Partial Data Sets Research Articles

Small-wedge synchrotron and serial XFEL datasets for Cysteinyl leukotriene GPCRs

Structural studies of challenging targets such as G protein-coupled receptors (GPCRs) have accelerated during the last several years due to the development of new approaches, including small-wedge and serial crystallography. Here, we describe the deposition of seven datasets consisting of X-ray diffraction images acquired from lipidic cubic phase (LCP) grown microcrystals of two human GPCRs, Cysteinyl leukotriene receptors 1 and 2 (CysLT1R and CysLT2R), in complex with various antagonists. Five datasets were collected using small-wedge synchrotron crystallography (SWSX) at the European Synchrotron Radiation Facility with multiple crystals under cryo-conditions. Two datasets were collected using X-ray free electron laser (XFEL) serial femtosecond crystallography (SFX) at the Linac Coherent Light Source, with microcrystals delivered at room temperature into the beam within LCP matrix by a viscous media microextrusion injector. All seven datasets have been deposited in the open-access databases Zenodo and CXIDB. Here, we describe sample preparation and annotate crystallization conditions for each partial and full datasets. We also document full processing pipelines and provide wrapper scripts for SWSX and SFX data processing.A Correction to this paper has been published: https://doi.org/10.1038/s41597-020-00759-w

STNReID: Deep Convolutional Networks With Pairwise Spatial Transformer Networks for Partial Person Re-Identification

Partial person re-identification (ReID) is a challenging task because only partial information of person images is available for matching target persons. Few studies, especially on deep learning, have focused on matching partial person images with holistic person images. This study presents a novel deep partial ReID framework based on pairwise spatial transformer networks (STNReID), which can be trained on existing holistic person datasets. STNReID includes a spatial transformer network (STN) module and a ReID module. The STN module samples an affined image (a semantically corresponding patch) from the holistic image to match the partial image. The ReID module extracts the features of the holistic, partial, and affined images. Competition (or confrontation) is observed between the STN module and the ReID module, and two-stage training is applied to acquire a strong STNReID for partial ReID. Experimental results show that our STNReID obtains 66.7% and 54.6% rank-1 accuracies on partial ReID and partial iLIDS datasets, respectively. These values are at par with those obtained with state-of-the-art methods.

Semantic-Aware Occlusion-Robust Network for Occluded Person Re-Identification

In recent years, deep learning-based person re-identification (Re-ID) methods have made significant progress. However, the performance of these methods substantially decreases when dealing with occlusion, which is ubiquitous in realistic scenarios. In this article, we propose a novel semantic-aware occlusion-robust network (SORN) that effectively exploits the intrinsic relationship between the tasks of person Re-ID and semantic segmentation for occluded person Re-ID. Specifically, the SORN is composed of three branches, including a local branch, a global branch, and a semantic branch. In particular, the local branch extracts part-based local features, and the global branch leverages a novel spatial-patch contrastive loss (SPC) to extract occlusion-robust global features. Meanwhile, the semantic branch generates a foreground-background mask for a pedestrian image, which indicates the non-occluded areas of the human body. The three branches are jointly trained in a unified multi-task learning network. Finally, pedestrian matching is performed based on the local features extracted from the non-occluded areas and the global features extracted from the whole pedestrian image. Extensive experimental results on a large-scale occluded person Re-ID dataset (i.e., Occluded-DukeMTMC) and two partial person Re-ID datasets (i.e., Partial-REID and Partial-iLIDS) show the superiority of the proposed method compared with several state-of-the-art methods for occluded and partial person Re-ID. We also demonstrate the effectiveness of the proposed method on two general person Re-ID datasets (i.e., Market-1501 and DukeMTMC-reID).

Multi-crystal data collection using synchrotron radiation as exemplified with low-symmetry crystals of Dps.

Multi-crystal data collection using synchrotron radiation was successfully applied to determine the three-dimensional structure of a triclinic crystal form of Dps from Escherichia coli at 2.0 Å resolution. The final data set was obtained by combining 261 partial diffraction data sets measured from crystals with an average size of approximately 5 µm. The most important features of diffraction data measurement and processing for low-symmetry crystals are discussed.

Inferring a complete genotype-phenotype map from a small number of measured phenotypes.

