Ethical issues and GenomEUtwin.

The post-genomic era is witnessing a proliferation of large-scale and population based genetic and genomic research projects. Many countries have or are establishing research biobanks and, as with GenomEUtwin, there is great interest in building multinational projects that link genotypic and phenotypic information from different centers. Clearly, the conduct of these projects raises multiple ethical issues, and the knowledge generated will continually recast the ethical, legal and social implications (ELSI) of such research. Maximising the scientific profit from this work while minimizing the risks to the participants requires full integration of ethics components into the structure and functioning of these projects. GenomEUtwin is organized around five intellectual cores, including an Ethics Core which operates across the entire project. This paper describes the role of the Ethics Core and presents an overview of the guidelines on which the principles followed in GenomEUtwin are based. We outline the major ethical concerns of our project and highlight complexities arising from diverse national legislations. Finally, the role of empirically based ethics research is discussed for understanding the ethical, legal, social and economic implications of human genetics and genomics research.     

Longevity studies in GenomEUtwin.

Previous twin studies have indicated that approximately 25% of the variation in life span can be attributed to genetic factors and recent studies have also suggested a moderate clustering of extreme longevity within families. Here we discuss various definitions of extreme longevity and some analytical approaches with special attention to the challenges due to censored data. Lexis diagrams are provided for the Danish, Dutch, Finnish, Italian, Norwegian, and Swedish Twin registries hereby outlining possibilities for longevity studies within GenomEUtwin. We extend previous analyses of lifespan for the Danish 1870-1900 twin cohorts to include the new 1901-1910 cohorts, which are consistent with the previous findings. The size of the twin cohorts in GenomEUtwin and the existence of population-based, nationwide health and death registers make epidemiological studies of longevity very powerful. The combined GenomEUtwin sample will also allow detailed age-specific heritability analyses of lifespan. Finally, it will provide a resource for identifying unusual sibships (i.e., dizygotic twin pairs) where both survived to extreme ages, as a basis for discovering genetic variants of importance for extreme survival.     

Analysis of genetic variation in the GenomEUtwin project.

Multiallelic short tandem repeat polymorphisms, or microsatellites, are useful markers in genome wide scans to identify chromosomal regions containing genes underlying disease loci. The biallelic single nucleotide polymorphism (SNP) can be used to fine map previously identified large candidate regions or to test functional candidate genes by association analysis. In the GenomEUtwin project the population based impact of susceptibility genes for six multifactorial traits will be studied. A genome wide panel of informative human microsatellite markers will be analyzed by fluorescent capillary electrophoresis in well characterized twin and population samples. Contrary to microsatellites, selection of the most informative panels of SNPs is hampered by imperfect data on the allele frequencies and population distribution of SNPs markers in the databases. Therefore, selection of SNPs requires a substantial amount of bioinformatics, and, the SNPs need to be validated experimentally in the relevant populations prior to genotyping large sample sets. In the GenomEUtwin project, large scale genotyping of SNPs will be performed using the SNPstreamUHT and MassARRAY genotyping systems that are based on the primer extension reaction principle combined with fluorescent and mass spectrometric detection, respectively. Production of the genotyping data will be a joint effort by GenomEUtwin partners at the University of Helsinki, the National Public Health Institute in Helsinki, Finland and Uppsala University, Sweden. All genotyping data will be stored in a common database established specifically for the GenomEUtwin project, from where it can be accessed by the twin research centres that provided the samples for genotyping.     

GenomEUtwin: a strategy to identify genetic influences on health and disease.

In this issue of Twin Research, we describe different facets of a European Community funded effort, GenomEUtwin, which capitalises on eight of the world's largest and best characterised twin registers and a multi-national population cohort, MORGAM. This international study, reaching beyond the geographical borders of Europe, is based on linkage and association strategies designed to identify genetic contributors to health and disease using integrated expertise of participating groups in genetics, epidemiology and biostatistics. By merging information from numerous epidemiological and genetic databases, GenomEUtwin will create an intellectual and technical infrastructure for future genetic epidemiological studies aiming to define genetic and life style risks for common human diseases.     

