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.

Trajectories of cannabis use beginning in adolescence associated with symptoms of posttraumatic stress disorder in the mid-thirties.

BACKGROUND: Posttraumatic stress disorder (PTSD) symptoms are related to a number of adverse consequences such as substance use and general medical conditions. The present longitudinal study seeks to find the longitudinal patterns of cannabis use as precursors of PTSD symptoms. Such information will serve as a guide for intervention programs for PTSD. METHODS: Growth mixture modeling was conducted to identify the cannabis use trajectory groups using a community sample of 674 participants (53% African Americans, 47% Hispanics of Puerto Rican decent; 60% females) from the Harlem Longitudinal Development Study. Logistic regression analyses were performed to examine the association between earlier trajectories of cannabis use (ages 14 to 36) and later symptoms of PTSD (at age 36) for the full model including the entire sample (N = 674) as well as the reduced model including only participants who had experienced a traumatic event (n = 205). RESULTS: Five trajectory groups of cannabis use were obtained. The chronic use group (full model: adjusted odds ratio [AOR] = 4.68, P < .01; reduced model: AOR = 4.27, P < .05), the late quitting group (full model: AOR = 6.18, P < .01; reduced model: AOR = 6.67, P < .01), and the moderate use group (full model: AOR = 3.97, P < .01; reduced model: AOR = 3.32, P < .05) were all associated with an increased likelihood of having PTSD symptoms at age 36 compared with the no use group. CONCLUSIONS: The findings provide information that PTSD symptoms in the mid-30s can possibly be reduced by decreasing membership in the chronic cannabis use trajectory group, the late quitting trajectory group, and the moderate cannabis use trajectory group.

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.

Trajectories of Cigarette Smoking Beginning in Adolescence Predict Insomnia in the Mid Thirties.

BACKGROUND: Insomnia is increasingly recognized as a public health concern in modern society. Insomnia diagnoses appear to be increasing and are associated with poor health outcomes. They may cost $100 billion annually in health services. OBJECTIVE: Given the adverse consequences of insomnia such as cardiovascular disease, diabetes, and depression, the present study was designed to examine the relationship of the trajectories of earlier cigarette smoking and later insomnia. The ultimate goal is to reduce the prevalence of insomnia. METHODS: 674 participants (53% African Americans, 47% Puerto Ricans, 60% females) were surveyed at 6 points in time. We employed the growth mixture model to obtain the trajectories of cigarette smoking from age 14 to 32. We used logistic regression analyses to examine the associations between the trajectories of smoking and insomnia. RESULTS: Males were less likely to have insomnia than females (Adjusted odds ratio: AOR = 0.34, p < .05). A higher Bayesian posterior probability (BPP) for the chronic smoking trajectory group (AOR = 2.69, p < .05) and for the moderate smoking trajectory group (AOR = 5.33, p < .01) was associated with an increased likelihood of having insomnia at age 36 compared with the BPP of the no or low smoking trajectory group. CONCLUSIONS: Prevention and treatment programs for individuals who suffer from insomnia should be implemented in parallel with programs for smoking cessation. From a public health perspective, our longitudinal study that examined the association between earlier smoking trajectories and later insomnia suggests that treatments designed to reduce or cease smoking may lessen the occurrence of symptoms of insomnia.

Trajectories of marijuana use from adolescence to adulthood predicting unemployment in the mid 30s.

