Latent triple trajectories of substance use as predictors for the onset of antisocial personality disorder among urban African American and Puerto Rican adults: A 22-year longitudinal study.

Background: Antisocial personality disorder (ASPD) incurs a high cost to society due to the high risk of violent and nonviolent offenses associated with this personality disorder, thus making the examination of the etiology and the onset of ASPD an important public health concern. Method: The present study consisted of five waves of data collection of the Harlem Longitudinal Development Study (N = 674). In the Cox proportional hazard model, latent multiple substance use trajectories from mid-adolescence to emerging adulthood (mean age 14 to mean age 24) were used as a predictor for the onset of ASPD during emerging adulthood to the mid-thirties (mean age 24 to mean age 36). The control variables were gender, ethnicity, problem behaviors, and victimization. Results: In the multiple Cox proportional hazard model, the high (HR = 2.74, p < 0.001) and the increasing frequency of (HR = 2.55, p < 0.001) use on alcohol, cigarette, and cannabis latent trajectory groups were associated with an increased hazard of ASPD onset as compared with the no or low frequency of use on alcohol, cigarette, and cannabis latent trajectory group after controlling for demographic factors and earlier problem behaviors as well as victimization. Conclusions: The implications of this study for the prediction of adult ASPD onset time may focus on the early use of alcohol, cigarette, and cannabis from mid adolescence to emerging adulthood.

Correlates of Cannabis Use Disorders among urban women of color: childhood abuse, relationship with spouse/partner, and media exposure.

OBJECTIVES: As the rate of cannabis use increases, it is expected that more individuals will develop a Cannabis Use Disorder (CUD). Relatively little is known, however, about the psychosocial correlates of CUDs among racial/ethnic minority women. This study, therefore, examined correlates of CUDs among a cohort of adult African American and Puerto Rican women. METHODS: The sample consisted of African American and Puerto Rican female participants (N = 343), who have been followed by the Harlem Longitudinal Development Study from mean age 14 to mean age 39 years. The bivariate and multivariate associations between CUDs at age 39 and variables from 5 domains - demographics, earlier cannabis use, childhood abuse, the relationship with the spouse/partner, and media exposure - were assessed using logistic regression analyses. RESULTS: The results showed that, with the exception of demographic factors, variables from each of the domains (e.g., sexual abuse in childhood, arguments with spouse/partner, and hours of visual media exposure) were related to CUDs at age 39. CONCLUSIONS: Findings suggest that in addition to treating CUDs, couples therapy may be indicated to strengthen the spousal/partner relationship, enlist the spouse/partner's support for cannabis use cessation. Furthermore, frequency of visual media exposure may need to be reduced.

Adolescent risk and protective factors predicting triple trajectories of substance use from adolescence into adulthood.

OBJECTIVES: Since the number of individuals who use substances in the United States has markedly increased every year, substance use is a significant public health concern. The current study examines the possible risk and protective factors associated with triple comorbid trajectories of longitudinal alcohol, tobacco, and cannabis use from age 14 to 36. METHODS: A community sample of 674 participants (53% African Americans and 47% Puerto Ricans; 60% females) were recruited from the Harlem Longitudinal Development Study. Multinomial logistic regression analyses were conducted to examine the associations between the risk (low self-control, peer drug use) and protective (parent-child attachment, family church attendance) factors at age 14 and membership in the triple trajectory groups derived from a multivariate growth mixture model. RESULTS: Low self-control and peer drug use were associated with an increased likelihood of being a member in the triple comorbid trajectory groups compared to the reference group (i.e., low alcohol, no tobacco, and no cannabis use). On the other hand, parent-child attachment and family church attendance were associated with a decreased likelihood of being a member in the triple comorbid trajectory groups compared to the reference group. CONCLUSIONS: Treatment programs for adolescents who use substances may be more helpful if their parents and/or friends could also participate together with the adolescent, rather than only the adolescent participates in the treatment programs. Further research is needed to gain a greater understanding of the conceptual nature of the relationship between earlier risk and protective factors and later substance use patterns.

Genetic characteristics of non-familial epilepsy.

