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Many modern genomic data analyses require implementing regressions where the number of parameters (p, e.g., the number of marker effects) exceeds sample size (n). Implementing these large-p-with-small-n regressions poses several statistical and computational challenges, some of which can be confronted using Bayesian methods. This approach allows integrating various parametric and nonparametric shrinkage and variable selection procedures in a unified and consistent manner. The BGLR R-package implements a large collection of Bayesian regression models, including parametric variable selection and shrinkage methods and semiparametric procedures (Bayesian reproducing kernel Hilbert spaces regressions, RKHS). The software was originally developed for genomic applications; however, the methods implemented are useful for many nongenomic applications as well. The response can be continuous (censored or not) or categorical (either binary or ordinal). The algorithm is based on a Gibbs sampler with scalar updates and the implementation takes advantage of efficient compiled C and Fortran routines. In this article we describe the methods implemented in BGLR, present examples of the use of the package, and discuss practical issues emerging in real-data analysis.

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New methods that incorporate the main and interaction effects of high-dimensional markers and of high-dimensional environmental covariates gave increased prediction accuracy of grain yield in wheat across and within environments. In most agricultural crops the effects of genes on traits are modulated by environmental conditions, leading to genetic by environmental interaction (G × E). Modern genotyping technologies allow characterizing genomes in great detail and modern information systems can generate large volumes of environmental data. In principle, G × E can be accounted for using interactions between markers and environmental covariates (ECs). However, when genotypic and environmental information is high dimensional, modeling all possible interactions explicitly becomes infeasible. In this article we show how to model interactions between high-dimensional sets of markers and ECs using covariance functions. The model presented here consists of (random) reaction norm where the genetic and environmental gradients are described as linear functions of markers and of ECs, respectively. We assessed the proposed method using data from Arvalis, consisting of 139 wheat lines genotyped with 2,395 SNPs and evaluated for grain yield over 8 years and various locations within northern France. A total of 68 ECs, defined based on five phases of the phenology of the crop, were used in the analysis. Interaction terms accounted for a sizable proportion (16 %) of the within-environment yield variance, and the prediction accuracy of models including interaction terms was substantially higher (17-34 %) than that of models based on main effects only. Breeding for target environmental conditions has become a central priority of most breeding programs. Methods, like the one presented here, that can capitalize upon the wealth of genomic and environmental information available, will become increasingly important.

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Genotyping-by-sequencing (GBS) technologies have proven capacity for delivering large numbers of marker genotypes with potentially less ascertainment bias than standard single nucleotide polymorphism (SNP) arrays. Therefore, GBS has become an attractive alternative technology for genomic selection. However, the use of GBS data poses important challenges, and the accuracy of genomic prediction using GBS is currently undergoing investigation in several crops, including maize, wheat, and cassava. The main objective of this study was to evaluate various methods for incorporating GBS information and compare them with pedigree models for predicting genetic values of lines from two maize populations evaluated for different traits measured in different environments (experiments 1 and 2). Given that GBS data come with a large percentage of uncalled genotypes, we evaluated methods using nonimputed, imputed, and GBS-inferred haplotypes of different lengths (short or long). GBS and pedigree data were incorporated into statistical models using either the genomic best linear unbiased predictors (GBLUP) or the reproducing kernel Hilbert spaces (RKHS) regressions, and prediction accuracy was quantified using cross-validation methods. The following results were found: relative to pedigree or marker-only models, there were consistent gains in prediction accuracy by combining pedigree and GBS data; there was increased predictive ability when using imputed or nonimputed GBS data over inferred haplotype in experiment 1, or nonimputed GBS and information-based imputed short and long haplotypes, as compared to the other methods in experiment 2; the level of prediction accuracy achieved using GBS data in experiment 2 is comparable to those reported by previous authors who analyzed this data set using SNP arrays; and GBLUP and RKHS models with pedigree with nonimputed and imputed GBS data provided the best prediction correlations for the three traits in experiment 1, whereas for experiment 2 RKHS provided slightly better prediction than GBLUP for drought-stressed environments, and both models provided similar predictions in well-watered environments.

