LPC$$_\mathrm{}$$: A local power controller using the frequency scheduling approach for virtualized servers

For more than a decade, the power consumption of data centers has been addressed from different perspectives. Many solutions have been proposed to reduce (or optimize) this power consumption, such as controlling the operation of the servers in data centers. However, these approaches have not yet reached their optimum goals. Existing power control solutions using CPU frequency with an ad hoc or frequency modulator approach are not sufficient. In this paper, we review the power consumption effects of different configuration settings applied to the server’s CPU. We propose our local power controller using frequency scheduling (LPC\(_\mathrm{FreqSchd}\)), which is a server-level power controller that depends on an extended gain scheduling technique. Our proposed LPC\(_\mathrm{FreqSchd}\) considers the impact of different CPU configuration settings that are typically not considered simultaneously, such as the allocated CPU credits and CPU frequency level. Through a real experimental test bed, our LPC\(_\mathrm{FreqSchd}\) exhibits effective power management of different types of machines and outperforms other existing approaches, such as ad hoc and frequency modulation, when the power budget is low. Moreover, our proposed LPC\(_\mathrm{FreqSchd}\) has a very lightweight control actuation overhead compared with other approaches: approximately \(1/10 \mathrm{th}\) of the ad hoc approach’s overhead and \(1/100 \mathrm{th}\) of the frequency modulator approach’s overhead. This lightweight control actuation overhead reduces the power consumption overhead caused by the controller, and it could be used by other controllers, such as performance or thermal controllers running on the same server.     

Antibody-Free Magnetic Cell Sorting of Genetically Modified Primary Human CD4+ T Cells by One-Step Streptavidin Affinity Purification

Existing methods for phenotypic selection of genetically modified mammalian cells suffer disadvantages of time, cost and scalability and, where antibodies are used to bind exogenous cell surface markers for magnetic selection, typically yield cells coated with antibody-antigen complexes and beads. To overcome these limitations we have developed a method termed Antibody-Free Magnetic Cell Sorting in which the 38 amino acid Streptavidin Binding Peptide (SBP) is displayed at the cell surface by the truncated Low Affinity Nerve Growth Receptor (LNGFRF) and used as an affinity tag for one-step selection with streptavidin-conjugated magnetic beads. Cells are released through competition with the naturally occurring vitamin biotin, free of either beads or antibody-antigen complexes and ready for culture or use in downstream applications. Antibody-Free Magnetic Cell Sorting is a rapid, cost-effective, scalable method of magnetic selection applicable to either viral transduction or transient transfection of cell lines or primary cells. We have optimised the system for enrichment of primary human CD4+ T cells expressing shRNAs and exogenous genes of interest to purities of >99%, and used it to isolate cells following Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/Cas9 genome editing.     

On the inference of complex phylogenetic networks by Markov Chain Monte-Carlo

For various species, high quality sequences and complete genomes are nowadays available for many individuals. This makes data analysis challenging, as methods need not only to be accurate, but also time efficient given the tremendous amount of data to process. In this article, we introduce an efficient method to infer the evolutionary history of individuals under the multispecies coalescent model in networks (MSNC). Phylogenetic networks are an extension of phylogenetic trees that can contain reticulate nodes, which allow to model complex biological events such as horizontal gene transfer, hybridization and introgression. We present a novel way to compute the likelihood of biallelic markers sampled along genomes whose evolution involved such events. This likelihood computation is at the heart of a Bayesian network inference method called SnappNet, as it extends the Snapp method inferring evolutionary trees under the multispecies coalescent model, to networks. SnappNet is available as a package of the well-known beast 2 software.Recently, the MCMC_BiMarkers method, implemented in PhyloNet, also extended Snapp to networks. Both methods take biallelic markers as input, rely on the same model of evolution and sample networks in a Bayesian framework, though using different methods for computing priors. However, SnappNet relies on algorithms that are exponentially more time-efficient on non-trivial networks. Using simulations, we compare performances of SnappNet and MCMC_BiMarkers. We show that both methods enjoy similar abilities to recover simple networks, but SnappNet is more accurate than MCMC_BiMarkers on more complex network scenarios. Also, on complex networks, SnappNet is found to be extremely faster than MCMC_BiMarkers in terms of time required for the likelihood computation. We finally illustrate SnappNet performances on a rice data set. SnappNet infers a scenario that is consistent with previous results and provides additional understanding of rice evolution.     