BACKGROUND AND OBJECTIVES: Unemployment (5.5% as of 2015) is a serious social and economic problem in our society. Since marijuana use is an important factor related to unemployment, identifying the trajectory of the use of marijuana may aid intervention programs and research on unemployment. METHODS: Six hundred seventy-four participants (53% African-Americans, 47% Puerto Ricans) were surveyed (60% females) from ages 14 to 36. The first data collection was held when the participants were students attending schools in the East Harlem area of New York City. RESULTS: We found that the chronic marijuana use (OR = 4.07, p < .001; AOR = 2.58, p < .05) and the late marijuana quitter (OR = 2.91, p < .05) trajectory groups were associated with an increased likelihood of unemployment compared with the no marijuana use trajectory group. CONCLUSIONS AND SCIENTIFIC SIGNIFICANCE: The results suggest that those who use marijuana chronically are at greater risk for being unemployed. Consequently, these individuals should have access to and participate in marijuana cessation treatment programs in order to reduce their risk of unemployment. Unemployment intervention programs should also consider focusing on the cessation of the use of marijuana to decrease the likelihood of later unemployment.

Triple comorbid trajectories of tobacco, alcohol, and marijuana use as predictors of antisocial personality disorder and generalized anxiety disorder among urban adults.

OBJECTIVES: We modeled triple trajectories of tobacco, alcohol, and marijuana use from adolescence to adulthood as predictors of antisocial personality disorder (ASPD) and generalized anxiety disorder (GAD). METHODS: We assessed urban African American and Puerto Rican participants (n = 816) in the Harlem Longitudinal Development Study, a psychosocial investigation, at 4 time waves (mean ages = 19, 24, 29, and 32 years). We used Mplus to obtain the 3 variable trajectories of tobacco, alcohol, and marijuana use from time 2 to time 5 and then conducted logistic regression analyses. RESULTS: A 5-trajectory group model, ranging from the use of all 3 substances (23%) to a nonuse group (9%), best fit the data. Membership in the trajectory group that used all 3 substances was associated with an increased likelihood of both ASPD (adjusted odds ratio [AOR] = 6.83; 95% CI = 1.14, 40.74; P < .05) and GAD (AOR = 4.35; 95% CI = 1.63, 11.63; P < .001) in adulthood, as compared with the nonuse group, with control for earlier proxies of these conditions. CONCLUSIONS: Adults with comorbid tobacco, alcohol, and marijuana use should be evaluated for use of other substances and for ASPD, GAD, and other psychiatric disorders. Treatment programs should address the use of all 3 substances to decrease the likelihood of comorbid psychopathology.

A novel method for analyzing genetic association with longitudinal phenotypes.

Knowledge of genes influencing longitudinal patterns may offer information about predicting disease progression. We developed a systematic procedure for testing association between SNP genotypes and longitudinal phenotypes. We evaluated false positive rates and statistical power to localize genes for disease progression. We used genome-wide SNP data from the Framingham Heart Study. With longitudinal data from two real studies unrelated to Framingham, we estimated three trajectory curves from each study. We performed simulations by randomly selecting 500 individuals. In each simulation replicate, we assigned each individual to one of the three trajectory groups based on the underlying hypothesis (null or alternative), and generated corresponding longitudinal data. Individual Bayesian posterior probabilities (BPPs) for belonging to a specific trajectory curve were estimated. These BPPs were treated as a quantitative trait and tested (using the Wald test) for genome-wide association. Empirical false positive rates and power were calculated. Our method maintained the expected false positive rate for all simulation models. Also, our method achieved high empirical power for most simulations. Our work presents a method for disease progression gene mapping. This method is potentially clinically significant as it may allow doctors to predict disease progression based on genotype and determine treatment accordingly.

Utilization of mental health services by minority urban adults: psychosocial predictors.

