Modeling missing pedigree with metafounders and validating single-step genomic predictions in a small dairy cattle population with a great influence of foreign genetics.

Genetic improvement in small countries rely heavily on foreign genetics. In an importing country such as Uruguay, consideration of unknown parent groups (UPG) for foreign sires is essential. However, the use of UPG in genomic model evaluations may lead to bias in genomic estimated breeding values (GEBV). The objective of this study was to study different models including UPG or metafounders (MF) in the Uruguayan Holstein evaluation and to analyze bias, dispersion, and accuracy of GEBV predictions in BLUP and single-step genomic BLUP (ssGBLUP). A gamma matrix (Γ) was estimated either by using base allele population frequencies obtained by bounded linear regression (MFbounded), or by using 2 values to design Γ (i.e., a single value for the diagonal and a different value for the off-diagonal [MFrobust]). Both Γ estimators performed well in terms of GEBV predictions, but MFbounded was the best option. There is, however, some bias whose origin was not completely understood. UPG or MF seem to model correctly genetic progress for unknown parents except for the very first groups (earlier time period). As for validation bulls, bias was observed across all models, whereas for validation cows it was only observed with UPG in BLUP. Overdispersion was found in all models, but it was mostly detected in validation bulls. Ratio of accuracies indicated that ssGBLUP gave better predictions than BLUP.

Entropy, purity and optical hysteresis in markovian optical modes.

We describe the synthesis of optical modes whose axial structure follows a random tandem array of Bessel beams of integer order. The array follows fluctuations of Markov-chain type and the amplitude values for each beam are linked to a sequence of random vectors. As a prototype, we describe the synthesis of optical fields for Markov-chain type Ehrenfest. This process models the thermodynamic equilibrium and then it can be related to the evolution and stability of optical systems, in this way, it offers a similitude with partially coherent processes where the coherence degree is now distributed between all the compounds of the resulting random vector. The matrix representation for the stochastic process allows incorporating entropy properties and the calculus of the purity for the optical field. This constitutes the basis to describe the interference between markovian modes. When the set of markovian modes type Ehrenfest reaches a stable configuration they become indistinguishability non-conservative optical field having associated hysteresis features. Computer simulations are presented.

The influence of high-density lipoprotein (HDL) and HDL subfractions on insulin secretion and cholesterol efflux in pancreatic derived ß-cells.

BACKGROUND: High-density lipoprotein (HDL) is considered a complex plasma-circulating particle with subfractions that vary in function, size, and chemical composition. We sought to test the effects of HDL, and HDL subfractions on insulin secretion and cholesterol efflux in the ß-cell line MIN-6. METHODS: We used total HDL and HDL subfractions 2a, 2b, 3a, 3b, and 3c, isolated from human plasma, to test insulin secretion under different glucose concentrations as well as insulin content and cholesterol efflux in the insulinoma MIN-6 cell line. RESULTS: Incubation of MIN-6 cells with low glucose and total HDL increased insulin release two-fold. Meanwhile, when high glucose and HDL were used, insulin release increased more than five times. HDL subfractions 2a, 2b, 3a, 3b, and 3c elicited higher insulin secretion and cholesterol efflux than their respective controls, at both low and high glucose concentrations. The insulin content of the MIN-6 cells incubated with low glucose and any of the five HDL subclasses had a modest reduction compared with their controls. However, there were no statistically significant differences between each HDL subfraction on their capacity of eliciting insulin secretion, insulin content, or cholesterol efflux. CONCLUSIONS: HDL can trigger insulin secretion under low, normal, and high glucose conditions. We found that all HDL subfractions exhibit very similar capacity to increase insulin secretion and cholesterol efflux. This is the first report demonstrating that HDL subfractions act both as insulin secretagogues (under low glucose) and insulin secretion enhancers (under high glucose) in the MIN-6 cell line.

Genomic and pedigree estimation of inbreeding depression for semen traits in the Basco-Béarnaise dairy sheep breed.