Application and microbial preparation of <Emphasis Type="SmallCaps">d</Emphasis>-valine

d-Valine is an important organic chiral source and has extensive industrial application, which is used as intermediate for the synthesis of agricultural pesticides, semi-synthetic veterinary antibiotics and pharmaceutical drugs. Its derivatives have shown great activity in clinical use, such as penicillamine for the treatment of immune-deficiency diseases, and actinomycin D for antitumor therapy. Fluvalinate, a pyrethroid pesticide made from d-valine, is a broad-spectrum insecticide with low mammalian toxicity. Valnemulin, a semi-synthetic pleuromutilin derivative synthesized from d-valine, is an antibiotic for animals. Moreover, d-valine is also used in cell culture for selectively inhibiting fibroblasts proliferation. Due to its widespread application, d-valine is gaining more and more attention and some approaches for d-valine preparation have been investigated. In comparison with other approaches, microbial preparation of d-valine is more competitive and promising because of its high stereo selectivity, mild reaction conditions and environmental friendly process. So far, microbial preparation of d-valine can be mainly classified into three categories: microbial asymmetric degradation of dl-valine, microbial stereoselective hydrolysis of N-acyl-dl-valine by d-aminoacylase, and microbial specific hydrolysis of dl-5-isopropylhydantoin by d-hydantoinase coupled with d-carbamoylase. In this paper, the industrial application of d-valine and its microbial preparation are reviewed.     

Limited Tryptic Degradation of Urea Denatured Glycinin

Digestibilities of native, 5 m urea-denatured and 8 m urea-denatured glycinin were studied. Urea was removed by dialysis before digestion. The tryptic digestion of the proteins are influenced by ionic strength. Under low ionic strength condition (0 m NaCl), the proteins, even native glycinin, are well degraded. On the other hand, under high ionic strength condition (0.5 m NaCl), native glycinin resists the tryptic attack and 5 m urea-denatured glycinin is best degraded. The digestibility of 8 m urea-denatured glycinin is lower than that of 5 m urea-denatured one under the condition. The gel filtration and electrophoretic properties show that the digestion intermediate like glycinin-T (the intermediate from native glycinin) is contained in the digestion products. These suggest that the urea-denatured protein contains the almost renatured component after removal of urea. A larger amount of the glycinin-T-like protein was detected at 8 m urea denaturation than at 5 m urea. Therefore, glycinin renatures more readily from 8 m urea denaturation. Probably this is the cause of the decreased digestibility at 8 m urea denaturation.     

Alternative methods in toxicology tests: In vitro toxicity

Toxicity testing is required for new chemicals being introduced onto the market. The use of animals in evaluating chemical safety is costly and time consuming. Furthermore, there is the ethical need to develope alternative methods to reduce the required number of animals. The newinvitro assays offer numerous advantages such as speed, reproducibility and control of test conditions, and increased sensitivity. Although the dermal irritation assays might be substituted by theinvitro tests in the near future (Duffy, 1989), much work is required to evaluate organ toxicity withinvitro methods. We present data regarding the use of Balb/3T3 mice fibroblasts and primary rat hepatocytes as test systems forinvitro toxicity. The end-points we have analysed are total protein content, dye accumulation in lysosomes, reductase mytochondrial activity, intracellular content and leakage of enzymes into the medium.     

The Role of a d-Mannosyl-Lipid as an Intermediate in the Synthesis of Polysaccharide in Phaseolus aureus Seedlings

Particulate preparations from Phaseolus aureus produce a d-mannosyl-lipid when treated with GDP-d-mannose. This lipid complex appears to be an active d-mannose donor, and some investigators have proposed that its role might be an obligatory intermediate in mannan synthesis of higher plants. When the partially purified d-mannosyl-lipids, isotopically labeled in the d-mannose moiety, were treated with particulate enzymes under a variety of conditions, a negligible amount of material was produced that behaved as a polysaccharide. Endogenous, particle-bound d-mannosyl-¹⁴C-lipid prepared from P. aureus particles readily transferred d-mannose to GDP to yield GDP-d-mannose and was hydrolyzed to free d-mannose when treated briefly with 0.01 n HCl at 100 C. The d-mannosyl-lipid, therefore, exhibits active d-mannose transfer potential in its endogenous state. When endogenous glycosyl-lipid was incubated in the absence of GDP-d-mannose-¹⁴C, little or no polysaccharide was produced. It was, instead, slowly degraded to d-mannose. Addition of several different unlabeled sugar nucleotides had no effect on the results. Our studies to date, therefore, offer no evidence that the mannosyl-lipid is an obligatory precursor of polysaccharide.     

