Combining disease models to test for gene-environment interaction in nuclear families

It is useful to have robust gene-environment interaction tests that can utilize a variety of family structures in an efficient way. This article 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.     

Variance components models for gene-environment interaction in twin analysis.

Gene-environment interaction is likely to be a common and important source of variation for complex behavioral traits. Often conceptualized as the genetic control of sensitivity to the environment, it can be incorporated in variance components twin analyses by partitioning genetic effects into a mean part, which is independent of the environment, and a part that is a linear function of the environment. The model allows for one or more environmental moderator variables (that possibly interact with each other) that may i). be continuous or binary ii). differ between twins within a pair iii). interact with residual environmental as well as genetic effects iv) have nonlinear moderating properties v). show scalar (different magnitudes) or qualitative (different genes) interactions vi). be correlated with genetic effects acting upon the trait, to allow for a test of gene-environment interaction in the presence of gene-environment correlation. Aspects and applications of a class of models are explored by simulation, in the context of both individual differences twin analysis and, in a companion paper (Purcell & Sham, 2002) sibpair quantitative trait locus linkage analysis. As well as elucidating environmental pathways, consideration of gene-environment interaction in quantitative and molecular studies will potentially direct and enhance gene-mapping efforts.     

Structured modeling and state estimation in a fermentation process: Lipase production by Candida rugosa

A simple structured mathematical model coupled with a methodology of state and parameter estimation is developed for lipase production by Candida rugosa in batch fermentation. The model describes the system according to the following qualitative observations and hypothesis: Lipase production is induced by extracellular oleic acid present in the medium. The acid is transported into the cell where it is consumed, transformed, and stored. Lipase is excreted to the medium where it is distributed between the available oil-water interphase and aqueous phase. Cell growth is modulated by the intracellular substrate concentration. Model parameters have been determined and the whole model validated against experiments not used in their determination. The estimation problem consists in the estimation of three state variables (biomass, intra- and extracellular substrate) and two kinetic parameters by using only the on-line measurement provided by exhaust gas analysis. The presented estimation strategy divides the complex problem into three subproblems that can be solved by stable algorithms. The estimation of biomass (X) and the specific growth rate (mu), is achieved by a recursive prediction error algorithm using the on-line measurement of the carbon dioxide evolution rate. mu is then used to perform an estimation of intracellular substrate and the other kinetic parameter related to substrate transport (A) by an adaptive observer. Extracellular substrate is then evaluated by means of the estimated values of intracellular substrate and biomass through the material balance of the reactor. Simulation and experimental tests showed good performance of the developed estimator, which appears suitable to be used for process control and monitoring. (c) 1995 John Wiley & Sons, Inc.     

Interpreting genotype-by-environment interaction using redundancy analysis

Summary Methods for the interpretation of genotype-by-environment interaction in the presense of explicitly measured environmental variables can be divided into two groups. Firstly, methods that extract environmental characterizations from the data itself, which are subsequently related to measured environmental variables, e.g., regression on the mean or singular value decomposition of the matrix of residuals from additivity, followed by correlation, or regression, methods. Secondly, methods that incorporate measured environmental variables directly into the model, e.g., multiple regression of individual genotypical responses on environmental variables, or factorial regression in which a genotype-by-environment matrix is modelled in terms of concomitant variables for the environmental factor. In this paper a redundancy analysis is presented, which can be derived from the singular-value decomposition of the residuals from additivity by imposing the restriction on the environmental scores of having to be linear combinations of environmental variables. At the same time, redundancy analysis is derivable from factorial regression by rotation of the axes in the space spanned by the fitted values of the factorial regression, followed by a reduction of dimensionality through discarding the least explanatory axes. Redundancy analysis is a member of the second group of methods, and can be an important tool in the interpretation of genotype-by-environment interaction, especially with reference to concomitant environmental information. A theoretical treatise of the method is given, followed by a practical example in which the results of the method are compared to the results of the other methods mentioned.     

Two-level Bayesian interaction analysis for survival data incorporating pathway information

Genetic interactions play an important role in the progression of complex diseases, providing explanation of variations in disease phenotype missed by main genetic effects. Comparatively, there are fewer studies on survival time, given its challenging characteristics such as censoring. In recent biomedical research, two-level analysis of both genes and their involved pathways has received much attention and been demonstrated as more effective than single-level analysis. However, such analysis is usually limited to main effects. Pathways are not isolated, and their interactions have also been suggested to have important contributions to the prognosis of complex diseases. In this paper, we develop a novel two-level Bayesian interaction analysis approach for survival data. This approach is the first to conduct the analysis of lower-level gene–gene interactions and higher-level pathway–pathway interactions simultaneously. Significantly advancing from the existing Bayesian studies based on the Markov Chain Monte Carlo (MCMC) technique, we propose a variational inference framework based on the accelerated failure time model with effective priors to accommodate two-level selection as well as censoring. Its computational efficiency is much desirable for high-dimensional interaction analysis. We examine performance of the proposed approach using extensive simulation. The application to TCGA melanoma and lung adenocarcinoma data leads to biologically sensible findings with satisfactory prediction accuracy and selection stability.     

