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

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

PVA-PDMS-Stearic acid composite nanofibrous mats with improved mechanical behavior for selective filtering applications

In this work, we report a facile way to fabricate composite nanofibrous mats of polyvinyl alcohol (PVA), polydimethylsiloxane (PDMS), and stearic acid (SA) by employing the electrospinning-technique, with PDMS fraction ranging from 40w% to nearly 80w%. The results show that for a predetermined fraction of PVA and SA, incorporation of an optimal amount of PDMS is necessary for which the mats exhibit the best mechanical behavior. Beyond this optimal PDMS fraction, the mechanical properties of the composite mats deteriorate. This result has been attributed to the ability of the SA molecules to mediate binding between the PVA and PDMS long-chain molecules via van-der-Waals bonding. The morphological, structural, mechanical, and thermal characterizations respectively using SEM, XRD, DMA/tensile test, and DSC lend support to this explanation. By this method, it is possible to control the hydrophilicity/oleophilicity of the mats, and the mats show an excellent selective permeability to oil as compared to water and successfully filter water from a water-in-oil emulsion. Incorporation of SA not only serves to aid in electrospinning of a PDMS-rich nanofibrous mat with good mechanical strength and control over hydrophilicity/oleophilicity, but also has a potential use in fabricating sheets impregnated with phase change materials for thermal energy storage