Multiple-trait random regression models for the estimation of genetic parameters for milk, fat, and protein yield in buffaloes

In this study, genetic parameters for test-day milk, fat, and protein yield were estimated for the first lactation. The data analyzed consisted of 1,433 first lactations of Murrah buffaloes, daughters of 113 sires from 12 herds in the state of São Paulo, Brazil, with calvings from 1985 to 2007. Ten-month classes of lactation days were considered for the test-day yields. The (co)variance components for the 3 traits were estimated using the regression analyses by Bayesian inference applying an animal model by Gibbs sampling. The contemporary groups were defined as herd-year-month of the test day. In the model, the random effects were additive genetic, permanent environment, and residual. The fixed effects were contemporary group and number of milkings (1 or 2), the linear and quadratic effects of the covariable age of the buffalo at calving, as well as the mean lactation curve of the population, which was modeled by orthogonal Legendre polynomials of fourth order. The random effects for the traits studied were modeled by Legendre polynomials of third and fourth order for additive genetic and permanent environment, respectively, the residual variances were modeled considering 4 residual classes. The heritability estimates for the traits were moderate (from 0.21-0.38), with higher estimates in the intermediate lactation phase. The genetic correlation estimates within and among the traits varied from 0.05 to 0.99. The results indicate that the selection for any trait test day will result in an indirect genetic gain for milk, fat, and protein yield in all periods of the lactation curve. The accuracy associated with estimated breeding values obtained using multi-trait random regression was slightly higher (around 8%) compared with single-trait random regression. This difference may be because to the greater amount of information available per animal. © 2013 American Dairy Science Association.

Random regression models using different functions to model milk flow in dairy cows

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Reaction norms for the study of genotype-environment interaction for growth and indicator traits of sexual precocity in Nellore cattle

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Random regression analyses using B-spline functions to model growth of Nellore cattle

The objective of this study was to estimate (co)variance components using random regression on B-spline functions to weight records obtained from birth to adulthood. A total of 82 064 weight records of 8145 females obtained from the data bank of the Nellore Breeding Program (PMGRN/Nellore Brazil) which started in 1987, were used. The models included direct additive and maternal genetic effects and animal and maternal permanent environmental effects as random. Contemporary group and dam age at calving (linear and quadratic effect) were included as fixed effects, and orthogonal Legendre polynomials of age (cubic regression) were considered as random covariate. The random effects were modeled using B-spline functions considering linear, quadratic and cubic polynomials for each individual segment. Residual variances were grouped in five age classes. Direct additive genetic and animal permanent environmental effects were modeled using up to seven knots (six segments). A single segment with two knots at the end points of the curve was used for the estimation of maternal genetic and maternal permanent environmental effects. A total of 15 models were studied, with the number of parameters ranging from 17 to 81. The models that used B-splines were compared with multi-trait analyses with nine weight traits and to a random regression model that used orthogonal Legendre polynomials. A model fitting quadratic B-splines, with four knots or three segments for direct additive genetic effect and animal permanent environmental effect and two knots for maternal additive genetic effect and maternal permanent environmental effect, was the most appropriate and parsimonious model to describe the covariance structure of the data. Selection for higher weight, such as at young ages, should be performed taking into account an increase in mature cow weight. Particularly, this is important in most of Nellore beef cattle production systems, where the cow herd is maintained on range conditions. There is limited modification of the growth curve of Nellore cattle with respect to the aim of selecting them for rapid growth at young ages while maintaining constant adult weight.

Random regression models using different functions to model test-day milk yield of Brazilian Holstein cows

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Peritoneal Dialysis-Related Peritonitis Due to Staphylococcus aureus: A Single-Center Experience over 15 Years

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Leishmania replication protein A-1 binds in vivo single-stranded telomeric DNA

Replication protein A (RPA) is a highly conserved heterotrimeric single-stranded DNA-binding protein involved in different events of DNA metabolism. In yeast, subunits 1 (RPA-1) and 2 (RPA-2) work also as telomerase recruiters and, in humans, the complex unfolds G-quartet structures formed by the 3' G-rich telomeric strand. In most eukaryotes, RPA-1 and RPA-2 bind DNA using multiple OB fold domains. In trypanosomatids, including Leishmania, RPA-1 has a canonical OB fold and a truncated RFA-1 structural domain. In Leishmania amazonensis, RPA-1 alone can form a complex in vitro with the telomeric G-rich strand. In this work, we show that LaRPA-1 is a nuclear protein that associates in vivo with Leishmania telomeres. We mapped the boundaries of the OB fold DNA-binding domain using deletion mutants. Since Leishmania and other trypanosomatids lack homologues of known telomere end binding proteins, our results raise questions about the function of RPA-1 in parasite telomeres. (C) 2007 Elsevier B.V. All rights reserved.