Inbreeding depression is associated with a decrease in performance and fitness of the animals. The goal of this study was to evaluate pedigree-based and genomic methods to estimate the level of inbreeding and inbreeding depression for 3 semen traits (volume, concentration, and motility score) in the Basco-Béarnaise sheep breed. Data comprised 16,196 (or 15,071) phenotypic records from 620 rams (of which 533 rams had genotypes of 36,464 SNPs). The pedigree included 8,266 animals, composed of the 620 rams and their ancestors. The number of equivalent complete generations for the 620 rams was 7.04. Inbreeding coefficients were estimated using genomic and pedigree-based information. Genomic inbreeding coefficients were estimated from individual SNP and using segments of homozygous SNP (runs of homozygosity, ROH). Short ROH are of old origin, whereas long ROH are due to recent inbreeding. Considering that the equivalent number of generations in Basco-Béarnaise was 6, inbreeding coefficients for ROH with a length >4 Mb refer to all (recent + old) inbreeding, those with a length >17 Mb correspond to recent inbreeding, and the difference between them indicates old inbreeding. Pedigree-based inbreeding coefficients were also estimated classically, or accounting for nonzero relationships for unknown parents, or including metafounder relationships (estimated using markers) to account for missing pedigree information. Finally, inbreeding coefficients combining genotyped and nongenotyped animal information were computed from matrix H of the single-step approach, also including metafounders. Inbreeding depression was estimated differently depending on the approach used to compute inbreeding coefficients. These 8 estimators of inbreeding coefficients were included as covariates in different animal models. No inbreeding depression was detected for sperm volume or sperm concentration. Inbreeding depression was significant for the motility of spermatozoa. The effect of old and recent inbreeding on motility was null and negative, respectively, demonstrating the existence of purging by selection of deleterious recessive alleles affecting motility. A 10% increase in inbreeding would result in a reduction in mean motility ranging between 0.09 and 0.22 points in the score (from 0 to 5). Motility is unfavorably affected by increasing recent inbreeding but the impact is very small. Runs of homozygosity and metafounders allow us to accurately estimate inbreeding depression and detect recent inbreeding.

Genetic dissection of reproductive performance of dairy cows under heat stress.

Heat stress negatively impacts the reproductive performance of dairy cows. The main objective of this study was to dissect the genetic basis underlying dairy cow fertility under heat stress conditions. Our first goal was to estimate genetic components of cow conception across lactations considering heat stress. Our second goal was to reveal individual genes and functional gene-sets that explain a cow's ability to conceive under thermal stress. Data consisted of 74 221 insemination records on 13 704 Holstein cows. Multitrait linear repeatability test-day models with random regressions on a function of temperature-humidity index values were used for the analyses. Heritability estimates for cow conception under heat stress were around 2-3%, whereas genetic correlations between general and thermotolerance additive genetic effects were negative and ranged between -0.35 and -0.82, indicating an unfavorable relationship between cows' ability to conceive under thermo-neutral vs. thermo-stress conditions. Whole-genome scans identified at least six genomic regions on BTA1, BTA10, BTA11, BTA17, BTA21 and BTA23 associated with conception under thermal stress. These regions harbor candidate genes such as BRWD1, EXD2, ADAM20, EPAS1, TAOK3, and NOS1, which are directly implicated in reproductive functions and cellular response to heat stress. The gene-set enrichment analysis revealed functional terms related to fertilization, developmental biology, heat shock proteins and oxidative stress, among others. Overall, our findings contribute to a better understanding of the genetics underlying the reproductive performance of dairy cattle under heat stress conditions and point out novel genomic strategies for improving thermotolerance and fertility via marker-assisted breeding.

Short communication: Methods to compute genomic inbreeding for ungenotyped individuals.

The genomic measure of inbreeding is closer to the actual inbreeding than the pedigree-based measure. However, it cannot be computed for ungenotyped animals. An estimate of genomic inbreeding comes from the diagonal of matrix H used in single-step methods. This matrix projects genomic relationships to all ungenotyped members of the pedigree. The diagonal element of H-1 gives an estimate of the genomic inbreeding coefficient. However, so far no computational methods are available to compute the diagonal of H. Here we propose 3 exact methods to compute this diagonal. The first uses an already-existing algorithm to compute, for each ungenotyped individual, products of the form Hx to obtain the corresponding diagonal element of H. The second method computes, for each ungenotyped individual, a term that can be written as a quadratic form involving pedigree and genomic relationships. For both methods, the computational burden is linear in the number of ungenotyped animals. The last method reorders the computations of the second method so that they become linear in the number of genotyped animals, which is usually much smaller. We tested the methods in 3 small data sets (with ~2,000 genotyped animals and 30,000-500,000 animals in pedigree) and in a large simulated population (with 1,220,000 animals in pedigree and 36,000 genotyped animals). Tests resulted in satisfactory computing times (<10 min in the largest example using 10 parallel threads). Computing times were much shorter for the third method, as expected. Using these methods, estimates of genomic inbreeding in ungenotyped animals can be obtained on a regular basis.

