Shaunna L. Clark

Sample Size Requirements for Structural Equation Models An Evaluation of Power, Bias, and Solution Propriety

Determining sample size requirements for structural equation modeling (SEM) is a challenge often faced by investigators, peer reviewers, and grant writers. Recent years have seen a large increase in SEMs in the behavioral science literature, but consideration of sample size requirements for applied SEMs often relies on outdated rules-of-thumb. This study used Monte Carlo data simulation techniques to evaluate sample size requirements for common applied SEMs. Across a series of simulations, we systematically varied key model properties, including number of indicators and factors, magnitude of factor loadings and path coefficients, and amount of missing data. We investigated how changes in these parameters affected sample size requirements with respect to statistical power, bias in the parameter estimates, and overall solution propriety. Results revealed a range of sample size requirements (i.e., from 30 to 460 cases), meaningful patterns of association between parameters and sample size, and highlight the limitations of commonly cited rules-of-thumb. The broad “lessons learned” for determining SEM sample size requirements are discussed.

Methylome-Wide Association Study of Schizophrenia: Identifying Blood Biomarker Signatures of Environmental Insults

IMPORTANCE Epigenetic studies present unique opportunities to advance schizophrenia research because they can potentially account for many of its clinical features and suggest novel strategies to improve disease management. OBJECTIVE To identify schizophrenia DNA methylation biomarkers in blood. DESIGN, SETTING, AND PARTICIPANTS The sample consisted of 759 schizophrenia cases and 738 controls (N = 1497) collected in Sweden. We used methyl-CpG-binding domain protein-enriched genome sequencing of the methylated genomic fraction, followed by next-generation DNA sequencing. We obtained a mean (SD) number of 68 (26.8) million reads per sample. This massive data set was processed using a specifically designed data analysis pipeline. Critical top findings from our methylome-wide association study (MWAS) were replicated in independent case-control participants using targeted pyrosequencing of bisulfite-converted DNA. MAIN OUTCOMES AND MEASURES Status of schizophrenia cases and controls. RESULTS Our MWAS suggested a considerable number of effects, with 25 sites passing the highly conservative Bonferroni correction and 139 sites significant at a false discovery rate of 0.01. Our top MWAS finding, which was located in FAM63B, replicated with P = 2.3 × 10-10. It was part of the networks regulated by microRNA that can be linked to neuronal differentiation and dopaminergic gene expression. Many other top MWAS results could be linked to hypoxia and, to a lesser extent, infection, suggesting that a record of pathogenic events may be preserved in the methylome. Our findings also implicated a site in RELN, one of the most frequently studied candidates in methylation studies of schizophrenia. CONCLUSIONS AND RELEVANCE To our knowledge, the present study is one of the first MWASs of disease with a large sample size using a technology that provides good coverage of methylation sites across the genome. Our results demonstrated one of the unique features of methylation studies that can capture signatures of environmental insults in peripheral tissues. Our MWAS suggested testable hypotheses about disease mechanisms and yielded biomarkers that can potentially be used to improve disease management.

A methylome-wide study of aging using massively parallel sequencing of the methyl-CpG-enriched genomic fraction from blood in over 700 subjects

The central importance of epigenetics to the aging process is increasingly being recognized. Here we perform a methylome-wide association study (MWAS) of aging in whole blood DNA from 718 individuals, aged 25-92 years (mean = 55). We sequenced the methyl-CpG-enriched genomic DNA fraction, averaging 67.3 million reads per subject, to obtain methylation measurements for the ∼27 million autosomal CpGs in the human genome. Following extensive quality control, we adaptively combined methylation measures for neighboring, highly-correlated CpGs into 4 344 016 CpG blocks with which we performed association testing. Eleven age-associated differentially methylated regions (DMRs) passed Bonferroni correction (P-value < 1.15 × 10(-8)). Top findings replicated in an independent sample set of 558 subjects using pyrosequencing of bisulfite-converted DNA (min P-value < 10(-30)). To examine biological themes, we selected 70 DMRs with false discovery rate of <0.1. Of these, 42 showed hypomethylation and 28 showed hypermethylation with age. Hypermethylated DMRs were more likely to overlap with CpG islands and shores. Hypomethylated DMRs were more likely to be in regions associated with polycomb/regulatory proteins (e.g. EZH2) or histone modifications H3K27ac, H3K4m1, H3K4m2, H3K4m3 and H3K9ac. Among genes implicated by the top DMRs were protocadherins, homeobox genes, MAPKs and ryanodine receptors. Several of our DMRs are at genes with potential relevance for age-related disease. This study successfully demonstrates the application of next-generation sequencing to MWAS, by interrogating a large proportion of the methylome and returning potentially novel age DMRs, in addition to replicating several loci implicated in previous studies using microarrays.

