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

Managing Dynamic User Communities in a Grid of Autonomous Resources

One of the fundamental concepts in Grid computing is the creation of Virtual Organizations (VO's): a set of resource consumers and providers that join forces to solve a common problem. Typical examples of Virtual Organizations include collaborations formed around the Large Hadron Collider (LHC) experiments. To date, Grid computing has been applied on a relatively small scale, linking dozens of users to a dozen resources, and management of these VO's was a largely manual operation. With the advance of large collaboration, linking more than 10000 users with a 1000 sites in 150 counties, a comprehensive, automated management system is required. It should be simple enough not to deter users, while at the same time ensuring local site autonomy. The VO Management Service (VOMS), developed by the EU DataGrid and DataTAG projects[1, 2], is a secured system for managing authorization for users and resources in virtual organizations. It extends the existing Grid Security Infrastructure[3] architecture with embedded VO affiliation assertions that can be independently verified by all VO members and resource providers. Within the EU DataGrid project, Grid services for job submission, file- and database access are being equipped with fine- grained authorization systems that take VO membership into account. These also give resource owners the ability to ensure site security and enforce local access policies. This paper will describe the EU DataGrid security architecture, the VO membership service and the local site enforcement mechanisms Local Centre Authorization Service (LCAS), Local Credential Mapping Service(LCMAPS) and the Java Trust and Authorization Manager.Comment: Talk from the 2003 Computing in High Energy and Nuclear Physics (CHEP03), La Jolla, Ca, USA, March 2003, 7 pages, LaTeX, 5 eps figures. PSN TUBT00

Optical frequency comb Fourier transform spectroscopy of formaldehyde in the 1250 to 1390 cm−1 range: Experimental line list and improved MARVEL analysis

We use optical frequency comb Fourier transform spectroscopy to record high-resolution, low-pressure, room-temperature spectra of formaldehyde (H212C16O) in the range of 1250 to 1390 cm−1. Through line-by-line fitting, we retrieve line positions and intensities of 747 rovibrational transitions: 558 from the ν6 band, 129 from the ν4 band, and 14 from the ν3 band, as well as 46 from four different hot bands. We incorporate the accurate and precise line positions (0.4 MHz median uncertainty) into the MARVEL (measured active vibration-rotation energy levels) analysis of the H2CO spectrum. This increases the number of MARVEL-predicted energy levels by 82 and of rovibrational transitions by 5382, and substantially reduces uncertainties of MARVEL-derived H2CO energy levels over a large range: from pure rotational levels below 200 cm−1 up to multiply excited vibrational levels at 6000 cm−1. This work is an important step toward filling the gaps in formaldehyde data in the HITRAN database

Genome-wide association scan meta-analysis identifies three Loci influencing adiposity and fat distribution.

To identify genetic loci influencing central obesity and fat distribution, we performed a meta-analysis of 16 genome-wide association studies (GWAS, N = 38,580) informative for adult waist circumference (WC) and waist-hip ratio (WHR). We selected 26 SNPs for follow-up, for which the evidence of association with measures of central adiposity (WC and/or WHR) was strong and disproportionate to that for overall adiposity or height. Follow-up studies in a maximum of 70,689 individuals identified two loci strongly associated with measures of central adiposity; these map near TFAP2B (WC, P = 1.9x10(-11)) and MSRA (WC, P = 8.9x10(-9)). A third locus, near LYPLAL1, was associated with WHR in women only (P = 2.6x10(-8)). The variants near TFAP2B appear to influence central adiposity through an effect on overall obesity/fat-mass, whereas LYPLAL1 displays a strong female-only association with fat distribution. By focusing on anthropometric measures of central obesity and fat distribution, we have identified three loci implicated in the regulation of human adiposity

Biological, clinical and population relevance of 95 loci for blood lipids.