Effectiveness of lumbar orthoses in low back pain: Review of the literature and our results.

Back pain and diseases of the spine are today a health disorder of outstanding epidemiological, medical, and health economic importance. The cost of care for patients with lumbosciatic complaints are steadily increasing. Accordingly, the guidelines and treatments are constantly renewed. One concept is the orthotic care. In the following we want to give an overview of the literature and the effectiveness of lumbar orthoses in low back pain supplemented by our own data. A prospective randomized study with 230 patients, divided into three groups, each with two subgroups. Three Orthoses by the TIGGES-Zours GmbH were prescribed; a demountable two-step lumbar orthosis, three-step bridging orthosis and a four-step flexion orthosis modular system. Each were compared to the nonmodular equivalent. All six groups showed improvement in pain intensity and functional capacity at 6 and 12 weeks. The modular groups were found to have improvement in the frequency of use. The subjective effectiveness and sensitivity for the modular and non-modular groups was assessed as being good. In the literature, there are no clear guidelines for an orthotic supply. The studies do not seem to be meaningful and universal due to the difficult ascertainability of pain. There is a need for further research here. Nevertheless, the authors of this review are of the opinion that the implementation of trunk orthoses is void of side effects and beneficial to patients. The modular systems seem to have an advantage as well as higher patient satisfaction.

Analyses of reaction norms reveal new chromosome regions associated with tick resistance in cattle.

Despite single nucleotide polymorphism (SNP) availability and frequent cost reduction has allowed genome-wide association studies even in complex traits as tick resistance, the use of this information source in SNP by environment interaction context is unknown for many economically important traits in cattle. We aimed at identifying putative genomic regions explaining differences in tick resistance in Hereford and Braford cattle under SNP by environment point of view as well as to identify candidate genes derived from outliers/significant markers. The environment was defined as contemporary group means of tick counts, since they seemed to be the most appropriate entities to describe the environmental gradient in beef cattle. A total of 4363 animals having tick counts (n=10 673) originated from 197 sires and 3966 dams were used. Genotypes were acquired on 3591 of these cattle. From top 1% SNPs (410) having the greatest effects in each environment, 75 were consistently relevant in all environments, which indicated SNP by environment interaction. The outliers/significant SNPs were mapped on chromosomes 1, 2, 5, 6, 7, 9, 11, 13, 14, 15, 16, 18, 21, 23, 24, 26 and 28, and potential candidate genes were detected across environments. The presence of SNP by environment interaction for tick resistance indicates that genetic expression of resistance depends upon tick burden. Markers with major portion of genetic variance explained across environments appeared to be close to genes with different direct or indirect functions related to immune system, inflammatory process and mechanisms of tissue destruction/repair, such as energy metabolism and cell differentiation.

Better Body Composition and Lipid Profile Can Be Associated with Vitamin D Status in Spanish Elderly? The PHYSMED Study.

Studies have shown that vitamin D deficiency increases the risk for lipid metabolism disorders, but this relationship has provided inconsistent results in elderly. Thus the aim was to assess the association between body composition and blood lipid profile levels on serum 25-hydrovitamin D [25(OH)D] concentration in Spanish elderly. A cross-sectional multicentre study was carried out in 383 participants (58.2% females) aged of 55-88 years. Fasting blood samples analyzed serum concentrations of 25(OH)D, low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglycerides (TG) and total cholesterol (TC). Body composition parameters (fat mass, fat free mass) were obtained by bioimpedance, waist circumference (WC), physical activity and vitamin D intake were also evaluated. BMI, fat mass and total fat mass were lower in vitamin D sufficient subjects than vitamin D insufficient and deficient subjects, but this difference was not significant (p>0,05). Those with vitamin D adequate levels also showed lower TC/HDL ratio than those who had inadequate (insufficient or deficient) vitamin D levels (p=0.04). Significant association between 25(OH)D and BMI, waist circumference, total muscle mass, TC/HDL-c ratio, HDL and TG (for all p≤ 0.05) was found after controlling for some confounders. Subjects with inadequate HDL levels (<40mg/dL) showed 1.7 times higher odds than vitamin D deficiency than those with adequate HDL levels (>60mg/dL) (95% CI= 1.10 to 2.85 p= 0.017) and WC was negatively associated with vitamin D status odds ratio of 0.98 (0.96 to 1.00; p= 0.04). Vitamin D concentration was positively correlated with HDL-c and total muscle mass, as well as negatively correlated with WC, LDL-c/HDL-c and TC/HDL-c independently of age, gender and some confounders.