Although most mental disorders have their first onset by young adulthood, there are few longitudinal studies of these problems and related help-seeking behavior. The present study examined some early and current predictors of the use of mental health services among African-American and Puerto Rican participants in their mid-30s. The 674 participants (52.8 % African Americans, 47.2 % Puerto Ricans; 60.1 % women) in this study were first seen in 1990 when the participants attended schools serving the East Harlem area of New York City. A structural equation model controlling for the participants' gender, educational level in emerging adulthood, and age at the most recent data collection showed significant standardized pathways from both ethnicity (ß = -0.28; z = -4.82; p < 0.001) and psychological symptoms (ß = 0.15; z = 2.41; p < 0.05), both measured in emerging adulthood, to smoking in the early 30s. That, in turn, was associated with certain physical diseases and symptoms (i.e., respiratory) in the mid-30s (ß = 0.16; z = 2.59; p < 0.05). These physical diseases and symptoms had a cross-sectional association with family financial difficulty in the mid-30s (ß = 0.21; z = 4.53; p < 0.001), which in turn also had a cross-sectional association with psychiatric disorders (ß = 0.30; z = 5.30; p < 0.001). Psychiatric disorders had a cross-sectional association with mental health services utilization (ß = 0.65; z = 13.25; p < 0.001). Additional pathways from the other domains to mental health services utilization in the mid-30s were also supported by the mediating role of psychiatric disorders. Results obtained from this research offer theoretical and practical information regarding the processes leading to the use of mental health services.

Impact of conjoint trajectories of body mass index and marijuana use on short sleep duration.

BACKGROUND AND OBJECTIVES: We examined the association between the conjoint developmental trajectories of body mass index (BMI) and marijuana use from age 24 to age 32 and short sleep duration. METHODS: The participants included 158 African American male, 267 African American female, 166 Puerto Rican male, and 225 Puerto Rican female young adults (N=816). Using Mplus, we obtained the conjoint trajectories of BMI and marijuana use. Logistic regression analyses examined the association between the conjoint trajectories and short sleep duration. RESULTS: Five conjoint trajectory groups were extracted: normal BMI and no or low marijuana use, obese and no or low marijuana use, morbidly obese and some marijuana use, normal BMI and high marijuana use, and obese and high marijuana use. Those in the obese and no or low marijuana use group, the morbidly obese and some marijuana use group, and the obese and high marijuana use group were more likely to report shorter sleep duration than those with normal BMI and no or low marijuana use group. CONCLUSIONS AND SCIENTIFIC SIGNIFICANCE: This study highlights the significance of examining joint trajectories over several developmental stages. In treating short sleep duration, we propose focusing on treating obesity, and also treating marijuana use if applicable.

Developmental trajectories of marijuana use from adolescence to adulthood: relationship with using weapons including guns.

This is the first study to assess the associations between the trajectories of marijuana use and other predictors of violent behavior with the use of guns or other weapons as well as stealing without the use of weapons among inner-city African Americans and Puerto Ricans (N = 838). Logistic regression analyses examined whether the longitudinal trajectories of marijuana use compared with the trajectory of no/low marijuana use predicted violent behavior. A higher Bayesian posterior probability (BPP) for the increasing marijuana use trajectory group (AOR = 3.37, P < .001), the moderate use of marijuana trajectory group (AOR = 1.98, P < .01), and the quitter trajectory group (AOR = 1.70, P < .05) was associated with an increased likelihood of engaging in violence (i.e., shooting or hitting someone with a weapon) compared with the BPP of the no use of marijuana trajectory group. Our results address a number of important public health and clinical issues. Public health funds might be spent on prevention programs focused on decreasing the use of marijuana, increasing educational retention, and decreasing contact with deviant associates. Understanding the psychosocial conditions related to the use of weapons is critical for individuals involved in the criminal justice system, physicians, and other health care providers in managing individuals who engage in violent behavior.

Voluntary smoking bans at home and in the car and smoking cessation, obesity, and self-control.

This study of female former and current smokers assessed the associations between voluntary smoking bans at home and in the car and smoking cessation, obesity, and self-control. Data from self-reported female smokers (N = 194) who were in a community-based random cohort at four points in time, from 1983 to 2009, were analyzed. These female participants (M age = 63.7 yr.) were given self-administered questionnaires. Analyses showed that complete smoking bans at home and in the car were positively associated with a greater likelihood of smoking cessation. Complete smoking bans at home and in the car were positively associated with greater self-control. Public health policies should focus on the positive health effects of smoking bans on smoking cessation and greater self-control.

