Development of multivariable models to predict perinatal depression before and after delivery using patient reported survey responses at weeks 4-10 of pregnancy.

BACKGROUND: Perinatal depression is estimated to affect ~ 12% of pregnancies and is linked to numerous negative outcomes. There is currently no model to predict perinatal depression at multiple time-points during and after pregnancy using variables ascertained early into pregnancy. METHODS: A prospective cohort design where 858 participants filled in a baseline self-reported survey at week 4-10 of pregnancy (that included social economics, health history, various psychiatric measures), with follow-up until 3 months after delivery. Our primary outcome was an Edinburgh Postnatal Depression Score (EPDS) score of 12 or more (a proxy for perinatal depression) assessed during each trimester and again at two time periods after delivery. Five gradient boosting machines were trained to predict the risk of having EPDS score > = 12 at each of the five follow-up periods. The predictors consisted of 21 variables from 3 validated psychometric scales. As a sensitivity analysis, we also investigated different predictor sets that contained: i) 17 of the 21 variables predictors by only including two of the psychometric scales and ii) including 143 additional social economics and health history predictors, resulting in 164 predictors. RESULTS: We developed five prognostic models: PND-T1 (trimester 1), PND-T2 (trimester 2), PND-T3 (trimester 3), PND-A1 (after delivery 1) and PND-A2 (delayed onset after delivery) that calculate personalised risks while only requiring that women be asked 21 questions from 3 validated psychometric scales at weeks 4-10 of pregnancy. C-statistics (also known as AUC) ranged between 0.69 (95% CI 0.65-0.73) and 0.77 (95% CI 0.74-0.80). At 50% sensitivity the positive predictive value ranged between 30%-50% across the models, generally identifying groups of patients with double the average risk. Models trained using the 17 predictors and 164 predictors did not improve model performance compared to the models trained using 21 predictors. CONCLUSIONS: The five models can predict risk of perinatal depression within each trimester and in two post-natal periods using survey responses as early as week 4 of pregnancy with modest performance. The models need to be externally validated and prospectively tested to ensure generalizability to any pregnant patient.

Leveraging Digital Technology in Conducting Longitudinal Research on Mental Health in Pregnancy: Longitudinal Panel Survey Study.

BACKGROUND: Collecting longitudinal data during and shortly after pregnancy is difficult, as pregnant women often avoid studies with repeated surveys. In contrast, pregnant women interact with certain websites at multiple stages throughout pregnancy and the postpartum period. This digital connection presents the opportunity to use a website as a way to recruit and enroll pregnant women into a panel study and collect valuable longitudinal data for research. These data can then be used to learn new scientific insights and improve health care. OBJECTIVE: The objective of this paper is to describe the approaches applied and lessons learned from designing and conducting an online panel for health care research, specifically perinatal mood disorders. Our panel design and approach aimed to recruit a large sample (N=1200) of pregnant women representative of the US population and to minimize attrition over time. METHODS: We designed an online panel to enroll participants from the pregnancy and parenting website BabyCenter. We enrolled women into the panel from weeks 4 to 10 of pregnancy (Panel 1) or from weeks 28 to 33 of pregnancy (Panel 2) and administered repeated psychometric assessments from enrollment through 3 months postpartum. We employed a combination of adaptive digital strategies to recruit, communicate with, and build trust with participants to minimize attrition over time. We were transparent at baseline about expectations, used monetary and information-based incentives, and sent personalized reminders to reduce attrition. The approach was participant-centric and leveraged many aspects of flexibility that digital methods afford. RESULTS: We recruited 1179 pregnant women-our target was 1200-during a 26-day period between August 25 and September 19, 2016. Our strategy to recruit participants using adaptive sampling tactics resulted in a large panel that was similar to the US population of pregnant women. Attrition was on par with existing longitudinal observational studies in pregnant populations, and 79.2% (934/1179) of our panel completed another survey after enrollment. There were 736 out of 1179 (62.4%) women who completed at least one assessment in both the prenatal and postnatal periods, and 709 out of 1179 (60.1%) women who completed the final assessment. To validate the data, we compared participation rates and factors of perinatal mood disorders ascertained from this study with prior research, suggesting reliability of our approach. CONCLUSIONS: A suitably designed online panel created in partnership with a digital media source that reaches the target audience is a means to leverage a conveniently sized and viable sample for scientific research. Our key lessons learned are as follows: sampling tactics may need to be adjusted to enroll a representative sample, attrition can be reduced by adapting to participants' needs, and study engagement can be boosted by personalizing interactions with the flexibility afforded by digital technologies.