Combined use of rapid d-dimer testing and estimation of clinical probability in the diagnosis of deep vein thrombosis: systematic review

Objective To summarise the evidence supporting the use of rapid d-dimer testing combined with estimation of clinical probability to exclude the diagnosis of deep venous thrombosis among outpatients.Data sources Medline (June 1993 to December 2003), the Database of Abstracts and Reviews (DARE), and reference lists of studies in English.Selection of studies We selected 12 studies from among 84 reviewed. The selected studies included more than 5000 patients and used a rapid d-dimer assay and explicit criteria to classify cases as having low, intermediate, or high clinical probability of deep vein thrombosis of the lower extremity among consecutive outpatients.Review methods Diagnosis required objective confirmation, and untreated patients had to have at least three months of follow up. The outcome was objectively documented venous thromboembolism. Two authors independently abstracted data by using a data collection form.Results When the less sensitive SimpliRED d-dimer assay was used the three month incidence of venous thromboembolism was 0.5% (95% confidence interval 0.07% to 1.1%) among patients with a low clinical probability of deep vein thrombosis and normal d-dimer concentrations. When a highly sensitive d-dimer assay was used, the three month incidence of venous thromboembolism was 0.4% (0.04% to 1.1%) among outpatients with low or moderate clinical probability of deep vein thrombosis and a normal d-dimer concentration.Conclusions The combination of low clinical probability for deep vein thrombosis and a normal result from the SimpliRED d-dimer test safely excludes a diagnosis of acute venous thrombosis A normal result from a highly sensitive d-dimer test effectively rules out deep vein thrombosis among patients classified as having either low or moderate clinical probability of deep vein thrombosis.     

A combinatorial approach to the structure elucidation of a pyoverdine siderophore produced by a Pseudomonas putida isolate and the use of pyoverdine as a taxonomic marker for typing P. putida subspecies

The structure of a pyoverdine produced by Pseudomonas putida, W15Oct28, was elucidated by combining mass spectrometric methods and bioinformatics by the analysis of non-ribosomal peptide synthetase genes present in the newly sequenced genome. The only form of pyoverdine produced by P. putida W15Oct28 is characterized to contain α-ketoglutaric acid as acyl side chain, a dihydropyoverdine chromophore, and a 12 amino acid peptide chain. The peptide chain is unique among all pyoverdines produced by Pseudomonas subspecies strains. It was characterized as –l-Asp-l-Ala-d-AOHOrn-l-Thr-Gly-c[l-Thr(O-)-l-Hse-d-Hya-l-Ser-l-Orn-l-Hse-l-Ser-O-]. The chemical formula and the detected and calculated molecular weight of this pyoverdine are: C₆₅H₉₃N₁₇O₃₂, detected mass 1624.6404 Da, calculated mass 1624.6245. Additionally, pyoverdine structures from both literature reports and bioinformatics prediction of the genome sequenced P. putida strains are summarized allowing us to propose a scheme based on pyoverdines structures as tool for the phylogeny of P. putida. This study shows the strength of the combination of in silico analysis together with analytical data and literature mining in determining the structure of secondary metabolites such as peptidic siderophores.     

Inactivation of Complement by <Emphasis Type="SmallCaps">L</Emphasis>-Asparaginase Preparations not Correlated with Enzyme Content

DELÁGE et al.¹ recently reported that L-asparaginase (L-A) in vitro inhibits human and guinea-pig whole serum complement and that L-A is a potent activator of C1. They therefore proposed that their results may “give a clue to the severe anaphylactic reactions noted in some patients on administration of the drug”. Because guinea-pig serum contains L-A in high quantities and Deláge et al. used reagents and methods that make their data difficult to interpret on a molecular basis, we have reinvestigated the effect of L-A preparations on human and guinea-pig complement.     

