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Identification of an autosomal recessive mode of inheritance in paediatric Behçet's families by segregation analysis.

We have conducted a segregation analysis in order to characterise the transmission of Behçet Disease (BD), a multifactorial condition with a strong genetic component. Complete information about BD status and pedigree was obtained on 104 probands from our database. We used the criteria of the International Study Group for BD (ISBD) to delineate the clinical status of the sibs: possible BD (patients meeting two criteria), or ascertained BD (patients meeting at least three criteria). A proband was defined as "paediatric" when he/she completed ISBD criteria before/by the age of 16 years. Families were distinguished as paediatric (n = 67) (ascertained through a paediatric proband), and non-paediatric (n = 37) ones. An Expectation Maximization (EM) algorithm was used to estimate the Mendelian segregation ratio P in nuclear families (two parents and their offspring). The maximum likelihood estimate: Pcirc; = 0.248, calculated in the paediatric data set, was consistent with the theoretical value of P = (1/4) for autosomal recessive inheritance, whereas the Pcirc; value was 0.08 when using the non-paediatric data set. Our work provides the first evidence of genetic heterogeneity in BD, and of the existence of a Mendelian entity in the paediatric BD subgroup. Previous studies failed to show any simple mode of inheritance in BD, probably because they were performed on the whole BD population.

Behcet Syndrome↗

Positive association of the AMPA receptor subunit GluR4 gene (GRIA4) haplotype with schizophrenia: linkage disequilibrium mapping using SNPs evenly distributed across the gene region.

The glutamatergic dysfunction hypothesis suggests that genes involved in the glutamate neurotransmitter system are candidates for schizophrenia-susceptibility genes. We have been conducting systematic studies of the association between glutamate receptors and schizophrenia. We report on a positive association of some haplotypes of the AMPA receptor subunit GluR4 gene (GRIA4) with schizophrenia. We genotyped 100 Japanese schizophrenics and 100 controls for six single nucleotide polymorphism (SNP) markers distributed at intervals of about 50 kb in the GRIA4 region, and estimated the degree of linkage disequilibrium (LD) between the SNPs. We constructed haplotypes of the SNPs in LD using the EM algorithm to test their association with schizophrenia. Significant associations were detected for the combination of SNP4-5 (chi(2) = 12.54, df = 3, P = 0.0057, P = 0.029 with Bonferroni correction) and for the combination of SNP3-4-5 (chi(2) = 18.9, df = 7, P = 0.0085, P = 0.043 with Bonferroni correction). These results suggest that at least one susceptibility locus for schizophrenia is located within or very close to the GRIA4 region in Japanese.

Alleles↗

Haplotype analysis of single nucleotide polymorphisms in VEGF gene for vascular dementia.

A case-control study was performed to identify single nucleotide variants of vascular endothelial growth factor (VEGF) gene associated with vascular dementia. Seven SNPs in promoter, 5'-UTR, 3'-UTR, and introns of VEGF gene were identified in 24 Koreans. Three of them, -1154G/A, -7C/T, and 13553C/T, were selected based on allele frequency and linkage disequilibrium (LD), and genotyped in 207 vascular dementia patients and 207 control subjects. Significant association with vascular dementia was not shown (P > 0.05) in the alleles and genotypes of single locus. Subsequent analysis of composing VEGF risk haplotypes associated with vascular dementia was performed with maximum likelihood estimates of their possible haplotypes employing the expectation-maximization (EM) algorithm. Of three-locus haplotypes, only GTC was significantly associated with vascular dementia, conferring a risk of 1.87 (P < 0.05). Of two-locus haplotypes, the risk was observed with the nested forms of the risk haplotype GTC, that is, GT at the loci -1154G/A and -7C/T and TC at the loci -7C/T and 13553C/T (P < 0.05). Our findings suggested some interaction among -1154G/A, -7C/T, and 13553C/T variants in the determination of risk for vascular dementia.