BACKGROUND: Knowledge of the genetic etiology of epilepsy can provide essential prognostic information and influence decisions regarding treatment and management, leading us into the era of precision medicine. However, the genetic basis underlying epileptogenesis or epilepsy pharmacoresistance is not well-understood, particularly in non-familial epilepsies with heterogeneous phenotypes that last until or start in adulthood. METHODS: We sought to determine the contribution of known epilepsy-associated genes (EAGs) to the causation of non-familial epilepsies with heterogeneous phenotypes and to the genetic basis underlying epilepsy pharmacoresistance. We performed a multi-center study for whole exome sequencing-based screening of 178 selected EAGs in 243 non-familial adult patients with primarily focal epilepsy (122 drug-resistant and 121 drug-responsive epilepsies). The pathogenicity of each variant was assessed through a customized stringent filtering process and classified according to the American College of Medical Genetics and Genomics guidelines. RESULTS: Possible causal genetic variants of epilepsy were uncovered in 13.2% of non-familial patients with primarily focal epilepsy. The diagnostic yield according to the seizure onset age was 25% (2/8) in the neonatal and infantile period, 11.1% (14/126) in childhood and 14.7% (16/109) in adulthood. The higher diagnostic yields were from ion channel-related genes and mTOR pathway-related genes, which does not significantly differ from the results of previous studies on familial or early-onset epilepsies. These potentially pathogenic variants, which were identified in genes that have been mainly associated with early-onset epilepsies with severe phenotypes, were also linked to epilepsies that start in or last until adulthood in this study. This finding suggested the presence of one or more disease-modifying factors that regulate the onset time or severity of epileptogenesis. The target hypothesis of epilepsy pharmacoresistance was not verified in our study. Instead, neurodevelopment-associated epilepsy genes, such as TSC2 or RELN, or structural brain lesions were more strongly associated with epilepsy pharmacoresistance. CONCLUSIONS: We revealed a fraction of possible causal genetic variants of non-familial epilepsies in which genetic testing is usually overlooked. In this study, we highlight the importance of earlier identification of the genetic etiology of non-familial epilepsies, which leads us to the best treatment options in terms of precision medicine and to future neurobiological research for novel drug development. This should be considered a justification for physicians determining the hidden genetics of non-familial epilepsies that last until or start in adulthood.

Single and dual diagnoses of major depressive disorder and posttraumatic stress disorder predicted by triple comorbid trajectories of tobacco, alcohol, and marijuana use among urban adults.

Background: The adverse consequences of major depressive disorder (MDD) and posttraumatic stress disorder (PTSD) affect a significant portion of the US population every year (i.e., 15 million for MDD; 8 million for PTSD) and are of public health concern. The current study examines tobacco, alcohol, and marijuana use as possible longitudinal predictors of MDD and/or PTSD. Methods: A community sample of 674 participants (53% African Americans and 47% Puerto Ricans; 405 females and 269 males) were recruited from the Harlem Longitudinal Development Study. We used Mplus software to obtain the triple trajectories of tobacco, alcohol, and marijuana use from mean age 14 to 36. Logistic regression analyses were then conducted to examine the associations between those triple trajectory groups and a single diagnosis of MDD or PTSD as well as a dual diagnosis of MDD with PTSD at age 36. Results: The observed percentages of MDD, PTSD, and the comorbidity of MDD and PTSD were 17%, 8%, and 5%, respectively. The heavy use of all 3 substances group was associated with an increased likelihood of having MDD (adjusted odds ratio [AOR] = 3.14, P < .01), PTSD (AOR = 3.91, P < .05), and MDD with PTSD (AOR = 6.64, P < .01), as compared with the tobacco and alcohol use group. Conclusions: Treatment programs to quit or reduce the use of tobacco, alcohol, and marijuana may help decrease the prevalence of MDD and PTSD. This could lead to improvements in individualized treatments for patients who use tobacco, alcohol, and marijuana and who have both MDD and PTSD.

Triple comorbid trajectories of alcohol, cigarette, and marijuana use from adolescence to adulthood predict insomnia in adulthood.

Approximately 9% of adults report the symptoms of insomnia, and there are a number of adverse consequences of insomnia. This could be a public health concern. The current study seeks plausible longitudinal predictors of insomnia for prevention purposes. A community sample of 674 participants (53% African Americans and 47% Puerto Ricans; 60% were females) were recruited from the Harlem Longitudinal Development Study. We applied a growth mixture model to obtain the triple trajectories of alcohol, cigarette, and marijuana use. Logistic regression analyses were then conducted to examine the associations between the triple trajectory groups from mean age 14 to 36 and insomnia at age 36. The estimated prevalence of insomnia is 7.1%. A five-group triple trajectory model was selected: A) Increasing use of all three substances (18%); B) Moderate use of alcohol and marijuana, and high use of cigarette (11%); C) Moderate use of alcohol and cigarette, and experimental use of marijuana (3%); D) Moderate use of all three substances (5%); and E) No or low use of all three substances (63%). Among the five trajectory groups, the increasing use of all three substances group (AOR = 2.71, p-value = .011) was associated with an increased likelihood of having insomnia as compared to the no or low use of all three substances group. Treatment programs to quit or reduce the use of alcohol, cigarette, and marijuana may help decrease the prevalence of insomnia. This could lead to improvements in individualized treatments for patients who have symptoms of insomnia and who also use substances.