Implications of SNP weighting on single-step genomic predictions for different reference population sizes.

We investigated the importance of SNP weighting in populations with 2,000 to 25,000 genotyped animals. Populations were simulated with two effective sizes (20 or 100) and three numbers of QTL (10, 50 or 500). Pedigree information was available for six generations; phenotypes were recorded for the four middle generations. Animals from the last three generations were genotyped for 45,000 SNP. Single-step genomic BLUP (ssGBLUP) and weighted ssGBLUP (WssGBLUP) were used to estimate genomic EBV using a genomic relationship matrix (G). The WssGBLUP performed better in small genotyped populations; however, any advantage for WssGBLUP was reduced or eliminated when more animals were genotyped. WssGBLUP had greater resolution for genome-wide association (GWA) as did increasing the number of genotyped animals. For few QTL, accuracy was greater for WssGBLUP than ssGBLUP; however, for many QTL, accuracy was the same for both methods. The largest genotyped set was used to assess the dimensionality of genomic information (number of effective SNP). The number of effective SNP was considerably less in weighted G than in unweighted G. Once the number of independent SNP is well represented in the genotyped population, the impact of SNP weighting becomes less important.

Technical note: Avoiding the direct inversion of the numerator relationship matrix for genotyped animals in single-step genomic best linear unbiased prediction solved with the preconditioned conjugate gradient.

This paper evaluates an efficient implementation to multiply the inverse of a numerator relationship matrix for genotyped animals () by a vector (). The computation is required for solving mixed model equations in single-step genomic BLUP (ssGBLUP) with the preconditioned conjugate gradient (PCG). The inverse can be decomposed into sparse matrices that are blocks of the sparse inverse of a numerator relationship matrix () including genotyped animals and their ancestors. The elements of were rapidly calculated with the Henderson's rule and stored as sparse matrices in memory. Implementation of was by a series of sparse matrix-vector multiplications. Diagonal elements of , which were required as preconditioners in PCG, were approximated with a Monte Carlo method using 1,000 samples. The efficient implementation of was compared with explicit inversion of with 3 data sets including about 15,000, 81,000, and 570,000 genotyped animals selected from populations with 213,000, 8.2 million, and 10.7 million pedigree animals, respectively. The explicit inversion required 1.8 GB, 49 GB, and 2,415 GB (estimated) of memory, respectively, and 42 s, 56 min, and 13.5 d (estimated), respectively, for the computations. The efficient implementation required <1 MB, 2.9 GB, and 2.3 GB of memory, respectively, and <1 sec, 3 min, and 5 min, respectively, for setting up. Only <1 sec was required for the multiplication in each PCG iteration for any data sets. When the equations in ssGBLUP are solved with the PCG algorithm, is no longer a limiting factor in the computations.

Genome-enabled prediction for tick resistance in Hereford and Braford beef cattle via reaction norm models.

Very few studies have been conducted to infer genotype × environment interaction (G×E) based in genomic prediction models using SNP markers. Therefore, our main objective was to compare a conventional genomic-based single-step model (HBLUP) with its reaction norm model extension (genomic 1-step linear reaction norm model [HLRNM]) to provide EBV for tick resistance as well as to compare predictive performance of these models with counterpart models that ignore SNP marker information, that is, a linear animal model (ABLUP) and its reaction norm extension (1-step linear reaction norm model [ALRNM]). Phenotypes included 10,673 tick counts on 4,363 Hereford and Braford animals, of which 3,591 were genotyped. Using the deviance information criterion for model choice, ABLUP and HBLUP seemed to be poorer fitting in comparison with their respective genomic model extensions. The HLRNM estimated lower average and reaction norm genetic variability compared with the ALRNM, whereas ABLUP and HBLUP seemed to be poorer fitting in comparison with their respective genomic reaction norm model extensions. Heritability and repeatability estimates varied along the environmental gradient (EG) and the genetic correlations were remarkably low between high and low EG, indicating the presence of G×E for tick resistance in these populations. Based on 5-fold -means partitioning, mean cross-validation estimates with their respective SE of predictive accuracy were 0.66 (SE 0.02), 0.67 (SE 0.02), 0.67 (SE 0.02), and 0.66 (SE 0.02) for ABLUP, HBLUP, HLRNM, and ALRNM, respectively. For 5-fold random partitioning, HLRNM (0.71 ± 0.01) was statistically different from ABLUP (0.67 ± 0.01). However, no statistical significance was reported when considering HBLUP (0.70 ± 0.01) and ALRNM (0.70 ± 0.01). Our results suggest that SNP marker information does not lead to higher prediction accuracies in reaction norm models. Furthermore, these accuracies decreased as the tick infestation level increased and as the relationship between animals in training and validation data sets decreased.