3' Flanking Region↗

Hierarchical models for the probabilities of conception.

In the last thirty years, there has been considerable interest in finding better models to fit data for probabilities of conception. An important early model was proposed by Barrett and Marshall (1969) and extended by Schwartz, MacDonald and Heuchel (1980). Recently, researchers have further extended these models by adding covariates. However, the increasingly complicated models are challenging to analyze with frequentist methods such as the EM algorithm. Bayesian models are more feasible, and the computation can be done via Markov chain Monte Carlo (MCMC). We consider a Bayesian model with an effect for protected intercourse to analyze data from the California Women's Reproductive Health Study and assess the effects of water contaminants and hormones. There are two main contributions in the paper. (1) For protected intercourse, we propose modeling the ratios of daily conception probabilities with protected intercourse to corresponding daily conception probabilities with unprotected intercourse. Due to the small sample size of our data set, we assume the ratios are the same for each day but unknown. (2) We consider Bayesian analysis under a unimodality assumption where the probabilities of conception increase before ovulation and decrease after ovulation. Gibbs sampling is used for finding the Bayesian estimates. There is some evidence that the two covariates affect fecundability.

Bayes Theorem↗

Quantification of fluorescence properties of lymphocytes in peripheral blood mononuclear cell suspensions using a latent class model.

Lymphocytes, monocytes, granulocytes, and other blood cells can be distinguished on the basis of their forward (FSC) and sideward (SSC) light scatter properties and their expression of CD45 and CD14. A FSC,SSC gate can be set to include > 95% of the lymphocytes using a "back gating" procedure on the CD45+, CD14- cells. However, nonlymphoid cells such as monocytes have light scattering properties similar to lymphocytes. This problem occurs particularly in patient populations where the light scattering properties of lymphocyte subsets have changed (e.g., due to activation) and are similar to those of the monocytes. Thus, immunophenotyping using antibodies specific for other markers than CD45 and CD14 does not allow a direct assessment of the percentage of all lymphocytes positive for those markers. In order to optimize immunophenotyping we have developed analytic model in which the FSC,SSC dot plot is partitioned into six nonoverlapping light scatter regions. Each light scatter region contains a mixture population of different cell types, i.e., lymphocytes, monocytes, granulocytes, and other cells. The proportions of each cell type are known from the CD45,CD14 expression within each light scatter region. Under the assumption of independence of fluorescence and scatter properties conditional on cell type, the expression of markers other than CD45 or CD14 are derived from the cell type composition and the fluorescence properties on the other markers of each light scatter region. The underlying statistical model is a latent class model, and maximum likelihood estimates are computed using the expectation-maximization (EM) algorithm. The application of the model for immunophenotyping of lymphocytes of healthy individuals and cancer patients receiving immunotherapy is shown.

Antibodies, Monoclonal↗

Three-marker phenotypic analysis of lymphocytes based on two-color immunofluorescence using a multinomial model for flow cytometric counts and maximum likelihood estimation.

The simultaneous flow cytometric study of multiple (> or = 3) markers on individual cells is restricted by technical reasons, e.g., the type of flow cytometer, and the availability of (monoclonal) antibodies (mAb) conjugated with the appropriate fluorochromes. However, a n-way classification (n > or = 3) may be derived from multiple 2-way classifications. The 2-way classifications are obtained by the use of only two fluorochromes, but each fluorochrome may be used for the simultaneous labelling of 2 or more mAb. We present a formal statistical model, based on an underlying multinomial distribution for the observed quadrant counts, by which data from such multiple 2-way classifications can be analyzed. The model is restricted to 3-marker phenotypic analyses, but can be extended to n-way classifications (n > 3). Maximum likelihood estimates are obtained by the application of the EM algorithm. The model was tested on 20 samples of peripheral blood mononuclear cells to study the coexpression of CD45RA, CD45RO, and Leu 8 by lymphocyte subsets defined by the CD56 (MHC-unrestricted cytotoxic cells) and CD3 (T cells) markers. Application of the model gave an excellent fit in all but one cases. In addition, the model reduced the effect of inter-assay variation on the estimates and it provided an analysis of consistency over the data, which allows the detection of outliers due to staining errors.