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The BLR (Bayesian linear regression) package of R implements several Bayesian regression models for continuous traits. The package was originally developed for implementing the Bayesian LASSO (BL) of Park and Casella (J Am Stat Assoc 103(482):681-686, 2008), extended to accommodate fixed effects and regressions on pedigree using methods described by de los Campos et al. (Genetics 182(1):375-385, 2009). In 2010 we further developed the code into an R-package, reprogrammed some internal aspects of the algorithm in the C language to increase computational speed, and further documented the package (Plant Genome J 3(2):106-116, 2010). The first version of BLR was launched in 2010 and since then the package has been used for multiple publications and is being routinely used for genomic evaluations in some animal and plant breeding programs. In this article we review the models implemented by BLR and illustrate the use of the package with examples.

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OBJECTIVE: To report the transcervical and transisthmic evacuation of a dichorionic interstitial twin pregnancy guided by ultrasound. DESIGN: Case study. SETTING: Fetal medicine unit of university hospital. PATIENT(S): Dichorionic twin fetuses. INTERVENTION(S): Three-dimensional/four-dimensional (3D/4D) ultrasound-guided transcervical and transisthmic evacuation under direct laparoscopic supervision. MAIN OUTCOME MEASURE(S): Maternal clinical outcome. RESULT(S): A dichorionic interstitial twin pregnancy was successfully evacuated without complications. CONCLUSION(S): The safety and effectiveness of ultrasound-guided transcervical and transisthmic evacuation of dichorionic interstitial twin pregnancy warrants further evaluation.

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El embarazo intersticial se ha visto incrementado en las últimas décadas como consecuencia del aumento de técnicas de reproducción asistida. A pesar del diagnóstico cada vez más temprano y el desarrollo de tratamientos cada vez más conservadores, presenta una morbilidad y mortalidad importantes. Presentamos 2 casos de embarazo intersticial tratado mediante legrado por aspiración bajo control ecográfico. En ambos, el procedimiento fue rápido, con mínimo sangrado y sin complicaciones. El legrado uterino ecoguiado es una alternativa efectiva y segura como tratamiento del embarazo ectópico intersticial (AU)

Interstitial pregnancy has increased in the last few decades due to greater use of assisted reproductive technology. Despite early diagnosis and the development of increasingly conservative treatment, maternal morbidity and mortality remain high. We report two cases of interstitial pregnancy treated by ultrasound-guided transcervical suction curettage. In both cases, the procedure was quick, bleeding was minimal and there were no complications. Ultrasound-guided transcervical curettage is a safe and effective alternative in interstitial pregnancy (AU)

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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.

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En los edificios, nos preocupamos de la confortabilidad, de los espacios, de la iluminación, ya no es suficiente que un ambiente sea agradable en temperatura, queremos que no huela mal, que no contenga substancias nocivas, en definitiva que sea saludable (AU)

In buildings, we care about the comfort, spaces, lighting is no longer enough for a pleasant atmosphere in temperature, we do not smell bad, that does not contain harmful substances, ultimately that is healthy (AU)

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The availability of dense molecular markers has made possible the use of genomic selection in plant and animal breeding. However, models for genomic selection pose several computational and statistical challenges and require specialized computer programs, not always available to the end user and not implemented in standard statistical software yet. The R-package BLR (Bayesian Linear Regression) implements several statistical procedures (e.g., Bayesian Ridge Regression, Bayesian LASSO) in a unifi ed framework that allows including marker genotypes and pedigree data jointly. This article describes the classes of models implemented in the BLR package and illustrates their use through examples. Some challenges faced when applying genomic-enabled selection, such as model choice, evaluation of predictive ability through cross-validation, and choice of hyper-parameters, are also addressed.