Anorexia nervosa and bulimia nervosa (BN) are rare, but eating disorders not otherwise specified (EDNOS) are relatively common among female participants. Our objective was to evaluate whether BN and subtypes of EDNOS are predictive of developing adverse outcomes.
This study comprised a prospective analysis of 8594 female participants from the ongoing Growing Up Today Study. Questionnaires were sent annually from 1996 through 2001, then biennially through 2007 and 2008. Participants who were 9 to 15 years of age in 1996 and completed at least 2 consecutive questionnaires between 1996 and 2008 were included in the analyses. Participants were classified as having BN (≥weekly binge eating and purging), binge eating disorder (BED; ≥weekly binge eating, infrequent purging), purging disorder (PD; ≥weekly purging, infrequent binge eating), other EDNOS (binge eating and/or purging monthly), or nondisordered.
BN affected ∼1% of adolescent girls; 2% to 3% had PD and another 2% to 3% had BED. Girls with BED were almost twice as likely as their nondisordered peers to become overweight or obese (odds ratio [OR]: 1.9 [95% confidence interval: 1.0–3.5]) or develop high depressive symptoms (OR: 2.3 [95% confidence interval: 1.0–5.0]). Female participants with PD had a significantly increased risk of starting to use drugs (OR: 1.7) and starting to binge drink frequently (OR: 1.8).
PD and BED are common and predict a range of adverse outcomes. Primary care clinicians should be made aware of these disorders, which may be underrepresented in eating disorder clinic samples. Efforts to prevent eating disorders should focus on cases of subthreshold severity.
adolescents; eating disorders; epidemiology; obesity; substance use
IL10 is an anti-inflammatory cytokine that has been found to have lower production in macrophages and mononuclear cells from asthmatics. Since reduced IL10 levels may influence the severity of asthma phenotypes, we examined IL10 single-nucleotide polymorphisms (SNPs) for association with asthma severity and allergy phenotypes as quantitative traits. Utilizing DNA samples from 518 Caucasian asthmatic children from the Childhood Asthma Management Program (CAMP) and their parents, we genotyped six IL10 SNPs: 3 in the promoter, 2 in introns, and one in the 3′ UTR. Using family-based association tests, each SNP was tested for association with asthma and allergy phenotypes individually. Population-based association analysis was performed with each SNP locus, the promoter haplotypes and the 6-loci haplotypes. The 3′ UTR SNP was significantly associated with FEV1 as a percent of predicted (FEV1PP) (P=0.0002) in both the family and population analyses. The promoter haplotype GCC was positively associated with IgE levels and FEV1PP (P=0.007 and 0.012, respectively). The promoter haplotype ATA was negatively associated with lnPC20 and FEV1PP (P=0.008 and 0.043, respectively). Polymorphisms in IL10 are associated with asthma phenotypes in this cohort. Further studies of variation in the IL10 gene may help elucidate the mechanism of asthma development in children.
interleukin 10 (IL10); single nucleotide polymorphism (SNP); genetic association; family-based association test (FBAT); haplotype; promoter; 3′; untranslated region (3′UTR)
Many Genome-Wide Association Studies (GWAS) have signals with unknown etiology. This paper addresses the question — is such an association signal caused by rare or common variants that lead to increased disease risk? For a genomic region implicated by a GWAS, we use Single Nucleotide Polymorphism (SNP) data in a case-control setting to predict how many common or rare variants there are, using a Bayesian analysis. Our objective is to compute posterior probabilities for configurations of rare and/or common variants. We use an extension of coalescent trees — the Ancestral Recombination Graphs (ARG) — to model the genealogical history of the samples based on marker data. As we expect SNPs to be in Linkage Disequilibrium (LD) with common disease variants, we can expect the trees to reflect on the type of variants. To demonstrate the application, we apply our method to candidate gene sequencing data from a German case-control study on nonsyndromic cleft lip with or without cleft palate (NSCL/P).
Coalescent Tree; Genetic Association; Rare Variant; Common Variant; Ancestral Recombination Graphs; Bayesian Modeling
To identify predictors of becoming eating disordered among adolescents.