Cell Adhesion Molecules↗

Testing for association with a case-parents design in the presence of genotyping errors.

Genotyping errors can create a problem for the analysis of case-parents data because some families will exhibit genotypes that are inconsistent with Mendelian inheritance. The problem with correcting Mendelian inconsistent genotype errors by regenotyping or removing families in which they occur is that the remaining unidentified genotype errors can produce excess type I (false positive) error for some family-based tests for association. We address this problem by developing a likelihood ratio test (LRT) for association in a case-parents design that incorporates nuisance parameters for a general genotype error model. We extend the likelihood approach for a single SNP to include short haplotypes consisting of 2 or 3 SNPs. The extension to haplotypes is based on assumptions of random mating, multiplicative penetrances, and at most a single genotype error per family. For a single SNP, we found, using Monte Carlo simulation, that type I error rate can be controlled for a number of genotype error models at different error rates. Simulation results suggest the same is true for 2 and 3 SNPs. In all cases, power declined with increasing genotyping error rates. In the absence of genotyping errors, power was similar whether nuisance parameters for genotype error were included in the LRT or not. The LRT developed here does not require prior specification of a particular model for genotype errors and it can be readily computed using the EM algorithm. Consequently, this test may be generally useful as a test of association with case-parents data in which Mendelian inconsistent families are observed.

Adult↗

Joint modeling of genetic association and population stratification using latent class models.

We show how latent class log-linear models can be used to test for an association between a candidate gene and a disease phenotype in a stratified population when the stratification is unobserved. The stratification may arise because of several ethnic groups or immigration and may lead to spurious associations between several loci and the disease. The information about the stratification is drawn from additional markers that are chosen to be independent of the disease and unlinked to the candidate gene and to each other within each population stratum. We use the EM algorithm to simultaneously estimate all the model parameters, including proportions of individuals in the latent population strata. The latent class model is used to test the phenotype association of single nucleotide polymorphism markers in four candidate regions in population-based case-control data selected from simulated Genetic Analysis Workshop (GAW) 12 population isolate 30. The analysis clearly demonstrates how the number of false positive associations can be reduced when the model accounts for population stratification.

Case-Control Studies↗

Analysis of case-control studies of genetic and environmental factors with missing genetic information and haplotype-phase ambiguity.

Case-control studies of unrelated subjects are now widely used to study the role of genetic susceptibility and gene-environment interactions in the etiology of complex diseases. Exploiting an assumption of gene-environment independence, and treating the distribution of environmental exposures as completely nonparametric, Chatterjee and Carroll recently developed an efficient retrospective maximum-likelihood method for analysis of case-control studies. In this article, we develop an extension of the retrospective maximum-likelihood approach to studies where genetic information may be missing on some study subjects. In particular, special emphasis is given to haplotype-based studies where missing data arise due to linkage-phase ambiguity of genotype data. We use a profile likelihood technique and an appropriate expectation-maximization (EM) algorithm to derive a relatively simple procedure for parameter estimation, with or without a rare disease assumption, and possibly incorporating information on the marginal probability of the disease for the underlying population. We also describe two alternative robust approaches that are less sensitive to the underlying gene-environment independence and Hardy-Weinberg-equilibrium assumptions. The performance of the proposed methods is studied using simulation studies in the context of haplotype-based studies of gene-environment interactions. An application of the proposed method is illustrated using a case-control study of ovarian cancer designed to investigate the interaction between BRCA1/2 mutations and reproductive risk factors in the etiology of ovarian cancer.

Algorithms↗

Power calculations for likelihood ratio tests for offspring genotype risks, maternal effects, and parent-of-origin (POO) effects in the presence of missing parental genotypes when unaffected siblings are available.

