULT AND CONCLUSION: The results show that the proposed method has higher prediction power and can handle participants from different ancestry backgrounds. We also provide practical recommendations for setting the linkage disequilibrium (LD) and p value thresholds.

Characteristics of statewide prescription drug monitoring programs and potentially inappropriate opioid prescribing to patients with non-cancer chronic pain: A machine learning application.

Unnecessary/unsafe opioid prescribing has become a major public health concern in the U.S. Statewide prescription drug monitoring programs (PDMPs) with varying characteristics have been implemented to improve safe prescribing practice. Yet, no studies have comprehensively evaluated the effectiveness of PDMP characteristics in reducing opioid-related potentially inappropriate prescribing (PIP) practices. The objective of the study is to apply machine learning methods to evaluate PDMP effectiveness by examining how different PDMP characteristics are associated with opioid-related PIPs for non-cancer chronic pain (NCCP) treatment. This was a retrospective observational study that included 802,926 adult patients who were diagnosed NCCP, obtained opioid prescriptions, and were continuously enrolled in plans of a major U.S. insurer for over a year. Four outcomes of opioid-related PIP practices, including dosage ≥50 MME/day and ≥90 MME/day, days supply ≥7 days, and benzodiazepine-opioid co-prescription were examined. Machine learning models were applied, including logistic regression, least absolute shrinkage and selection operation regression, classification and regression trees, random forests, and gradient boost modeling (GBM). The SHapley Additive exPlanations (SHAP) method was applied to interpret model results. The results show that among 1,886,146 NCCP opioid-related claims, 22.8% had an opioid dosage ≥50 MME/day and 8.9% ≥90 MME/day, 70.3% had days supply ≥7 days, and 10.3% were when benzodiazepine was filled ≤7 days ago. GBM had superior model performance. We identified the most salient PDMP characteristics that predict opioid-related PIPs (e.g., broader access to patient prescription history, monitoring Schedule IV controlled substances), which could be informative to the states considering the redesign of PDMPs.

Home e-cigarette rules and youth's vulnerability to initiate and sustain e-cigarette use.

Existing studies of the impact of home rules on youth's vulnerability to e-cigarette use were based on cross-sectional data, youth or parent reports alone, as well as youth's perceptions and susceptibility. This study capitalizes on the restricted-use data of the Population Assessment of Tobacco and Health (PATH) Study to examine the longitudinal association between home rules for e-cigarette use and youth's vulnerability including initiation of use and regular use two years later. Secondary analysis was conducted on 1203 parent-youth pairs who participated in both Wave 4 (2016-2018) and Wave 5 (2018-2019) assessment of the PATH Study and while the youth were age 12-16 at Wave 4. Linear and logistic regressions were performed to examine the associations between having a strict home rule for e-cigarette use at Wave 4 and the youth's outcomes including perceived social norms, expectancies, susceptibility, initiation of use, and regular use of e-cigarettes at Wave 5, controlling for parent and youth factors. The results show that having a strict home rule for e-cigarette use was associated with youth's heightened level of perceived injunctive norms (ß = 0.22, p < 0.01), higher expectancy of harmfulness (ß = 0.28, p < 0.01) and lower odds for regular e-cigarette use (OR = 0.36, p < 0.05). In conclusion, the findings of this study support the potential protective effects of implementing a strict home rule for e-cigarette use. Future intervention efforts may promote parents' awareness of the potential protective effects of a strict home e-cigarette rule on youth's normative belief, harm expectancy, and behavior of e-cigarette use.

Alcohol and cannabis co-use and longitudinal gray matter volumetric changes in early and late adolescence.