Triple trajectories of alcohol use, tobacco use, and depressive symptoms as predictors of cannabis use disorders among urban adults.

Heavy cannabis use is associated with a wide array of physical, mental, and functional problems. Therefore, cannabis use disorders (CUDs) may be a major public health concern. Given the adverse health consequences of CUDs, the present study seeks to find possible precursors of CUDs. The current study consisted of 5 waves of data collection from the Harlem Longitudinal Development Study. Among 816 participants, about half are African Americans (52%), and the other half are Puerto Ricans (48%). We used Mplus to obtain the triple trajectories of alcohol use, tobacco use, and depressive symptoms. Logistic regression analyses were then conducted to examine the associations between the trajectory groups and CUDs. The 5 trajectory groups were (1) moderate alcohol use, high tobacco use, and high depressive symptoms (MHH; 12%); (2) moderate alcohol use, high tobacco use, and low depressive symptoms (MHL; 26%); (3) moderate alcohol use, low tobacco use, and low depressive symptoms (MLL; 18%); (4) low alcohol use, no tobacco use, and high depressive symptoms (LNH; 11%); and (5) low alcohol use, no tobacco use, and low depressive symptoms (LNL; 33%). The MHH, MHL, MLL, and LNH trajectory groups were associated with an increased likelihood of having CUDs compared to the LNL trajectory group after controlling for a number of confounding factors (e.g., CUDs in the late 20s). The findings of the current longitudinal study suggest that treatments designed to reduce or quit drinking as well as smoking and to relieve depressive symptoms may reduce the prevalence of CUDs. (PsycINFO Database Record

An Analytic Solution to the Computation of Power and Sample Size for Genetic Association Studies under a Pleiotropic Mode of Inheritance.

Our motivation here is to calculate the power of 3 statistical tests used when there are genetic traits that operate under a pleiotropic mode of inheritance and when qualitative phenotypes are defined by use of thresholds for the multiple quantitative phenotypes. Specifically, we formulate a multivariate function that provides the probability that an individual has a vector of specific quantitative trait values conditional on having a risk locus genotype, and we apply thresholds to define qualitative phenotypes (affected, unaffected) and compute penetrances and conditional genotype frequencies based on the multivariate function. We extend the analytic power and minimum-sample-size-necessary (MSSN) formulas for 2 categorical data-based tests (genotype, linear trend test [LTT]) of genetic association to the pleiotropic model. We further compare the MSSN of the genotype test and the LTT with that of a multivariate ANOVA (Pillai). We approximate the MSSN for statistics by linear models using a factorial design and ANOVA. With ANOVA decomposition, we determine which factors most significantly change the power/MSSN for all statistics. Finally, we determine which test statistics have the smallest MSSN. In this work, MSSN calculations are for 2 traits (bivariate distributions) only (for illustrative purposes). We note that the calculations may be extended to address any number of traits. Our key findings are that the genotype test usually has lower MSSN requirements than the LTT. More inclusive thresholds (top/bottom 25% vs. top/bottom 10%) have higher sample size requirements. The Pillai test has a much larger MSSN than both the genotype test and the LTT, as a result of sample selection. With these formulas, researchers can specify how many subjects they must collect to localize genes for pleiotropic phenotypes.

Transmission Disequilibrium Tests Based on Read Counts for Low-Coverage Next-Generation Sequence Data.

The purpose of this paper is the introduction of new statistical methods for case-parent trio association studies based on the read counts that can be obtained from next-generation sequencing (NGS) experiments. This work focuses on the inclusion of low-coverage data into the case-parent trio design without genotype classification or imputation. Two different approaches are considered: (1) a likelihood-based approach implementing a 15-component parametric mixture model and (2) a model-free approach that applies non-parametric statistical methods to the ratios of the read counts to coverage. Simulation studies are conducted to evaluate the performances of the proposed tests. In addition, the non-centrality parameters of the mixture likelihood-based tests are derived to determine sample sizes and coverage for a NGS experimental design. As an example, the sample sizes to maintain specified powers of a published adolescent idiopathic scoliosis (AIS) study are presented. The simulation results show that the tests using the genotypes classified by the maximum Bayesian posterior probability have significantly inflated type I error rates for low-coverage data. The tests using the posterior probabilities instead of the classified genotypes show lower power than the proposed tests. Generally, power for the likelihood-based approach is higher than that for the non-parametric ratio-based approach. For the AIS example, approximately 654 trios with 4× coverage are necessary to maintain 90% power when detecting an association of odds ratio 2 at a locus with a minor allele frequency of 0.35 at the level of significance α = 5 × 10(-8). By comparison, approximately 416 trios with 25× coverage are required to maintain the same power with the same settings. The R and C source codes to calculate the proposed test statistics, the sample sizes and power can be obtained by contacting the author (wkim@cau.ac.kr).