Prospective cohort study.
Girls (n=6916) and boys (n=5618), aged 9 to 15 years at baseline, in the ongoing Growing Up Today Study (GUTS).
Parent, peer, and media influences.
Main Outcome Measures
Onset of starting to binge eat or purge (ie, vomiting or using laxatives) at least weekly.
During 7 years of follow-up, 4.3% of female subjects and 2.3% of male subjects (hereafter referred to as “females” and “males”) started to binge eat and 5.3% of females and 0.8% of males started to purge to control their weight. Few participants started to both binge eat and purge. Rates and risk factors varied by sex and age group (<14 vs ≥14 years). Females younger than 14 years whose mothers had a history of an eating disorder were nearly 3 times more likely than their peers to start purging at least weekly (odds ratio, 2.8; 95% confidence interval, 1.3–5.9); however, maternal history of an eating disorder was unrelated to risk of starting to binge eat or purge in older adolescent females. Frequent dieting and trying to look like persons in the media were independent predictors of binge eating in females of all ages. In males, negative comments about weight by fathers was predictive of starting to binge at least weekly.
Risk factors for the development of binge eating and purging differ by sex and by age group in females. Maternal history of an eating disorder is a risk factor only in younger adolescent females.
For genetic association studies in designs of unrelated individuals, current statistical methodology typically models the phenotype of interest as a function of the genotype and assumes a known statistical model for the phenotype. In the analysis of complex phenotypes, especially in the presence of ascertainment conditions, the specification of such model assumptions is not straight-forward and is error-prone, potentially causing misleading results.
In this paper, we propose an alternative approach that treats the genotype as the random variable and conditions upon the phenotype. Thereby, the validity of the approach does not depend on the correctness of assumptions about the phenotypic model. Misspecification of the phenotypic model may lead to reduced statistical power. Theoretical derivations and simulation studies demonstrate both the validity and the advantages of the approach over existing methodology. In the COPDGene study (a GWAS for Chronic Obstructive Pulmonary Disease (COPD)), we apply the approach to a secondary, quantitative phenotype, the Fagerstrom nicotine dependence score, that is correlated with COPD affection status. The software package that implements this method is available.
The flexibility of this approach enables the straight-forward application to quantitative phenotypes and binary traits in ascertained and unascertained samples. In addition to its robustness features, our method provides the platform for the construction of complex statistical models for longitudinal data, multivariate data, multi-marker tests, rare-variant analysis, and others.
Genetic associations studies; Secondary phenotypes; Case-control; Ascertainment; Semi-parametric
We previously reported that asthmatic children with GSTM1 null genotype may be more susceptible to the acute effect of ozone on the small airways and might benefit from antioxidant supplementation. This study aims to assess the acute effect of ozone on lung function (FEF25-75) in asthmatic children according to dietary intake of vitamin C and the number of putative risk alleles in three antioxidant genes: GSTM1, GSTP1 (rs1695), and NQO1 (rs1800566).
257 asthmatic children from two cohort studies conducted in Mexico City were included. Stratified linear mixed models with random intercepts and random slopes on ozone were used. Potential confounding by ethnicity was assessed. Analyses were conducted under single gene and genotype score approaches.
The change in FEF25-75 per interquartile range (60 ppb) of ozone in persistent asthmatic children with low vitamin C intake and GSTM1 null was −91.2 ml/s (p = 0.06). Persistent asthmatic children with 4 to 6 risk alleles and low vitamin C intake showed an average decrement in FEF25-75 of 97.2 ml/s per 60 ppb of ozone (p = 0.03). In contrast in children with 1 to 3 risk alleles, acute effects of ozone on FEF25-75 did not differ by vitamin C intake.
Our results provide further evidence that asthmatic children predicted to have compromised antioxidant defense by virtue of genetic susceptibility combined with deficient antioxidant intake may be at increased risk of adverse effects of ozone on pulmonary function.
