Biomedical subjects

F F Cardoso

Publications and source records attributed to F F Cardoso.

5 recordsLinked to original sources

PubMed2006

Accounting for outliers and heteroskedasticity in multibreed genetic evaluations of postweaning gain of Nelore-Hereford cattle.

The objectives of this study were to demonstrate the utility of hierarchical Bayesian models combining residual heteroskedasticity with robustness for outlier detection and muting and to evaluate the effects of such joint modeling in multibreed genetic evaluations. A 3 x 2 factorial specification of 6 residual variance models based on several distributional (Gaussian, Student's t, or Slash) and variability (homoskedastic or heteroskedastic) assumptions was used to analyze 22,717 postweaning gain records from a Nelore-Hereford population (40,082 animals in the pedigree). To illustrate the utility of the 2 robust distributional specifications (Student's t and Slash) for outlier detection and muting, 3 records from the same contemporary group (an extreme residual outlier, a mild residual outlier, and a near-zero residual) were chosen for further study. The posterior densities of the corresponding weighting variables of these records were used to assess their degree of Gaussian outlyingness and the ability of the robust models to mute the effects of deviant records. The Student's t heteroskedastic provided the best-fit model among the 6 specifications and was preferred for genetic merit inference. Kendall rank correlations of the posterior means of the additive genetic effects of the animals, used to compare the selection order of the Student's t and Gaussian models, were reasonably high across all animals within the most frequent genotypes, ranging from 0.83 to 0.91 and from 0.89 to 0.95 for the homoskedastic and the heteroskedastic versions, respectively. However, when considering only animals ranked in the top 10% by the customary Gaussian homoskedastic model, these rank correlations were reduced considerably, ranging from 0.29 to 0.57 and from 0.72 to 0.85 between the 2 residual densities within the homoskedastic and heteroskedastic versions, respectively. Rank correlations between the homoskedastic and heteroskedastic versions within each of the Gaussian and Student's t error models tended to be smaller, with a range from 0.68 to 0.90 across all animals and from 0.28 to 0.67 for animals ranked in the top 10%. These results support the implementation of robust models accounting for sources of heteroskedasticity to increase the precision and stability of multibreed genetic evaluations with proper statistical treatment of deviant records.

Analysis of Variance↗

PubMed2005

Multiple-breed genetic inference using heavy-tailed structural models for heterogeneous residual variances.

Multiple-breed genetic models recently have been demonstrated to account for the heterogenous genetic variances that exist between different beef cattle breed groups. We extend these models to allow for residual heteroskedasticity (heterogeneous residual variances), specified as a function of fixed effects (e.g., sex, breed proportion, breed group heterozygosity) and random effects such as contemporary groups (CG). We additionally specify the residual distributions to be either Gaussian or based on heavier-tailed alternatives such as the Student's t or Slash densities. For each of these three residual densities using either homoskedastic (homogeneous variance) or heteroskedastic error specifications, we analyzed 22,717 postweaning gain records from a Nelore-Hereford population based on a Markov chain Monte Carlo animal model implementation. The heteroskedastic Student's t error model (with estimated df = 7.33 +/- 0.48) was clearly the best-fitting model based on a pseudo-Bayes factor criterion. Breed group heterozygosity and, to a lesser extent, calf sex seemed to be marginally important sources of residual heteroskedasticity. Specifically, the residual variance in F1 animals was estimated to be 0.70 +/- 0.16 times that for purebreds, whereas the male residual variance was estimated to be 1.13 +/- 0.09 times that for females. The CG effects were important random sources of residual heteroskedasticity (i.e., the coefficient of variation of CG-specific residual variances was estimated to be 0.72 +/- 0.06). Purebred Nelores were estimated to have a larger genetic variance (124.84 +/- 21.75 kg2) compared with Herefords (40.89 +/- 6.70 kg2) under the heteroskedastic Student's t error model; however, the converse was observed from results based on a homoskedastic Student's t error model (46.24 +/- 10.90 kg2 and 60.11 +/- 8.54 kg2, respectively). These results indicate that allowing for robustness to outliers and accounting for heteroskedasticity of residual variances has potentially important implications for variance component and genetic parameter estimates from data on multiple-breed populations.