Perinatal depressive symptoms often start in the prenatal rather than postpartum period: results from a longitudinal study.

Depressive symptoms during and after pregnancy confer risks for adverse outcomes in both the mother and child. Postpartum depression is traditionally diagnosed after birth of the child. Perinatal depression is a serious, prevalent heterogeneous syndrome that can occur during the period from conception through several months after childbirth. Onset and course are not well understood. There is a paucity of longitudinal studies of the disorder that include the antenatal period in population-based samples. We used an Internet panel of pregnant women recruited in 2 cohorts; 858 ascertained in the first and 322 ascertained in the third trimesters of pregnancy. We recruited the second cohort in order to assure sufficient sample to examine depressive symptoms later in pregnancy and in the postpartum period. Assessments included standard psychometric measures, health history, and pregnancy experience. The Edinburgh Postnatal Depression Scale was used for the assessment of depressive symptoms. Nearly 10% of women entered the pregnancy with depressive symptoms. Prevalence was about the same at 4 weeks and 3 months postpartum. During pregnancy, prevalence increased to 16% in the third trimester. Among incident cases, 80% occurred during pregnancy, with 1/3 occurring in the first trimester. We describe predictors of incident depressive symptoms and covariates associated with time-to-onset which include health history (psychiatric and medical) and social support covariates. The majority of incident depressive symptoms occur during pregnancy rather than afterward. This finding underscores the mandate for mental health screening early in pregnancy and throughout gestation. It will be important to find safe and effective interventions that prevent, mitigate, or delay the onset of depressive symptoms that can be implemented during pregnancy.

Evaluation of disability in patients exposed to fluoroquinolones.

BACKGROUND: Fluoroquinolones are used for conditions including sinusitis, bronchitis, and urinary tract infections. It has been suggested that exposure to fluoroquinolones for these conditions is associated with disability resulting from adverse events in 2 or more organ systems. The objectives were to: describe: 1) fluoroquinolone, azithromycin, and sulfamethoxazole / trimethoprim utilization for these infections; 2) the rate of disability associated with exposure to each of these antibiotic classes and adverse events in 2 or more system organ classes, and 3) compare outcome rates for each of the antibiotic classes. METHODS: This study was conducted using administrative data to mitigate the limitations of spontaneous reports. The sampling frame was a U.S. population with both medical and disability insurance, including patients with the above uncomplicated infections who were prescribed the antibiotics of interest. The primary outcome was an incident short-term disability claim associated with adverse events in 2 different organ systems within 120 days of exposure. A matched analysis was used to compare the outcome for patients receiving each of the drug classes. RESULTS: After propensity score matching, there were 119,653 individuals in each of the exposure groups. There were 264 fluoroquinolone associated disability events and 243 azithromycin/ sulfamethoxazole associated disability events (relative risk =1.09 (95% CI: 0.92-1.30; calibrated p = 0.84)). The results were not significantly different from the null hypothesis of no difference between groups. CONCLUSION: Comparative assessments are difficult to conduct in spontaneous reports. This examination of disability associated with adverse events in different system organ classes showed no difference between fluoroquinolones and azithromycin or sulfamethoxazole in administrative data.

Risperdal® CONSTA® Needle Detachment. Incidence Rates Before and After Kit Redesign: A Retrospective Study using Electronic Health Records and Natural Language Processing in the Department of Veterans Affairs.