Implementation of genomic recursions in single-step genomic best linear unbiased predictor for US Holsteins with a large number of genotyped animals.

The objectives of this study were to develop and evaluate an efficient implementation in the computation of the inverse of genomic relationship matrix with the recursion algorithm, called the algorithm for proven and young (APY), in single-step genomic BLUP. We validated genomic predictions for young bulls with more than 500,000 genotyped animals in final score for US Holsteins. Phenotypic data included 11,626,576 final scores on 7,093,380 US Holstein cows, and genotypes were available for 569,404 animals. Daughter deviations for young bulls with no classified daughters in 2009, but at least 30 classified daughters in 2014 were computed using all the phenotypic data. Genomic predictions for the same bulls were calculated with single-step genomic BLUP using phenotypes up to 2009. We calculated the inverse of the genomic relationship matrix GAPY(-1) based on a direct inversion of genomic relationship matrix on a small subset of genotyped animals (core animals) and extended that information to noncore animals by recursion. We tested several sets of core animals including 9,406 bulls with at least 1 classified daughter, 9,406 bulls and 1,052 classified dams of bulls, 9,406 bulls and 7,422 classified cows, and random samples of 5,000 to 30,000 animals. Validation reliability was assessed by the coefficient of determination from regression of daughter deviation on genomic predictions for the predicted young bulls. The reliabilities were 0.39 with 5,000 randomly chosen core animals, 0.45 with the 9,406 bulls, and 7,422 cows as core animals, and 0.44 with the remaining sets. With phenotypes truncated in 2009 and the preconditioned conjugate gradient to solve mixed model equations, the number of rounds to convergence for core animals defined by bulls was 1,343; defined by bulls and cows, 2,066; and defined by 10,000 random animals, at most 1,629. With complete phenotype data, the number of rounds decreased to 858, 1,299, and at most 1,092, respectively. Setting up GAPY(-1) for 569,404 genotyped animals with 10,000 core animals took 1.3h and 57 GB of memory. The validation reliability with APY reaches a plateau when the number of core animals is at least 10,000. Predictions with APY have little differences in reliability among definitions of core animals. Single-step genomic BLUP with APY is applicable to millions of genotyped animals.

Technical note: Acceleration of sparse operations for average-information REML analyses with supernodal methods and sparse-storage refinements.

The objective of this study was to remove bottlenecks generally found in a computer program for average-information REML. The refinements included improvements to setting-up mixed-model equations on a hash table with a faster hash function as sparse matrix storage, changing sparse structures in calculation of traces, and replacing a sparse matrix package using traditional methods (FSPAK) with a new package using supernodal methods (YAMS); the latter package quickly processed sparse matrices containing large, dense blocks. Comparisons included 23 models with data sets from broiler, swine, beef, and dairy cattle. Models included single-trait, multiple-trait, maternal, and random regression models with phenotypic data; selected models used genomic information in a single-step approach. Setting-up mixed model equations was completed without abnormal termination in all analyses. Calculations in traces were accelerated with a hash format, especially for models with a genomic relationship matrix, and the maximum speed was 67 times faster. Computations with YAMS were, on average, more than 10 times faster than with FSPAK and had greater advantages for large data and more complicated models including multiple traits, random regressions, and genomic effects. These refinements can be applied to general average-information REML programs.

Genetic evaluation using single-step genomic best linear unbiased predictor in American Angus.