Biochemical Aspects of 2-Keto-l-gulonate Accumulation from 2,5-Diketo-d-gluconate by Corynebacterium sp. and Its Mutants

Corynebacterium sp. SHS 0007 accumulated 2-keto-l-gulonate and 2-keto-d-gluconate simultaneously with 2,5-diketo-d-gluconate utilization. This strain, however, possibly metabolized 2,5- diketo-d-gluconate through two pathways leading to d-gluconate as a common intermediate: via 2- keto-d-gluconate, and via 2-keto-l-gulonate, l-idonate and 5-keto-d-gluconate. A polysaccharide- negative, 2-keto-l-gulonate-negative and 5-keto-d-gluconate-negative mutant produced only calcium 2-keto-l-gulonate from calcium 2,5-diketo-d-gluconate, in a 90.5 mol% yield. The addition of a hydrogen donor such as d-glucose was essential for its production. This mutant possessed the direct oxidation route of d-glucose to d-gluconate, the pentose cycle pathway and a possible Embden-Meyerhof-Parnas pathway, indicating that d-glucose was metabolized through these three pathways and provided NADPH for the reduction of 2,5-diketo-d-gluconate.     

Estimating Breeding Values With Molecular Relatedness and Reconstructed Pedigrees in Natural Mating Populations of Common Sole,Solea Solea

Captive populations where natural mating in groups is used to obtain offspring typically yield unbalanced population structures with highly skewed parental contributions and unknown pedigrees. Consequently, for genetic parameter estimation, relationships need to be reconstructed or estimated using DNA marker data. With missing parents and natural mating groups, commonly used pedigree reconstruction methods are not accurate and lead to loss of data. Relatedness estimators, however, infer relationships between all animals sampled. In this study, we compared a pedigree relatedness method and a relatedness estimator (“molecular relatedness”) method using accuracy of estimated breeding values. A commercial data set of common sole, Solea solea, with 51 parents and 1953 offspring (“full data set”) was used. Due to missing parents, for 1338 offspring, a pedigree could be reconstructed with 10 microsatellite markers (“reduced data set”). Cross-validation of both methods using the reduced data set showed an accuracy of estimated breeding values of 0.54 with pedigree reconstruction and 0.55 with molecular relatedness. Accuracy of estimated breeding values increased to 0.60 when applying molecular relatedness to the full data set. Our results indicate that pedigree reconstruction and molecular relatedness predict breeding values equally well in a population with skewed contributions to families. This is probably due to the presence of few large full-sib families. However, unlike methods with pedigree reconstruction, molecular relatedness methods ensure availability of all genotyped selection candidates, which results in higher accuracy of breeding value estimation.To estimate genetic parameters, additive genetic relationships between individuals are inferred from known pedigrees (Falconer and Mackay 1996; Lynch and Walsh 1997). However, in natural populations (Ritland 2000; Thomas et al. 2002) and in captive species where natural mating in groups is used to obtain offspring (Brown et al. 2005; Fessehaye et al. 2006; Blonk et al. 2009) pedigrees are reconstructed. In these populations there is no control on mating structure, and typically unbalanced population structures with highly skewed parental contributions are obtained (Bekkevold et al. 2002; Brown et al. 2005; Fessehaye et al. 2006; Blonk et al. 2009). To reconstruct pedigrees, parental allocation methods are often used (Marshall et al. 1998; Avise et al. 2002; Duchesne et al. 2002). These methods require that all parents be known. For situations where parental information is not available, numerous DNA-marker-based methods for estimating molecular relatedness have been developed (Lynch 1988; Queller and Goodnight 1989; Ritland 2000; Toro et al. 2002). These relatedness estimators determine relationship values between individuals on a continuous scale. Evaluation of relatedness estimators within real and simulated data in both plants and animals (e.g., see Van de Casteele et al. 2001 ; Milligan 2003; Oliehoek et al. 2006; Rodríguez-Ramilo et al. 2007; Bink et al. 2008) has generally focused on bias and sampling error of estimated genetic variances or relatedness values. Relatively little attention has been paid to their efficiency for estimation of breeding values.Two types of relatedness estimators are currently available: method-of-moments estimators and maximum-likelihood estimators. Method-of-moments estimators (e.g., Queller and Goodnight 1989; Li et al. 1993; Ritland 1996; Lynch and Ritland 1999; Toro et al. 2002) determine relationships while calculating sharing of alleles between pairs in different ways. A variant of method-of-moments estimators is the transformation of continuous relatedness values to categorical genealogical relationships using “explicit pedigree reconstruction” (Fernández and Toro 2006) or thresholds (Rodríguez-Ramilo et al. 2007). However, correlations of transformed coancestries with known genealogical coancestries are low (Rodríguez-Ramilo et al. 2007). Several studies have compared different method-of-moments estimators but none revealed one single best estimator (Van de Casteele et al. 2001; Oliehoek et al. 2006; Rodríguez-Ramilo et al. 2007; Bink et al. 2008).Maximum-likelihood (ML) approaches classify animals into a limited number of relationship classes (Mousseau et al. 1998; Thomas et al. 2002; Wang 2004; Herbinger et al. 2006; Anderson and Weir 2007). For each pair a likelihood to fall into a possible relatedness class (e.g., full sib vs. unrelated) is calculated given its genotype and phenotype. ML techniques combined with a Markov chain Monte Carlo approach reconstruct groups with specific relationships jointly and are therefore more efficient than other ML approaches. To minimize standard errors, all discussed ML methods require balanced population structures, large sibling groups, and a large variance of relatedness (Thomas et al. 2002; Wang 2004; Anderson and Weir 2007). Therefore, these methods may not be suitable for natural mating systems.Unlike parental allocation methods, a benefit from relatedness estimators is that essentially all selection candidates are maintained for breeding value estimation, even with missing parents. The question is, however, whether such relatedness estimators also give accurate breeding values to perform selection.In this study, we test suitability of a relatedness estimator to obtain breeding values in a population of common sole, Solea solea (n = 1953) obtained by natural mating. First, we estimate breeding values using pedigree relatedness of animals for which a pedigree could be reconstructed (using parental allocation). This data set (n = 1338) is further referred to as “reduced data set.” We compare results with estimated breeding values using a simple but robust method-of-moments relatedness estimator: “molecular relatedness” (Toro et al. 2002, 2003). Next, we estimate breeding values using molecular relatedness in the full data set (n = 1953). Results show that accuracies of estimated breeding values obtained with molecular relatedness and pedigree relatedness are comparable. Accuracy increases when breeding values are estimated with molecular relatedness in the full data set. This implies that a molecular relatedness estimator can be used to estimate breeding values in captive natural mating populations.     

