Quantitative genetic modeling and inference in the presence of nonignorable missing data.

Natural selection is typically exerted at some specific life stages. If natural selection takes place before a trait can be measured, using conventional models can cause wrong inference about population parameters. When the missing data process relates to the trait of interest, a valid inference requires explicit modeling of the missing process. We propose a joint modeling approach, a shared parameter model, to account for nonrandom missing data. It consists of an animal model for the phenotypic data and a logistic model for the missing process, linked by the additive genetic effects. A Bayesian approach is taken and inference is made using integrated nested Laplace approximations. From a simulation study we find that wrongly assuming that missing data are missing at random can result in severely biased estimates of additive genetic variance. Using real data from a wild population of Swiss barn owls Tyto alba, our model indicates that the missing individuals would display large black spots; and we conclude that genes affecting this trait are already under selection before it is expressed. Our model is a tool to correctly estimate the magnitude of both natural selection and additive genetic variance.

Regional-scale integration of multi-scale hydrological and geophysical data using a two-step Bayesian sequential simulation approach

Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale for the purpose of improving predictions of groundwater flow and solute transport. However, extending corresponding approaches to the regional scale still represents one of the major challenges in the domain of hydrogeophysics. To address this problem, we have developed a regional-scale data integration methodology based on a two-step Bayesian sequential simulation approach. Our objective is to generate high-resolution stochastic realizations of the regional-scale hydraulic conductivity field in the common case where there exist spatially exhaustive but poorly resolved measurements of a related geophysical parameter, as well as highly resolved but spatially sparse collocated measurements of this geophysical parameter and the hydraulic conductivity. To integrate this multi-scale, multi-parameter database, we first link the low- and high-resolution geophysical data via a stochastic downscaling procedure. This is followed by relating the downscaled geophysical data to the high-resolution hydraulic conductivity distribution. After outlining the general methodology of the approach, we demonstrate its application to a realistic synthetic example where we consider as data high-resolution measurements of the hydraulic and electrical conductivities at a small number of borehole locations, as well as spatially exhaustive, low-resolution estimates of the electrical conductivity obtained from surface-based electrical resistivity tomography. The different stochastic realizations of the hydraulic conductivity field obtained using our procedure are validated by comparing their solute transport behaviour with that of the underlying ?true? hydraulic conductivity field. We find that, even in the presence of strong subsurface heterogeneity, our proposed procedure allows for the generation of faithful representations of the regional-scale hydraulic conductivity structure and reliable predictions of solute transport over long, regional-scale distances.

Clustering and hot spot detection in socio-economic spatio-temporal data

Distribution of socio-economic features in urban space is an important source of information for land and transportation planning. The metropolization phenomenon has changed the distribution of types of professions in space and has given birth to different spatial patterns that the urban planner must know in order to plan a sustainable city. Such distributions can be discovered by statistical and learning algorithms through different methods. In this paper, an unsupervised classification method and a cluster detection method are discussed and applied to analyze the socio-economic structure of Switzerland. The unsupervised classification method, based on Ward's classification and self-organized maps, is used to classify the municipalities of the country and allows to reduce a highly-dimensional input information to interpret the socio-economic landscape. The cluster detection method, the spatial scan statistics, is used in a more specific manner in order to detect hot spots of certain types of service activities. The method is applied to the distribution services in the agglomeration of Lausanne. Results show the emergence of new centralities and can be analyzed in both transportation and social terms.

The UniProtKB/Swiss-Prot Tox-Prot program: A central hub of integrated venom protein data.

Animal toxins are of interest to a wide range of scientists, due to their numerous applications in pharmacology, neurology, hematology, medicine, and drug research. This, and to a lesser extent the development of new performing tools in transcriptomics and proteomics, has led to an increase in toxin discovery. In this context, providing publicly available data on animal toxins has become essential. The UniProtKB/Swiss-Prot Tox-Prot program (http://www.uniprot.org/program/Toxins) plays a crucial role by providing such an access to venom protein sequences and functions from all venomous species. This program has up to now curated more than 5000 venom proteins to the high-quality standards of UniProtKB/Swiss-Prot (release 2012_02). Proteins targeted by these toxins are also available in the knowledgebase. This paper describes in details the type of information provided by UniProtKB/Swiss-Prot for toxins, as well as the structured format of the knowledgebase.

Alcohol-related and hepatocellular cancer deaths by country of birth in England and Wales: analysis of mortality and census data.