Genotype-based likelihood-ratio tests (LRT) of association that examine maternal and parent-of-origin effects have been previously developed in the framework of log-linear and conditional logistic regression models. In the situation where parental genotypes are missing, the expectation-maximization (EM) algorithm has been incorporated in the log-linear approach to allow incomplete triads to contribute to the LRT. We present an extension to this model which we call the Combined_LRT that incorporates additional information from the genotypes of unaffected siblings to improve assignment of incompletely typed families to mating type categories, thereby improving inference of missing parental data. Using simulations involving a realistic array of family structures, we demonstrate the validity of the Combined_LRT under the null hypothesis of no association and provide power comparisons under varying levels of missing data and using sibling genotype data. We demonstrate the improved power of the Combined_LRT compared with the family-based association test (FBAT), another widely used association test. Lastly, we apply the Combined_LRT to a candidate gene analysis in Autism families, some of which have missing parental genotypes. We conclude that the proposed log-linear model will be an important tool for future candidate gene studies, for many complex diseases where unaffected siblings can often be ascertained and where epigenetic factors such as imprinting may play a role in disease etiology.

Algorithms↗

Identification of novel SNPs in the interleukin 6 receptor gene (IL6R).

The human interleukin-6 receptor (IL6R) is responsible for signal transduction of IL6 that might have associations with immune diseases and various infectious diseases. We have sequenced all 10 exons including the putative splicing site to identify single nucleotide polymorphisms in IL6R. Seven novel single nucleotide polymorphisms (SNPs) were identified; one SNP in the promoter region (-183G>A), one synonymous SNP in exon 2 (24013G>A: Ala31Ala), one non-synonymous SNP in exon 9 (48892A>C: Asp358Ala) and four in introns (29753A>C, 42700T>C, 48869T>A and 59818C>T). The frequencies of each SNP in the Korean population (n=300) were 0.48 (-183 G>A), 0.13 (24013 G>A), 0.41 (29753A>C), 0.41 (42700T>C), 0.1 (48869T>A), 0.42 (48892A>C), and 0.07 (59818C>T), respectively. Haplotypes and their frequencies were estimated by the EM algorithm. Linkage disequilibrium coefficients (/D'/) between each SNP pair were also calculated. The information on SNPs and haplotypes in IL6R would be useful for genetic studies of this gene.

Chromosome Mapping↗

Identification of novel variants in transforming growth factor-beta 1 (TGFB1) gene and association analysis with bone mineral density.

Human transforming growth factor-beta1 (TGFB1) is a family of polypeptides that regulate cell growth, cell differentiation, and cell function as a multifunctional regulator of cellular activity. TGFB1 is produced by osteoblasts and stored in substantial amounts in the bone matrix, which is an important regulator of both skeletal development and homeostasis of bone metabolism. In the present study, we identified four new polymorphisms in TGFB1 and examined whether these polymorphisms are risk factors for osteoporosis. We have sequenced all exons including in the promoter region up to -1,800bp to identify additional genetic polymorphisms in TGFB1. Four novel polymorphisms were newly identified: one in 5' region (g.14129555_14129557dupAGG), one in promoter region (g.14128838C>T), and two in intron (g.14106505G>A and g.14106215G>A). Two known SNPs (g.14128554C>T and g.14127139T>C) were also confirmed. The frequencies of each SNP were 0.479 (g.14129555_14129557dupAGG), 0.007 (g.14128838C>T), 0.478 (g.14128554C>T), 0.476 (g.14127139T>C), 0.016 (g.14106505G>A), and 0.004 (g.14106215G>A) in the Korean population (n=1,885), respectively. Haplotypes and their frequencies were estimated by EM algorithm, and linkage disequilibrium coefficients (mid R:/D'/: and r2) between polymorphism pairs were calculated. We analyzed genetic associations of TGFB1 polymorphisms and haplotypes with spinal bone mineral density (BMD) value of 433 postmenopausal Korean women. By statistical analysis, we could not find any associations with spinal BMD. The information from this study of the critical TGFB1 would be useful for genetic studies of other diseases.