Biodegradation pathway of l-glutamatediacetate by Rhizobium radiobacter strain BG-1

An aerobic bacterium was isolated from activated sludge in a medium containing l-glutamate-N,N-diacetate (l-GLDA) as sole carbon and energy source. The isolate was identified as a Rhizobium radiobacter species. Besides l-GLDA, the strain utilized nitrilotriacetate (NTA) and proposed intermediates in l-GLDA metabolism such as glyoxylate and l-glutamate. l-GLDA-grown cells oxidized l-GLDA, l-glutamate but not iminodiacetate (IDA), and trans-ketoglutaconate, indicating removal of a carboxymethyl group as an initial degradation reaction. The removal of the first carboxymethyl group of l-GLDA is catalyzed by an NADH-dependent mono-oxygenase. The oxidative deamination of l-glutamate by a dehydrogenase resulting in the formation of oxoglutarate was also detected in cell-free extracts of R. radiobacter sp. A pathway for the metabolism of l-GLDA R. radiobacter sp. is proposed: First, l-GLDA leads to l-glutamate-N-monoacetate (l-GLMA) which in turn leads to l-glutamate. Then, l-glutamate leads to oxoglutarate, an intermediate of the TCA cycle.     

Genome-scale identification of Caenorhabditis elegans regulatory elements by tiling-array mapping of DNase I hypersensitive sites

Background

High throughput techniques have generated a huge set of biological data, which are deposited in various databases. Efficient exploitation of these databases is often hampered by a lack of appropriate tools, which allow easy and reliable identification of genes that miss functional characterization but are correlated with specific biological conditions (e.g. organotypic expression).