BACKGROUND: Previous studies have characterized the impact of substance use on cerebral structure and function in adolescents. Yet, the great majority of prior studies employed a small sample, presented cross-sectional findings, and omitted potential sex differences. METHODS: Using data based on 724 adolescents (370 females) curated from the NCANDA study, we investigated how gray matter volumes (GMVs) decline longitudinally as a result of alcohol and cannabis use. The impacts of alcohol and cannabis co-use and how these vary across assigned sex at birth and age were examined. Brain imaging data comprised the GMVs of 34 regions of interest and the results were evaluated with a Bonferroni correction. RESULTS: Mixed-effects modeling showed faster volumetric declines in the caudal middle frontal cortex, fusiform, inferior frontal, superior temporal (STG), and supramarginal (SMG) gyri, at -0.046 to -0.138 cm3 /year in individuals with prior-year alcohol and cannabis co-use, but not those engaged in alcohol or cannabis use only. These findings cannot be explained by more severe alcohol use among co-users. Further, alcohol and cannabis co-use in early versus late adolescence predicted faster volumetric decline in the STG and SMG across assigned sex at birth. CONCLUSIONS: Findings highlight the longitudinal impact of alcohol and cannabis co-use on brain development, especially among youth reporting early adolescent onset of use. The volumetric decline was noted in cortical regions in support of attention, memory, executive control, and social cognition, suggesting the pervasive effect of alcohol and cannabis co-use on brain development.

Chronic pain and delinquency partially explain the effect of the DRD4 gene polymorphism on adult substance use.

Background: The dopamine receptor D4 [DRD4] has been reported to be associated with substance use. Yet, the roles that health conditions and behaviors may play in such association are understudied.Objective: This longitudinal study investigated the potential mediation effects of chronic pain and delinquency in adolescence on the association between the DRD4 2-repeat allele and substance use in adulthood. Sex, witnessing violence, and experiencing violence were also examined as potential moderators for the mediation pathways.Methods: We used the restricted and candidate gene data from the National Longitudinal Study of Adolescent to Adult Health (Waves I-IV) to conduct secondary analysis (N = 8,671; 47% male). A two-step approach was adopted to examine the mediation effects regarding four substance use outcomes in adulthood: number of lifetime alcohol use disorder symptoms, lifetime regular smoker status, past-month smoking, and lifetime "pain killer" misuse. The moderation effects were investigated using stratification and permutation.Results: The DRD4 2-repeat allele was associated with all adulthood substance use outcomes through adolescent chronic pain and delinquency (AORs/IRR range 1.08-3.78; all ps<0.01). The association between delinquency and smoking was higher among females. The association between delinquency and substance use was lower among the participants who witnessed violence in adolescence.Conclusions: This study identified modifiable mediators underlying the association between the DRD4 2-repeat allele and substance use behaviors, concluding that chronic pain and delinquency partially explain the effect of the DRD4 gene polymorphism on adult substance use.

A new statistical model for longitudinal ecological momentary assessment data on dual use of electronic and combustible cigarettes.

Background: Existing studies of dual use of electronic and combustible cigarettes either collected longitudinal data with long gaps in between waves or conducted ecological momentary assessment (EMA) over a short period of time. In recent years, the measurement burst design that embeds an EMA protocol in each wave assessment of a traditional longitudinal study has become more popular and yet conventional generalized linear mixed models (GLMM) have important limitations for handling data from this design.Objectives: This study proposed a new statistical method to analyze data from the measurement burst design.Methods: This new statistical method was designed to model the short-term (within-wave) as well as long-term (between-wave) changes and was validated by a simulation study. Secondary analysis was conducted to analyze data from 205 dual users (52% male) and 146 exclusive smokers (50% male) who participated in a recent study using the measurement burst design.Results: The simulation study shows that the proposed method can handle the gap between waves well and is also robust to nonlinear changes across waves. Although no short-term change in smoking was found, dual users reported a long-term reduction in cigarette use that was more rapid compared to exclusive smokers (߈=-0.0127,p=.0167). Vaping more was associated with smoking less (߈=-0.0058,p=.0054).Conclusion: The proposed method is highly applicable as it can be easily implemented by substance use researchers and the results can be straightforwardly interpreted. The results suggest that e-cigarette use may play a role in promoting a long-term reduction in smoking among dual users.

Validating E-Cigarette Dependence Scales Based on Dynamic Patterns of Vaping Behaviors.