INTRODUCTION: Janssen received reports of needle detachments for Risperdal® CONSTA® and, in response, redesigned the kit. OBJECTIVE: The study objective was to estimate the rate of Risperdal® CONSTA® needle detachments prior to and after the introduction of a redesigned kit. METHODS: This retrospective study used record abstraction in the US Department of Veterans Affairs (VA). The 3 phases included: (1) a pilot study for methods evaluation in a sample of 6 hospitals with previously reported detachments; (2) a baseline study to ascertain the baseline detachment rate; and (3) a follow-up study to ascertain the rate for the redesigned kit. Administrative codes and natural language processing with clinical review were used to identify detachments. RESULTS: Pilot: we identified a subset of spontaneously reported detachments and several previously unreported events. In the baseline study (original device), from January through December 2013, 22 needle detachments were identified among 47,934 administrations of the drug in a census of administrations in the VA; an incidence of 0.0459%. In the follow-up study (redesigned device), from December 2015 through December 2016, there were 14 reported detachments in 41,819 injections, 0.0335%. This represents a reduction of 27% from the baseline. CONCLUSION: This approach enabled us to identify needle detachments we would not have otherwise found ("solicited"). However, it likely resulted in incomplete outcome ascertainment. While this may have resulted in lower overall rates, it did not bias the comparison of the baseline and follow-up studies. The results showed that the redesigned Risperdal® CONSTA® kit reduced the incidence of needle detachment events in the VA. FUNDING: Janssen Pharmaceuticals, Inc.

Liver Test Monitoring: Real-World Compliance for Drugs with Monitoring Requirements at 2-Week Intervals or More Frequently.

BACKGROUND: Monitoring risk is often an important component of therapy. Some compounds require liver test (LT) monitoring, with the frequency detailed in the product label. Compliance with these instructions is generally unknown. OBJECTIVE: The goal of this short study was to describe LT compliance for compounds with monitoring recommended at 2-week intervals or more frequently in three US administrative claims databases. METHODS: The sample was drawn from three US claims databases during the period 1 January 2015 through 30 June 2018. This study examined nine compounds and five types of LTs. We looked at compounds in a published list of drugs requiring LTs at 2-week intervals or more frequently. Descriptive statistics about the days between tests were reported, as were the number and proportion of tests associated with each drug that met the recommended frequency. RESULTS: Compliance was < 33% with four drugs (ketoconazole, succimer, pentamidine, and felbamate) and > 60% with five drugs (oxaliplatin, rifampin, tolcapone, albendazole, and azathioprine). Among drugs with more than 1000 drug eras observed (all but succimer and tolcapone), LT compliance was highest for oxaliplatin (75.3%) and lowest for pentamidine (20.6%), with little difference in overall compliance by type of test (range 41-46). CONCLUSION: Compliance with frequent LT monitoring differed for the drugs examined. Two strata were found: compliance > 60% (oxaliplatin, rifampin, tolcapone, albendazole, and azathioprine) and compliance 20-30% (ketoconazole, succimer, pentamidine, and felbamate). No drug reached 80% compliance.

The Open Translational Science in Schizophrenia (OPTICS) project: an open-science project bringing together Janssen clinical trial and NIMH data.

Clinical trial data are the gold standard for evaluating pharmaceutical safety and efficacy. There is an ethical and scientific imperative for transparency and data sharing to confirm published results and generate new knowledge. The Open Translational Science in Schizophrenia (OPTICS) Project was an open-science initiative aggregating Janssen clinical trial and NIH/NIMH data from real-world studies and trials in schizophrenia. The project aims were to show the value of using shared data to examine: therapeutic safety and efficacy; disease etiologies and course; and methods development. The success of project investigators was due to collaboration from project applications through analyses, with support from the Harvard Catalyst. Project work was independent of Janssen; all intellectual property was dedicated to the public. Efforts such as this are necessary to gain deeper insights into the biology of disease, foster collaboration, and to achieve the goal of developing better treatments, reducing the overall public health burden of devastating brain diseases.