MBD-seq as a cost-effective approach for methylome-wide association studies: demonstration in 1500 case–control samples

AIM We studied the use of methyl-CpG binding domain (MBD) protein-enriched genome sequencing (MBD-seq) as a cost-effective screening tool for methylome-wide association studies (MWAS). MATERIALS & METHODS Because MBD-seq has not yet been applied on a large scale, we first developed and tested a pipeline for data processing using 1500 schizophrenia cases and controls plus 75 technical replicates with an average of 68 million reads per sample. This involved the use of technical replicates to optimize quality control for multi- and duplicate-reads, an in silico experiment to identify CpGs in loci with alignment problems, CpG coverage calculations based on multiparametric estimates of the fragment size distribution, a two-stage adaptive algorithm to combine data from correlated adjacent CpG sites, principal component analyses to control for confounders and new software tailored to handle the large data set. RESULTS We replicated MWAS findings in independent samples using a different technology that provided single base resolution. In an MWAS of age-related methylation changes, one of our top findings was a previously reported robust association involving GRIA2. Our results also suggested that owing to the many confounding effects, a considerable challenge in MWAS is to identify those effects that are informative about disease processes. CONCLUSION This study showed the potential of MBD-seq as a cost-effective tool in large-scale disease studies.

CBCL Pediatric Bipolar Disorder Profile and ADHD: Comorbidity and Quantitative Trait Loci Analysis

OBJECTIVE The pediatric bipolar disorder profile of the Child Behavior Checklist (CBCL-PBD), a parent-completed measure that avoids clinician ideological bias, has proven useful in differentiating patients with attention-deficit/hyperactivity disorder (ADHD). We used CBCL-PBD profiles to distinguish patterns of comorbidity and to search for quantitative trait loci in a genomewide scan in a sample of multiple affected ADHD sibling pairs. METHOD A total of 540 ADHD subjects ages 5 to 18 years were assessed with the Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime version and CBCL. Parents were assessed with the Schedule for Affective Disorders and Schizophrenia-Lifetime version supplemented by the Schedule for Affective Disorders and Schizophrenia for School-Age Children for disruptive behavioral disorders. Patterns of psychiatric comorbidity were contrasted based on the CBCL-PBD profile. A quantitative trait loci variance component analysis was used to identify potential genomic regions that may harbor susceptibility genes for the CBCL-PBD quantitative phenotype. RESULTS Bipolar spectrum disorders represented less than 2% of the overall sample. The CBCL-PBD classification was associated with increased generalized anxiety disorder (p =.001), oppositional defiant disorder (p =.008), conduct disorder (p =.003), and parental substance abuse (p =.005). A moderately significant linkage signal (multipoint maximum lod score = 2.5) was found on chromosome 2q. CONCLUSIONS The CBCL-PBD profile distinguishes a subset of ADHD patients with significant comorbidity. Linkage analysis of the CBCL-PBD phenotype suggests certain genomic regions that merit further investigation for genes predisposing to severe psychopathology.

Models and Strategies for Factor Mixture Analysis: An Example Concerning the Structure Underlying Psychological Disorders

The factor mixture model (FMM) uses a hybrid of both categorical and continuous latent variables. The FMM is a good model for the underlying structure of psychopathology because the use of both categorical and continuous latent variables allows the structure to be simultaneously categorical and dimensional. This is useful because both diagnostic class membership and the range of severity within and across diagnostic classes can be modeled concurrently. Although the conceptualization of the FMM has been explained in the literature, the use of the FMM is still not prevalent. One reason is that there is little research about how such models should be applied in practice and, once a well-fitting model is obtained, how it should be interpreted. In this article, the FMM is explored by studying a real data example on conduct disorder. By exploring this example, this article aims to explain the different formulations of the FMM, the various steps in building a FMM, and how to decide between an FMM and alternative models.