Understanding evolution requires detailed knowledge of genotype-phenotype maps; however, it can be a herculean task to measure every phenotype in a combinatorial map. We have developed a computational strategy to predict the missing phenotypes from an incomplete, combinatorial genotype-phenotype map. As a test case, we used an incomplete genotype-phenotype dataset previously generated for the malaria parasite's 'chloroquine resistance transporter' (PfCRT). Wild-type PfCRT (PfCRT3D7) lacks significant chloroquine (CQ) transport activity, but the introduction of the eight mutations present in the 'Dd2' isoform of PfCRT (PfCRTDd2) enables the protein to transport CQ away from its site of antimalarial action. This gain of a transport function imparts CQ resistance to the parasite. A combinatorial map between PfCRT3D7 and PfCRTDd2 consists of 256 genotypes, of which only 52 have had their CQ transport activities measured through expression in the Xenopus laevis oocyte. We trained a statistical model with these 52 measurements to infer the CQ transport activity for the remaining 204 combinatorial genotypes between PfCRT3D7 and PfCRTDd2. Our best-performing model incorporated a binary classifier, a nonlinear scale, and additive effects for each mutation. The addition of specific pairwise- and high-order-epistatic coefficients decreased the predictive power of the model. We evaluated our predictions by experimentally measuring the CQ transport activities of 24 additional PfCRT genotypes. The R2 value between our predicted and newly-measured phenotypes was 0.90. We then used the model to probe the accessibility of evolutionary trajectories through the map. Approximately 1% of the possible trajectories between PfCRT3D7 and PfCRTDd2 are accessible; however, none of the trajectories entailed eight successive increases in CQ transport activity. These results demonstrate that phenotypes can be inferred with known uncertainty from a partial genotype-phenotype dataset. We also validated our approach against a collection of previously published genotype-phenotype maps. The model therefore appears general and should be applicable to a large number of genotype-phenotype maps.

Validation of single-step GBLUP genomic predictions from threshold models using the linear regression method: An application in chicken mortality.

The objective of this study was to determine whether the linear regression (LR) method could be used to validate genomic threshold models. Statistics for the LR method were computed from estimated breeding values (EBVs) using the whole and truncated data sets with variances from the reference and validation populations. The method was tested using simulated and real chicken data sets. The simulated data set included 10 generations of 4,500 birds each; genotypes were available for the last three generations. Each animal was assigned a continuous trait, which was converted to a binary score assuming an incidence of failure of 7%. The real data set included the survival status of 186,596 broilers (mortality rate equal to 7.2%) and genotypes of 18,047 birds. Both data sets were analysed using best linear unbiased predictor (BLUP) or single‐step GBLUP (ssGBLUP). The whole data set included all phenotypes available, whereas in the partial data set, phenotypes of the most recent generation were removed. In the simulated data set, the accuracies based on the LR formulas were 0.45 for BLUP and 0.76 for ssGBLUP, whereas the correlations between true breeding values and EBVs (i.e. true accuracies) were 0.37 and 0.65, respectively. The gain in accuracy by adding genomic information was overestimated by 0.09 when using the LR method compared to the true increase in accuracy. However, when the estimated ratio between the additive variance computed based on pedigree only and on pedigree and genomic information was considered, the difference between true and estimated gain was <0.02. Accuracies of BLUP and ssGBLUP with the real data set were 0.41 and 0.47, respectively. This small improvement in accuracy when using ssGBLUP with the real data set was due to population structure and lower heritability. The LR method is a useful tool for estimating improvements in accuracy of EBVs due to the inclusion of genomic information when traditional validation methods as k‐fold validation and predictive ability are not applicable.

Deriving probabilistic soil distribution coefficients (Kd). Part 2: Reducing caesium Kd uncertainty by accounting for experimental approach and soil properties

The solid-liquid distribution coefficient (Kd) is a key input parameter in radioecological models. However, its large variability hampers its usefulness in modelling transport processes as well as its accuracy in representing soil-radionuclide interactions. For the specific case of radiocaesium, the analyses of a Cs Kd soil dataset (769 entries) showed that values varied over a five order of magnitude range, and the resulting Cs Kd best estimate (calculated as a geometric mean = 2.5 × 103 L kg−1) lacked reliability and representativity. Grouping data and creation of partial datasets based on the experimental approach (short-term (< ~1 yr) vs. long-term experiments (> ~1 yr)) and soil factors affecting Cs interaction (i.e., the ratio of the radiocaesium interception potential (RIP) to the potassium content in soil solution (Kss); organic matter content (OM) and soil texture) succeeded in reducing variability a few orders of magnitude, with Cs Kd best estimates also differing by one-two orders of magnitude depending on the type of soil and experimental approach. The statistical comparison of the Cs Kd best estimates and related cumulative distribution functions of the partial datasets revealed a relevant effect of the sorption dynamics on Cs Kd values (with long-term values systematically higher than short-term ones), and that the RIP/Kss ratio was an excellent predictor of Cs Kd for short-term scenarios, whereas the RIP parameter could be predicted on the basis of texture information. The OM threshold to distinguish between OM threshold to distinguish between Mineral and Organic soils subclasses, regarding Cs interaction was determined to be 50% and 90% OM for short- and long-term scenarios, respectively. It was then recommended to select the Cs Kd input data depending on the soils and scenarios to be assessed (e.g., short- vs. long-term; OM %) to improve the reliability and decrease the uncertainty of the radioecological models.