Single-variant and multi-variant trend tests for genetic association with next-generation sequencing that are robust to sequencing error.

As with any new technology, next-generation sequencing (NGS) has potential advantages and potential challenges. One advantage is the identification of multiple causal variants for disease that might otherwise be missed by SNP-chip technology. One potential challenge is misclassification error (as with any emerging technology) and the issue of power loss due to multiple testing. Here, we develop an extension of the linear trend test for association that incorporates differential misclassification error and may be applied to any number of SNPs. We call the statistic the linear trend test allowing for error, applied to NGS, or LTTae,NGS. This statistic allows for differential misclassification. The observed data are phenotypes for unrelated cases and controls, coverage, and the number of putative causal variants for every individual at all SNPs. We simulate data considering multiple factors (disease mode of inheritance, genotype relative risk, causal variant frequency, sequence error rate in cases, sequence error rate in controls, number of loci, and others) and evaluate type I error rate and power for each vector of factor settings. We compare our results with two recently published NGS statistics. Also, we create a fictitious disease model based on downloaded 1000 Genomes data for 5 SNPs and 388 individuals, and apply our statistic to those data. We find that the LTTae,NGS maintains the correct type I error rate in all simulations (differential and non-differential error), while the other statistics show large inflation in type I error for lower coverage. Power for all three methods is approximately the same for all three statistics in the presence of non-differential error. Application of our statistic to the 1000 Genomes data suggests that, for the data downloaded, there is a 1.5% sequence misclassification rate over all SNPs. Finally, application of the multi-variant form of LTTae,NGS shows high power for a number of simulation settings, although it can have lower power than the corresponding single-variant simulation results, most probably due to our specification of multi-variant SNP correlation values. In conclusion, our LTTae,NGS addresses two key challenges with NGS disease studies; first, it allows for differential misclassification when computing the statistic; and second, it addresses the multiple-testing issue in that there is a multi-variant form of the statistic that has only one degree of freedom, and provides a single p value, no matter how many loci.

Growth mixture modeling as an exploratory analysis tool in longitudinal quantitative trait loci analysis.

We examined the properties of growth mixture modeling in finding longitudinal quantitative trait loci in a genome-wide association study. Two software packages are commonly used in these analyses: Mplus and the SAS TRAJ procedure. We analyzed the 200 replicates of the simulated data with these programs using three tests: the likelihood-ratio test statistic, a direct test of genetic model coefficients, and the chi-square test classifying subjects based on the trajectory model's posterior Bayesian probability. The Mplus program was not effective in this application due to its computational demands. The distributions of these tests applied to genes not related to the trait were sensitive to departures from Hardy-Weinberg equilibrium. The likelihood-ratio test statistic was not usable in this application because its distribution was far from the expected asymptotic distributions when applied to markers with no genetic relation to the quantitative trait. The other two tests were satisfactory. Power was still substantial when we used markers near the gene rather than the gene itself. That is, growth mixture modeling may be useful in genome-wide association studies. For markers near the actual gene, there was somewhat greater power for the direct test of the coefficients and lesser power for the posterior Bayesian probability chi-square test.

Computing power and sample size for case-control association studies with copy number polymorphism: application of mixture-based likelihood ratio test.

Recent studies suggest that copy number polymorphisms (CNPs) may play an important role in disease susceptibility and onset. Currently, the detection of CNPs mainly depends on microarray technology. For case-control studies, conventionally, subjects are assigned to a specific CNP category based on the continuous quantitative measure produced by microarray experiments, and cases and controls are then compared using a chi-square test of independence. The purpose of this work is to specify the likelihood ratio test statistic (LRTS) for case-control sampling design based on the underlying continuous quantitative measurement, and to assess its power and relative efficiency (as compared to the chi-square test of independence on CNP counts). The sample size and power formulas of both methods are given. For the latter, the CNPs are classified using the Bayesian classification rule. The LRTS is more powerful than this chi-square test for the alternatives considered, especially alternatives in which the at-risk CNP categories have low frequencies. An example of the application of the LRTS is given for a comparison of CNP distributions in individuals of Caucasian or Taiwanese ethnicity, where the LRTS appears to be more powerful than the chi-square test, possibly due to misclassification of the most common CNP category into a less common category.

