A saturated map of common genetic variants associated with human height

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

Atrial fibrillation genetic risk differentiates cardioembolic stroke from other stroke subtypes

AbstractObjectiveWe sought to assess whether genetic risk factors for atrial fibrillation can explain cardioembolic stroke risk.MethodsWe evaluated genetic correlations between a prior genetic study of AF and AF in the presence of cardioembolic stroke using genome-wide genotypes from the Stroke Genetics Network (N = 3,190 AF cases, 3,000 cardioembolic stroke cases, and 28,026 referents). We tested whether a previously-validated AF polygenic risk score (PRS) associated with cardioembolic and other stroke subtypes after accounting for AF clinical risk factors.ResultsWe observed strong correlation between previously reported genetic risk for AF, AF in the presence of stroke, and cardioembolic stroke (Pearson’s r=0.77 and 0.76, respectively, across SNPs with p &lt; 4.4 × 10−4 in the prior AF meta-analysis). An AF PRS, adjusted for clinical AF risk factors, was associated with cardioembolic stroke (odds ratio (OR) per standard deviation (sd) = 1.40, p = 1.45×10−48), explaining ∼20% of the heritable component of cardioembolic stroke risk. The AF PRS was also associated with stroke of undetermined cause (OR per sd = 1.07, p = 0.004), but no other primary stroke subtypes (all p &gt; 0.1).ConclusionsGenetic risk for AF is associated with cardioembolic stroke, independent of clinical risk factors. Studies are warranted to determine whether AF genetic risk can serve as a biomarker for strokes caused by AF.</jats:sec

A saturated map of common genetic variants associated with human height.

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 sizes1. 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 panel2) 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

The applicability of the GHS classification criteria to nanomaterials

The report reviews the applicability of the Globally Harmonised System of Classification and Labelling of Chemicals (GHS) hazard classification criteria to manufactured nanomaterials considering the recent data generated and compiled in the nanomaterial testing program under the OECD Working Party on Manufactured Nanomaterials. In addition, data from the EU NANoREG project, the EU NanoSafety Cluster projects, REACH registrations and publicly available literature were used. The project focused on four nanomaterials and selected health hazard classes. The available test data were evaluated with respect to applicability of the test methods, applicability of the GHS criteria, identified data gaps and uncertainties and need for revision of GHS criteria or guidance. The report also highlights specific issues to be considered when classifying nanomaterials

Inhibition of T cell proliferation by selective block of Ca(2+)-activated K(+) channels

T lymphocytes express a plethora of distinct ion channels that participate in the control of calcium homeostasis and signal transduction. Potassium channels play a critical role in the modulation of T cell calcium signaling, and the significance of the voltage-dependent K channel, Kv1.3, is well established. The recent cloning of the Ca(2+)-activated, intermediate-conductance K(+) channel (IK channel) has enabled a detailed investigation of the role of this highly Ca(2+)-sensitive K(+) channel in the calcium signaling and subsequent regulation of T cell proliferation. The role IK channels play in T cell activation and proliferation has been investigated by using various blockers of IK channels. The Ca(2+)-activated K(+) current in human T cells is shown by the whole-cell voltage-clamp technique to be highly sensitive to clotrimazole, charybdotoxin, and nitrendipine, but not to ketoconazole. Clotrimazole, nitrendipine, and charybdotoxin block T cell activation induced by signals that elicit a rise in intracellular Ca(2+)—e.g., phytohemagglutinin, Con A, and antigens such as Candida albicans and tetanus toxin in a dose-dependent manner. The release of IFN-γ from activated T cells is also inhibited after block of IK channels by clotrimazole. Clotrimazole and cyclosporin A act synergistically to inhibit T cell proliferation, which confirms that block of IK channels affects the process downstream from T cell receptor activation. We suggest that IK channels constitute another target for immune suppression