INTRODUCTION: Existing e-cigarette dependence scales are mainly validated based on retrospective overall consumption or perception. Further, given that the majority of adult e-cigarette users also use combustible cigarettes, it is important to determine whether e-cigarette dependence scales capture the product-specific dependence. This study fills in the current knowledge gaps by validating e-cigarette dependence scales using novel indices of dynamic patterns of e-cigarette use behaviors and examining the association between dynamic patterns of smoking and e-cigarette dependence among dual users. METHODS: Secondary analysis was conducted on the 2-week ecological momentary assessment data from 116 dual users. The Smoothly Clipped Absolute Deviation penalty (SCAD) was adopted to select important indices for dynamic patterns of consumption or craving and estimate their associations with e-cigarette dependence scales. RESULTS: The fitted linear regression models support the hypothesis that higher e-cigarette dependence is associated with higher levels of e-cigarette consumption and craving as well as lower instability of e-cigarette consumption. Controlling for dynamic patterns of vaping, dual users with lower e-cigarette dependence tend to report higher day-to-day dramatic changes in combustible cigarette consumption but not higher average levels of smoking. CONCLUSIONS: We found that more stable use patterns are related to higher levels of dependence, which has been demonstrated in combustible cigarettes and we have now illustrated in e-cigarettes. Furthermore, the e-cigarette dependence scales may capture the product-specific average consumption but not product-specific instability of consumption. IMPLICATIONS: This study provides empirical support for three e-cigarette dependence measures: PS-ECDI, e-FTCD, and e-WISDM, based on dynamic patterns of e-cigarette consumption and craving revealed by EMA data that have great ecological validity. This is the first study that introduces novel indices of dynamic patterns and demonstrates their potential applications in vaping research.

Deposition of E-cigarette aerosol in human airways through passive vaping.

Secondary exposure to e-cigarette aerosol (passive vaping) will soon become a pressing public health issue in the world. Yet, the current knowledge about respiratory depositions of e-cigarette aerosol through passive vaping in human airways is limited due to critical weaknesses of traditional experimental methods. To fill in this important knowledge gap, this study proposed a special approach involving an upgraded Mobile Aerosol Lung Deposition Apparatus (MALDA) that consists of a set of human airway replicas including a head airway, tracheobronchial airways down to the 11th lung generation, and a representative alveolar section. In addition to the comprehensive coverage of human airways, the MALDA is easily transportable for providing efficient estimations of aerosol respiratory deposition. In this study, the MALDA was first evaluated in the laboratory and then applied to estimate the respiratory deposition associated with passive vaping in an indoor real-life setting. The results showed that the respiratory deposition data aligned closely with the conventional respiratory deposition curves not only in the head-to-TB region but also in the alveolar region. The strengths of MALDA demonstrate great promise for a wide variety of applications in real-life settings that could provide crucial information for future public health and indoor air quality studies.

Resilience as a Mediator between Adverse Childhood Experiences and Prescription Opioid Misuse among U.S. Adults.

BACKGROUND:: Studies have shown some linkage between adverse childhood experiences (ACEs) and prescription opioid misuse. While preventable, once an individual is exposed to ACEs, they can have irreparable health impacts. Resilience could have protective effects on preventing those with ACEs from adulthood health-risk behaviors. Objectives: To examine how resilience mediates the association between ACEs and adulthood prescription opioid misuse among U.S. adults. Methods: Adult respondents (n = 33,613) from the National Epidemiologic Survey on Alcohol and Related Conditions-III (2012-2013) were included. A count of ten types of ACEs constituted the ACE score (range: 0-10). A latent variable was created to measure resilience based on the Connor-Davidson Resilience Scale. Generalized structural equation modeling (GSEM) was utilized to conduct the mediation analysis. The Problem Behavior Theory was used to guide covariate selection. Results: The GSEM measurement model estimated the latent variable resilience and determined that tolerance of negative effects, self-control, acceptance of change, and spiritual influence were all associated with resilience, holding personal competence constrained for estimation purposes. Respondents with a higher ACE score were more likely to misuse prescription opioids in the past year (OR = 1.08; p < 0.01), where 40.8% of the association was mediated by resilience (indirect effect OR = 1.06). Conclusion: Reducing prescription opioid misuse is an essential step in alleviating the current opioid epidemic. Findings suggested that resilience mediated the relationship between ACEs and adulthood prescription opioid misuse. To prevent adults with ACEs from misusing prescription opioids, interventions designed to address ACEs' impact should include behavioral and educational components for enhancing the priority dimensions of resilience.

Aggressive-Depressive Trajectories in Childhood and Their Associations with Drinking Behaviors and Problems in Adolescence and Emerging Adulthood.