Data mining of RNA expression and DNA genotype data: presentation group 5 contributions to Genetic Analysis Workshop 15.

The complexity of data available in human genetics continues to grow at an explosive rate. With that growth, the challenges to understanding the meaning of the underlying information also grow. A currently popular approach to dissecting such information falls under the broad category of data mining. This can apply to any approach that tries to extract relevant information from large amounts of data, but often refers to methods that deal, in a non-linear fashion, with very large numbers of variables that cannot be simultaneously handled by more conventional statistical methods. To explore the usefulness of some of these approaches, 13 groups applied a variety of strategies to the first dataset provided to GAW 15 participants. With the extensive microarray and SNP data provided for 14 CEPH families, these groups explored multistage analyses, machine learning methods, network construction, and other techniques to try to answer questions about gene-gene interaction, functional similarities, co-regulated gene expression and the mapping of gene expression determinants, among others. In general, the methods offered strategies to provide a better understanding of the complex pathways involved in gene expression and function. These are still "works in progress," often exploratory in nature, but they provide insights into ways in which the data might be interpreted. Despite the still preliminary nature of some of these methods and the diversity of the approaches, some common themes emerged. The collection of papers and methods offer a starting point for further exploration of complex interactions in human genetic data now readily available.

A dose-response test via closed-form solutions for constrained MLEs in survival/sacrifice experiments.

In most survival-sacrifice experiments in animal carcinogenicity studies, the onset of the tumour of interest is not clinically observable. Due to the complexity of constraints for a biological justification, recently developed methods for estimating the tumour onset function and tumour-specific survival function employ computer-intensive numerical solutions. In this paper, closed-form solutions for nonparametric maximum likelihood estimators are derived under explicit and implicit inequality constraints obtained from the monotonicity of the survival functions. Our methods do not require cause-of-death information. The proposed methods can be used to estimate the tumour onset function and the survival function of the tumour of interest. We use the proposed estimators for the development of our new dose-response trend test. A modification of the Poly-k test is made by replacing the time-at-risk weight to a function of the tumour onset survival function. The weighted least square regression method is applied to the estimated survival functions in order to construct a dose-response trend test. A simulation study is conducted to evaluate the performance of the proposed test and compare it with existing trend tests. A real example is used to illustrate the methods.

The effects of SNP genotyping errors on the power of the Cochran-Armitage linear trend test for case/control association studies.

The questions addressed in this paper are: What single nucleotide polymorphism (SNP) genotyping errors are most costly, in terms of minimum sample size necessary (MSSN) to maintain constant asymptotic power and significance level, when performing case-control studies of genetic association applying the Cochran-Armitage trend test? And which trend test or chi2 test is more powerful under standard genetic models with genotyping errors? Our strategy is to expand the non-centrality parameter of the asymptotic distribution of the trend test to approximate the MSSN using a Taylor series linear in the genotyping error rates. We apply our strategy to example scenarios that assume recessive, dominant, additive, or over-dominant disease models. The most costly errors are recording the more common homozygote as the less common homozygote, and the more common homozygote as the heterozygote, with MSSN that become indefinitely large as the minor SNP allele frequency approaches zero. Misclassifying the heterozygote as the less common homozygote is costly when using the recessive trend test on data from a recessive model. The chi2 test has power close to, but less than, the optimal trend test and is never dominated over all genetic models studied by any specific trend test.

Mixture modeling of microarray gene expression data.

About 28% of genes appear to have an expression pattern that follows a mixture distribution. We use first- and second-order partial correlation coefficients to identify trios and quartets of non-sex-linked genes that are highly associated and that are also mixtures. We identified 18 trio and 35 quartet mixtures and evaluated their mixture distribution concordance. Concordance was defined as the proportion of observations that simultaneously fall in the component with the higher mean or simultaneously in the component with the lower mean based on their Bayesian posterior probabilities. These trios and quartets have a concordance rate greater than 80%. There are 33 genes involved in these trios and quartets. A factor analysis with varimax rotation identifies three gene groups based on their factor loadings. One group of 18 genes has a concordance rate of 56.7%, another group of 8 genes has a concordance rate of 60.8%, and a third group of 7 genes has a concordance rate of 69.6%. Each of these rates is highly significant, suggesting that there may be strong biological underpinnings for the mixture mechanisms of these genes. Bayesian factor screening confirms this hypothesis by identifying six single-nucleotide polymorphisms that are significantly associated with the expression phenotypes of the five most concordant genes in the first group.