Longitudinal determinants of substance use disorders.

Substance use and substance use disorders (SUDs) have been linked with marital discord. Relatively little is known, however, about the antecedents of SUDs, the mediators of these factors over time, or their associations with the spousal/partner relationship among urban adults. A better understanding of the longitudinal pathways to marital conflict and to SUDs should help prevention and intervention programs target their precursors within the developmental period in which they occur. The present study, therefore, examined the longitudinal predictors of an unsupportive spousal/partner relationship and SUDs among a community sample of urban African American and Puerto Rican adults from East Harlem, NY. Participants (N = 816) completed structured questionnaires at five time waves, from adolescence to adulthood (mean ages = 14, 19, 24, 29, and 32 years). Structural equation modeling examined the effects of earlier environmental and social stressors and intrapersonal and interpersonal factors on later SUDs in adulthood. There was a good fit of the structural equation model (CFI = 0.91; RMSEA = 0.06; and SRMR = 0.06), which revealed three main pathways from adolescence to the spousal/partner relationship and SUDs in adulthood. One pathway linked a weak parent-adolescent attachment relationship with the participant's psychological symptoms in emerging adulthood (p < 0.01), which in turn were related to affiliation with deviant and drug-using peers, also in emerging adulthood (p < 0.001). Peer deviance and drug use were associated with the participant's substance use in young adulthood (p < 0.001), which predicted both an unsupportive spousal/partner relationship (p < 0.05) and SUDs (p < 0.001) later in adulthood. Other pathways highlighted the continuity of psychological symptoms as related to both substance use in young adulthood (p < 0.001) and an unsupportive spousal/partner relationship in adulthood (p < 0.001). Findings showed that the associations of both distal stressors and the parent-adolescent relationship with more proximal intra- and interpersonal problems predicted unsupportive spousal/partner relationships and SUDs among urban adults. Several aspects of the individual's life, at different developmental stages, provide opportunities for interventions to prevent or reduce unsupportive spousal/partner relationships and SUDs.

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.

Long-term consequences of membership in trajectory groups of delinquent behavior in an urban sample: violence, drug use, interpersonal, and neighborhood attributes.

Research on stability and change in delinquent behavior over time has important implications for both the individual and the criminal justice system. The present research looks at this issue by examining the associations between the trajectories of delinquent behavior in adolescence and adult functioning. Data for the present study are from a four-wave longitudinal study of African American and Hispanic participants. Participants provided information at mean ages 14, 19, 24, and 29. We used growth mixture modeling to extract trajectory groups of delinquent behavior in adolescence and young adulthood. Regression analyses were conducted to examine whether memberships in the trajectory groups of delinquent behavior from mean age 14 to mean age 24 were associated with violence, substance abuse and dependence, partner discord, peer substance use, and residence in a high-crime neighborhood at mean age 29 when compared with the reference trajectory group of participants with low or no delinquent behavior. Four trajectory groups of delinquent behavior were identified: the no/low, the decreasing, the moderate, and the high persistent trajectory groups. Memberships in the trajectory groups were significantly correlated with variations in adult functioning. Memberships in some trajectory groups of delinquent behavior are significant predictors of later violent behavior, substance abuse and dependence, partner discord, peer substance use, and residence in a high-crime neighborhood. The findings reinforce the importance of investing in interventions to address different patterns of delinquent behavior. Findings are discussed in relation to previous investigations with non-Hispanic White samples.

Physical factors, personal characteristics, and substance use: associations with obesity.

