Alpha-2 macroglobulin is genetically associated with Alzheimer disease.

Alpha-2-macroglobulin (alpha-2M; encoded by the gene A2M) is a serum pan-protease inhibitor that has been implicated in Alzheimer disease (AD) based on its ability to mediate the clearance and degradation of A beta, the major component of beta-amyloid deposits. Analysis of a deletion in the A2M gene at the 5' splice site of 'exon II' of the bait region (exon 18) revealed that inheritance of the deletion (A2M-2) confers increased risk for AD (Mantel-Haenzel odds ratio=3.56, P=0.001). The sibship disequilibrium test (SDT) also revealed a significant association between A2M and AD (P=0.00009). These values were comparable to those obtained for the APOE-epsilon4 allele in the same sample, but in contrast to APOE-epsilon4, A2M-2 did not affect age of onset. The observed association of A2M with AD did not appear to account for the previously published linkage of AD to chromosome 12, which we were unable to confirm in this sample. A2M, LRP1 (encoding the alpha-2M receptor) and the genes for two other LRP ligands, APOE and APP (encoding the amyloid beta-protein precursor), have now all been genetically linked to AD, suggesting that these proteins may participate in a common neuropathogenic pathway leading to AD.

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.

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.

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.

Generalized linear mixture models for handling nonignorable dropouts in longitudinal studies.

This paper presents a method for analysing longitudinal data when there are dropouts. In particular, we develop a simple method based on generalized linear mixture models for handling nonignorable dropouts for a variety of discrete and continuous outcomes. Statistical inference for the model parameters is based on a generalized estimating equations (GEE) approach (Liang and Zeger, 1986). The proposed method yields estimates of the model parameters that are valid when nonresponse is nonignorable under a variety of assumptions concerning the dropout process. Furthermore, the proposed method can be implemented using widely available statistical software. Finally, an example using data from a clinical trial of contracepting women is used to illustrate the methodology.

A 40-year perspective on the prevalence of depression: the Stirling County Study.

BACKGROUND: According to epidemiologic studies that use recall of lifetime episodes, the prevalence of depression is increasing. This report from the Stirling County Study compares rates of current depression among representative samples of adults from a population in Atlantic Canada. METHODS: Sample sizes were 1003, 1201, and 1396 in 1952, 1970, and 1992, respectively. The depression component of the study's method, the DPAX (DP for depression and AX for anxiety), was employed. The original procedure (DPAX-1) was applied in all years. A revision (DPAX-2) was used in 1970 and 1992. The Diagnostic Interview Schedule (DIS) was also used in 1992. RESULTS: With the DPAX-1, the overall prevalence of current depression was steady at 5% over the 2 early samples but declined in 1992 because of vernacular changes referring to dysphoria. The DPAX-2 gave a stable overall prevalence of 5% in the 2 recent samples, but indicated that women and younger people were at greater risk in 1992 than in 1970. The DIS, like the DPAX-2, found a current 1992 rate of 5% for major depressive episodes combined with dysthymia. Recalled lifetime rates using the DIS showed the same profile interpreted in other studies as suggesting an increase in depression over time. CONCLUSIONS: Three samples over a 40-year period showed a stable current prevalence of depression using the DPAX methods that was comparable in 1992 with the current rates using the DIS. This casts doubt on the interpretation that depression is generally increasing. Within the overall steady rate observed in this study, historical change was a matter of redistribution by sex and age, with a higher rate among younger women being of recent origin.

A comparison of diagnostic interviews for depression in the Stirling County study: challenges for psychiatric epidemiology.

BACKGROUND: High prevalence rates in psychiatric epidemiologic studies raise questions about whether data-gathering procedures identify transient responses rather than clinical disorders. This issue is explored relevant to depression using data from the Stirling County Study. METHODS: The study's customary method, the DPAX (DP for depression and AX for anxiety) was compared with the Diagnostic Interview Schedule (DIS), both of which were administered to a sample of 1396 subjects selected in 1992. Reasons for discordance were analyzed, and demographic correlates of responses to questions about dysphoria were examined. These lay-administered interviews were then compared with clinician-administered interviews that used the Structured Clinical Interview for DSM-III-R (SCID) with 139 subjects. The kappa statistic and logistic regression were used for statistical assessment. RESULTS: For the level of agreement between the DPAX and the DIS for current and lifetime depression, kappa = 0.40 and kappa = 0.33, respectively. Subjects diagnosed only by the DPAX tended to have less education than those diagnosed only by the DIS. Some idioms for dysphoria seemed to work better than others. Using SCID interviews as a clinical standard, the DPAX had 15% sensitivity and 96% specificity and the DIS had 25% sensitivity and 98% specificity. CONCLUSIONS: Comprehension of an interview can be improved by using multiple questions for dysphoria and a simpler mode of inquiry. Clinician-administered interviews tend to corroborate disorders identified in lay-administered interviews but suggest that survey methods underestimate prevalence. Further research is needed to evaluate the validity of both types of interviews, but evidence from a 16-year follow-up evaluation indicates that depression diagnosed by the DPAX is a serious disorder in terms of morbidity and mortality.

