Hundreds of variants clustered in genomic loci and biological pathways affect human height.

Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P < 0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented among variants that alter amino-acid structure of proteins and expression levels of nearby genes. Our data explain approximately 10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to approximately 16% of phenotypic variation (approximately 20% of heritable variation). Although additional approaches are needed to dissect the genetic architecture of polygenic human traits fully, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.

Automated scoring of AFLPs using RawGeno v 2.0, a free R CRAN library.

Amplified Fragment Length Polymorphisms (AFLPs) are a cheap and efficient protocol for generating large sets of genetic markers. This technique has become increasingly used during the last decade in various fields of biology, including population genomics, phylogeography, and genome mapping. Here, we present RawGeno, an R library dedicated to the automated scoring of AFLPs (i.e., the coding of electropherogram signals into ready-to-use datasets). Our program includes a complete suite of tools for binning, editing, visualizing, and exporting results obtained from AFLP experiments. RawGeno can either be used with command lines and program analysis routines or through a user-friendly graphical user interface. We describe the whole RawGeno pipeline along with recommendations for (a) setting the analysis of electropherograms in combination with PeakScanner, a program freely distributed by Applied Biosystems; (b) performing quality checks; (c) defining bins and proceeding to scoring; (d) filtering nonoptimal bins; and (e) exporting results in different formats.

Association of genetic variation with systolic and diastolic blood pressure among African Americans: the Candidate Gene Association Resource study.

The prevalence of hypertension in African Americans (AAs) is higher than in other US groups; yet, few have performed genome-wide association studies (GWASs) in AA. Among people of European descent, GWASs have identified genetic variants at 13 loci that are associated with blood pressure. It is unknown if these variants confer susceptibility in people of African ancestry. Here, we examined genome-wide and candidate gene associations with systolic blood pressure (SBP) and diastolic blood pressure (DBP) using the Candidate Gene Association Resource (CARe) consortium consisting of 8591 AAs. Genotypes included genome-wide single-nucleotide polymorphism (SNP) data utilizing the Affymetrix 6.0 array with imputation to 2.5 million HapMap SNPs and candidate gene SNP data utilizing a 50K cardiovascular gene-centric array (ITMAT-Broad-CARe [IBC] array). For Affymetrix data, the strongest signal for DBP was rs10474346 (P= 3.6 × 10(-8)) located near GPR98 and ARRDC3. For SBP, the strongest signal was rs2258119 in C21orf91 (P= 4.7 × 10(-8)). The top IBC association for SBP was rs2012318 (P= 6.4 × 10(-6)) near SLC25A42 and for DBP was rs2523586 (P= 1.3 × 10(-6)) near HLA-B. None of the top variants replicated in additional AA (n = 11 882) or European-American (n = 69 899) cohorts. We replicated previously reported European-American blood pressure SNPs in our AA samples (SH2B3, P= 0.009; TBX3-TBX5, P= 0.03; and CSK-ULK3, P= 0.0004). These genetic loci represent the best evidence of genetic influences on SBP and DBP in AAs to date. More broadly, this work supports that notion that blood pressure among AAs is a trait with genetic underpinnings but also with significant complexity.

Estimating biophysical variable dependences with kernels

This paper introduces a nonlinear measure of dependence between random variables in the context of remote sensing data analysis. The Hilbert-Schmidt Independence Criterion (HSIC) is a kernel method for evaluating statistical dependence. HSIC is based on computing the Hilbert-Schmidt norm of the cross-covariance operator of mapped samples in the corresponding Hilbert spaces. The HSIC empirical estimator is very easy to compute and has good theoretical and practical properties. We exploit the capabilities of HSIC to explain nonlinear dependences in two remote sensing problems: temperature estimation and chlorophyll concentration prediction from spectra. Results show that, when the relationship between random variables is nonlinear or when few data are available, the HSIC criterion outperforms other standard methods, such as the linear correlation or mutual information.

The αVβ3/αVβ5 integrin inhibitor cilengitide augments tumor response to melphalan isolated limb perfusion in a sarcoma model.