Air pollution; Asthmatic children; Antioxidant genes; Mexico City; Vitamin C
Genome-wide association studies have been able to identify disease associations with many common variants; however most of the estimated genetic contribution explained by these variants appears to be very modest. Rare variants are thought to have larger effect sizes compared to common SNPs but effects of rare variants cannot be tested in the GWAS setting. Here we propose a novel method to test for association of rare variants obtained by sequencing in family-based samples by collapsing the standard family-based association test (FBAT) statistic over a region of interest. We also propose a suitable weighting scheme so that low frequency SNPs that may be enriched in functional variants can be upweighted compared to common variants. Using simulations we show that the family-based methods perform at par with the population-based methods under no population stratification. By construction, family-based tests are completely robust to population stratification; we show that our proposed methods remain valid even when population stratification is present.
Two recent metaanalyses of genome-wide association studies conducted by the CHARGE and SpiroMeta consortia identified novel loci yielding evidence of association at or near genome-wide significance (GWS) with FEV1 and FEV1/FVC. We hypothesized that a subset of these markers would also be associated with chronic obstructive pulmonary disease (COPD) susceptibility. Thirty-two single-nucleotide polymorphisms (SNPs) in or near 17 genes in 11 previously identified GWS spirometric genomic regions were tested for association with COPD status in four COPD case-control study samples (NETT/NAS, the Norway case-control study, ECLIPSE, and the first 1,000 subjects in COPDGene; total sample size, 3,456 cases and 1,906 controls). In addition to testing the 32 spirometric GWS SNPs, we tested a dense panel of imputed HapMap2 SNP markers from the 17 genes located near the 32 GWS SNPs and in a set of 21 well studied COPD candidate genes. Of the previously identified GWS spirometric genomic regions, three loci harbored SNPs associated with COPD susceptibility at a 5% false discovery rate: the 4q24 locus including FLJ20184/INTS12/GSTCD/NPNT, the 6p21 locus including AGER and PPT2, and the 5q33 locus including ADAM19. In conclusion, markers previously associated at or near GWS with spirometric measures were tested for association with COPD status in data from four COPD case-control studies, and three loci showed evidence of association with COPD susceptibility at a 5% false discovery rate.
It is useful to have robust gene-environment interaction tests that can utilize a variety of family structures in an efficient way. This paper focuses on tests for gene-environment interaction in the presence of main genetic and environmental effects. The objective is to develop powerful tests that can combine trio data with parental genotypes and discordant sibships when parents genotypes are missing. We first make a modest improvement on a method for discordant sibs (discordant on phenotype), but the approach does not allow one to use families when all offspring are affected, e.g. trios. We then make a modest improvement on a Mendelian transmission-based approach that is inefficient when discordant sibs are available, but can be applied to any nuclear family. Finally, we propose a hybrid approach that utilizes the most efficient method for a specific family type, then combines over families. We utilize this hybrid approach to analyze a chronic obstructive pulmonary disorder dataset to test for gene-environment interaction in the Serpine2 gene with smoking. The methods are freely available in the R package fbati.
Gene-Environment Interaction; Family-Based Association Tests; Candidate Gene Analysis; Binary Trait; COPD; Serpine2
Compositional epistasis is said to be present when the effect of a genetic factor at one locus is masked by a variant at another locus. Although such compositional epistasis is not equivalent to the presence of an interaction in a statistical model, non-standard tests can sometimes be used to detect compositional epistasis. In this paper we consider empirical tests for compositional epistasis under models for the joint effect of two genetic factors which place no restrictions on the main effects of each factor but constrain the interactive effects of the two factors so as to be captured by a single parameter in the model. We describe the implications of these tests for cohort, case-control, case-only and family-based study designs and we illustrate the methods using an example of gene-gene interaction already reported in the literature.
Published studies suggest associations between circadian gene polymorphisms and bipolar I disorder (BPI), as well as schizoaffective disorder (SZA) and schizophrenia (SZ). The results are plausible, based on prior studies of circadian abnormalities. As replications have not been attempted uniformly, we evaluated representative, common polymorphisms in all three disorders.
