Polygenic risk for schizophrenia and neurocognitive performance in patients with schizophrenia.

Both neurocognitive deficits and schizophrenia are highly heritable. Genetic overlap between neurocognitive deficits and schizophrenia has been observed in both the general population and in the clinical samples. This study aimed to examine if the polygenic architecture of susceptibility to schizophrenia modified neurocognitive performance in schizophrenia patients. Schizophrenia polygenic risk scores (PRSs) were first derived from the Psychiatric Genomics Consortium (PGC) on schizophrenia, and then the scores were calculated in our independent sample of 1130 schizophrenia trios, who had PsychChip data and were part of the Schizophrenia Families from Taiwan project. Pseudocontrols generated from the nontransmitted parental alleles of the parents in these trios were compared with alleles in schizophrenia patients in assessing the replicability of PGC-derived susceptibility variants. Schizophrenia PRS at the P-value threshold (PT) of 0.1 explained 0.2% in the variance of disease status in this Han-Taiwanese samples, and the score itself had a P-value 0.05 for the association test with the disorder. Each patient underwent neurocognitive evaluation on sustained attention using the continuous performance test and executive function using the Wisconsin Card Sorting Test. We applied a structural equation model to construct the neurocognitive latent variable estimated from multiple measured indices in these 2 tests, and then tested the association between the PRS and the neurocognitive latent variable. Higher schizophrenia PRS generated at the PT of 0.1 was significantly associated with poorer neurocognitive performance with explained variance 0.5%. Our findings indicated that schizophrenia susceptibility variants modify the neurocognitive performance in schizophrenia patients.

Using linkage information to weight a genome-wide association of bipolar disorder.

Issues of multiple-testing and statistical significance in genomewide association studies (GWAS) have prompted statistical methods utilizing prior data to increase the power of association results. Using prior findings from genome-wide linkage studies on bipolar disorder (BPD), we employed a weighted false discovery approach (wFDR; [Roeder et al. 2006. Am J Hum Genet 78(2): 243­252]) to previously reported GWAS data drawn from the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD). Using this method, association signals are up or down-weighted given the linkage score in that genomic region. Although no SNPs in our sample reached genome-wide significance through the wFDR approach, the strongest single SNP result from the original GWAS results (rs4939921 in myosin VB) is strongly up-weighted as it occurs on a linkage peak of chromosome 18. We also identify regions on chromosome 9, 17, and 18 where modestly associated SNP clusters coincide with strong linkage scores, implicating them as possible candidate regions for further analysis. Moving forward, we believe the application of prior linkage information will be increasingly useful to future GWAS studies that incorporate rarer variants into their analysis.

Fitting ACE structural equation models to case-control family data.

Investigators interested in whether a disease aggregates in families often collect case-control family data, which consist of disease status and covariate information for members of families selected via case or control probands. Here, we focus on the use of case-control family data to investigate the relative contributions to the disease of additive genetic effects (A), shared family environment (C), and unique environment (E). We describe an ACE model for binary family data; this structural equation model, which has been described previously, combines a general-family extension of the classic ACE twin model with a (possibly covariate-specific) liability-threshold model for binary outcomes. We then introduce our contribution, a likelihood-based approach to fitting the model to singly ascertained case-control family data. The approach, which involves conditioning on the proband's disease status and also setting prevalence equal to a prespecified value that can be estimated from the data, makes it possible to obtain valid estimates of the A, C, and E variance components from case-control (rather than only from population-based) family data. In fact, simulation experiments suggest that our approach to fitting yields approximately unbiased estimates of the A, C, and E variance components, provided that certain commonly made assumptions hold. Further, when our approach is used to fit the ACE model to Austrian case-control family data on depression, the resulting estimate of heritability is very similar to those from previous analyses of twin data.

Obesity and weight gain in relation to depression: findings from the Stirling County Study.