BACKGROUND: Because obesity has become a major public health problem, attention to a range of its predictors is needed. This study examined the association of physical factors, personal characteristics, and substance use with obesity in a sample (N = 815) of African American and Puerto Rican young adults with a mean age of 32. METHODS: Body mass index (BMI) was calculated to assess obesity. Bivariate and multivariate logistic regression analyses were conducted. RESULTS: Bivariate analyses showed that protective factors such as physical activity (adjusted odds ratio [AOR] = .82, 95% confidence interval [CI] = .74-.91), healthy diet (AOR = .96, 95% CI = .93-.99), self-control (AOR = .93, 95% CI = .87-.98), and life satisfaction (AOR = .97, 95% CI = .95-.99) were associated with a reduced probability of being obese. Marijuana use was also associated with a decreased probability of obesity (AOR = .89, 95% CI = .80-.99), but was not considered a protective factor. Risk factors such as short sleep duration (AOR = 1.70, 95% CI = 1.24-2.33) and depressive mood (AOR = 1.05, 95% CI = 1.01-1.09) were associated with an increased probability of being obese. CONCLUSIONS: For African Americans and Puerto Ricans, programs to treat obesity should focus on increasing sleep, physical activity, and life satisfaction.

Adult work commitment, financial stability, and social environment as related to trajectories of marijuana use beginning in adolescence.

BACKGROUND: The objective of this study is to examine trajectories of marijuana use among African Americans and Puerto Ricans from adolescence to adulthood, with attention paid to work commitment, financial stability, drug use, and violence. METHODS: Participants (N = 816) completed in-class questionnaires as students in the East Harlem area of New York City at the first wave and provided follow-up data at 4 additional points in time (mean ages = 14, 19, 24, 29, and 32 years). Among 816 participants, there were 60% females, 52% African American, and 48% Puerto Ricans. RESULTS: The chronic marijuana user trajectory group compared with the none or low, increasing, and/or moderate marijuana user trajectory group was associated with negative aspects of work commitment, financial stability, and the social environment. The chronic marijuana user group was similar to the increasing marijuana user group on work commitment and financial stability. CONCLUSIONS: These results suggest that treating marijuana use in late adolescence may reduce difficulty in the assumption of adult roles. Because chronic marijuana users experienced the most adverse effects in each of the domains, they require more intense clinical intervention than moderate marijuana users.

Determinants of obesity: results from a longitudinal study of adolescents and adults living in an urban area.

This study examined the relation of cigarette smoking, psychological symptoms (e.g., depressive symptoms, anxiety), physical activity, and body mass index (BMI) separately by sex. The sample consisted of 815 African Americans and Puerto Ricans (324 males, 491 females). The participants were originally 14 years of age, and were followed to 32 years of age, and gave information on smoking, depressive symptoms, anxiety, physical activity, and BMI. Structural equation modeling showed that for males cigarette smoking in mid/late adolescence was related to cigarette smoking in emerging adulthood and early adulthood. Finally, cigarette smoking in early adulthood was negatively related to BMI in adulthood only for male participants. For female participants, cigarette smoking in adolescence was related to psychological symptoms (e.g., depressive symptoms, anxiety) in emerging adulthood and early adulthood. Psychological symptoms in early adulthood predicted less physical activity in adulthood, which in turn, was related to BMI. With one exception, all of the standardized coefficients were statistically significant. Implications for preventive interventions are discussed.

TDT-HET: a new transmission disequilibrium test that incorporates locus heterogeneity into the analysis of family-based association data.