Pharmacogenomic study of side-effects for antidepressant treatment options in STAR*D

BACKGROUND Understanding individual differences in susceptibility to antidepressant therapy side-effects is essential to optimize the treatment of depression. METHOD We performed genome-wide association studies (GWAS) to search for genetic variation affecting the susceptibility to side-effects. The analysis sample consisted of 1439 depression patients, successfully genotyped for 421K single nucleotide polymorphisms (SNPs), from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study. Outcomes included four indicators of side-effects: general side-effect burden, sexual side-effects, dizziness and vision/hearing-related side-effects. Our criterion for genome-wide significance was a prespecified threshold ensuring that, on average, only 10% of the significant findings are false discoveries. RESULTS Thirty-four SNPs satisfied this criterion. The top finding indicated that 10 SNPs in SACM1L mediated the effects of bupropion on sexual side-effects (p = 4.98 × 10(-7), q = 0.023). Suggestive findings were also found for SNPs in MAGI2, DTWD1, WDFY4 and CHL1. CONCLUSIONS Although our findings require replication and functional validation, this study demonstrates the potential of GWAS to discover genes and pathways that could mediate adverse effects of antidepressant medication.

School Attendance Problems and Youth Psychopathology: Structural Cross-Lagged Regression Models in Three Longitudinal Data Sets

This study tests a model of reciprocal influences between absenteeism and youth psychopathology using 3 longitudinal datasets (Ns = 20,745, 2,311, and 671). Participants in 1st through 12th grades were interviewed annually or biannually. Measures of psychopathology include self-, parent-, and teacher-report questionnaires. Structural cross-lagged regression models were tested. In a nationally representative data set (Add Health), middle school students with relatively greater absenteeism at Study Year 1 tended toward increased depression and conduct problems in Study Year 2, over and above the effects of autoregressive associations and demographic covariates. The opposite direction of effects was found for both middle and high school students. Analyses with 2 regionally representative data sets were also partially supportive. Longitudinal links were more evident in adolescence than in childhood.

Genome-wide pharmacogenomic study of citalopram- induced side effects in STAR*D

Affecting about 1 in 12 Americans annually, depression is a leading cause of the global disease burden. While a range of effective antidepressants are now available, failure and relapse rates remain substantial, with intolerable side effect burden the most commonly cited reason for discontinuation. Thus, understanding individual differences in susceptibility to antidepressant therapy side effects will be essential to optimize depression treatment. Here we perform genome-wide association studies (GWAS) to identify genetic variation influencing susceptibility to citalopram-induced side effects. The analysis sample consisted of 1762 depression patients, successfully genotyped for 421K single-nucleotide polymorphisms (SNPs), from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study. Outcomes included five indicators of citalopram side effects: general side effect burden, overall tolerability, sexual side effects, dizziness and vision/hearing side effects. Two SNPs met our genome-wide significance criterion (q<0.1), ensuring that, on average, only 10% of significant findings are false discoveries. In total, 12 additional SNPs demonstrated suggestive associations (q<0.5). The top finding was rs17135437, an intronic SNP within EMID2, mediating the effects of citalopram on vision/hearing side effects (P=3.27 × 10−8, q=0.026). The second genome-wide significant finding, representing a haplotype spanning ∼30 kb and eight genotyped SNPs in a gene desert on chromosome 13, was associated with general side effect burden (P=3.22 × 10−7, q=0.096). Suggestive findings were also found for SNPs at LAMA1, AOX2P, EGFLAM, FHIT and RTP2. Although our findings require replication and functional validation, this study demonstrates the potential of GWAS to discover genes and pathways that potentially mediate adverse effects of antidepressant medications.

Analysis of efficacy and side effects in CATIE demonstrates drug response subgroups and potential for personalized medicine.

OBJECTIVE This study aims to improve understanding of antipsychotic non/response and assess the potential for personalized schizophrenia treatment. METHODS We used data from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE). Efficacy measures included the Positive and Negative Syndrome Scale (PANSS) and neurocognitive functioning. Side effect measures included weight, lipids, glucose, heart rate and QT prolongation. Latent class analysis was conducted for each of the five drugs on the individual treatment effects to study whether there were subgroups of drug responders. The posterior probabilities of belonging to a particular response group were correlated across drugs to examine if patients not responding to one drug are likely to respond to a different drug and whether response to one drug may help to predict response to another drug. RESULTS We identified four qualitatively distinct response groups: Optimal Responders, Average Responders, Global Responders and Non-Responders. Different patterns of correlations with demographics and clinical variables across classes provided further support for the validity of these groups. The low correlations between posterior probabilities of the same response groups across drugs implied that patients generally belonged to different response groups for different drugs. CONCLUSIONS Our results demonstrate the existence of subgroups of patients characterized by distinct patterns of drug response. Further, findings suggest that patients who experience a poor response to one drug may be an optimal responder to another antipsychotic. Taken together these findings demonstrate the potential to personalize schizophrenia treatment and highlight the importance of identifying better predictors of drug response.