Research indicates that externalizing and internalizing pathways emanated in childhood are connected to later drinking behaviors; however, no study has investigated the contemporaneous effects of the two pathways using a person-centered approach that categorizes individuals based on their various developmental patterns. This study examined the distinct patterns of concurrent development of aggression and depressive symptoms in childhood and their associations with later drinking behaviors using data from a 15-year Taiwanese cohort since age 8 (N = 2854, 49% females). Group-based multi-trajectory modeling identified four aggressive-depressive trajectory groups: Moderate, Aggressive, Depressive, and Comorbid, which manifested a sequential risk gradient in alcohol use. Comorbid group, characterized by persistently high levels of aggression and depressive symptoms, has the highest levels of alcohol use and drinking problems and the earliest onset of drinking. Aggressive and Depressive groups have higher levels of alcohol use and earlier onset of drinking than Moderate group. These findings imply the importance of monitoring aggression and depressive symptoms simultaneously and continually in childhood to prevent later drinking.

Time-varying Effects of GABRG1 and Maladaptive Peer Behavior on Externalizing Behavior from Childhood to Adulthood: Testing Gene × Environment × Development Effects.

Engagement in externalizing behavior is problematic. Deviant peer affiliation increases risk for externalizing behavior. Yet, peer effects vary across individuals and may differ across genes. This study determines gene × environment × development interactions as they apply to externalizing behavior from childhood to adulthood. A sample (n = 687; 68% male, 90% White) of youth from the Michigan Longitudinal Study was assessed from ages 10 to 25. Interactions between γ-amino butyric acid type A receptor γ1 subunit (GABRG1; rs7683876, rs13120165) and maladaptive peer behavior on externalizing behavior were examined using time-varying effect modeling. The findings indicate a sequential risk gradient in the influence of maladaptive peer behavior on externalizing behavior depending on the number of G alleles during childhood through adulthood. Individuals with the GG genotype are most vulnerable to maladaptive peer influences, which results in greater externalizing behavior during late childhood through early adulthood.

Interstate data sharing of prescription drug monitoring programs and associated opioid prescriptions among patients with non-cancer chronic pain.

All fifty states have implemented prescription drug monitoring programs (PDMPs) to reduce misuse and diversion of controlled drugs. Interstate PDMP data sharing has been called for by clinical practitioners, but evidence to support the effectiveness of PDMP data sharing is lacking. This study examined whether PDMP interstate data sharing with bordering states was associated with prescriptions of opioids. This was a cross-sectional study that included patients with non-cancer chronic pain from the 2014 National Ambulatory Medical Care Survey (weighted N = 66,198,751; unweighted N = 2846). Multinomial logistic regression was performed to examine the association between PDMP interstate data sharing status and patients' being prescribed opioids for pain treatment, controlling for covariates guided by the Eisenberg's model of physician decision-making. Findings indicated that patients residing in states with interstate PDMP data sharing with all or partial bordering states were not less likely to be prescribed opioids compared to those living in states without interstate data sharing. Other factors such as patient age, health insurance type, new patient status, and physician adoption of electronic medical records were associated with the likelihood of patients' being prescribed opioids. This study concluded that current practice of interstate PDMP data sharing with bordering states was not associated with patients' being prescribed opioids for non-cancer chronic pain treatment. Future studies and policy efforts that unravel technological, legal, and political barriers to reciprocal and equal interstate data sharing with bordering states should be warranted to inform PDMP redesign and in turn, augment overall PDMP effectiveness in reducing misuse of prescription opioids.

Identifying pleiotropic genes in genome-wide association studies from related subjects using the linear mixed model and Fisher combination function.

BACKGROUND: A multivariate genome-wide association test is proposed for analyzing data on multivariate quantitative phenotypes collected from related subjects. The proposed method is a two-step approach. The first step models the association between the genotype and marginal phenotype using a linear mixed model. The second step uses the correlation between residuals of the linear mixed model to estimate the null distribution of the Fisher combination test statistic. RESULTS: The simulation results show that the proposed method controls the type I error rate and is more powerful than the marginal tests across different population structures (admixed or non-admixed) and relatedness (related or independent). The statistical analysis on the database of the Study of Addiction: Genetics and Environment (SAGE) demonstrates that applying the multivariate association test may facilitate identification of the pleiotropic genes contributing to the risk for alcohol dependence commonly expressed by four correlated phenotypes. CONCLUSIONS: This study proposes a multivariate method for identifying pleiotropic genes while adjusting for cryptic relatedness and population structure between subjects. The two-step approach is not only powerful but also computationally efficient even when the number of subjects and the number of phenotypes are both very large.