We assayed 276 publicly available ‘tag’ single nucleotide polymorphisms (SNPs) at 21 circadian genes among 523 patients with BPI, 527 patients with SZ/SZA, and 477 screened adult controls. Detected associations were evaluated in relation to two published genome-wide association studies (GWAS).
Using gene-based tests, suggestive associations were noted between EGR3 and BPI (p = 0.017), and between NPAS2 and SZ/SZA (p = 0.034). Three SNPs were associated with both sets of disorders (NPAS2: rs13025524 and rs11123857; RORB: rs10491929; p < 0.05). None of the associations remained significant following corrections for multiple comparisons. Approximately 15% of the analyzed SNPs overlapped with an independent study that conducted GWAS for BPI; suggestive overlap between the GWAS analyses and ours was noted at ARNTL.
Several suggestive, novel associations were detected with circadian genes and BPI and SZ/SZA, but the present analyses do not support associations with common polymorphisms that confer risk with odds ratios greater than 1.5. Additional analyses using adequately powered samples are warranted to further evaluate these results.
association; bipolar disorder; circadian; gene; schizoaffective disorder; schizophrenia
In clinical trials multiple outcomes are often used to assess treatment interventions. This paper presents an evaluation of likelihood-based methods for jointly testing treatment effects in clinical trials with multiple continuous outcomes. Specifically, we compare the power of joint tests of treatment effects obtained from joint models for the multiple outcomes with univariate tests based on modelling the outcomes separately. We also consider the power and bias of tests when data are missing, a common feature of many trials, especially in psychiatry. Our results suggest that joint tests capitalize on the correlation of multiple outcomes and are more powerful than standard univariate methods, especially when outcomes are missing completely at random. When outcomes are missing at random, test procedures based on correctly specified joint models are unbiased, while standard univariate procedures are not. Results of a simulation study are reported, and the methods are illustrated in an example from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) for schizophrenia.
joint tests; multiple outcomes; power; missing data; psychiatry
Investigators sometimes use information about a given variable obtained from multiple informants. We focus on estimating the effect of a predictor on a continuous outcome, when that predictor cannot be observed directly but is measured by two informants. We describe various approaches to using information from two informants to estimate a regression or correlation coefficient for the effect of the (true) predictor on the outcome. These approaches include methods we refer to as single informant, simple average, optimal weighted average, principal components analysis, and classical measurement error. Each of these five methods effectively uses a weighted average of the informants' reports as a proxy for the true predictor in calculating the correlation or regression coefficient. We compare the performance of these methods in simulation experiments that assume a rounded congeneric measurement model for the relationship between the informants' reports and a true predictor that is a mixture of zeros and positively-distributed continuous values. We also compare the methods' performance in a real data example -the relationship between vigorous physical activity (the predictor) and body mass index (the continuous outcome). The results of the simulations and the example suggest that the simple average is a reasonable choice when there are only two informants.
Genetic Analysis Workshop 17 (GAW17) provided a platform for evaluating existing statistical genetic methods and for developing novel methods to analyze rare variants that modulate complex traits. In this article, we present an overview of the 1000 Genomes Project exome data and simulated phenotype data that were distributed to GAW17 participants for analyses, the different issues addressed by the participants, and the process of preparation of manuscripts resulting from the discussions during the workshop.
Linkage- and association-based methods have been proposed for mapping disease-causing rare variants. Based on the family information provided in the Genetic Analysis Workshop 17 data set, we formulate a two-pronged approach that combines both methods. Using the identity-by-descent information provided for eight extended pedigrees (n = 697) and the simulated quantitative trait Q1, we explore various traditional nonparametric linkage analysis methods; the best result is obtained by assuming between-family heterogeneity and applying the Haseman-Elston regression to each pedigree separately. We discover strong signals from two genes in two different families and weaker signals for a third gene from two other families. As an exploratory approach, we apply an association test based on a modified family-based association test statistic to all rare variants (frequency < 1% or < 3%) designated as causal for Q1. Family-based association tests correctly identified causal single-nucleotide polymorphisms for four genes (KDR, VEGFA, VEGFC, and FLT1). Our results suggest that both linkage and association tests with families show promise for identifying rare variants.