Deriving probabilistic soil distribution coefficients (Kd). Part 3: Reducing variability of americium Kd best estimates using soil properties and chemical and geological material analogues

The solid-liquid distribution coefficient (Kd) is a key input parameter in radioecological risk models. However, its large variability hampers its usefulness in modelling transport processes as well as its accuracy in representing soil-radionuclide interactions. To assist in the selection of Kd values and their cumulative distribution functions for study areas without site specific information, a critically reviewed dataset was developed, containing more than 5000 soil Kd entries for 83 elements and an additional 2000 entries of Kd data for 75 elements gathered from a selection of other geological materials. For the specific case of americium (Am), the dataset contained 109 entries for soils and 33 additional entries for sediment and subsoils. The analysis of the Am Kd soil dataset showed that values varied 4-orders of magnitude, and consequently the resulting Am Kd best estimate (geometric mean (GM): 4.6 × 103 L kg−1) lacked sufficient reliability. The objective of this study was to calculate cumulative distribution functions and statistically evaluate this dataset to determine if the Am Kd variability in soils could be reduced by considering various factors, including: 1) measurement methods, 2) key soil properties, 3) the use of chemical analogue data, and 4) the use of analogue data. Accounting for Am Kd experimental method (i.e., sorption vs. desorption; long-vs. short-term experiments) had little effect on reducing variability. However, accounting for key soil factors (i.e., organic matter content (OM), pH, soil texture) succeeded in reducing variability of Am Kd, especially when combining pH and OM. While previous data sets have used 20% OM content as a critical value to distinguish between mineral and organic soils, this study shows that this critical value should be reduced to 10% OM to minimize Am Kd variability. The inclusion in the dataset of Am Kd from other geological materials (e.g., gyttjas, tills, and subsoils) and Kd values from trivalent lanthanides (Ln (III)) and actinides (An (III)) (172 additional entries) did not statistically affect the Am Kd geometric means of the various pH and OM partial datasets. The larger composite dataset (> 310 entries), with both chemical analogues and geological material analogues to address data gaps, increased the statistical power for calculating Kd best estimates with lower variability, thereby enhancing their usefulness for radionuclide risk calculations.

Deriving probabilistic soil distribution coefficients (Kd). Part 1: General approach to decreasing and describing variability and example using uranium Kd values

A general approach is presented to derive probabilistic radionuclide distribution coefficients (Kd) in soils from a Kd dataset. The main aim was to derive informed estimates with a low inherent uncertainty by restricting the Kd value data to subsets based on key soil factors and the experimental approach used to calculate the Kd value (e.g., sorption and desorption tests). As an example, the general approach was applied to uranium (U) Kd values that are part of a critically reviewed dataset containing more than 5000 soil Kd entries for 83 elements and an additional 2000 entries of Kd data for 75 elements gathered from a selection of other, non-soil, geological materials. The overall soil U Kd dataset included 196 values spanning a range of four orders of magnitude (1–67,000 L kg−1), with additional 50 entries for other geological materials. Whereas the effect of the experimental approach could be disregarded, major factors in decreasing U Kd variability were pH and organic matter content (OM). Limitation in the number of entries made it difficult to use texture information (sand, silt, clay) to further decrease U Kd variability. The integrated combination of pH + OM permitted some soil groups to have U Kd confidence intervals as narrow as two orders of magnitude. Specifically for U Kd, data in the Mineral (< 20% OM) and Organic (≥ 20% OM) partial datasets were significantly different. Analogue data from geological materials other than soils, such as subsoil, till and gyttja (a lacustrine mud having elevated organic matter (OM) contents), were also statistically evaluated to determine whether they could be used to fill U Kd data gaps. It was shown that U Kd from subsoils and tills, but not gyttjas, could be used to enhance soil U Kd datasets. Selection of probabilistic Kd values for risk modelling can be made more reliably and with less uncertainty by using appropriate geochemical data representative of the study site to narrow the wide range of potential Kd values.