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.

Tutorial in Biostatistics: Evaluating the impact of 'critical periods' in longitudinal studies of growth using piecewise mixed effects models.

Recent developments in modern multivariate methods provide applied researchers with the means to address many important research questions that arise in studies with repeated measures data collected on individuals over time. One such area of applied research is focused on studying change associated with some event or critical period in human development. This tutorial deals with the use of the general linear mixed model for regression analysis of correlated data with a two-piece linear function of time corresponding to the pre- and post-event trends. The model assumes a continuous outcome is linearly related to a set of explanatory variables, but allows for the trend after the event to be different from the trend before it. This task can be accomplished using a piecewise linear random effects model for longitudinal data where the response depends upon time of the event. A detailed example that examines the influence of menarche on changes in body fat accretion will be presented using data from a prospective study of 162 girls measured annually from approximately age 10 until 4 years post menarche.

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.

Inter-laboratory variability in Ames assay results.

The Ames test is widely used in the screening of chemicals and compounds for potential carcinogenic effect. There is, however, considerable inter-laboratory variability in results from this assay. Using data from the RTI Collaborative Study of the EPA Ames Test Protocol, we show that their reported standard errors of estimates of mutagenicity fall far short of capturing day-to-day or laboratory-to-laboratory variation. We estimate the factors by which the standard errors must be inflated to account for these sources of variation. The laboratory protocol and previous studies suggest that much of this variation may be caused by factors that are relatively constant within days (e.g. technician, incubation temperature, S9 liver homogenate preparation) but vary over days and across laboratories. Therefore, such variation might be reduced through use of a reference compound tested on the same day and under the same conditions as the test chemical. This conjecture was, however, not supported by analyses that considered the positive control compound and a pure chemical as possible reference assays.

Increasing efficiency from censored survival data by using random effects to model longitudinal covariates.

When estimating a survival time distribution, the loss of information due to right censoring results in a loss of efficiency in the estimator. In many circumstances, however, repeated measurements on a longitudinal process which is associated with survival time are made throughout the observation time, and these measurements may be used to recover information lost to censoring. For example, patients in an AIDS clinical trial may be measured at regular intervals on CD4 count and viral load. We describe a model for the joint distribution of a survival time and a repeated measures process. The joint distribution is specified by linking the survival time to subject-specific random effects characterizing the repeated measures, and is similar in form to the pattern mixture model for multivariate data with nonignorable nonresponse. We also describe an estimator of survival derived from this model. We apply the methods to a long-term AIDS clinical trial, and study properties of the survival estimator. Monte Carlo simulation is used to estimate gains in efficiency when the survival time is related to the location and scale of the random effects distribution. Under relatively light censoring (20%), the methods yield a modest gain in efficiency for estimating three-year survival in the AIDS clinical trial. Our simulation study, which mimics characteristics of the clinical trial, indicates that much larger gains in efficiency can be realized under heavier censoring or with studies designed for long term follow up on survival.

Maximum likelihood analysis of generalized linear models with missing covariates.

Missing data is a common occurrence in most medical research data collection enterprises. There is an extensive literature concerning missing data, much of which has focused on missing outcomes. Covariates in regression models are often missing, particularly if information is being collected from multiple sources. The method of weights is an implementation of the EM algorithm for general maximum-likelihood analysis of regression models, including generalized linear models (GLMs) with incomplete covariates. In this paper, we will describe the method of weights in detail, illustrate its application with several examples, discuss its advantages and limitations, and review extensions and applications of the method.

Longitudinal studies with continuous responses.

The analysis of serial measurements obtained in longitudinal studies plays an increasingly prominent role in applied research. The last few years have seen the development of many new techniques for carrying out analyses, including computer software. These methods can be used in a variety of standard problems, including repeated measures and cross-over designs, as well as growth curve analyses. We review these new methods, their application, and available computer packages. Data from a longitudinal study of lung function is used to illustrate the methods.

Kartagener's syndrome. A blinded, controlled study of cilia ultrastructure.

We investigated respiratory mucosa cilia ultrastructure in patients homozygous for the gene for Kartagener's syndrome (KS) and patients apparently phenotypic for KS who had bronchiectasis and sinusitis but without situs inversus. Parents, as obligate carriers of the recessive KS gene, were also evaluated among other control groups. The four patients with KS had significantly fewer cilia outer dynein arms than normal subjects or parents of patients with KS. Two of five patients apparently phenotypic for KS demonstrated distinctive ultrastructural changes. No other subjects demonstrated explicit ultrastructural abnormalities. Internal control specimens showed that the number of outer dynein arms was consistent within a subject compared with variation between subjects. The outer dynein arm serves as a dependable ultrastructural marker. Carriers of KS do not demonstrate distinctive morphologic cilia abnormalities. Not every patient with chronic bronchiectasis and sinusitis demonstrates abnormal cilia ultrastructure.