Results

We have developed a simple algorithm (DGSA = Database-dependent Gene Selection and Analysis) to identify genes with unknown functions involved in organ development concentrating on the heart. Using our approach, we identified a large number of yet uncharacterized genes, which are expressed during heart development. An initial functional characterization of genes by loss-of-function analysis employing morpholino injections into zebrafish embryos disclosed severe developmental defects indicating a decisive function of selected genes for developmental processes.

Conclusion

We conclude that DGSA is a versatile tool for database mining allowing efficient selection of uncharacterized genes for functional analysis.     

Importing statistical measures into Artemis enhances gene identification in the <Emphasis Type="Italic">Leishmania</Emphasis> genome project

Background

Seattle Biomedical Research Institute (SBRI) as part of the Leishmania Genome Network (LGN) is sequencing chromosomes of the trypanosomatid protozoan species Leishmania major. At SBRI, chromosomal sequence is annotated using a combination of trained and untrained non-consensus gene-prediction algorithms with ARTEMIS, an annotation platform with rich and user-friendly interfaces.

Results

Here we describe a methodology used to import results from three different protein-coding gene-prediction algorithms (GLIMMER, TESTCODE and GENESCAN) into the ARTEMIS sequence viewer and annotation tool. Comparison of these methods, along with the CODON USAGE algorithm built into ARTEMIS, shows the importance of combining methods to more accurately annotate the L. major genomic sequence.

Conclusion

An improvised and powerful tool for gene prediction has been developed by importing data from widely-used algorithms into an existing annotation platform. This approach is especially fruitful in the Leishmania genome project where there is large proportion of novel genes requiring manual annotation.

     

Prediction of Genetic Values of Quantitative Traits in Plant Breeding Using Pedigree and Molecular Markers

The availability of dense molecular markers has made possible the use of genomic selection (GS) for plant breeding. However, the evaluation of models for GS in real plant populations is very limited. This article evaluates the performance of parametric and semiparametric models for GS using wheat (Triticum aestivum L.) and maize (Zea mays) data in which different traits were measured in several environmental conditions. The findings, based on extensive cross-validations, indicate that models including marker information had higher predictive ability than pedigree-based models. In the wheat data set, and relative to a pedigree model, gains in predictive ability due to inclusion of markers ranged from 7.7 to 35.7%. Correlation between observed and predictive values in the maize data set achieved values up to 0.79. Estimates of marker effects were different across environmental conditions, indicating that genotype × environment interaction is an important component of genetic variability. These results indicate that GS in plant breeding can be an effective strategy for selecting among lines whose phenotypes have yet to be observed.PEDIGREE-BASED prediction of genetic values based on the additive infinitesimal model (Fisher 1918) has played a central role in genetic improvement of complex traits in plants and animals. Animal breeders have used this model for predicting breeding values either in a mixed model (best linear unbiased prediction, BLUP) (Henderson 1984) or in a Bayesian framework (Gianola and Fernando 1986). More recently, plant breeders have incorporated pedigree information into linear mixed models for predicting breeding values (Crossa et al. 2006, 2007; Oakey et al. 2006; Burgueño et al. 2007; Piepho et al. 2007).The availability of thousands of genome-wide molecular markers has made possible the use of genomic selection (GS) for prediction of genetic values (Meuwissen et al. 2001) in plants (e.g., Bernardo and Yu 2007; Piepho 2009; Jannink et al. 2010) and animals (Gonzalez-Recio et al. 2008; VanRaden et al. 2008; Hayes et al. 2009; de los Campos et al. 2009a). Implementing GS poses several statistical and computational challenges, such as how models can cope with the curse of dimensionality, colinearity between markers, or the complexity of quantitative traits. Parametric (e.g., Meuwissen et al. 2001) and semiparametric (e.g., Gianola et al. 2006; Gianola and van Kaam 2008) methods address these problems differently.In standard genetic models, phenotypic outcomes, , are viewed as the sum of a genetic value, , and a model residual, ; that is, . In parametric models for GS, is described as a regression on marker covariates (j = 1,  …  , p molecular markers) of the form , such that(or , in matrix notation), where is the regression of on the jth marker covariate .Estimation of via multiple regression by ordinary least squares (OLS) is not feasible when p > n. A commonly used alternative is to estimate marker effects jointly using penalized methods such as ridge regression (Hoerl and Kennard 1970) or the Least Absolute Shrinkage and Selection Operator (LASSO) (Tibshirani 1996) or their Bayesian counterpart. This approach yields greater accuracy of estimated genetic values and can be coupled with geostatistical techniques commonly used in plant breeding to model multienvironments trials (Piepho 2009).In ridge regression (or its Bayesian counterpart) the extent of shrinkage is homogeneous across markers, which may not be appropriate if some markers are located in regions that are not associated with genetic variance, while markers in other regions may be linked to QTL (Goddard and Hayes 2007). To overcome this limitation, many authors have proposed methods that use marker-specific shrinkage. In a Bayesian setting, this can be implemented using priors of marker effects that are mixtures of scaled-normal densities. Examples of this are methods Bayes A and Bayes B of Meuwissen et al. (2001) and the Bayesian LASSO of Park and Casella (2008).An alternative to parametric regressions is to use semiparametric methods such as reproducing kernel Hilbert spaces (RKHS) regression (Gianola and van Kaam 2008). The Bayesian RKHS regression regards genetic values as random variables coming from a Gaussian process centered at zero and with a (co)variance structure that is proportional to a kernel matrix K (de los Campos et al. 2009b); that is, , where , are vectors of marker genotypes for the ith and jth individuals, respectively, and is a positive definite function evaluated in marker genotypes. In a finite-dimensional setting this amounts to modeling the vector of genetic values, , as multivariate normal; that is, where is a variance parameter. One of the most attractive features of RKHS regression is that the methodology can be used with almost any information set (e.g., covariates, strings, images, graphs). A second advantage is that with RKHS the model is represented in terms of n unknowns, which gives RKHS a great computational advantage relative to some parametric methods, especially when p ≫ n.This study presents an evaluation of several methods for GS, using two extensive data sets. One contains phenotypic records of a series of wheat trials and recently generated genomic data. The other data set pertains to international maize trials in which different traits were measured in maize lines evaluated under severe drought and well-watered conditions.     