A network embedding based method for partial multi-omics integration in cancer subtyping.

Integrative analysis of multiple omics offers the opportunity to uncover coordinated cellular processes acting across different omics layers. The ever-increasing of multi-omics data provides us a comprehensive insight into cancer subtyping. Many multi-omics integrative methods have been developed, but few of them can deal with partial datasets in which some samples only have data for a subset of the omics. In this study, we propose a partial multi-omics integrative method, MSNE (Multiple Similarity Network Embedding), for cancer subtyping. MSNE integrates the multi-omics information by embedding the neighbor relations of samples defined by the random walk on multiple similarity networks. We compared MSNE with five existing multi-omics integrative methods on twelve datasets in both full and partial scenarios. MSNE achieved the best result on pan-cancer and image datasets. Furthermore, on ten cancer subtyping datasets, MSNE got the most enriched clinical parameters and comparable log-rank test P-values in survival analysis. In conclusion, MSNE is an effective and efficient integrative method for multi-omics data and, especially, has a strong power on partial datasets.

Incomplete Descriptor Mining With Elastic Loss for Person Re-Identification

In this paper, we propose a novel person Re-ID model, Consecutive Batch DropBlock Network (CBDB-Net), to capture the attentive and robust person descriptor for the person Re-ID task. The CBDB-Net contains two novel designs: the Consecutive Batch DropBlock Module (CBDBM) and the Elastic Loss (EL). In the Consecutive Batch DropBlock Module (CBDBM), we firstly conduct uniform partition on the feature maps. And then, we independently and continuously drop each patch from top to bottom on the feature maps, which can output multiple incomplete feature maps. In the training stage, these multiple incomplete features can better encourage the Re-ID model to capture the robust person descriptor for the Re-ID task. In the Elastic Loss (EL), we design a novel weight control item to help the Re-ID model adaptively balance hard sample pairs and easy sample pairs in the whole training process. Through an extensive set of ablation studies, we verify that the Consecutive Batch DropBlock Module (CBDBM) and the Elastic Loss (EL) each contribute to the performance boosts of CBDB-Net. We demonstrate that our CBDB-Net can achieve the competitive performance on the three standard person Re-ID datasets (the Market-1501, the DukeMTMC-Re-ID, and the CUHK03 dataset), three occluded Person Re-ID datasets (the Occluded DukeMTMC, the Partial-REID, and the Partial iLIDS dataset), and a general image retrieval dataset (In-Shop Clothes Retrieval dataset).

Preliminary information on the prevalence and molecular description of Taenia hydatigena isolates in Pakistan based on mitochondrial cox1 gene

Taenia hydatigena is a cestode of veterinary importance. Infection with the metacestode larval stage results in cysticercosis, which poses a serious challenge to the livestock industry worldwide. Globally, there are numerous reports on cysticercosis caused by T. hydatigena in sheep and goat but a lack of data on the prevalence and genetic diversity exists for Pakistan. We designed this study to provide an insight into the disease status as well as investigate the genetic variation among the recovered isolates based on the mitochondrial cox1 gene. In this study, we examined small ruminants (sheep and goats) slaughtered in Faisalabad in eastern Punjab province of Pakistan for T. hydatigena metacestodes and described the population structure and genetic variation using the cytochrome c oxidase subunit 1 (cox1) mitochondrial gene. Overall, a prevalence of 4.40% (goat =4.67% sheep = 4.07%) from a total of 2225 small ruminant carcasses (sheep = 983, goats = 1242) was observed. Based on the NCBI BLAST search and Bayesian phylogeny, the identity of all isolates was confirmed via their nucleotide sequences. The diversity indices indicated a high haplotype and a low nucleotide diversity with 43 haplotypes from 98 isolates. The results also show the existence of unique haplotypes of T. hydatigena in Pakistan as demonstrated by the significant negative values of Tajima's D and Fu's Fs neutrality test suggesting a recent population expansion. The median-joining network of the partial cox1 sequence dataset showed the existence of two main haplotypes detected in both sheep and goat populations. This study shows that the prevalence of cycticercosis due to T. hydatigena is below 5% in sheep and goats in Faisalabad, Punjab, Pakistan. The molecular analysis of the partial cox1 gene also indicates a high degree of genetic variation with the existence of rare haplotypes. These findings represent a preliminary report on the prevalence and genetic variation of T. hydatigena in Pakistan and serve as baseline information for future studies on the prevalence and population structure of T. hydatigena in the country.