Clinical phenotypes of perinatal depression and time of symptom onset: analysis of data from an international consortium.

BACKGROUND: The perinatal period is a time of high risk for onset of depressive disorders and is associated with substantial morbidity and mortality, including maternal suicide. Perinatal depression comprises a heterogeneous group of clinical subtypes, and further refinement is needed to improve treatment outcomes. We sought to empirically identify and describe clinically relevant phenotypic subtypes of perinatal depression, and further characterise subtypes by time of symptom onset within pregnancy and three post-partum periods. METHODS: Data were assembled from a subset of seven of 19 international sites in the Postpartum Depression: Action Towards Causes and Treatment (PACT) Consortium. In this analysis, the cohort was restricted to women aged 19-40 years with information about onset of depressive symptoms in the perinatal period and complete prospective data for the ten-item Edinburgh postnatal depression scale (EPDS). Principal components and common factor analysis were used to identify symptom dimensions in the EPDS. The National Institute of Mental Health research domain criteria functional constructs of negative valence and arousal were applied to the EPDS dimensions that reflect states of depressed mood, anhedonia, and anxiety. We used k-means clustering to identify subtypes of women sharing symptom patterns. Univariate and bivariate statistics were used to describe the subtypes. FINDINGS: Data for 663 women were included in these analyses. We found evidence for three underlying dimensions measured by the EPDS: depressed mood, anxiety, and anhedonia. On the basis of these dimensions, we identified five distinct subtypes of perinatal depression: severe anxious depression, moderate anxious depression, anxious anhedonia, pure anhedonia, and resolved depression. These subtypes have clear differences in symptom quality and time of onset. Anxiety and anhedonia emerged as prominent symptom dimensions with post-partum onset and were notably severe. INTERPRETATION: Our findings show that there might be different types and severity of perinatal depression with varying time of onset throughout pregnancy and post partum. These findings support the need for tailored treatments that improve outcomes for women with perinatal depression. FUNDING: Janssen Research & Development.

Causal inference methods to assess safety upper bounds in randomized trials with noncompliance.

BACKGROUND: Premature discontinuation and other forms of noncompliance with treatment assignment can complicate causal inference of treatment effects in randomized trials. The intent-to-treat analysis gives unbiased estimates for causal effects of treatment assignment on outcome, but may understate potential benefit or harm of actual treatment. The corresponding upper confidence limit can also be underestimated. PURPOSE: To compare estimates of the hazard ratio and upper bound of the two-sided 95% confidence interval from causal inference methods that account for noncompliance with those from the intent-to-treat analysis. METHODS: We used simulations with parameters chosen to reflect cardiovascular safety trials of diabetes drugs, with a focus on upper bound estimates relative to 1.3, based on regulatory guidelines. A total of 1000 simulations were run under each parameter combination for a hypothetical trial of 10,000 total subjects randomly assigned to active treatment or control at 1:1 ratio. Noncompliance was considered in the form of treatment discontinuation and cross-over at specified proportions, with an assumed true hazard ratio of 0.9, 1, and 1.3, respectively. Various levels of risk associated with being a non-complier (independent of treatment status) were evaluated. Hazard ratio and upper bound estimates from causal survival analysis and intent-to-treat were obtained from each simulation and summarized under each parameter setting. RESULTS: Causal analysis estimated the true hazard ratio with little bias in almost all settings examined. Intent-to-treat was unbiased only when the true hazard ratio = 1; otherwise it underestimated both benefit and harm. When upper bound estimates from intent-to-treat were ≥1.3, corresponding estimates from causal analysis were also ≥1.3 in almost 100% of the simulations, regardless of the true hazard ratio. When upper bound estimates from intent-to-treat were <1.3 and the true hazard ratio = 1, corresponding upper bound estimates from causal analysis were ≥1.3 in up to 66% of the simulations under some settings. LIMITATIONS: Simulations cannot cover all scenarios for noncompliance in real randomized trials. CONCLUSION: Causal survival analysis was superior to intent-to-treat in estimating the true hazard ratio with respect to bias in the presence of noncompliance. However, its large variance should be considered for safety upper bound exclusion especially when the true hazard ratio = 1. Our simulations provided a broad reference for practical considerations of bias-variance trade-off in dealing with noncompliance in cardiovascular safety trials of diabetes drugs. Further research is warranted for the development and application of causal inference methods in the evaluation of safety upper bounds.