Isolated limb perfusion (ILP) with melphalan and tumor necrosis factor (TNF)-α is used to treat bulky, locally advanced melanoma and sarcoma. However, TNF toxicity suggests a need for better-tolerated drugs. Cilengitide (EMD 121974), a novel cyclic inhibitor of alpha-V integrins, has both anti-angiogenic and direct anti-tumor effects and is a possible alternative to TNF in ILP. In this study, rats bearing a hind limb soft tissue sarcoma underwent ILP using different combinations of melphalan, TNF and cilengitide in the perfusate. Further groups had intra-peritoneal (i.p.) injections of cilengitide or saline 2 hr before and 3 hr after ILP. A 77% response rate (RR) was seen in animals treated i.p. with cilengitide and perfused with melphalan plus cilengitide. The RR was 85% in animals treated i.p. with cilengitide and ILP using melphalan plus both TNF and cilengitide. Both RRs were significantly greater than those seen with melphalan or cilengitide alone. Histopathology showed that high RRs were accompanied by disruption of tumor vascular endothelium and tumor necrosis. Compared with ILP using melphalan alone, the addition of cilengitide resulted in a three to sevenfold increase in melphalan concentration in tumor but not in muscle in the perfused limb. Supportive in vitro studies indicate that cilengitide both inhibits tumor cell attachment and increases endothelial permeability. Since cilengitide has low toxicity, these data suggest the agent is a good alternative to TNF in the ILP setting.

Vascular Effects of RWJ-676070, a Selective Combined V-1a/V-2 Vasopressin Receptor Antagonist

In recent years, several vasopressin antagonists have been developed that block V-1 receptors either selectively or nonselectively.(1,2) To date, one combined V-1/V-2 antagonist (primarily a V-2 antagonist, as determined on the basis of human receptor binding data), conivaptan, has been approved for the treatment of euvolemic hyponatremia.(3,4) We have previously shown that the vascular properties of a vasopressin V-1 antagonist can be investigated safely and reliably in healthy subjects. We used the measurement of skin blood flow after intradermic injection of exogenous arginine vasopressin on a skin area prevasodilated with calcitonin gene-related peptide (CGRP).(3,5) This technique enables the documentation of the dose-dependent effects of vasopressin or vasopressin antagonists. In this study, we have characterized the V-1a pharmacodynamic profile of increasing doses of RWJ-676070, a new orally active dual V-1a/V-2 receptor antagonist, in healthy subjects.(5)

Multiple Kernel Learning of Environmental Data. Case study: Analysis and Mapping of Wind Fields

The paper presents the Multiple Kernel Learning (MKL) approach as a modelling and data exploratory tool and applies it to the problem of wind speed mapping. Support Vector Regression (SVR) is used to predict spatial variations of the mean wind speed from terrain features (slopes, terrain curvature, directional derivatives) generated at different spatial scales. Multiple Kernel Learning is applied to learn kernels for individual features and thematic feature subsets, both in the context of feature selection and optimal parameters determination. An empirical study on real-life data confirms the usefulness of MKL as a tool that enhances the interpretability of data-driven models.

Role of the V-ATPase in regulation of the vacuolar fission-fusion equilibrium.

Like numerous other eukaryotic organelles, the vacuole of the yeast Saccharomyces cerevisiae undergoes coordinated cycles of membrane fission and fusion in the course of the cell cycle and in adaptation to environmental conditions. Organelle fission and fusion processes must be balanced to ensure organelle integrity. Coordination of vacuole fission and fusion depends on the interactions of vacuolar SNARE proteins and the dynamin-like GTPase Vps1p. Here, we identify a novel factor that impinges on the fusion-fission equilibrium: the vacuolar H(+)-ATPase (V-ATPase) performs two distinct roles in vacuole fission and fusion. Fusion requires the physical presence of the membrane sector of the vacuolar H(+)-ATPase sector, but not its pump activity. Vacuole fission, in contrast, depends on proton translocation by the V-ATPase. Eliminating proton pumping by the V-ATPase either pharmacologically or by conditional or constitutive V-ATPase mutations blocked salt-induced vacuole fragmentation in vivo. In living cells, fission defects are epistatic to fusion defects. Therefore, mutants lacking the V-ATPase display large single vacuoles instead of multiple smaller vacuoles, the phenotype that is generally seen in mutants having defects only in vacuolar fusion. Its dual involvement in vacuole fission and fusion suggests the V-ATPase as a potential regulator of vacuolar morphology and membrane dynamics.