Recent advances in next-generation sequencing technologies have made it possible to generate large amounts of sequence data with rare variants in a cost-effective way. Statistical methods that test variants individually are underpowered to detect rare variants, so it is desirable to perform association analysis of rare variants by combining the information from all variants. In this study, we use a Bayesian regression method to model all variants simultaneously to identify rare variants in a data set from Genetic Analysis Workshop 17. We studied the association between the quantitative risk traits Q1, Q2, and Q4 and the single-nucleotide polymorphisms and identified several positive single-nucleotide polymorphisms for traits Q1 and Q2. However, the model also generated several apparent false positives and missed many true positives, suggesting that there is room for improvement in this model.
Longitudinal studies are an important tool for analysing traits that change over time, depending on individual characteristics and environmental exposures. Complex quantitative traits, such as lung function, may change over time and appear to depend on genetic and environmental factors, as well as on potential gene-environment interactions. There is a growing interest in modelling both marginal genetic effects and gene-environment interactions. In an admixed population, the use of traditional statistical models may fail to adjust for confounding by ethnicity, leading to bias in the genetic effect estimates. A variety of methods have been developed to account for the genetic substructure of human populations. Family-based designs provide an important resource for avoiding confounding due to admixture. To date, however, most genetic analyses have been applied to cross-sectional designs. In this paper, we propose a methodology which aims to improve the assessment of main genetic effect and gene-environment interaction effects by combining the advantages of both longitudinal studies for continuous phenotypes, and the family-based designs. This approach is based on an extension of ordinary linear mixed models for quantitative phenotypes, which incorporates information from a case-parent design. Our results indicate that use of this method allows both main genetic and gene-environment interaction effects to be estimated without bias, even in the presence of population substructure.
gene-environment interaction; longitudinal phenotypes; power; bias; population substructure
Numerous studies have demonstrated associations between genetic markers and COPD, but results have been inconsistent. One reason may be heterogeneity in disease definition. Unsupervised learning approaches may assist in understanding disease heterogeneity.
We selected 31 phenotypic variables and 12 SNPs from five candidate genes in 308 subjects in the National Emphysema Treatment Trial (NETT) Genetics Ancillary Study cohort. We used factor analysis to select a subset of phenotypic variables, and then used cluster analysis to identify subtypes of severe emphysema. We examined the phenotypic and genotypic characteristics of each cluster.
We identified six factors accounting for 75% of the shared variability among our initial phenotypic variables. We selected four phenotypic variables from these factors for cluster analysis: 1) post-bronchodilator FEV1 percent predicted, 2) percent bronchodilator responsiveness, and quantitative CT measurements of 3) apical emphysema and 4) airway wall thickness. K-means cluster analysis revealed four clusters, though separation between clusters was modest: 1) emphysema predominant, 2) bronchodilator responsive, with higher FEV1; 3) discordant, with a lower FEV1 despite less severe emphysema and lower airway wall thickness, and 4) airway predominant. Of the genotypes examined, membership in cluster 1 (emphysema-predominant) was associated with TGFB1 SNP rs1800470.
Cluster analysis may identify meaningful disease subtypes and/or groups of related phenotypic variables even in a highly selected group of severe emphysema subjects, and may be useful for genetic association studies.
Results of behavioral genetic and molecular genetic studies have converged to suggest that genes substantially contribute to the development of attention deficit/hyperactivity disorder (ADHD), a common disorder that onsets in childhood. Yet, despite numerous linkage and candidate gene studies, strongly consistent and replicable association has eluded detection. To search for ADHD susceptibility genes, we genotyped approximately 600,000 SNPs in 958 ADHD affected family trios. After cleaning the data, we analyzed 438,784 SNPs in 2803 individuals comprising 909 complete trios using ADHD diagnosis as phenotype. We present the initial TDT findings as well as considerations for cleaning family-based TDT data. None of the SNP association tests achieved genome-wide significance, indicating that larger samples may be required to identify risk loci for ADHD. We additionally identify a systemic bias in family-based association, and suggest that variable missing genotype rates may be the source of this bias.