A single nucleotide deletion at the C1 inhibitor gene as the cause of hereditary angioedema: insights from a Brazilian family

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Recanalization Rates after Acute Deep Vein Thrombosis: A Single-center Experience Using a Newly Proposed Vein Diameter Variation Index

Background: Duplex ultrasound scanning (DUS) is the method of choice for diagnosis of deep vein thrombosis (DVT). However, only a few studies have performed prospective serial DUS after an acute episode of DVT to assess its evolution. This study aimed to report our experience using DUS combined with a thrombosis score (TS) and a newly proposed vein diameter variation index (VDVI) to evaluate the rate of resolution of DVT by assessing and quantifying the early stages of vein recanalization in proximal vein segments within 6 months after an episode of acute lower extremity DVT.Methods: Twelve patients with first episode of acute lower extremity DVT confirmed by DUS as occurring in <= 10 days after the onset of venous thrombosis symptoms were followed up prospectively for 6 months. TS and VDVI were calculated at 1, 3, and 6 months to assess vein recanalization. Intra-thrombus arteriovenous fistula formation was also investigated and related to the recanalization process.Results: Seven (58%) women were included, with a total cohort median age of 53.5 +/- 19 years. The left lower extremity was affected in 7 (58%) patients. DVT was diagnosed in 55 proximal vein segments. All patients had proximal DVT, with involvement of the external iliac, femoral, and popliteal veins. After 6 months, there was a significant decrease in TS and increase in VDVI (P < 0.001) in all proximal vein segments assessed, indicating thrombus regression. The more distal the DVT was, the faster was the VDVI increase, with most popliteal veins being recanalized at 3 months (P < 0.001). Intra-thrombus arteriovenous fistula was identified in 50% of patients at 1 month while on anticoagulation.Conclusions: The combined use of two different DUS-based assessment tools, TS and the proposed VDVI, provided an effective method to prospectively assess vein recanalization rates after an episode of acute lower extremity DVT in this series of patients and may allow a correct evaluation of DVT and its resolution or progression.

Predicting breeding values for milk yield of Guzera (Bos indicus) cows using random regression models

Given the importance of Guzera breeding programs for milk production in the tropics, the objective of this study was to compare alternative random regression models for estimation of genetic parameters and prediction of breeding values. Test-day milk yields records (TDR) were collected monthly, in a maximum of 10 measurements. The database included 20,524 records of first lactation from 2816 Guzera cows. TDR data were analyzed by random regression models (RRM) considering additive genetic, permanent environmental and residual effects as random and the effects of contemporary group (CG), calving age as a covariate (linear and quadratic effects) and mean lactation curve as fixed. The genetic additive and permanent environmental effects were modeled by RRM using Wilmink, All and Schaeffer and cubic B-spline functions as well as Legendre polynomials. Residual variances were considered as heterogeneous classes, grouped differently according to the model used. Multi-trait analysis using finite-dimensional models (FDM) for testday milk records (TDR) and a single-trait model for 305-days milk yields (default) using the restricted maximum likelihood method were also carried out as further comparisons. Through the statistical criteria adopted, the best RRM was the one that used the cubic B-spline function with five random regression coefficients for the genetic additive and permanent environmental effects. However, the models using the Ali and Schaeffer function or Legendre polynomials with second and fifth order for, respectively, the additive genetic and permanent environmental effects can be adopted, as little variation was observed in the genetic parameter estimates compared to those estimated by models using the B-spline function. Therefore, due to the lower complexity in the (co)variance estimations, the model using Legendre polynomials represented the best option for the genetic evaluation of the Guzera lactation records. An increase of 3.6% in the accuracy of the estimated breeding values was verified when using RRM. The ranks of animals were very close whatever the RRM for the data set used to predict breeding values. Considering P305, results indicated only small to medium difference in the animals' ranking based on breeding values predicted by the conventional model or by RRM. Therefore, the sum of all the RRM-predicted breeding values along the lactation period (RRM305) can be used as a selection criterion for 305-day milk production. (c) 2014 Elsevier B.V. All rights reserved.

A note on model uncertainty in linear regression

We consider model selection uncertainty in linear regression. We study theoretically and by simulation the approach of Buckland and co-workers, who proposed estimating a parameter common to all models under study by taking a weighted average over the models, using weights obtained from information criteria or the bootstrap. This approach is compared with the usual approach in which the 'best' model is used, and with Bayesian model averaging. The weighted predictor behaves similarly to model averaging, with generally more realistic mean-squared errors than the usual model-selection-based estimator.

Precision of distances and ordering of microsatellite markers in consensus linkage maps of chromosomes 1, 3 and 4 from two reciprocal chicken populations using bootstrap sampling

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)