Structured modelling of animal cells

Recent advances in computer technology have promoted the design and use of detailed, computer-based models for biological systems. For many non-biological systems, the complexity of such simulations may be considered inappropriate and unwieldy, but in biological systems, and more specifically in animal cell culture, this level of complexity simply mimics what is only beginning to be understood about metabolic processs. With this in mind, we contend that complex, structured models are vital tools in the investigation of fundamental biological processes. An example of such a simulation, which describes the commercial production of therapeutic proteins by animal cell cultures, is considered.     

Exploiting gene-environment independence for analysis of case-control studies: an empirical Bayes-type shrinkage estimator to trade-off between bias and efficiency

Summary .   Standard prospective logistic regression analysis of case–control data often leads to very imprecise estimates of gene-environment interactions due to small numbers of cases or controls in cells of crossing genotype and exposure. In contrast, under the assumption of gene-environment independence, modern "retrospective" methods, including the "case-only" approach, can estimate the interaction parameters much more precisely, but they can be seriously biased when the underlying assumption of gene-environment independence is violated. In this article, we propose a novel empirical Bayes-type shrinkage estimator to analyze case–control data that can relax the gene-environment independence assumption in a data-adaptive fashion. In the special case, involving a binary gene and a binary exposure, the method leads to an estimator of the interaction log odds ratio parameter in a simple closed form that corresponds to an weighted average of the standard case-only and case–control estimators. We also describe a general approach for deriving the new shrinkage estimator and its variance within the retrospective maximum-likelihood framework developed by Chatterjee and Carroll (2005, Biometrika 92, 399–418). Both simulated and real data examples suggest that the proposed estimator strikes a balance between bias and efficiency depending on the true nature of the gene-environment association and the sample size for a given study.     

Semiparametric Bayesian analysis of case-control data under conditional gene-environment independence

In case-control studies of gene-environment association with disease, when genetic and environmental exposures can be assumed to be independent in the underlying population, one may exploit the independence in order to derive more efficient estimation techniques than the traditional logistic regression analysis (Chatterjee and Carroll, 2005, Biometrika92, 399-418). However, covariates that stratify the population, such as age, ethnicity and alike, could potentially lead to nonindependence. In this article, we provide a novel semiparametric Bayesian approach to model stratification effects under the assumption of gene-environment independence in the control population. We illustrate the methods by applying them to data from a population-based case-control study on ovarian cancer conducted in Israel. A simulation study is conducted to compare our method with other popular choices. The results reflect that the semiparametric Bayesian model allows incorporation of key scientific evidence in the form of a prior and offers a flexible, robust alternative when standard parametric model assumptions do not hold.     

Variance components models for gene-environment interaction in quantitative trait locus linkage analysis.

Gene-environment interaction (G x E) is likely to be a common and important source of variation for complex behavioral traits. Gene-environment interaction, or genetic control of sensitivity to the environment, can be incorporated into variance components twin and sib-pair analyses by partitioning genetic effects into a mean part, which is independent of the environment, and a part that is a linear function of the environment. An approach described in a companion paper (Purcell, 2002) is applied to sib-pair variance components linkage analysis in two ways: allowing for quantitative trait locus by environment interaction and utilizing information on any residual interactions detected prior to analysis. As well as elucidating environmental pathways, consideration of G x E in quantitative and molecular studies will potentially direct and enhance gene-mapping efforts.     

Structured models of metapopulation dynamics

I develop models of metapopulation dynamics that describe changes in the numbers of individuals within patches. These models are analogous to structured population models, with patches playing the role of individuals. Single species models which do not include the effect of immigration on local population dynamics of occupied patches typically lead to a unique equilibrium. The models can be used to study the distributions of numbers of individuals among patches, showing that both metapopulations with local outbreaks and metapopulations without outbreaks can occur in systems with no underlying environmental variability. Distributions of local population sizes (in occupied patches) can vary independently of the total population size, so both patterns of distributions of local population sizes are compatible with either rare or common species. Models which include the effect of immigration on local population dynamics can lead to two positive equilibria, one stable and one unstable, the latter representing a threshold between regional extinction and persistence.     