Stability of exploratory multivariate data modeling in longitudinal data

Exploratory data-driven multivariate analysis provides a means of investigating underlying structure in complex data. To explore the stability of multivariate data modeling, we have applied a common method of multivariate modeling (factor analysis) to the Genetic Analysis Workshop 13 (GAW13) Framingham Heart Study data. Given the longitudinal nature of the data, multivariate models were generated independently for a number of different time points (corresponding to cross-sectional clinic visits for the two cohorts), and compared. In addition, each multivariate model was used to generate factor scores, which were then used as a quantitative trait in variance component-based linkage analysis to investigate the stability of linkage signals over time. We found surprisingly good correlation between factor models (i.e., predicted factor structures), maximum LOD scores, and locations of maximum LOD scores (0.81< rho <0.94 for factor scores; rho >0.99 for peak locations; and 0.67< rho <0.93 for peak LOD scores). Furthermore, the regions implicated by linkage analysis with these factor scores have also been observed in other studies, further validating our exploratory modeling.     

Effective integration of protein data through better data modeling

Protein data, from sequence and structure to interaction, is being generated through many diverse methodologies; it is stored and reported in numerous forms and multiple places. The magnitude of the data limits researchers abilities to utilize all information generated. Effective integration of protein data can be accomplished through better data modeling. We demonstrate this through the MIPD project.     

Data collection and capture systems for microbial modeling

Summary Microbial modeling experiments require an integrated and efficient design to overcome constraints on time and human resources. The choice of an experimental system is effected by first determining the goals and scope of the model to be constructed. Kinetic studies, for example, require a different approach from single end-point models, such as time to toxin detection or growth probability. Studies have been conducted in liquid broth tubes or batch culture, agar plates, and food matrices. These traditional systems are labor intensive, however, which constrains experimental size, and thus, a model's scope and validity. To maximize experimental size, experimental systems should be automated and linked to electronic data manipulation, analysis, and presentation. Microbial modelers should also consider the relationship between the experimental environmental factors, such as pH,a_w, or temperature, and their impact on growth, virulence or toxigenesis determinants. Attaining these goals will increase the probability that the model will accurately predict microbial responses in food systems.     

Conductance measurements for data generation in predictive modeling

Summary The electrical resistance of a growth medium inoculated with bacteria may be automatically recorded throughout an incubation period without the necessity for sampling. The rate of change in conductance is dependent on the bacteria studied, the medium composition and the prevailing growth conditions.The effect of growth medium composition, growth conditions and inoculum level on the conductance response was studied forYersinia enterocolitica O:3. A large number of combinations of factors affecting the growth/activity of the bacteria could be studied simultaneously due to the large instrumental capacity of the Malthus 2000. A polynomial model based on conductance measurements was developed forY. enterocolitica describing the effect of temperature, pH andl-lactate level on conductance response curve parameters. The model was used for predicting growth rates. Growth rates calculated from bacterial counts ofY. enterocolitica growing in minced pork corresponded to growth rates predicted using the polynomial conductance models.     

Multilevel pharmacokinetics-driven modeling of metabolomics data

Introduction

Multilevel modeling is a quantitative statistical method to investigate variability and relationships between variables of interest, taking into account population structure and dependencies. It can be used for prediction, data reduction and causal inference from experiments and observational studies allowing for more efficient elucidation of knowledge.

Objectives

In this study we introduced the concept of multilevel pharmacokinetics (PK)-driven modelling for large-sample, unbalanced and unadjusted metabolomics data comprising nucleoside and creatinine concentration measurements in urine of healthy and cancer patients.

Methods

A Bayesian multilevel model was proposed to describe the nucleoside and creatinine concentration ratio considering age, sex and health status as covariates. The predictive performance of the proposed model was summarized via area under the ROC, sensitivity and specificity using external validation.