OBJECTIVE: This study concerns the question of whether obese subjects in a community sample experience depression in a different way from the nonobese, especially whether they overeat to the point of gaining weight during periods of depression. DESIGN: A representative sample of adults was interviewed regarding depression and obesity. SUBJECTS: The sample consisted of 1396 subjects whose interviews were studied regarding relationships between obesity and depression and among whom 114 had experienced a major depressive episode at some point in their lives and provided information about the symptoms experienced during the worst or only episode of major depression. MEASUREMENTS: The Diagnostic Interview Schedule (DIS) was used to identify major depressive episodes. Information was also derived from the section on Depression and Anxiety (DPAX) of the Stirling Study Schedule. Obesity was calculated as a body mass index >30. Logistic regressions were employed to assess relationships, controlling for age and gender, by means of odds ratios and 95% confidence intervals. RESULTS: In the sample as a whole, obesity was not related to depression although it was associated with the symptom of hopelessness. Among those who had ever experienced a major depressive episode, obese persons were 5 times more likely than the nonobese to overeat leading to weight gain during a period of depression (P<0.002). These obese subjects, compared to the nonobese, also experienced longer episodes of depression, a larger number of episodes, and were more preoccupied with death during such episodes. CONCLUSIONS: Depression among obese subjects in a community sample tends to be more severe than among the nonobese. Gaining weight while depressed is an important marker of that severity. Further research is needed to understand and possibly prevent the associations, sequences and outcomes among depression, obesity, weight gain and other adversities.

A new statistical screening approach for finding pharmacokinetics-related genes in genome-wide studies.

Biomedical researchers usually test the null hypothesis that there is no difference of the population mean of pharmacokinetics (PK) parameters between genotypes by the Kruskal-Wallis test. Although a monotone increasing pattern with a number of alleles is expected for PK-related genes, the Kruskal-Wallis test does not consider a monotonic response pattern. For detecting such patterns in clinical and toxicological trials, a maximum contrast method has been proposed. We show how that method can be used with pharmacogenomics data to a develop test of association. Further, using simulation studies, we compare the power of the modified maximum contrast method to those of the maximum contrast method and the Kruskal-Wallis test. On the basis of the results of those studies, we suggest rules of thumb for which statistics to use in a given situation. An application of all three methods to an actual genome-wide pharmacogenomics study illustrates the practical relevance of our discussion.

Evidence for an association of the dopamine D5 receptor gene on age at onset of attention deficit hyperactivity disorder.

The purpose of this study was to determine whether the single nucleotide polymorphisms (SNPs) within candidate genes for attention deficit hyperactivity disorder (ADHD) are associated with the age at onset for ADHD. One hundred and forty-three SNPs were genotyped across five candidate genes (DRD5, SLC6A3, HTR1B, SNAP25, DRD4) for ADHD in 229 families with at least one affected offspring. SNPs with the highest estimated power to detect an association with age at onset were selected for each candidate gene, using a power-based screening procedure that does not compromise the nominal significance level. A time-to-onset analysis for family-based samples was performed on these SNPs to determine if an association exists with age at onset for ADHD. Seven consecutive SNPs surrounding the D5 dopamine receptor gene (DRD5), were associated with the age at onset for ADHD; FDR adjusted q-values ranged from 0.008 to 0.023. This analysis indicates that individuals with the risk genotype develop ADHD earlier than individuals with any other genotype. A haplotype analysis across the 6 significant SNPs that were in linkage disequilibrium with one another, CTCATA, was also found to be significant (p-value = 0.02). We did not observe significant associations with age at onset for the other candidate loci tested. Although definitive conclusions await independent replication, these results suggest that a variant in DRD5 may affect age at onset for ADHD.

A study of how socioeconomic status moderates the relationship between SNPs encompassing BDNF and ADHD symptom counts in ADHD families.

