Mapping genetic variations to three- dimensional protein structures to enhance variant interpretation: a proposed framework

The translation of personal genomics to precision medicine depends on the accurate interpretation of the multitude of genetic variants observed for each individual. However, even when genetic variants are predicted to modify a protein, their functional implications may be unclear. Many diseases are caused by genetic variants affecting important protein features, such as enzyme active sites or interaction interfaces. The scientific community has catalogued millions of genetic variants in genomic databases and thousands of protein structures in the Protein Data Bank. Mapping mutations onto three-dimensional (3D) structures enables atomic-level analyses of protein positions that may be important for the stability or formation of interactions; these may explain the effect of mutations and in some cases even open a path for targeted drug development. To accelerate progress in the integration of these data types, we held a two-day Gene Variation to 3D (GVto3D) workshop to report on the latest advances and to discuss unmet needs. The overarching goal of the workshop was to address the question: what can be done together as a community to advance the integration of genetic variants and 3D protein structures that could not be done by a single investigator or laboratory? Here we describe the workshop outcomes, review the state of the field, and propose the development of a framework with which to promote progress in this arena. The framework will include a set of standard formats, common ontologies, a common application programming interface to enable interoperation of the resources, and a Tool Registry to make it easy to find and apply the tools to specific analysis problems. Interoperability will enable integration of diverse data sources and tools and collaborative development of variant effect prediction methods

A many-analysts approach to the relation between religiosity and well-being

The relation between religiosity and well-being is one of the most researched topics in the psychology of religion, yet the directionality and robustness of the effect remains debated. Here, we adopted a many-analysts approach to assess the robustness of this relation based on a new cross-cultural dataset (N=10,535 participants from 24 countries). We recruited 120 analysis teams to investigate (1) whether religious people self-report higher well-being, and (2) whether the relation between religiosity and self-reported well-being depends on perceived cultural norms of religion (i.e., whether it is considered normal and desirable to be religious in a given country). In a two-stage procedure, the teams first created an analysis plan and then executed their planned analysis on the data. For the first research question, all but 3 teams reported positive effect sizes with credible/confidence intervals excluding zero (median reported β=0.120). For the second research question, this was the case for 65% of the teams (median reported β=0.039). While most teams applied (multilevel) linear regression models, there was considerable variability in the choice of items used to construct the independent variables, the dependent variable, and the included covariates

Multiyear trans-horizon radio propagation measurements at 3.5 GHz:system design and measurement results over land and wetland paths in the Netherlands

The design, realization, and measurement results of a high-accuracy multiyear 3.5 GHz trans-horizon radio propagation measurement system are discussed, with both emphasis on the results and implemented technical measures to enhance the accuracy and overall reliability of the measurements. The propagation measurements have been performed on two different paths of 253 and 234 km length, using two transmitters and one receiver in the period September 2013 till November 2016. One of the paths travels over wetland; the other path can be considered as a land path. On each path, an additional transmitter is placed at 107 km (in the 253 km path) and 84 km (in the 234 km path) from the receiver. With this arrangement, the correlation between two nonaligned paths of comparable length, and two aligned paths of dissimilar length, was studied. The measurements show that for the land path, the predicted ITU-R P.452-16 cumulative distribution function (CDF) typically shows 5 dB higher path loss than the actual measured CDF for the region of interest; anomalous propagation. This means that the measured signal is on average weaker than predicted (a higher path loss). For the wetland path, the actual CDF is very close to the predicted CDF. Also, the measurements reveal that typically 30% of the anomalous propagation occurrences are correlated with other paths