Discovery and fine mapping of serum protein loci through transethnic meta-analysis.

Many disorders are associated with altered serum protein concentrations, including malnutrition, cancer, and cardiovascular, kidney, and inflammatory diseases. Although these protein concentrations are highly heritable, relatively little is known about their underlying genetic determinants. Through transethnic meta-analysis of European-ancestry and Japanese genome-wide association studies, we identified six loci at genome-wide significance (p < 5 × 10(-8)) for serum albumin (HPN-SCN1B, GCKR-FNDC4, SERPINF2-WDR81, TNFRSF11A-ZCCHC2, FRMD5-WDR76, and RPS11-FCGRT, in up to 53,190 European-ancestry and 9,380 Japanese individuals) and three loci for total protein (TNFRS13B, 6q21.3, and ELL2, in up to 25,539 European-ancestry and 10,168 Japanese individuals). We observed little evidence of heterogeneity in allelic effects at these loci between groups of European and Japanese ancestry but obtained substantial improvements in the resolution of fine mapping of potential causal variants by leveraging transethnic differences in the distribution of linkage disequilibrium. We demonstrated a functional role for the most strongly associated serum albumin locus, HPN, for which Hpn knockout mice manifest low plasma albumin concentrations. Other loci associated with serum albumin harbor genes related to ribosome function, protein translation, and proteasomal degradation, whereas those associated with serum total protein include genes related to immune function. Our results highlight the advantages of transethnic meta-analysis for the discovery and fine mapping of complex trait loci and have provided initial insights into the underlying genetic architecture of serum protein concentrations and their association with human disease.

Multi-scale support vector algorithms for hot spot detection and modelling

The algorithmic approach to data modelling has developed rapidly these last years, in particular methods based on data mining and machine learning have been used in a growing number of applications. These methods follow a data-driven methodology, aiming at providing the best possible generalization and predictive abilities instead of concentrating on the properties of the data model. One of the most successful groups of such methods is known as Support Vector algorithms. Following the fruitful developments in applying Support Vector algorithms to spatial data, this paper introduces a new extension of the traditional support vector regression (SVR) algorithm. This extension allows for the simultaneous modelling of environmental data at several spatial scales. The joint influence of environmental processes presenting different patterns at different scales is here learned automatically from data, providing the optimum mixture of short and large-scale models. The method is adaptive to the spatial scale of the data. With this advantage, it can provide efficient means to model local anomalies that may typically arise in situations at an early phase of an environmental emergency. However, the proposed approach still requires some prior knowledge on the possible existence of such short-scale patterns. This is a possible limitation of the method for its implementation in early warning systems. The purpose of this paper is to present the multi-scale SVR model and to illustrate its use with an application to the mapping of Cs137 activity given the measurements taken in the region of Briansk following the Chernobyl accident.

Plasmodium falciparum merozoite surface protein 2: epitope mapping and biological activity analysis of specific antibodies

Background: Plasmodium falciparum(P. falciparum) merozoite surfaceprotein 2 (MSP-2) is one of bloodstage proteins that are associated withprotection from malaria. MSP-2 consistsof a highly polymorphic centralrepeat region flanked by a dimorphicregion that defines the two allelicfamilies, 3D7 and FC27; N- and Cterminalregions are conserved domains.Long synthetic peptides (LSP)representing the two allelic familiesof MSP-2 and constant regions arerecognized by sera from donors livingin endemic areas; and specific antibodies(Abs) are associated with protectionand active in antibody dependentcellular inhibition (ADCI) in vitro.However, the fine specificity ofAb response to the two allelic familiesof MSP-2 is unknown. Methods: Peptidesrepresenting dimorphic regionof 3D7 and FC27 families and theirC-terminal (common fragment to thetwo families) termed 3D7-D (88 aa),FC27-D (48 aa) and C (40 aa) respectivelywere synthesized. Overlapping20 mer peptides covering dimorphicand constant regions of two familieswere also synthesized for epitopemapping. Human sera were obtainedfrom donors living in malaria endemicareas. SpecificDand CregionsAbs were purified from single or poolhuman sera. Sera from mice were obtainedafter immunization with thetwo families LSP mixture in three differentadjuvants: alhydrogel (Alum),Glucopyranosyl Lipid Adjuvant-Stableoil-in-water Emulsion (GLA-SE)and Virosome. For ADCI, P. falciparum(strain 3D7) parasite wasmaintained in culture at 0.5% parasitemiaand 4% hematocrit in air tightbox at love oxygen (2%) and 37 ºC.Results: We identified several epitopesfrom the dimorphic and constantregions of both families of MSP-2, inmice and humans (adults and children).In human, most recognizedepitopes were the same in differentendemic regions for each domain ofthe two families of MSP-2. In mice,the differential recognition of epitopewas depending on the strain of mouseand interestingly on the adjuvantused. GLA-SE and alum as adjuvantswere more often associated with therecognition of multiple epitopes thanvirosomes. Epitope-specific Abs recognizednative merozoites of P.falciparum and were active in ADCIto block development of parasite.Conclusion: The delineation of a limitednumber of epitopes could be exploitedto develop MSP-2 vaccinesactive on both allelic families ofMSP-2.