Recent animal research suggests that brain-derived neurotrophic factor (BDNF), may mediate response to different environmental stimuli. In this paper, we evaluated the possible role of BDNF as a moderator of attention deficit hyperactivity disorder (ADHD) in the context of different socioeconomic classes. We genotyped ten single nucleotide polymorphisms (SNPs) in and around BDNF in 229 families and evaluate whether there are SNP-by-socioeconomic status (SES) interactions for attention deficit hyperactivity. We developed three quantitative phenotypes for ADHD from nine inattentive and nine hyperactive-impulsive symptoms that were used in SNP-by-SES interaction analyses using a new methodology implemented in the computer program PBAT. Findings were adjusted for multiple comparisons using the false discovery rate. We found multiple significant SNP-by-SES interactions using the inattentive symptom count. This study suggests that different SES classes may modify the effect of the functional variant(s) in and around BDNF to have an impact on the number of ADHD symptom counts that are observed. The two exons within BDNF represent potential functional variants that may be causing the observed associations.

Approaches to handling incomplete data in family-based association testing.

The high throughput of data arising from the complete sequence of the human genome has left statistical geneticists with a rich and extensive information source. The wide availability of software and the increase in computing power has improved the possibilities to access and process such data. One problem is incompleteness of the data: unobserved or partially observed data points due to technical reasons or reasons associated with the patient's status or erroneous measurements of phenotype or genotype, to name a few. When not properly accounted for, these sources of incompleteness may seriously jeopardize the credibility of results from analyses. In this paper we provide some perspectives on the occurrence and analysis of different forms of incomplete data in family-based genetic association testing.

Incidence of adverse events and negligence in hospitalized patients: results of the Harvard Medical Practice Study I. 1991.

BACKGROUND: As part of an interdisciplinary study of medical injury and malpractice litigation, we estimated the incidence of adverse events, defined as injuries caused by medical management, and of the subgroup of such injuries that resulted from negligent or substandard care. METHODS: We reviewed 30121 randomly selected records from 51 randomly selected acute care, non-psychiatric hospitals in New York State in 1984. We then developed population estimates of injuries and computed rates according to the age and sex of the patients as well as the specialties of the physicians. RESULTS: Adverse events occurred in 3.7% of the hospitalizations (95% confidence interval 3.2 to 4.2), and 27.6% of the adverse events were due to negligence (95% confidence interval 22.5 to 32.6). Although 70.5% of the adverse events gave rise to disability lasting less than 6 months, 2.6% caused permanently disabling injuries and 13.6% led to death. The percentage of adverse events attributable to negligence increased in the categories of more severe injuries (Wald test chi(2) = 21.04, p<0.0001). Using weighted totals we estimated that among the 2671863 patients discharged from New York hospitals in 1984 there were 98609 adverse events and 27179 adverse events involving negligence. Rates of adverse events rose with age (p<0.0001). The percentage of adverse events due to negligence was markedly higher among the elderly (p<0.01). There were significant differences in rates of adverse events among categories of clinical specialties (p<0.0001), but no differences in the percentage due to negligence. CONCLUSIONS: There is a substantial amount of injury to patients from medical management, and many injuries are the result of substandard care.

Anxiety and depression: a 40-year perspective on relationships regarding prevalence, distribution, and comorbidity.

OBJECTIVE: Building on a report about the prevalence of depression over time, this paper examines historical trends regarding anxiety in terms of its prevalence, its distribution by age and gender, and its comorbidity with depression. Methods for conducting such time trend analysis are reviewed. METHOD: Representative samples of adults were selected and interviewed in 1952, 1970, and 1992. Logistic regressions were used for statistical analysis. RESULTS: Although twice as common as depression, the prevalence of anxiety was equally stable. Anxiety was consistently and significantly more characteristic of women than men. A re-distribution of rates in 1992 indicated that depression but not anxiety had significantly increased among younger women (P = 0.03). Throughout the study, approximately half of the cases of anxiety also suffered depression. CONCLUSION: The relationships between anxiety and depression remained similar over time with the exception that depression came to resemble anxiety as a disorder to which women were significantly more vulnerable than men. Social and historical factors should be investigated to assess their relevance to this change.