A time-varying effect model for examining group differences in trajectories of zero-inflated count outcomes with applications in substance abuse research.

This study proposes a time-varying effect model for examining group differences in trajectories of zero-inflated count outcomes. The motivating example demonstrates that this zero-inflated Poisson model allows investigators to study group differences in different aspects of substance use (e.g., the probability of abstinence and the quantity of alcohol use) simultaneously. The simulation study shows that the accuracy of estimation of trajectory functions improves as the sample size increases; the accuracy under equal group sizes is only higher when the sample size is small (100). In terms of the performance of the hypothesis testing, the type I error rates are close to their corresponding significance levels under all settings. Furthermore, the power increases as the alternative hypothesis deviates more from the null hypothesis, and the rate of this increasing trend is higher when the sample size is larger. Moreover, the hypothesis test for the group difference in the zero component tends to be less powerful than the test for the group difference in the Poisson component. Copyright © 2016 John Wiley & Sons, Ltd.

An efficient genome-wide association test for multivariate phenotypes based on the Fisher combination function.

BACKGROUND: In genome-wide association studies (GWAS) for complex diseases, the association between a SNP and each phenotype is usually weak. Combining multiple related phenotypic traits can increase the power of gene search and thus is a practically important area that requires methodology work. This study provides a comprehensive review of existing methods for conducting GWAS on complex diseases with multiple phenotypes including the multivariate analysis of variance (MANOVA), the principal component analysis (PCA), the generalizing estimating equations (GEE), the trait-based association test involving the extended Simes procedure (TATES), and the classical Fisher combination test. We propose a new method that relaxes the unrealistic independence assumption of the classical Fisher combination test and is computationally efficient. To demonstrate applications of the proposed method, we also present the results of statistical analysis on the Study of Addiction: Genetics and Environment (SAGE) data. RESULTS: Our simulation study shows that the proposed method has higher power than existing methods while controlling for the type I error rate. The GEE and the classical Fisher combination test, on the other hand, do not control the type I error rate and thus are not recommended. In general, the power of the competing methods decreases as the correlation between phenotypes increases. All the methods tend to have lower power when the multivariate phenotypes come from long tailed distributions. The real data analysis also demonstrates that the proposed method allows us to compare the marginal results with the multivariate results and specify which SNPs are specific to a particular phenotype or contribute to the common construct. CONCLUSIONS: The proposed method outperforms existing methods in most settings and also has great applications in GWAS on complex diseases with multiple phenotypes such as the substance abuse disorders.

Longitudinal phenotypes for alcoholism: Heterogeneity of course, early identifiers, and life course correlates.

Alcoholism is a heterogeneous disorder; however, characterization of life-course variations in symptomatology is almost nonexistent, and developmentally early predictors of variations are very poorly characterized. In this study, the course of alcoholic symptomatology over 32 years is differentiated, and predictors and covariates of trajectory class membership are identified. A community sample of alcoholic and neighborhood matched control families, 332 men and 336 women, was recruited based on alcoholism in the men. Symptoms were assessed retrospectively at baseline (mean age = 32) back to age 15 and prospectively from baseline every 3 years for 15 years. Trajectory classes were established using growth mixture modeling. Men and women had very similarly shaped trajectory classes: developmentally limited (men: 29%, women: 42%), developmentally cumulative (men: 26%, women: 38%), young adult onset (men: 31%, women: 21%), and early onset severe (men: 13%). Three factors at age 15 predicted class membership: family history of alcoholism, age 15 symptoms, and level of childhood antisocial behavior. Numerous measures of drinking and other psychopathology were also associated with class membership. The findings suggest that clinical assessments can be crafted where the profile of current and historical information can predict not only severity of prognosis but also future moderation of symptoms and/or remission over intervals as long as decades.