Predictive ability of genomic EBV when using single-step genomic BLUP (ssGBLUP) in Angus cattle was investigated. Over 6 million records were available on birth weight (BiW) and weaning weight (WW), almost 3.4 million on postweaning gain (PWG), and over 1.3 million on calving ease (CE). Genomic information was available on, at most, 51,883 animals, which included high and low EBV accuracy animals. Traditional EBV was computed by BLUP and genomic EBV by ssGBLUP and indirect prediction based on SNP effects was derived from ssGBLUP; SNP effects were calculated based on the following reference populations: ref_2k (contains top bulls and top cows that had an EBV accuracy for BiW ≥0.85), ref_8k (contains all parents that were genotyped), and ref_33k (contains all genotyped animals born up to 2012). Indirect prediction was obtained as direct genomic value (DGV) or as an index of DGV and parent average (PA). Additionally, runs with ssGBLUP used the inverse of the genomic relationship matrix calculated by an algorithm for proven and young animals (APY) that uses recursions on a small subset of reference animals. An extra reference subset included 3,872 genotyped parents of genotyped animals (ref_4k). Cross-validation was used to assess predictive ability on a validation population of 18,721 animals born in 2013. Computations for growth traits used multiple-trait linear model and, for CE, a bivariate CE-BiW threshold-linear model. With BLUP, predictivities were 0.29, 0.34, 0.23, and 0.12 for BiW, WW, PWG, and CE, respectively. With ssGBLUP and ref_2k, predictivities were 0.34, 0.35, 0.27, and 0.13 for BiW, WW, PWG, and CE, respectively, and with ssGBLUP and ref_33k, predictivities were 0.39, 0.38, 0.29, and 0.13 for BiW, WW, PWG, and CE, respectively. Low predictivity for CE was due to low incidence rate of difficult calving. Indirect predictions with ref_33k were as accurate as with full ssGBLUP. Using the APY and recursions on ref_4k gave 88% gains of full ssGBLUP and using the APY and recursions on ref_8k gave 97% gains of full ssGBLUP. Genomic evaluation in beef cattle with ssGBLUP is feasible while keeping the models (maternal, multiple trait, and threshold) already used in regular BLUP. Gains in predictivity are dependent on the composition of the reference population. Indirect predictions via SNP effects derived from ssGBLUP allow for accurate genomic predictions on young animals, with no advantage of including PA in the index if the reference population is large. With the APY conditioning on about 10,000 reference animals, ssGBLUP is potentially applicable to a large number of genotyped animals without compromising predictive ability.

Genomic prediction for tick resistance in Braford and Hereford cattle.

One of the main animal health problems in tropical and subtropical cattle production is the bovine tick, which causes decreased performance, hide devaluation, increased production costs with acaricide treatments, and transmission of infectious diseases. This study investigated the utility of genomic prediction as a tool to select Braford (BO) and Hereford (HH) cattle resistant to ticks. The accuracy and bias of different methods for direct and blended genomic prediction was assessed using 10,673 tick counts obtained from 3,435 BO and 928 HH cattle belonging to the Delta G Connection breeding program. A subset of 2,803 BO and 652 HH samples were genotyped and 41,045 markers remained after quality control. Log transformed records were adjusted by a pedigree repeatability model to estimate variance components, genetic parameters, and breeding values (EBV) and subsequently used to obtain deregressed EBV. Estimated heritability and repeatability for tick counts were 0.19 ± 0.03 and 0.29 ± 0.01, respectively. Data were split into 5 subsets using k-means and random clustering for cross-validation of genomic predictions. Depending on the method, direct genomic value (DGV) prediction accuracies ranged from 0.35 with Bayes least absolute shrinkage and selection operator (LASSO) to 0.39 with BayesB for k-means clustering and between 0.42 with BayesLASSO and 0.45 with BayesC for random clustering. All genomic methods were superior to pedigree BLUP (PBLUP) accuracies of 0.26 for k-means and 0.29 for random groups, with highest accuracy gains obtained with BayesB (39%) for k-means and BayesC (55%) for random groups. Blending of historical phenotypic and pedigree information by different methods further increased DGV accuracies by values between 0.03 and 0.05 for direct prediction methods. However, highest accuracy was observed with single-step genomic BLUP with values of 0.48 for -means and 0.56, which represent, respectively, 84 and 93% improvement over PBLUP. Observed random clustering cross-validation breed-specific accuracies ranged between 0.29 and 0.36 for HH and between 0.55 and 0.61 for BO, depending on the blending method. These moderately high values for BO demonstrate that genomic predictions could be used as a practical tool to improve genetic resistance to ticks and in the development of resistant lines of this breed. For HH, accuracies are still in the low to moderate side and this breed training population needs to be increased before genomic selection could be reliably applied to improve tick resistance.