Sparse factors for the positive and negative syndrome scale: which symptoms and stage of illness?

The Positive and Negative Syndrome Scale (PANSS) is frequently described with five latent factors, yet published factor models consistently fail to replicate across samples and related disorders. We hypothesize that (1) a subset of the PANSS, instead of the entire PANSS scale, would produce the most replicable five-factor models across samples, and that (2) the PANSS factor structure may be different depending on the treatment phase, influenced by the responsiveness of the positive symptoms to treatment. Using exploratory factor analysis, confirmatory factor analysis and cross validation on baseline and post-treatment observations from 3647 schizophrenia patients, we show that five-factor models fit best across samples when substantial subsets of the PANSS items are removed. The optimal model at baseline (five factors) omits 12 items: Motor Retardation, Grandiosity, Somatic Concern, Lack of Judgment and Insight, Difficulty in Abstract Thinking, Mannerisms and Posturing, Disturbance of Volition, Preoccupation, Disorientation, Excitement, Guilt Feelings and Depression. The PANSS factor models fit differently before and after patients have been treated. Patients with larger treatment response in positive symptoms have larger variations in factor structure across treatment stage than the less responsive patients. Negative symptom scores better predict the positive symptoms scores after treatment than before treatment. We conclude that sparse factor models replicate better on new samples, and the underlying disease structure of Schizophrenia changes upon treatment.

Major depressive disorder subtypes to predict long-term course.

BACKGROUND: Variation in the course of major depressive disorder (MDD) is not strongly predicted by existing subtype distinctions. A new subtyping approach is considered here. METHODS: Two data mining techniques, ensemble recursive partitioning and Lasso generalized linear models (GLMs), followed by k-means cluster analysis are used to search for subtypes based on index episode symptoms predicting subsequent MDD course in the World Mental Health (WMH) surveys. The WMH surveys are community surveys in 16 countries. Lifetime DSM-IV MDD was reported by 8,261 respondents. Retrospectively reported outcomes included measures of persistence (number of years with an episode, number of years with an episode lasting most of the year) and severity (hospitalization for MDD, disability due to MDD). RESULTS: Recursive partitioning found significant clusters defined by the conjunctions of early onset, suicidality, and anxiety (irritability, panic, nervousness-worry-anxiety) during the index episode. GLMs found additional associations involving a number of individual symptoms. Predicted values of the four outcomes were strongly correlated. Cluster analysis of these predicted values found three clusters having consistently high, intermediate, or low predicted scores across all outcomes. The high-risk cluster (30.0% of respondents) accounted for 52.9-69.7% of high persistence and severity, and it was most strongly predicted by index episode severe dysphoria, suicidality, anxiety, and early onset. A total symptom count, in comparison, was not a significant predictor. CONCLUSIONS: Despite being based on retrospective reports, results suggest that useful MDD subtyping distinctions can be made using data mining methods. Further studies are needed to test and expand these results with prospective data.

Pain qualities and satisfaction with therapy: a survey of subjects with neuropathic pain.