Asian People↗

Service inputs and costs of care related to outcomes among cognitively impaired nursing home residents.

BACKGROUND: There are over 17 000 nursing homes in the United States. Within these, special care units (SCUs) provide a separate residential and/or activity locus for residents, and are expected to provide more staff time and more specialized staff assignments. This paper addresses a fundamental issue relating to the nature, quality and quantity of resident care inputs: what impacts of SCUs are associated with added service inputs, and thus with personnel costs, recognizing that personnel account for the majority of costs associated with nursing home care? AIMS OF THE STUDY: The aim of this aspect of the study was to determine the extent to which additions of staff would result in a diminution of deviant behaviors among residents of special care and of traditional care units. METHOD: The data were collected from a random sample of ten downstate nursing homes located in New York State. Using rigorous sampling procedures, random samples of 40 residents were drawn from each of the facilities, equally divided between special care unit and traditional care unit residents. Thus, the sampling design involved two levels of clustering: subjects were clustered within units and units were clustered within facilities. The observational behaviour measure was taken from the INCARE (institutional version of the Comprehensive Assessment and Referral Evaluation). The behavioral observation measure contains 23 items such as "disruptive of others", "picks/pulls clothing", "repetitive movements", "repetitive questioning"and "wandering". Each item is rated as to frequency of occurrence; ratings are collected on three occasions, and averaged. Outcome and covariate data (e.g., behavior and cognition) were collected by trained research staff who visited each site for three to four weeks of intensive data collection, accomplished through direct resident interviews, staff interviews and questionnaires and chart data abstraction. The clinical staff time data were collected using the InfoAide system, whereby each care provider used a portable barcode scanner to record the type of care given, the recipient and the duration of care. A random effects model using the SAS mixed procedure was applied to the data; adhering to this model, some effects were fixed and some random. The random effects were comprised of the subject (intercept or subject starting point at baseline) and the unit; used here was restricted maximum likelihood (REML) with the EM algorithm. RESULTS: There was a significant reduction of behavior disorder associated with more provision of aide time in SCUs as contrasted with non-SCUs. The greater the service provided, the greater the slope, i.e., the greater the reduction. That is, while SCU residents showed improvements in behavior accompanying increases in aide time, no such change was observed among non-SCU residents. IMPLICATIONS FOR HEALTH CARE PROVISION AND USE: The significant effect of SCU + time + aide-minutes indicates that more aide time on SCUs was associated with improvement in behavior. It appears that the important ingredient in relation to the reduction of behavior disorder is not membership in an SCU per se, but the provision of more aide time within SCUs. Those SCUs that provide more aide time have a better behavior outcome. Thus, these findings point to additional paths for exploration, i.e., future research needs to focus on elements of SCUs such as the available amounts of program and staff resources rather than on SCU status alone.

Journal Article↗

Hierarchical Multi-Label Classification With Gene-Environment Interactions in Disease Modeling.

In biomedical studies, gene-environment (G-E) interactions have been demonstrated to have important implications for analyzing disease outcomes beyond the main G and main E effects. Many approaches have been developed for G-E interaction analysis, yielding important findings. However, hierarchical multi-label classification, which provides insightful information on disease outcomes, remains unexplored in G-E analysis literature. Moreover, unlabeled data are commonly observed in practical settings but omitted by many existing methods of hierarchical multi-label classification. In this study, we consider a semi-supervised scenario and develop a novel approach for the two-layer hierarchical response with G-E interactions. A two-step penalized estimation is then proposed using an efficient expectation-maximization (EM) algorithm. Simulation shows that it has superior performance in classification and feature selection. The analysis of The Cancer Genome Atlas (TCGA) data on lung cancer demonstrates the practical utility of the proposed method. Overall, this study can fill the important knowledge gap in G-E interaction analysis by providing a widely applicable framework for hierarchical multi-label classification of complex disease outcomes.