细胞黏附分子互作网络的构建与分析

借助网络分析可对基因调控、蛋白质互作和信号转导等细胞活动进行全局和局部性质分析.以细胞黏附的蛋白质相互作用为对象,通过数据挖掘和可视化软件构建了整合蛋白介导的黏附分子互作网络,该分子互作网络由156种蛋白质通过690种相互作用相连,其平均节点度为8.66、平均聚集系数为0.24,平均路径长度为2.6.黏附分子互作网络中包含数个功能模块,这些模块涉及网络内部多种分子相互作用的启动与停止,并进一步影响细胞的黏附、迁移和骨架组织.对黏附分子网络进行模体筛选和比较,发现一些数量相对较少、以三元复合物为主要结构的关键模体,同时对各网络模块和模体对细胞黏附的调控作用进行了探讨.     

Biomolecular interaction analysis for carbon nanotubes and for biocompatibility prediction

The interactions between carbon nanotubes (CNTs) and biologics have been commonly studied by various microscopy and spectroscopy methods. We tried biomolecular interaction analysis to measure the kinetic interactions between proteins and CNTs. The analysis demonstrated that wheat germ agglutinin (WGA) and other proteins have high affinity toward carboxylated CNT (f-MWCNT) but essentially no binding to normal CNT (p-MWCNT). The binding of f-MWCNT–protein showed dose dependence, and the observed kinetic constants were in the range of 10⁻⁹ to 10⁻¹¹ M with very small off-rates (10⁻³ to 10⁻⁷ s⁻¹), indicating a relatively tight and stable f-MWCNT–protein complex formation. Interestingly in hemolysis assay, p-MWCNT showed good biocompatibility, f-MWCNT caused 30% hemolysis, but WGA-coated f-MWCNT did not show hemolysis. Furthermore, the f-MWCNT–WGA complex demonstrated enhanced cytotoxicity toward cancer cells, perhaps through the glycoproteins expressed on the cells' surface. Taken together, biomolecular interaction analysis is a precise method that might be useful in evaluating the binding affinity of biologics to CNTs and in predicting biological actions.     

Structured proteins and proteins with intrinsic disorder

Until recently, the point of view that the unique tertiary structure is necessary for protein function has prevailed. However, recent data have demonstrated that many cell proteins do not possess such structure in isolation, although displaying a distinct function under physiological conditions. These proteins were named the naturally, or intrinsically, disordered proteins. The fraction of intrinsically disordered regions in such proteins may vary from several amino acid residues to a completely unordered sequence of several tens or even several hundreds of residues. The main distinction of these proteins from structured (globular) proteins is that they have no unique tertiary structure in isolation and acquire it only upon interaction with their partners. The conformation of these proteins in a complex is determined not only by their own amino acid sequence (as is typical of structured, or globular, proteins) but also by the interacting partner. This review discusses the structure-function relationships in structured and intrinsically disordered proteins. The intricateness of this problem and the possible ways to solve it are illustrated by the example of the EF1A elongation factor family.     

具有年龄结构的种群模型平衡解的稳定性

本文考虑具有年龄结构的种群动力系统模型,讨论系统具有多个平衡解时其解的渐近性质,最后给出了其平衡解中那些是稳定的,哪些是不稳定的.     

Gene environment interaction in preterm delivery with special reference to organochlorine pesticide: a case control study

Objectives: To assess the Gene-Environmental interaction between maternal organochlorine pesticides (OCPs) level and CYP17 gene polymorphism with the risk of preterm delivery (PTD). Materials and methods: Maternal blood samples of hundred cases (n = 100) of PTD and of equal number of healthy controls were collected at the time of delivery. OCPs levels were estimated by Gas chromatography system equipped with electron capture detector and PCR-RFLP was used for polymorphic analysis of CYP17 gene. Results: Significantly (p < 0.05) higher levels of α-HCH, β-HCH, and γ-HCH were found in maternal blood samples of PTD cases as compared to controls. We did not found any significant difference in the frequency genotype distribution CYP17 gene in PTD cases as compared to controls. When gene environmental interaction between the CYP17 gene polymorphism and OCPs level was considered, a significant interaction was observed between ≥ 50th percentile of γ-HCH and CYP17 A1A1 (wild type) genotype. Conclusions: Higher levels of OCPs along with wild type state of CYP17 gene (A1A1) in women may be considered as an important etiological factor in ‘idiopathic’ PTD. The present study provides evidence that genetic variation and its interaction with the environmental exposure may increase the risk of PTD.