OBJECTIVES: Pain qualities may reflect neurobiological mechanisms and guide therapy. The objective was to assess whether pain qualities were associated with satisfaction with pain relief in subjects with neuropathic pain. METHODS: Subjects responded to a web survey that included current pain qualities, type of medications, and satisfaction with pain relief. RESULTS: A total of 1,502 subjects, primarily with diabetic neuropathy, completed the survey and were grouped into six clusters based on their pain qualities. Subjects in the Broadest spectrum endorsed all pain types (paroxysmal, throbbing, paresthesias, evoked pain, and numbness). Subjects in the Broad spectrum endorsed all types of pain, but to a lesser degree. Subjects in the Sharp paroxysmal and paresthesias endorsed mainly stabbing-like pain, subjects in the throbbing dull pain endorsed throbbing and pressure, subjects in the numbness endorsed mainly numbness, and subjects in the least affected endorsed few symptoms. The degree of satisfaction in each cluster varied with the type of medication. Two clusters were dissatisfied with antidepressants: subjects with paroxysmal pain and paresthesias (odds ratio [OR] = 0.38; 95% confidence interval [CI], 0.19-0.74) and subjects with the broadest spectrum of symptoms (OR = 0.63; 95% CI, 0.41-0.97). Two clusters were dissatisfied with opioids: subjects with throbbing and dull pain (OR = 0.44; 95% CI, 0.26-0.75) and subjects with numbness (OR = 0.48; 95% CI, 0.23-0.99). DISCUSSION: Pain qualities may help guide pain therapy and permit individualization of therapy.

Detecting rare variant associations: methods for testing haplotypes and multiallelic genotypes.

We summarize the work done by the contributors to Group 13 at Genetic Analysis Workshop 17 (GAW17) and provide a synthesis of their data analyses. The Group 13 contributors used a variety of approaches to test associations of both rare variants and common single-nucleotide polymorphisms (SNPs) with the GAW17 simulated traits, implementing analytic methods that incorporate multiallelic genotypes and haplotypes. In addition to using a wide variety of statistical methods and approaches, the contributors exhibited a remarkable amount of flexibility and creativity in coding the variants and their genes and in evaluating their proposed approaches and methods. We describe and contrast their methods along three dimensions: (1) selection and coding of genetic entities for analysis, (2) method of analysis, and (3) evaluation of the results. The contributors consistently presented a strong rationale for using multiallelic analytic approaches. They indicated that power was likely to be increased by capturing the signals of multiple markers within genetic entities defined by sliding windows, haplotypes, genes, functional pathways, and the entire set of SNPs and rare variants taken in aggregate. Despite this variability, the methods were fairly consistent in their ability to identify two associated genes for each simulated trait. The first gene was selected for the largest number of causal alleles and the second for a high-frequency causal SNP. The presumed model of inheritance and choice of genetic entities are likely to have a strong effect on the outcomes of the analyses.

Rare variant collapsing in conjunction with mean log p-value and gradient boosting approaches applied to Genetic Analysis Workshop 17 data.

In addition to methods that can identify common variants associated with susceptibility to common diseases, there has been increasing interest in approaches that can identify rare genetic variants. We use the simulated data provided to the participants of Genetic Analysis Workshop 17 (GAW17) to identify both rare and common single-nucleotide polymorphisms and pathways associated with disease status. We apply a rare variant collapsing approach and the usual association tests for common variants to identify candidates for further analysis using pathway-based and tree-based ensemble approaches. We use the mean log p-value approach to identify a top set of pathways and compare it to those used in simulation of GAW17 dataset. We conclude that the mean log p-value approach is able to identify those pathways in the top list and also related pathways. We also use the stochastic gradient boosting approach for the selected subset of single-nucleotide polymorphisms. When compared the result of this tree-based method with the list of single-nucleotide polymorphisms used in dataset simulation, in addition to correct SNPs we observe number of false positives.

Comparison of methods for correcting population stratification in a genome-wide association study of rheumatoid arthritis: principal-component analysis versus multidimensional scaling.

Population stratification (PS) represents a major challenge in genome-wide association studies. Using the Genetic Analysis Workshop 16 Problem 1 data, which include samples of rheumatoid arthritis patients and healthy controls, we compared two methods that can be used to evaluate population structure and correct PS in genome-wide association studies: the principal-component analysis method and the multidimensional-scaling method. While both methods identified similar population structures in this dataset, principal-component analysis performed slightly better than the multidimensional-scaling method in correcting for PS in genome-wide association analysis of this dataset.