Humans↗

Estimation of an errors-in-variables regression model when the variances of the measurement errors vary between the observations.

It is common in the analysis of aggregate data in epidemiology that the variances of the aggregate observations are available. The analysis of such data leads to a measurement error situation, where the known variances of the measurement errors vary between the observations. Assuming multivariate normal distribution for the 'true' observations and normal distributions for the measurement errors, we derive a simple EM algorithm for obtaining maximum likelihood estimates of the parameters of the multivariate normal distributions. The results also facilitate the estimation of regression parameters between the variables as well as the 'true' values of the observations. The approach is applied to re-estimate recent results of the WHO MONICA Project on cardiovascular disease and its risk factors, where the original estimation of the regression coefficients did not adjust for the regression attenuation caused by the measurement errors.

Algorithms↗

Detecting and eliminating erroneous gestational ages: a normal mixture model.

In perinatal research and clinical practice, gestational age is a crucial variable for measuring foetal 'growth' (birth weight for gestational age) and for estimating the risk of mortality and morbidity, yet reported gestational age values are affected by random and systematic errors due to the absence of a gold standard measure. Previous investigators have used birth weight (which is measured with greater validity and precision than is gestational age) to correct such errors, but existing methods are inadequate due to unreasonable assumptions about the distributions of birth weight and gestational age. We propose a new method for identifying and correcting implausible observations using the expectation-maximization (EM) algorithm. Using population-based data from U.S. birth certificates, we compare the resulting gestational ages, birth weight distributions at each gestational age, and gestational age-specific infant mortality based on the new method with those on the same population produced by previous published correction methods. The new method gives the best birth weight distributions for gestational age and the most realistic gestational-age-specific mortality rates, while each of the other methods has at least one significant flaw.

Algorithms↗

Maximum likelihood estimation in the joint analysis of time-to-event and multiple longitudinal variables.

Joint modelling of longitudinal and survival data has received much attention in recent years. Most have concentrated on a single longitudinal variable. This paper considers joint modelling in the presence of multiple longitudinal variables. We explore direct association of time-to-event and multiple longitudinal processes through a frailty model and use a mixed effects model for each of the longitudinal variables. Correlations among the longitudinal variables are induced through correlated random effects. We allow effects of categorical and continuous covariates on both longitudinal and time-to-event responses and explore interactions between the longitudinal variables and other covariates on time-to-event. Estimates of the parameters are obtained by maximizing the joint likelihood for the longitudinal variable processes and the event process. We use a one-step-late EM algorithm to handle the direct dependence of the event process on the modelled longitudinal variables along with the presence of other fixed covariates in both processes. We argue that such a joint analysis with multiple longitudinal variables is advantageous to one with only a single longitudinal variable in revealing interplay among multiple longitudinal variables and the time-to-event.

Head and Neck Neoplasms↗

Survival curve estimation with partial non-random exposure information.

The objective of this paper is to estimate survival curves for two different exposure groups when the exposure group is not known for all observations, and the data is subject to left truncation and right censoring. The situation we consider is when the probability that the exposure group is missing may depend on whether the observation is censored or uncensored, in which case the exposure is not missing at random. The problem was motivated by a study of Alzheimer's disease to estimate the distribution of ages at diagnosis for individuals with and without an apolipoprotein E4 allele (the exposure group). Genotyping for this risk factor was incomplete and performed more frequently on the cases of Alzheimer's disease (the uncensored observations) than the censored observations. The survival curves are estimated in discrete time using an EM algorithm. A bootstrapping procedure is proposed that guarantees each bootstrap sample has the same proportion of observations with missing exposure. A simulation is performed to evaluate the bias of the estimators and to investigate design and efficiency issues. The methods are applied to the Alzheimer's disease study.

Adult↗