Tests of gene-environment interaction for case-parent triads with general environmental exposures.

As knowledge of the human genome continues to grow, more progress is being made towards not only identifying the genes involved in disease susceptibility but also in defining the synergistic role genes play with environmental exposures. The detection of gene-environment interactions is important as it can offer clinicians a potential means of intervention. The discovery of interactions relies heavily on powerful statistical methods. We present a test, FBAT-I, that can be used to investigate gene-environment interaction. The test uses the case-parent triad design and protects the statistical inference from potential spurious results due to population admixture.

Family study of the aggregation of eating disorders and mood disorders.

BACKGROUND: Family studies have suggested that eating disorders and mood disorders may coaggregate in families. To study further this question, data from a family interview study of probands with and without major depressive disorder was examined. METHOD: A bivariate proband predictive logistic regression model was applied to data from a family interview study, conducted in Innsbruck, Austria, of probands with (N = 64) and without (N = 58) major depressive disorder, together with 330 of their first-degree relatives. RESULTS: The estimated odds ratio (OR) for the familial aggregation of eating disorders (anorexia nervosa, bulimia nervosa and binge-eating disorder) was 7.0 (95 % CI 1.4, 28; P = 0.006); the OR for the familial aggregation of mood disorders (major depression and bipolar disorder) was 2.2 (0.92, 5.4; P = 0.076); and for the familial coaggregation of eating disorders with mood disorders the OR was 2.2 (1.1, 4.6; P = 0.035). CONCLUSIONS: The familial coaggregation of eating disorders with mood disorders was significant and of the same magnitude as the aggregation of mood disorders alone--suggesting that eating disorders and mood disorders have common familial causal factors.

Estimation and tests of haplotype-environment interaction when linkage phase is ambiguous.

In the study of complex traits, the utility of linkage analysis and single marker association tests can be limited for researchers attempting to elucidate the complex interplay between a gene and environmental covariates. For these purposes, tests of gene-environment interactions are needed. In addition, recent studies have indicated that haplotypes, which are specific combinations of nucleotides on the same chromosome, may be more suitable as the unit of analysis for statistical tests than single genetic markers. The difficulty with this approach is that, in standard laboratory genotyping, haplotypes are often not directly observable. Instead, unphased marker phenotypes are collected. In this article, we present a method for estimating and testing haplotype-environment interactions when linkage phase is potentially ambiguous. The method builds on the work of Schaid et al. [2002] and is applicable to any trait that can be placed in the generalized linear model framework. Simulations were run to illustrate the salient features of the method. In addition, the method was used to test for haplotype-smoking exposure interaction with data from the Childhood Asthma Management Program.

Univariate and multivariate family-based association analysis of the IL-13 ARG130GLN polymorphism in the Childhood Asthma Management Program.

Interleukin 13 (IL-13) has been demonstrated to have a crucial role in animal models of allergy and asthma. In human case-control genetic-association studies, the Arg130Gln polymorphism has been associated with elevated total serum IgE and an asthma diagnosis in atopic and nonatopic individuals (Graves et al. [2000] J. Allergy Clin. Immunol. 105:506-513; Heinzmann et al. [2000] Hum. Mol. Genet. 9:549-559). To apply family-based association methods, we obtained DNA samples from 685 asthmatic children from 640 sibships and their parents in the Childhood Asthma Management Program (CAMP). Six hundred and sixty-six asthmatic children had complete phenotypic information and were used for this analysis. We performed quantitative association analysis using the transmission disequilibrium test (TDT) on 22 individual phenotypes and 5 grouped phenotypes relating to allergy, airway responsiveness, pulmonary function, bronchodilator responsiveness, and asthma severity, using genotypes at the Arg130Gln polymorphism of the IL-13 gene. A positive association was obtained between Arg130Gln and a grouped phenotype of allergy (consisting of the individual phenotypes of eosinophils, IgE, and positive skin tests), using FBAT-GEE, a multivariate extension of the family-based association test (Lange et al. [2002] Biostatistics 1:1-15). The three phenotypes were then evaluated individually and revealed a significant association between total eosinophil count and the Arg130Gln locus; there was a trend for association between total IgE and the Arg130Gln polymorphism. The Arg130Gln polymorphism is associated with an elevated eosinophil count as well as with a grouped allergy phenotype, in children with mild to moderate asthma. No evidence for association was found between Arg130Gln and airway responsiveness, asthma diagnosis, or asthma severity.