Animals↗

PubMed2004

Hierarchical Bayes multiple-breed inference with an application to genetic evaluation of a Nelore-Hereford population.

The primary objective of this study was to demonstrate the utility of a hierarchical Bayes implementation of a multiple-breed animal model (MBAM) to estimate breed composition means and additive genetic variances as well as on variances due to the segregation between breeds. The MBAM and a conventional animal model (AM) were both applied to five simulated data sets derived from each of two different populations. Population I consisted of crosses between two breeds having a twofold difference in genetic variance and a nonzero segregation variance. Population II had the same population structure as Population I, except that the two breeds had the same genetic variance with no segregation variance; that is, Population II was essentially single breed in its genetic architecture. For Population I, posterior means of all variance components obtained by MBAM were unbiased, with 95% posterior probability intervals (PPI) having the expected coverage based on five replicates. The MBAM showed a slightly superior performance over the AM for genetic predictions in Population I, although there was no evidence that the use of the MBAM translated into greater genetic gains relative to the use of the AM. Nevertheless, the MBAM was clearly demonstrated to have superior fit to the data using pseudo-Bayes factors (PBF) as the basis for model choice. As expected, the MBAM and AM performed equally well in Population II. A data set consisting of 22,717 postweaning gain (PWG) records of a Nelore-Hereford population (40,082 animals in the pedigree) also was analyzed. The MBAM inference on Nelore and Hereford additive heritabilities (h2A) substantially differed. Herefords had a posterior mean h2A of 0.20 with a 95% PPI of 0.15 to 0.25, whereas the corresponding values for the Nelores were 0.07 and 0.04 to 0.11, respectively. The posterior mean genetic variance due to the segregation between these breeds was 8.4 kg2, with a 95% PPI of 2.3 to 24.8 kg2, and represented 35.4% of the Nelore but only 9.9% of the Hereford posterior mean genetic variance. The posterior mean h2A using the AM was 0.15, presumed common across the two breeds, with a 95% PPI of 0.11 to 0.19. The PBF heavily favored the MBAM over the AM for the PWG data. Accordingly, the MBAM represents a viable alternative to AM for multiple-breed genetic evaluations, providing the necessary flexibility in modeling heteroskedastic genetic variances of breed composition groups.

Algorithms↗

PubMed2004

Hyperimmunization of steers with J5 Escherichia coli bacterin: effects on isotype-specific serum antibody responses and cross reactivity with heterogeneous gram-negative bacteria.

Isotype-specific antibody responses and cross reactivity were profiled following hyperimmunization of steers with J5 Escherichia coli bacterin. The vaccine was administered at time 0, 30 d later, and every 2 wk for 10 subsequent immunizations. Blood was collected preimmunization and multiple times following each immunization. Isotype-specific anti-J5 Escherichia coli antibody response profiles in diluted sera harvested from each sample were assayed by ELISA and recorded as optical density. Selected sera were assayed for anti-J5 Escherichia coli antibody titers and used to determine cross reactivity against a variety of gram-negative bacteria. Immunization number and day postimmunization influenced response profiles for anti-J5 E. coli IgM, IgG(1)and IgG(2) antibodies. Two immunizations increased mean serum IgM and the IgG(1)antibody profiles above preimmunization levels, but 5 immunizations were required to detect significant IgG(2) antibody responses that were above preimmunization levels. Isotype-specific cross reactivity of the serum antibodies with a variety of heterologous gram-negative bacteria was also increased by hyperimmunization. However, no cross reactivity was observed for Staphylococcus aureus, purified lipopolysaccharide, or lipid A. Our results indicate that multiple booster doses of J5 E. coli bacterin may be required to elicit high levels of cross-reactive serum IgG(2) antibodies.

Animals↗

PubMedDate not supplied

Basic concepts in statistics for veterinary ophthalmologists.

Abstract This review article provides a comprehensive coverage of basic statistical procedures commonly used in biologic experiments and is intended to be a basic guide to the selection of appropriate tests and also to aid the reader in the evaluation of published studies. The focus is on the use, interpretation and presentation of the main statistical tests and their key concepts for the practicing veterinary ophthalmologist. Several examples derived from actual ophthalmic research are presented in the text. Lastly, the article discusses computer-based statistical analysis, provides a list of different software available and gives a practical example of a 'real life' statistical analysis.

Animals↗