Genome-wide association study for empirically derived metabolic phenotypes in the Framingham Heart Study offspring cohort.

We used data reduction and clustering methods to identify five phenotypically homogeneous groups of study participants with similar profiles for cardiovascular disease risk factors. We constructed both qualitative (binary subgroup membership) and quantitative traits (probability of subgroup membership) for each individual. The Cluster 1 comprised individuals who were generally healthy and had some history of smoking. Cluster 2 was dropped from the analyses due to the preponderance of missing data. Cluster 3 was used as the control group, healthy non-smokers. Members of Cluster 4 had features of the metabolic syndrome and were generally not as obese as Cluster 5. Obesity was the hallmark of Cluster 5, the members of which also had some features of the metabolic syndrome.We then examined the genetic associations with both qualitative and quantitative representations of these empirically derived traits. Genetic analyses of the qualitative traits were conducted, comparing each of the affected groups with the unaffected cluster alone and, to increase statistical power, the unaffected group and healthy smokers combined. One single-nucleotide polymorphism on chromosome 4 met a conservative genome-wide significance level, but the effect was muted when we accounted for population stratification. The results for the quantitative traits were similar, with a small number of genome-wide significant findings muted by control for admixture. The directional findings will provide the basis for hypothesis generation for syndromes such as the metabolic syndrome and obesity.

Phenotype definition and development--contributions from Group 7.

The papers in Genetic Analysis Workshop 16 Group 7 covered a wide range of topics. The effects of confounder misclassification and selection bias on association results were examined by one group. Another focused on bias introduced by various methods of accounting for treatment effects. Two groups used related methods to derive phenotypic traits. They used different analytic strategies for genetic associations with non-overlapping results (but because they used different sets of single-nucleotide polymorphisms (SNPs) and significance criteria, this is not surprising). Another group relied on the well-characterized definition of type 2 diabetes to show benefits of a novel predictive test. Transmission-ratio distortion was the focus of another paper. The results were extended to show a potential secondary benefit of the test to identify potentially mis-called SNPs.

The validity of cocaine dependence subtypes.

Cocaine dependence (CD) is a multifactorial disorder, variable in its manifestations, and heritable. We examined the concurrent validity of homogeneous subgroups of CD as phenotypes for genetic analysis. We applied data reduction methods and an empirical cluster-analytic approach to measures of cocaine use, cocaine-related effects, and cocaine treatment history in 1393 subjects, from 660 small nuclear families. Four of the six clusters that were derived yielded heritability estimates in excess of 0.3. Linkage analysis showed genome-wide significant results for two of the clusters. Here we examine the concurrent validity of the six clusters using a variety of demographic and substance-related measures. In addition to being differentiated by a variety of cocaine-related measures, the clusters differed significantly on measures that were independent of those used to generate the clusters, i.e., demographic features and prevalence rates of co-morbid substance use and psychiatric disorders. These findings support the validity of the methods used to derive homogeneous subgroups of CD subjects and the resulting CD subtypes. Independent replication of these findings would provide further validation of this approach.

Genetic association with rheumatoid arthritis-Genetic Analysis Workshop 15: summary of contributions from Group 2.

The papers in presentation group 2 of Genetic Analysis Workshop 15 (GAW15) conducted association analyses of rheumatoid arthritis data. The analyses were carried out primarily in the data provided by the North American Rheumatoid Arthritis Consortium (NARAC). One group conducted analyses in the data provided by the Canadian Rheumatoid Arthritis Genetics Study (CRAGS). Analysis strategies included genome-wide scans, the examination of candidate genes, and investigations of a region of interest on chromosome 18q21. Most authors employed relatively new methods, proposed extensions of existing methods, or introduced completely novel methods for aspects of association analysis. There were several common observations; a group of papers using a variety of methods found stronger association, on chromosomes 6 and 18 and in candidate gene PTPN22 among women with early onset. Generally, models that considered haplotypes or multiple markers showed stronger evidence for association than did single marker analyses.