Multiple informants: mortality associated with psychiatric disorders in the Stirling County Study.

This paper applies new statistical procedures for analyzing multiple-source information about the relation of psychiatric diagnoses to mortality. The data come from the Stirling County Study, a longitudinal community investigation of adults, that collected multiple-source reports (self-report and physician-report) about psychiatric disorders. These reports are used as predictors of mortality risk over a 16-year follow-up period (1952-1968). Despite extensive efforts, one or both of these reports were sometimes missing. Missingness of self-report was related to demographic characteristics as well as to physician-reports of psychiatric diagnosis. The statistical procedures used here draw together into a single frame of reference both informant reports for the initial Stirling survey and relate these to mortality risk using weighted generalized estimating equation regression models for time to event data. This unified method has two advantages over traditional approaches: 1) the relative predictiveness of each informant can be assessed and 2) all subjects contribute to the analysis. The methods are applicable to other areas of epidemiology where multiple informant reports are used. The results for self-reports and physician-reports of disorders were comparable: Psychiatric diagnosis was associated with higher mortality, particularly among younger subjects.

Impact of overweight on the risk of developing common chronic diseases during a 10-year period.

BACKGROUND: Overweight adults are at an increased risk of developing numerous chronic diseases. METHODS: Ten-year follow-up (1986-1996) of middle-aged women in the Nurses' Health Study and men in the Health Professionals Follow-up Study to assess the health risks associated with overweight. RESULTS: The risk of developing diabetes, gallstones, hypertension, heart disease, and stroke increased with severity of overweight among both women and men. Compared with their same-sex peers with a body mass index (BMI) (calculated as weight in kilograms divided by the square of height in meters) between 18.5 and 24.9, those with BMI of 35.0 or more were approximately 20 times more likely to develop diabetes (relative risk [RR], 17.0; 95% confidence interval [CI], 14.2-20.5 for women; RR, 23.4; 95% CI, 19.4-33.2 for men). Women who were overweight but not obese (ie, BMI between 25.0 and 29.9) were also significantly more likely than their leaner peers to develop gallstones (RR, 1.9), hypertension (RR, 1.7), high cholesterol level (RR, 1.1), and heart disease (RR, 1.4). The results were similar in men. CONCLUSIONS: During 10 years of follow-up, the incidence of diabetes, gallstones, hypertension, heart disease, colon cancer, and stroke (men only) increased with degree of overweight in both men and women. Adults who were overweight but not obese (ie, 25.0 < or = BMI < or = 29.9) were at significantly increased risk of developing numerous health conditions. Moreover, the dose-response relationship between BMI and the risk of developing chronic diseases was evident even among adults in the upper half of the healthy weight range (ie, BMI of 22.0-24.9), suggesting that adults should try to maintain a BMI between 18.5 and 21.9 to minimize their risk of disease.

Genetic association analysis of behavioral inhibition using candidate loci from mouse models.