Several family-based approaches for testing genetic association with traits obtained from longitudinal or repeated measurement studies have been previously proposed. These approaches utilize the multivariate data more efficiently by using estimated optimal weights to combine univariate tests. We show that these FBAT approaches are still robust against hidden population stratification, but their power can be heavily affected since the estimated weights might provide poor approximation of the true theoretical optimal weights with the presence of population stratification. We introduce a permutation-based approach FBAT-MinP and an equal combination approach FBAT-EW, both of which do not involve the use of estimated weights. Through simulation studies, FBAT-MinP and FBAT-EW are shown to be powerful even in the presence of population stratification, when other approaches may substantially lose their power. An application of these approaches to the Childhood Asthma Management Program (CAMP) study data for testing an association between body mass index and a previously reported candidate SNP is given as an example.
A time-to-onset analysis for family-based samples was performed on the genomewide association (GWAS) data for Attention Deficit Hyperactivity Disorder (ADHD) to determine if associations exist with the age at onset of ADHD. The initial dataset consisted of 958 parent-offspring trios that were genotyped on the Perlegen 600,000 SNP array. After data cleaning procedures, 429,981 autosomal SNPs and 930 parent-offspring trios were used found suitable for use and a family-based logrank analysis was performed using that age at first ADHD symptoms as the quantitative trait of interest. No SNP achieved genome-wide significance, and the lowest p-values had a magnitude of 10−7. Several SNPs among a pre-specified list of candidate genes had nominal associations including SLC9A9, DRD1, ADRB2, SLC6A3, NFIL3, ADRB1, SYT1, HTR2A, ARRB2, and CHRNA4. Of these findings SLC9A9 stood out as a promising candidate, with nominally significant SNPs in six distinct regions of the gene.
The INSIG2 rs7566605 polymorphism was identified for obesity (BMI≥30 kg/m2) in one of the first genome-wide association studies, but replications were inconsistent. We collected statistics from 34 studies (n = 74,345), including general population (GP) studies, population-based studies with subjects selected for conditions related to a better health status (‘healthy population’, HP), and obesity studies (OB). We tested five hypotheses to explore potential sources of heterogeneity. The meta-analysis of 27 studies on Caucasian adults (n = 66,213) combining the different study designs did not support overall association of the CC-genotype with obesity, yielding an odds ratio (OR) of 1.05 (p-value = 0.27). The I2 measure of 41% (p-value = 0.015) indicated between-study heterogeneity. Restricting to GP studies resulted in a declined I2 measure of 11% (p-value = 0.33) and an OR of 1.10 (p-value = 0.015). Regarding the five hypotheses, our data showed (a) some difference between GP and HP studies (p-value = 0.012) and (b) an association in extreme comparisons (BMI≥32.5, 35.0, 37.5, 40.0 kg/m2 versus BMI<25 kg/m2) yielding ORs of 1.16, 1.18, 1.22, or 1.27 (p-values 0.001 to 0.003), which was also underscored by significantly increased CC-genotype frequencies across BMI categories (10.4% to 12.5%, p-value for trend = 0.0002). We did not find evidence for differential ORs (c) among studies with higher than average obesity prevalence compared to lower, (d) among studies with BMI assessment after the year 2000 compared to those before, or (e) among studies from older populations compared to younger. Analysis of non-Caucasian adults (n = 4889) or children (n = 3243) yielded ORs of 1.01 (p-value = 0.94) or 1.15 (p-value = 0.22), respectively. There was no evidence for overall association of the rs7566605 polymorphism with obesity. Our data suggested an association with extreme degrees of obesity, and consequently heterogeneous effects from different study designs may mask an underlying association when unaccounted for. The importance of study design might be under-recognized in gene discovery and association replication so far.