High-throughput in situ experimental phasing.

In this article, a new approach to experimental phasing for macromolecular crystallography (MX) at synchrotrons is introduced and described for the first time. It makes use of automated robotics applied to a multi-crystal framework in which human intervention is reduced to a minimum. Hundreds of samples are automatically soaked in heavy-atom solutions, using a Labcyte Inc. Echo 550 Liquid Handler, in a highly controlled and optimized fashion in order to generate derivatized and isomorphous crystals. Partial data sets obtained on MX beamlines using an in situ setup for data collection are processed with the aim ofproducing good-quality anomalous signal leading to successful experimental phasing.

Combining Partially Overlapping Multi-Omics Data in Databases Using Relationship Matrices.

Private and public breeding programs, as well as companies and universities, have developed different genomics technologies that have resulted in the generation of unprecedented amounts of sequence data, which bring new challenges in terms of data management, query, and analysis. The magnitude and complexity of these datasets bring new challenges but also an opportunity to use the data available as a whole. Detailed phenotype data, combined with increasing amounts of genomic data, have an enormous potential to accelerate the identification of key traits to improve our understanding of quantitative genetics. Data harmonization enables cross-national and international comparative research, facilitating the extraction of new scientific knowledge. In this paper, we address the complex issue of combining high dimensional and unbalanced omics data. More specifically, we propose a covariance-based method for combining partial datasets in the genotype to phenotype spectrum. This method can be used to combine partially overlapping relationship/covariance matrices. Here, we show with applications that our approach might be advantageous to feature imputation based approaches; we demonstrate how this method can be used in genomic prediction using heterogeneous marker data and also how to combine the data from multiple phenotypic experiments to make inferences about previously unobserved trait relationships. Our results demonstrate that it is possible to harmonize datasets to improve available information across gene-banks, data repositories, or other data resources.

Recent observation and measurements of diboson processes from the ATLAS experiment

This review covers results at a centre-of-mass energy of [Formula: see text] TeV from the ATLAS experiment that have been published, or submitted for publication, up to April 2020. It summarizes results on the inclusive production cross-section measurements of boson pairs and of the electroweak production of diboson in association with two jets. The measurements either use the full integrated luminosity of 139 fb[Formula: see text] collected by the ATLAS detector at the LHC from 2015 to 2018, or a partial dataset of 36 fb[Formula: see text]. The inclusive production rates of diboson are studied to high precision. These measurements provide stringent tests of the electroweak sector of the Standard Model and allow search for new physics via anomalous triple and quartic gauge boson couplings.

Partial domain adaptation based on shared class oriented adversarial network

Most existing domain adaptation methods assume that the label space of the source domain is the same as the label space of the target domain. However, this assumption is generally untenable due to the differences between the two domains. Therefore, a novel domain adaptation paradigm called Partial Domain Adaptation (PDA), which only assumes that the source label space is large enough to subsume the target label space has been proposed recently to relax such strict assumption. Previous partial domain adaptation methods mainly utilize weighting mechanisms to alleviate negative transfer caused by outlier classes samples. Though these methods have achieved high performance in PDA tasks, all the heterogeneous data is retained during the whole training process, which still contributes to negative transfer. In this work, we propose a shared class oriented adversarial network (SCOAN) for partial domain adaptation. Outlier samples are excluded from training process via weighting strategy to entirely circumvent negative transfer and positive transfer is performed by combining adversarial network and Maximum Mean Discrepancy (MMD) to bridge domain gap. Multi-classifier module is proposed to further improve the generalization ability of the network. Extensive experiments show that SCOAN achieves state-of-the-art results on several benchmark partial domain adaptation datasets.

Making a difference in multi-data-set crystallography: simple and deterministic data-scaling/selection methods.

Phasing by single-wavelength anomalous diffraction (SAD) from multiple crystallographic data sets can be particularly demanding because of the weak anomalous signal and possible non-isomorphism. The identification and exclusion of non-isomorphous data sets by suitable indicators is therefore indispensable. Here, simple and robust data-selection methods are described. A multi-dimensional scaling procedure is first used to identify data sets with large non-isomorphism relative to clusters of other data sets. Within each cluster that it identifies, further selection is based on the weighted ΔCC1/2, a quantity representing the influence of a set of reflections on the overall CC1/2 of the merged data. The anomalous signal is further improved by optimizing the scaling protocol. The success of iterating the selection and scaling steps was verified by substructure determination and subsequent structure solution. Three serial synchrotron crystallography (SSX) SAD test cases with hundreds of partial data sets and one test case with 62 complete data sets were analyzed. Structure solution was dramatically simplified with this procedure, and enabled solution of the structures after a few selection/scaling iterations. To explore the limits, the procedure was tested with much fewer data than originally required and could still solve the structure in several cases. In addition, an SSX data challenge, minimizing the number of (simulated) data sets necessary to solve the structure, was significantly underbid.