Plasma concentrations of total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglycerides are among the most important risk factors for coronary artery disease (CAD) and are targets for therapeutic intervention. We screened the genome for common variants associated with plasma lipids in >100,000 individuals of European ancestry. Here we report 95 significantly associated loci (P < 5 x 10(-8)), with 59 showing genome-wide significant association with lipid traits for the first time. The newly reported associations include single nucleotide polymorphisms (SNPs) near known lipid regulators (for example, CYP7A1, NPC1L1 and SCARB1) as well as in scores of loci not previously implicated in lipoprotein metabolism. The 95 loci contribute not only to normal variation in lipid traits but also to extreme lipid phenotypes and have an impact on lipid traits in three non-European populations (East Asians, South Asians and African Americans). Our results identify several novel loci associated with plasma lipids that are also associated with CAD. Finally, we validated three of the novel genes-GALNT2, PPP1R3B and TTC39B-with experiments in mouse models. Taken together, our findings provide the foundation to develop a broader biological understanding of lipoprotein metabolism and to identify new therapeutic opportunities for the prevention of CAD

New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk.

Levels of circulating glucose are tightly regulated. To identify new loci influencing glycemic traits, we performed meta-analyses of 21 genome-wide association studies informative for fasting glucose, fasting insulin and indices of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) in up to 46,186 nondiabetic participants. Follow-up of 25 loci in up to 76,558 additional subjects identified 16 loci associated with fasting glucose and HOMA-B and two loci associated with fasting insulin and HOMA-IR. These include nine loci newly associated with fasting glucose (in or near ADCY5, MADD, ADRA2A, CRY2, FADS1, GLIS3, SLC2A2, PROX1 and C2CD4B) and one influencing fasting insulin and HOMA-IR (near IGF1). We also demonstrated association of ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 with type 2 diabetes. Within these loci, likely biological candidate genes influence signal transduction, cell proliferation, development, glucose-sensing and circadian regulation. Our results demonstrate that genetic studies of glycemic traits can identify type 2 diabetes risk loci, as well as loci containing gene variants that are associated with a modest elevation in glucose levels but are not associated with overt diabetes

Algorithms for learning parsimonious context trees

Parsimonious context trees, PCTs, provide a sparse parameterization of conditional probability distributions. They are particularly powerful for modeling context-specific independencies in sequential discrete data. Learning PCTs from data is computationally hard due to the combinatorial explosion of the space of model structures as the number of predictor variables grows. Under the score-and-search paradigm, the fastest algorithm for finding an optimal PCT, prior to the present work, is based on dynamic programming. While the algorithm can handle small instances fast, it becomes infeasible already when there are half a dozen four-state predictor variables. Here, we show that common scoring functions enable the use of new algorithmic ideas, which can significantly expedite the dynamic programming algorithm on typical data. Specifically, we introduce a memoization technique, which exploits regularities within the predictor variables by equating different contexts associated with the same data subset, and a bound-and-prune technique, which exploits regularities within the response variable by pruning parts of the search space based on score upper bounds. On real-world data from recent applications of PCTs within computational biology the ideas are shown to reduce the traversed search space and the computation time by several orders of magnitude in typical cases.Peer reviewe

On Model Selection, Bayesian Networks, and the Fisher Information Integral

We study BIC-like model selection criteria and in particular, their refinements that include a constant term involving the Fisher information matrix. We perform numerical simulations that enable increasingly accurate approximation of this constant in the case of Bayesian networks. We observe that for complex Bayesian network models, the constant term is a negative number with a very large absolute value that dominates the other terms for small and moderate sample sizes. For networks with a fixed number of parameters, d, the leading term in the complexity penalty, which is proportional to d, is the same. However, as we show, the constant term can vary significantly depending on the network structure even if the number of parameters is fixed. Based on our experiments, we conjecture that the distribution of the nodes’ outdegree is a key factor. Furthermore, we demonstrate that the constant term can have a dramatic effect on model selection performance for small sample sizes.Peer reviewe