Genes influence the development of anxiety disorders, but the specific loci involved are not known. Genetic association studies of anxiety disorders are complicated by the complexity of the phenotypes and the difficulty in identifying appropriate candidate loci. We have begun to examine the genetics of behavioral inhibition to the unfamiliar (BI), a heritable temperamental predisposition that is a developmental and familial risk factor for panic and phobic disorders. Specific loci associated with homologous phenotypes in mouse models provide compelling candidate genes for human BI. We conducted family-based association analyses of BI using four genes derived from genetic studies of mouse models with features of behavioral inhibition. The sample included families of 72 children classified as inhibited by structured behavioral assessments. We observed modest evidence of association (P = 0.05) between BI and the glutamic acid decarboxylase gene (65 kDA isoform), which encodes an enzyme involved in GABA synthesis. No significant evidence of association was observed for the genes encoding the adenosine A(1A) receptor, the adenosine A(2A) receptor, or preproenkephalin. This study illustrates the potential utility of using candidate genes derived from mouse models to dissect the genetic basis of BI, a possible intermediate phenotype for panic and phobic disorders.

The family based association test method: strategies for studying general genotype--phenotype associations.

With possibly incomplete nuclear families, the family based association test (FBAT) method allows one to evaluate any test statistic that can be expressed as the sum of products (covariance) between an arbitrary function of an offspring's genotype with an arbitrary function of the offspring's phenotype. We derive expressions needed to calculate the mean and variance of these test statistics under the null hypothesis of no linkage. To give some guidance on using the FBAT method, we present three simple data analysis strategies for different phenotypes: dichotomous (affection status), quantitative and censored (eg, the age of onset). We illustrate the approach by applying it to candidate gene data of the NIMH Alzheimer Disease Initiative. We show that the RC-TDT is equivalent to a special case of the FBAT method. This result allows us to generalise the RC-TDT to dominant, recessive and multi-allelic marker codings. Simulations compare the resulting FBAT tests to the RC-TDT and the S-TDT. The FBAT software is freely available.

Maximum likelihood analysis of logistic regression models with incomplete covariate data and auxiliary information.

This article presents a new method for maximum likelihood estimation of logistic regression models with incomplete covariate data where auxiliary information is available. This auxiliary information is extraneous to the regression model of interest but predictive of the covariate with missing data. Ibrahim (1990, Journal of the American Statistical Association 85, 765-769) provides a general method for estimating generalized linear regression models with missing covariates using the EM algorithm that is easily implemented when there is no auxiliary data. Vach (1997, Statistics in Medicine 16, 57-72) describes how the method can be extended when the outcome and auxiliary data are conditionally independent given the covariates in the model. The method allows the incorporation of auxiliary data without making the conditional independence assumption. We suggest tests of conditional independence and compare the performance of several estimators in an example concerning mental health service utilization in children. Using an artificial dataset, we compare the performance of several estimators when auxiliary data are available.

An alternative parameterization of the general linear mixture model for longitudinal data with non-ignorable drop-outs.

This paper considers the mixture model methodology for handling non-ignorable drop-outs in longitudinal studies with continuous outcomes. Recently, Hogan and Laird have developed a mixture model for non-ignorable drop-outs which is a standard linear mixed effects model except that the parameters which characterize change over time depend also upon time of drop-out. That is, the mean response is linear in time, other covariates and drop-out time, and their interactions. One of the key attractions of the mixture modelling approach to drop-outs is that it is relatively easy to explore the sensitivity of results to model specification. However, the main drawback of mixture models is that the parameters that are ordinarily of interest are not immediately available, but require marginalization of the distribution of outcome over drop-out times. Furthermore, although a linear model is assumed for the conditional mean of the outcome vector given time of drop out, after marginalization, the unconditional mean of the outcome vector is not, in general, linear in the regression parameters. As a result, it is not possible to parsimoniously describe the effects of covariates on the marginal distribution of the outcome in terms of regression coefficients. The need to explicitly average over the distribution of the drop-out times and the absence of regression coefficients that describe the effects of covariates on the outcome are two unappealing features of the mixture modelling approach. In this paper we describe a particular parameterization of the general linear mixture model that circumvents both of these problems.