Results

Cancer was associated with an increase in methylthioadenosine/creatinine excretion rate by a factor of 1.42 (1.09–2.03) which constituted the highest increase among all nucleosides. Age influenced nucleosides/creatinine excretion rates for all nucleosides in the same direction which was likely caused by a decrease in creatinine clearance with age. There was a small evidence of sex-related differences for methylthioadenosine. The individual a posteriori prediction of patient classification as area under the ROC with 5th and 95th percentile was 0.57(0.5–0.67) with sensitivity and specificity of 0.59(0.42–0.76) and 0.57(0.45–0.7), respectively suggesting limited usefulness of 13 nucleosides/creatinine urine concentration measurements in predicting disease in this population.

Conclusion

Bayesian multilevel pharmacokinetics-driven modeling in metabolomics may be useful in understanding the data and may constitute a new tool for searching towards potential candidates of disease indicators.

     

The Protein Data Bank and lessons in data management

The Protein Data Bank (PDB) is a widely used biological databaseof macromolecular structures with a long history. This historyis treated as lessons learned and is used to highlight whatare believed to be the best practices important to developersof biological databases today. While the focus is on data quality,data representation and the information technology to supportthese data, the non-data and technology issues cannot be ignored.The role of the human factor in the form of users, collaborators,scientific society and ad hoc committees is also included.      

Monte Carlo modeling of small-angle scattering data from non-interacting homogeneous and heterogeneous particles in solution.

A Monte Carlo algorithm that rapidly generates the scattered intensity function for complex heterogeneous particles is described. The heterogeneous particles are built from any number and orientation of simple building blocks, which include ellipsoidal shells, hollow ellipsoidal cylinders, ellipsoidal helices, triangular prisms, rectangular prisms, and semi-ellipsoidal shells. Applications are discussed such as real proteins and their complexes, polysaccharides, void effects on I(q), Guinier range estimation, and calculation of Stuhrmann plots.     

群体遗传学研究中的数据处理方法I．RAPD数据的AMOVA分析

近年来,RAPD数据和AMOVA分析广泛地应用于群体遗传学和保护遗传学研究。然而,由于RAPD标记具显性特点。加上目前进行AMOVA分析所依赖的RAPDistance软件不完善,使得对RAPD数据进行AMOVA分析时存在许多不足。本文介绍了AMOVA分析的基本过程,同时引入一个新的程序DCFA用以替代RADistance并详述了将DCFA与WINAMOVA联用,对RAPD数据进行AMOVA分析的具体步骤与注意事项,最后,以产自中国和巴西8个普通野生稻（Oryza furipogon)天然群体为例,演示了对RAPD表型数据进行AMOVA分析的过程,讨论了AMOVA分析结果在群体遗传结构上的意义。通过对AMOVA算法的分析,同时比较4种距离系数所得AMOVA结果,我们认为在进行AMOVA分析时选择NEI－LI距离和欧氏距离平方较为合适,而目前国内使用较多的JACCARD系数不适合AMOVA分析。     

Comparison of logistic regression and linear regression in modeling percentage data

Percentage is widely used to describe different results in food microbiology, e.g., probability of microbial growth, percent inactivated, and percent of positive samples. Four sets of percentage data, percent-growth-positive, germination extent, probability for one cell to grow, and maximum fraction of positive tubes, were obtained from our own experiments and the literature. These data were modeled using linear and logistic regression. Five methods were used to compare the goodness of fit of the two models: percentage of predictions closer to observations, range of the differences (predicted value minus observed value), deviation of the model, linear regression between the observed and predicted values, and bias and accuracy factors. Logistic regression was a better predictor of at least 78% of the observations in all four data sets. In all cases, the deviation of logistic models was much smaller. The linear correlation between observations and logistic predictions was always stronger. Validation (accomplished using part of one data set) also demonstrated that the logistic model was more accurate in predicting new data points. Bias and accuracy factors were found to be less informative when evaluating models developed for percentage data, since neither of these indices can compare predictions at zero. Model simplification for the logistic model was demonstrated with one data set. The simplified model was as powerful in making predictions as the full linear model, and it also gave clearer insight in determining the key experimental factors.