Use of genomic recursions and algorithm for proven and young animals for single-step genomic BLUP analyses--a simulation study.

The purpose of this study was to examine accuracy of genomic selection via single-step genomic BLUP (ssGBLUP) when the direct inverse of the genomic relationship matrix (G) is replaced by an approximation of G(-1) based on recursions for young genotyped animals conditioned on a subset of proven animals, termed algorithm for proven and young animals (APY). With the efficient implementation, this algorithm has a cubic cost with proven animals and linear with young animals. Ten duplicate data sets mimicking a dairy cattle population were simulated. In a first scenario, genomic information for 20k genotyped bulls, divided in 7k proven and 13k young bulls, was generated for each replicate. In a second scenario, 5k genotyped cows with phenotypes were included in the analysis as young animals. Accuracies (average for the 10 replicates) in regular EBV were 0.72 and 0.34 for proven and young animals, respectively. When genomic information was included, they increased to 0.75 and 0.50. No differences between genomic EBV (GEBV) obtained with the regular G(-1) and the approximated G(-1) via the recursive method were observed. In the second scenario, accuracies in GEBV (0.76, 0.51 and 0.59 for proven bulls, young males and young females, respectively) were also higher than those in EBV (0.72, 0.35 and 0.49). Again, no differences between GEBV with regular G(-1) and with recursions were observed. With the recursive algorithm, the number of iterations to achieve convergence was reduced from 227 to 206 in the first scenario and from 232 to 209 in the second scenario. Cows can be treated as young animals in APY without reducing the accuracy. The proposed algorithm can be implemented to reduce computing costs and to overcome current limitations on the number of genotyped animals in the ssGBLUP method.

Hot topic: Use of genomic recursions in single-step genomic best linear unbiased predictor (BLUP) with a large number of genotypes.

The purpose of this study was to evaluate the accuracy of genomic selection in single-step genomic BLUP (ssGBLUP) when the inverse of the genomic relationship matrix (G) is derived by the "algorithm for proven and young animals" (APY). This algorithm implements genomic recursions on a subset of "proven" animals. Only a relationship matrix for animals treated as "proven" needs to be inverted, and the extra costs of adding animals treated as "young" are linear. Analyses involved 10,102,702 final scores on 6,930,618 Holstein cows. Final score, which is a composite of type traits, is popular trait in the United States and was easily available for this study. A total of 100,000 animals with genotypes were used in the analyses and included 23,000 sires (16,000 with >5 progeny), 27,000 cows, and 50,000 young animals. Genomic EBV (GEBV) were calculated with a regular inverse of G, and with the G inverse approximated by APY. Animals in the proven subset included only sires (23,000), sires+cows (50,000), only cows (27,000), or sires with >5 progeny (16,000). The correlations of GEBV with APY and regular GEBV for young genotyped animals were 0.994, 0.995, 0.992, and 0.992, respectively Later, animals in the proven subset were randomly sampled from all genotyped animals in sets of 2,000, 5,000, 10,000, 15,000, and 20,000; each sample was replicated 4 times. Respective correlations were 0.97 (5,000 sample), 0.98 (10,000 sample), and 0.99 (20,000 sample), with minimal difference between samples of the same size. Genomic EBV with APY were accurate when the number of animals used in the subset is between 10,000 and 20,000, with little difference between the ways of creating the subset. Due to the approximately linear cost of APY, ssGBLUP with APY could support any number of genotyped animals without affecting accuracy.

Characterization of the dengue outbreak in Nuevo Leon state, Mexico, 2010.

We studied serotypes circulating dengue virus (DENV) cases, entomological Breteau index, rain-fall index and epidemiology of groups affected during the 2010 outbreak in Nuevo Leon, Mexico. From 2,271 positive cases, 94% were dengue classic and 6% dengue hemorrhagic fever; DENV1 was mainly isolated (99%) (Central-American lineage of American-African-genotype). We found correlation between two environmental phenomena (Increment of rainfall and vector-indexes) (p ≤ 0.05) with epidemiological, clinical and risk of DENV-1 ongoing transmission.