BACKGROUND: Locus heterogeneity is one of the most documented phenomena in genetics. To date, relatively little work had been done on the development of methods to address locus heterogeneity in genetic association analysis. Motivated by Zhou and Pan's work, we present a mixture model of linked and unlinked trios and develop a statistical method to estimate the probability that a heterozygous parent transmits the disease allele at a di-allelic locus, and the probability that any trio is in the linked group. The purpose here is the development of a test that extends the classic transmission disequilibrium test (TDT) to one that accounts for locus heterogeneity. RESULTS: Our simulations suggest that, for sufficiently large sample size (1000 trios) our method has good power to detect association even the proportion of unlinked trios is high (75%). While the median difference (TDT-HET empirical power - TDT empirical power) is approximately 0 for all MOI, there are parameter settings for which the power difference can be substantial. Our multi-locus simulations suggest that our method has good power to detect association as long as the markers are reasonably well-correlated and the genotype relative risk are larger. Results of both single-locus and multi-locus simulations suggest our method maintains the correct type I error rate.Finally, the TDT-HET statistic shows highly significant p-values for most of the idiopathic scoliosis candidate loci, and for some loci, the estimated proportion of unlinked trios approaches or exceeds 50%, suggesting the presence of locus heterogeneity. CONCLUSIONS: We have developed an extension of the TDT statistic (TDT-HET) that allows for locus heterogeneity among coded trios. Benefits of our method include: estimates of parameters in the presence of heterogeneity, and reasonable power even when the proportion of linked trios is small. Also, we have extended multi-locus methods to TDT-HET and have demonstrated that the empirical power may be high to detect linkage. Last, given that we obtain PPBs, we conjecture that the TDT-HET may be a useful method for correctly identifying linked trios. We anticipate that researchers will find this property increasingly useful as they apply next-generation sequencing data in family based studies.

Earlier joint trajectories of cigarette smoking and low perceived self-control as predictors of later poor health for women in their mid-60s.

INTRODUCTION: The current study was designed to assess the prospective relationship between the earlier joint trajectories of cigarette smoking and low perceived self-control (X age = 40-48) and later health problems (X age = 65.2) within a community sample of understudied females. METHODS: The participants were given self-administered questionnaires. RESULTS: The results of the growth mixture model suggested 5 joint trajectories of cigarette smoking and perceived self-control, which consisted of 1 at-risk group (chronic smoking and low perceived self-control), 1 low-risk group (infrequent or nonsmoking and high perceived self-control), and 3 intermediate groups (i.e., high on one factor and low on the other). The results from logistic regression analyses supported a model by which (a) women in the at-risk group, in comparison with the low-risk group, were more likely to report 5 or more diseases (adjusted odds ratio [AOR] = 4.81; p < .001) and poor or very poor general health (AOR = 5.98; p < .001); (b) women in the at-risk group as compared with women in the intermediate groups were also more likely to report 5 or more diseases (AOR = 2.36; p < .05) and poor or very poor general health (AOR = 2.86; p < .01); and (c) women in the intermediate group were more likely to report 5 or more diseases (AOR = 2.04; p < .05) and poor or very poor general health (AOR = 2.09; p < .05) than women in the low-risk group. CONCLUSIONS: The findings highlight the significance of targeting dispositional factors (e.g., perceived self-control) in conjunction with smoking in designing programs for promoting the health of women in midlife.

A new expectation-maximization statistical test for case-control association studies considering rare variants obtained by high-throughput sequencing.

Genome-wide association studies (GWAS) have been successful in identifying common genetic variation reproducibly associated with disease. However, most associated variants confer very small risk and after meta-analysis of large cohorts a large fraction of expected heritability still remains unexplained. A possible explanation is that rare variants currently undetected by GWAS with SNP arrays could contribute a large fraction of risk when present in cases. This concept has spurred great interest in exploring the role of rare variants in disease. As the cost of sequencing continue to plummet, it is becoming feasible to directly sequence case-control samples for testing disease association including rare variants. We have developed a test statistic that allows for association testing among cases and controls using data directly from sequencing reads. In addition, our method allows for random errors in reads. We determine the probability of a true genotype call based on the observed base pair reads using the expectation-maximization algorithm. We apply the SumStat procedure to obtain a single statistic for a group of multiple rare variant loci. We document the validity of our method through simulations. Our results suggest that our statistic maintains the correct type I error rate, even in the presence of differential misclassification for sequence reads, and that it has good power under a number of scenarios. Finally, our SumStat results show power at least as good as the maximum single locus results.