BACKGROUND: The incidence of and mortality from alcohol-related conditions, liver disease and hepatocellular cancer (HCC) are increasing in the UK. We compared mortality rates by country of birth to explore potential inequalities and inform clinical and preventive care. DESIGN: Analysis of mortality for people aged 20 years and over using the 2001 Census data and death data from 1999 and 2001-2003. SETTING: England and Wales. MAIN OUTCOME MEASURES: Standardized mortality ratios (SMRs) for alcohol-related deaths and HCC. RESULTS: Mortality from alcohol-related deaths (23 502 deaths) was particularly high for people born in Ireland (SMR for men [M]: 236, 95% confidence interval [CI]: 219-254; SMR for women [F]: 212, 95% CI: 191-235) and Scotland (SMR-M: 187, CI: 173-213; SMR-F 182, CI: 163-205) and men born in India (SMR-M: 161, CI: 144-181). Low alcohol-related mortality was found in women born in other countries and men born in Bangladesh, Middle East, West Africa, Pakistan, China and Hong Kong, and the West Indies. Similar mortality patterns were observed by country of birth for alcoholic liver disease and other liver diseases. Mortality from HCC (8266 deaths) was particularly high for people born in Bangladesh (SMR-M: 523, CI: 380-701; SMR-F: 319, CI: 146-605), China and Hong Kong (SMR-M: 492, CI: 168-667; SMR-F: 323, CI: 184-524), West Africa (SMR-M: 440, CI, 308-609; SMR-F: 319, CI: 165-557) and Pakistan (SMR-M: 216, CI: 113-287; SMR-F: 215, CI: 133-319). CONCLUSIONS: These findings show persistent differences in mortality by country of birth for both alcohol-related and HCC deaths and have important clinical and public health implications. New policy, research and practical action are required to address these differences.This resource was contributed by The National Documentation Centre on Drug Use.

Interpretation of array comparative genome hybridization data: a major challenge.

The advent and application of high-resolution array-based comparative genome hybridization (array CGH) has led to the detection of large numbers of copy number variants (CNVs) in patients with developmental delay and/or multiple congenital anomalies as well as in healthy individuals. The notion that CNVs are also abundantly present in the normal population challenges the interpretation of the clinical significance of detected CNVs in patients. In this review we will illustrate a general clinical workflow based on our own experience that can be used in routine diagnostics for the interpretation of CNVs.

Catherine Comiskey Describing Data

Introducing and describing data and understanding the normal distribution.

Archiving and Accessing Qualitative Data Programme

There is a growing body of qualitative research data being made available in data archives in both Ireland and the UK. Examples of Irish qualitative data currently available for further analysis include Growing Up in Ireland (GUI) and Life Histories and Social Change in 20th Century Ireland. The Timescapes Research Programme, at the University of Leeds, hosts a wide variety of economic and social research data relevant to child and family researchers; data which is available to researchers in both Northern Ireland and the Republic of Ireland. Access to this data provides researchers with greater opportunities to explore the issues that affect children, their families and their  communities and which may ultimately contribute to informing children's policy. In this the second master class of the Children's Research Network for Ireland and Northern Ireland, participants will learn about the various qualitativ edatasets that are publicly-available for research purposes in the Republic of Ireland and Northern Ireland.

Growing up in Ireland data in the Irish Qualitative Data Archive

Brian Merriman's presentation on the Growing up in Ireland data in the Irish Qualitative Data Archive

CDI Tallaght RACCER data in the Irish Qualitative Data Archive

Tara Murphy's presentation on preparing RACCER data for the Irish Qualitative Data Archive

The Irish Qualitative Data Archive

Jane Gray's presentation on the Irish Qualitative Data Archive

Webcast Archiving and Accessing Qualitative Data

View the webcast of the presentations at the Archiving and Accessing Qualitative data master class, February 2012.

Life Expectancy and All Age All Cause Mortality Monitoring (Overall and Health Inequalities) - Update to include data for 2007

The latest annual update on life expectancy data and all age all cause mortality rates, with data updated to 2005-07, which are used to monitor progress against Department of Health targets for overall life expectancy in England, and for the gap in life expectancy between the areas with the worst health and deprivation indicators (the Spearhead group) and the England average, was released on 13th November 2008 according to the arrangements approved by the UK Statistics Authority.

The Marmot Review: Updated data and information 2 years on

To mark the two year anniversary since The Marmot Review ('Fair Society, Healthy Lives') was published, on the 15th of February the UCL Institute of Health Equity published new data on key health inequalities indicators at local authority level in England.Main Findings:Life Expectancy – this has historically been one of the main indicators of health inequalities.The Marmot Indicators from this year’s charts show the average life expectancy for eachlocal authority and the level of inequality within each authority area (7):-While overall life expectancy at birth in England increased by 0.3 years for both menand women between 2007-9 and 2008-10, inequalities in life expectancy betweenneighbourhoods increased by 0.1 years for men and showed no change for women-Among the 150 upper tier local authorities in England, life expectancy improved inthe majority of cases (133 areas saw improvements for men and 125 sawimprovements for women). However inequalities also increased in the majority ofareas (104 for men and 92 for women).-The largest increase in inequality in life expectancy was in West Berkshire for men(2.0 years) and inMiddlesbrough for women (2 years). The largest decreases ininequality were in Kensington and Chelsea for both men and women (1.9 and 1.1years respectively. To find out more, please read: - The press release, including key figures and main findings. - A blog by Michael Marmot about the data and it's implications. - Press coverage of the data in national and local newspapers and websites. - A powerpoint presentation on the key findings.