Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes(1). Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel(2)) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries. A large genome-wide association study of more than 5 million individuals reveals that 12,111 single-nucleotide polymorphisms account for nearly all the heritability of height attributable to common genetic variants.

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.

DALI is a popular resource for comparing protein structures. The software is based on distance-matrix alignment. The associated web server provides tools to navigate, integrate and organize some data pushed out by genomics and structural genomics. The server has been running continuously for the past 25 years. Structural biologists routinely use DALI to compare a new structure against previously known protein structures. If significant similarities are discovered, it may indicate a distant homology, that is, that the structures are of shared origin. This may be significant in determining the molecular mechanisms, as these may remain very similar from a distant predecessor to the present day, for example, from the last common ancestor of humans and bacteria. Meta-analysis of independent reference-based evaluations of alignment accuracy and fold discrimination shows DALI at top rank in six out of 12 studies. The web server and standalone software are available from .

BACKGROUND: Economic, cultural, and geographical reasons usually limit the access to specialized health centers in developing countries, especially in rural areas. Peruvian health system indicators still highlight significant unmet clinical need for neurosurgical patients. Our project is to develop the first highly specialized neurosurgical center in the EsSalud hospital of Trujillo, with the goal to improve the treatment of neurosurgical diseases in that region, thus optimizing their outcomes while decreasing expensive and risky patients transfer to the neurosurgical departments in the capital district. METHODS: After an initial center evaluation, 2 neurosurgeons and 2 nurses from the Helsinki University Central Hospital provided the microneurosurgical training for the local team. Moreover, our team worked closely with the local staff to develop standardized protocols for surgical procedures and postoperative management. RESULTS: From February to May 2016, 59 surgeries were performed in the new Neurosurgical Center, including cerebrovascular and skull-base cases that were never performed before in Trujillo. Moreover, the first "Cerebral Bypass and Vascular Microsurgery Live Course" was held in Trujillo in May 2016. After we left, the local team continued to work following the same protocols we introduced, and built up together. CONCLUSIONS: An effective and adequate operative skill transfer to the local staff may be accomplished in a reasonable amount of time, thus guaranteeing a longlasting improvement of neurosurgical care, while minimizing expenditures on personnel and capital. We believe that this is possible following a general microsurgical philosophy that can be simplified as follows: "simple, clean, fast, and preserving normal anatomy."

Drug Target Commons (DTC) is a web platform (database with user interface) for community-driven bioactivity data integration and standardization for comprehensive mapping, reuse and analysis of compound-target interaction profiles. End users can search, upload, edit, annotate and export expert-curated bioactivity data for further analysis, using an application programmable interface, database dump or tab-delimited text download options. To guide chemical biology and drug-repurposing applications, DTC version 2.0 includes updated clinical development information for the compounds and target gene-disease associations, as well as cancer-type indications for mutant protein targets, which are critical for precision oncology developments.

Background Identifying associations among biological variables is a major challenge in modern quantitative biological research, particularly given the systemic and statistical noise endemic to biological systems. Drug sensitivity data has proven to be a particularly challenging field for identifying associations to inform patient treatment. Results To address this, we introduce two semi-parametric variations on the commonly used concordance index: the robust concordance index and the kernelized concordance index (rCI, kCI), which incorporate measurements about the noise distribution from the data. We demonstrate that common statistical tests applied to the concordance index and its variations fail to control for false positives, and introduce efficient implementations to compute p-values using adaptive permutation testing. We then evaluate the statistical power of these coefficients under simulation and compare with Pearson and Spearman correlation coefficients. Finally, we evaluate the various statistics in matching drugs across pharmacogenomic datasets. Conclusions We observe that the rCI and kCI are better powered than the concordance index in simulation and show some improvement on real data. Surprisingly, we observe that the Pearson correlation was the most robust to measurement noise among the different metrics.

Studies of nostalgia are one of the research subfields of recalled consumption experiences. In addition to the nostalgic recall, the consumers' remembered experiences situate in other temporal frames, a theme rarely touched in the extant research. The aim of this research was to examine the differences between nostalgic and other recalled consumption experiences by identifying and analysing the characteristics of the temporal frames. The data set for this task comprised 480 descriptions of consumers' experiences involving an everyday consumer object. An interpretive approach was utilized to analyse the temporal frames. The results of the study indicate that the consumers described their memories in four temporal structures. These are the strong nostalgia from childhood, light nostalgia from youth, descriptions of recent past and memories linked to consumption practices and traditions that will be fostered in the future. The article proposes a conceptual framework describing the temporal frames of consumers' remembered consumption experiences that opens further avenues for research alongside of nostalgic recall.

Researchers have assembled thousands of eukaryotic genomes using Illumina reads, but traditional mate-pair libraries cannot span all repetitive elements, resulting in highly fragmented assemblies. However, both chromosome conformation capture techniques, such as Hi-C and Dovetail Genomics Chicago libraries and long-read sequencing, such as Pacific Biosciences and Oxford Nanopore, help span and resolve repetitive regions and therefore improve genome assemblies. One important livestock species of arid regions that does not have a high-quality contiguous reference genome is the dromedary (Camelus dromedarius). Draft genomes exist but are highly fragmented, and a high-quality reference genome is needed to understand adaptation to desert environments and artificial selection during domestication. Dromedaries are among the last livestock species to have been domesticated, and together with wild and domestic Bactrian camels, they are the only representatives of the Camelini tribe, which highlights their evolutionary significance. Here we describe our efforts to improve the North African dromedary genome. We used Chicago and Hi-C sequencing libraries from Dovetail Genomics to resolve the order of previously assembled contigs, producing almost chromosome-level scaffolds. Remaining gaps were filled with Pacific Biosciences long reads, and then scaffolds were comparatively mapped to chromosomes. Long reads added 99.32 Mbp to the total length of the new assembly. Dovetail Chicago and Hi-C libraries increased the longest scaffold over 12-fold, from 9.71 Mbp to 124.99 Mbp and the scaffold N50 over 50-fold, from 1.48 Mbp to 75.02 Mbp. We demonstrate that Illumina de novo assemblies can be substantially upgraded by combining chromosome conformation capture and long-read sequencing.