Applying Semantic Role Labeling and Spreading Activation Techniques for Semantic Information Retrieval

Semantically enhanced information retrieval (IR) is aimed at improving classical IR methods and goes way beyond plain Boolean keyword matching with the main goal of better serving implicit and ambiguous information needs. As a de-facto pre-requisite to semantic IR, different information extraction (IE) techniques are used to mine unstructured text for underlying knowledge. In this paper we present a method that combines both IE and IR to enable semantic search in natural language texts. First, we apply semantic role labeling (SRL) to automatically extract event-oriented information found in natural language texts to an RDF knowledge graph leveraging semantic web technology. Second, we investigate how a custom flavored graph traversal spreading activation algorithm can be employed to interpret user’s information needs on top of the prior-extracted knowledge base. Finally, we present an assessment on the applicability of our method for semantically enhanced IR. An experimental evaluation on partial WikiQA dataset shows the strengths of our approach and also unveils common pitfalls that we use as guidelines to draw further work directions in the open-domain semantic search field.

In-depth Mendelian randomization analysis of causal factors for coronary artery disease

Selecting a set of valid genetic variants is critical for Mendelian randomization (MR) to correctly infer risk factors causing a disease. We here developed a method for selecting genetic variants as valid instrumental variables for inferring risk factors causing coronary artery disease (CAD). Using this method, we selected two sets of single-nucleotide-polymorphism (SNP) genetic variants (SNP338 and SNP363) associated with each of the three potential risk factors for CAD including low density lipoprotein cholesterol (LDL-c), high density lipoprotein cholesterol (HDL-c) and triglycerides (TG) from two independent GWAS datasets. We performed in-depth multivariate MR (MVMR) analyses and the results from both datasets consistently showed that LDL-c was strongly associated with increased risk for CAD (β = 0.396,OR = 1.486 per 1 SD (equivalent to 38 mg/dL), 95CI = (1.38, 1.59) in SNP338; and β = 0.424, OR = 1.528 per 1 SD, 95%CI = (1.42, 1.65) in SNP363); HDL-c was strongly associated with reduced risk for CAD (β = −0.315, OR = 0.729 per 1 SD (equivalent to 16 mg/dL), 95CI = (0.68, 0.78) in SNP338; and β = −0.319, OR = 0.726 per 1 SD, 95%CI = (0.66, 0.80), in SNP363). In case of TG, when using the full datasets, an increased risk for CAD (β = 0.184, OR = 1.2 per 1 SD (equivalent to 89 mg/dL), 95%CI = (1.12, 1.28) in SNPP338; and β = 0.207, OR = 1.222 per 1 SD, 95%CI = (1.10, 1.36) in SNP363) was observed, while using partial datasets that contain shared and unique SNPs showed that TG is not a risk factor for CAD. From these results, it can be inferred that TG itself is not a causal risk factor for CAD, but it’s shown as a risk factor due to pleiotropic effects associated with LDL-c and HDL-c SNPs. Large-scale simulation experiments without pleiotropic effects also corroborated these results.

Exploring the inclusion of genomic information and metafounders in Latxa dairy sheep genetic evaluations

The availability of genomic marker panels has made possible more precise estimates of breeding values. Sheep breeding programs are implementing genomic selection. In Latxa dairy sheep breed, a previous study using pre-corrected data and a small number of genotyped animals did not show a clear advantage of genomic selection. The objective of the present study was to ascertain the possible benefits of GS for the Latxa breed based on more data than before and using better tools, in particular single-step genomic BLUP using metafounders to model missing pedigree. Goodness of prediction of pedigree and genomic evaluations was analyzed by cross-validation comparing predictions of young rams using whole and partial (truncated) data sets. The results showed that with the current available data, genetic and genomic evaluations have the same accuracy. Contrary to the previous study, predictions were nearly unbiased, which shows the advantage of using single-step genomic BLUP. However, genomic information did not yield more precise evaluations. This could be explained by the small number of sibs in the young rams.