A polymorphism of the INSIG2 gene was identified as being associated with obesity in one of the first genome-wide association studies. However, this association has since then been highly debated upon inconsistent subsequent reports. We collected association information from 34 studies including a total of 74,000 participants. In a meta-analysis of the 27 studies including 66,000 Caucasian adults, we found no overall association of this polymorphism rs7566605 with obesity, comparing subjects with a body-mass-index (BMI)≥30 kg/m2 with normal BMI subjects (BMI<30 kg/m2). Our data suggested an association of this polymorphism with extreme obesity (e.g., BMI≥37.5 kg/m2) compared to normal controls. Such an association with extreme obesity might induce heterogeneous effects from different study designs depending on the proportion of extreme obesity included by the design. However, further studies would be required to substantiate this finding. The importance of study design might be under-recognized in gene discovery and association replication so far.
Motivation: Estimating the frequency distribution of copy number variants (CNVs) is an important aspect of the effort to characterize this new type of genetic variation. Currently, most studies report a strong skew toward low-frequency CNVs. In this article, our goal is to investigate the frequencies of CNVs. We employ a two-step procedure for the CNV frequency estimation process. We use family information a posteriori to select only the most reliable CNV regions, i.e. those showing high rates of Mendelian transmission.
Results: Our results suggest that the current skew toward low-frequency CNVs may not be representative of the true frequency distribution, but may be due, among other reasons, to the non-negligible false negative rates that characterize CNV detection methods. Moreover, false positives are also likely, as low-frequency CNVs are hard to detect with small sample sizes and technologies that are not ideally suited for their detection. Without appropriate validation methods, such as incorporation of biologically relevant information (for example, in our case, the transmission of heritable CNVs from parents to offspring), it is difficult to assess the validity of specific CNVs, and even harder to obtain reliable frequency estimates.
Availability: Software implementing the methods described in this article is available for download at the following address: http://www.isites.harvard.edu/icb/icb.do?keyword=k36162
Supplementary informantion: Supplementary data are available at Bioinformatics online.
This study concerns the question of whether obese subjects in a community sample experience depression in a different way from the non-obese, especially whether they over-eat to the point of gaining weight during periods of depression.
A representative sample of adults was interviewed regarding depression and obesity.
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.
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 (BMI) >30. Logistic regressions were employed to assess relationships, controlling for age and gender, by means of Odds Ratios and 95% Confidence Intervals.
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 non-obese to over-eat leading to weight gain during a period of depression (p <0.002). These obese subjects, compared to the non-obese, also experienced longer episodes of depression, a larger number of episodes, and were more preoccupied with death during such episodes.
Depression among obese subjects in a community sample tends to be more severe than among the non-obese. 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.
Obesity; Major Depression; Over-eating; Gaining Weight; Atypical Depression
Recent technological advances in continuous biological monitoring and personal exposure assessment have led to the collection of subject-specific functional data. A primary goal in such studies is to assess the relationship between the functional predictors and the functional responses. The historical functional linear model (HFLM) can be used to model such dependencies of the response on the history of the predictor values. An estimation procedure for the regression coefficients that uses a variety of regularization techniques is proposed. An approximation of the regression surface relating the predictor to the outcome by a finite-dimensional basis expansion is used, followed by penalization of the coefficients of the neighboring basis functions by restricting the size of the coefficient differences to be small. Penalties based on the absolute values of the basis function coefficient differences (corresponding to the LASSO) and the squares of these differences (corresponding to the penalized spline methodology) are studied. The fits are compared using an extension of the Akaike Information Criterion that combines the error variance estimate, degrees of freedom of the fit and the norm of the bases function coefficients. The performance of the proposed methods is evaluated via simulations. The LASSO penalty applied to the linearly transformed coefficients yields sparser representations of the estimated regression surface, while the quadratic penalty provides solutions with the smallest L2-norm of the basis functions coefficients. Finally, the new estimation procedure is applied to the analysis of the effects of occupational particulate matter (PM) exposure on the heart rate variability (HRV) in a cohort of boilermaker workers. Results suggest that the strongest association between PM exposure and HRV in these workers occurs as a result of point exposures to the increased levels of particulate matter corresponding to smoking breaks.
environmental assessment; functional data; heart rate variability; LASSO; penalized regression splines