Première découverte d’un squelette de Channidae (Poisson téléostéen) dans le Miocène inférieur d’Illerkirchberg, près d’Ulm (Wurtemberg, Allemagne)

A skeleton of a Channid fish is reported for the first time from the European Lower Miocene. Because of the lack of any appropriate diagnostic character, it is nevertheless impossible to determine if it belongs either to the Asiatic genusChanna Scopoli or to the African one,Parachanna Teugels &Daget. For this reason, its palaeobiogeographical significance remains unclear. Otoliths (sagitta) that very likely belong to the same Channid species have been described from Illerkirchberg asChanna elliptica (vonSalis). They are compared to the sagitta from recent species of the generaChanna (Scopoli) andParachanna (Teugels &Daget). Their morphological characters are intermediate between those characterizing the sagitta of both genera.     

Reducing GWAS Complexity

Genome-wide association studies (GWAS) have revealed numerous genomic 'hits' associated with complex phenotypes. In most cases these hits, along with surrogate genetic variation as measure by numerous single nucleotide polymorphisms (SNPs) that are in linkage disequilibrium, are not in coding genes making assignment of functionality or causality intractable. Here we propose that fine-mapping along with the matching of risk SNPs at chromatin biofeatures lessen this complexity by reducing the number of candidate functional/causal SNPs. For example, we show here that only on average 2 SNPs per prostate cancer risk locus are likely candidates for functionality/causality; we further propose that this manageable number should be taken forward in mechanistic studies. The candidate SNPs can be looked up for each prostate cancer risk region in 2 recent publications in 2015^1,2 Han Y, Hazelett DJ, Wiklund F, Schumacher FR, Stram DO, Berndt SI, Wang Z, Rand KA, Hoover RN, Machiela MJ, et al. Integration of Multiethnic Fine-mapping and Genomic Annotation to Prioritize Candidate Functional SNPs at Prostate Cancer Susceptibility Regions. Hum Mol Genet 2015; 24(19):5603–18. Amin Al Olama A, Dadaev T, Hazelett DJ, Li Q, Leongamornlert D, Saunders EJ, Stephens S, Cieza-Borrella C, Whitmore I, Benlloch Garcia S, et al. Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans. Hum Mol Genet 2015; 24(19):5589–602.  from our groups.