Biomedical subjects

A Reverter

Publications and source records attributed to A Reverter.

17 recordsLinked to original sources

PubMed2005

Gene expression profiling of muscle tissue in Brahman steers during nutritional restriction.

Expression profiling using microarrays allows for the detailed characterization of the gene networks that regulate an animal's response to environmental stresses. During nutritional restriction, processes such as protein turnover, connective tissue remodeling, and muscle atrophy take place in the skeletal muscle of the animal. These processes and their regulation are of interest in the context of managing livestock for optimal production efficiency and product quality. Here we expand on recent research applying complementary DNA (cDNA) microarray technology to the study of the effect of nutritional restriction on bovine skeletal muscle. Using a custom cDNA microarray of 9,274 probes from cattle muscle and s.c. fat libraries, we examined the differential gene expression profile of the LM from 10 Brahman steers under three different dietary treatments. The statistical approach was based on mixed-model ANOVA and model-based clustering of the BLUP solutions for the gene x diet interaction effect. From the results, we defined a transcript profile of 156 differentially expressed array elements between the weight loss and weight gain diet substrates. After sequence and annotation analyses, the 57 upregulated elements represented 29 unique genes, and the 99 downregulated elements represented 28 unique genes. Most of these co-regulated genes cluster into groups with distinct biological function related to protein turnover and cytoskeletal metabolism and contribute to our mechanistic understanding of the processes associated with remodeling of muscle tissue in response to nutritional stress.

Analysis of Variance↗

PubMed2004

A rapid method for computationally inferring transcriptome coverage and microarray sensitivity.

MOTIVATION: There are many different gene expression technologies, including cDNA and oligo-based microarrays, SAGE and MPSS. For each organism of interest, coverage of the transcriptome and the genome will be different. We address the question of what level of coverage is required to exploit the sensitivity of the different technologies, and what is the sensitivity of the different approaches in the experimental study. RESULTS: We estimate the transcriptome coverage by randomly sampling transcripts from a pre-defined tag-to-gene mapping function. For a given microarray experiment, we locate the thresholds in intensities that define the distribution of transcript abundance. These values are compared against the distribution obtained by applying the same thresholds to the intensities from differentially expressed genes. The ratio of these two distributions meets at the equilibrium defining sensitivity. We conclude that a collection of approximately 340,000 sequences is adequate for microarrays, but not large enough for maximum utilization of tag-based technologies. In the absence of large-scale sequencing, the majority of the tags detected by the latter approaches will remain unidentified until the genome sequence is available.

Algorithms↗

PubMed2004

Joint analysis of multiple cDNA microarray studies via multivariate mixed models applied to genetic improvement of beef cattle.

In functional genomic laboratories, it is common to use the same microarray slide across studies, each investigating a unique biological question, and each analyzed separately due to computational limitations and/or because there is no hybridization of samples from different studies on one slide. However, the question of analyzing data from multiple studies is a major current issue in microarray data analysis because there are gains to be made in the accuracy of estimated effects by exploiting a covariance structure between gene expression data across studies. We propose an approach for combining multiple studies using multivariate mixed models, with the assumption of a nonzero correlation among genes across experiments, while imposing a null residual covariance. We applied this method to jointly analyze three experiments in genetics of cattle with a total of 54 arrays, each with 19,200 spots and 7,638 elements. The resulting seven-variate model contains 752,476 equations and 56 covariances. To identify differentially expressed genes, we applied model-based clustering to a linear combination of the random gene x variety interaction effect. We enhanced the biological interpretation of the results by applying an iterative algorithm to identify the gene ontology classes that significantly changed in each experiment. We found 118 elements with coordinate expression that clustered into distinct biological functions such as adipogenesis and protein turnover. These results contribute to our understanding of the mechanistic processes involved in adipogenesis and nutrient partitioning.

Animals↗

PubMed2004

A mixed-model approach for the analysis of cDNA microarray gene expression data from extreme-performing pigs after infection with Actinobacillus pleuropneumoniae.

We proposed a novel statistical approach for the analysis of cDNA experiments based on mixed-model methodology combined with mixtures of distributions. Our objective was to detect genes that may be involved in conferring heritable differences in susceptibility to common infections in intensive pig production. We employed a microarray expression profiling strategy and a mixed-model approach to the analysis of the expression data. A cDNA microarray of pig with 6,420 probes from immune tissues and cells was used to compare gene expression in peripheral blood leukocytes of two pigs showing extreme performance in their response to infection with Actinobacillus pleuropneumoniae. Principal components analyses were used to identify the two most extreme-performing pigs after infection (i.e., pigs whose measured responses to infection fell at the extremes). Blood samples and expression profiles from 0 to 24 h after infection were compared using a bivariate, mixed-model approach, in which the effect gene x immunological status interaction was treated as a random effect. Bayesian model-based clustering via mixtures of normal distributions of the resulting BLUP of the random interaction was approached and resulted in a list of 307 differentially expressed genes, of which 179 were down-regulated in the susceptible pig. The majority of the differentially expressed genes were derived from a cDNA library of leukocytes of A. pleuropneumoniae-challenged pigs that were subtracted against leukocytes before the challenge. These results provide evidence that the proposed statistical approach was useful in enhancing the knowledge of the mechanisms involved in the genetics of the immune response.

Actinobacillus Infections↗

PubMed2003

A mixture model-based cluster analysis of DNA microarray gene expression data on Brahman and Brahman composite steers fed high-, medium-, and low-quality diets.

The objective of this study is to explore aspects of the statistical analysis of gene expression response at the muscle tissue level to varying levels of energy and protein in the diet. Eleven Brahman and Brahman composite steers (weighing 302 +/- 9.8 kg, on average) were allocated randomly into high- (HIGH), medium- (MED), and low- (LOW) quality forage diets for 27 d. After this period, a biopsy of the longissimus dorsi muscle was taken from each animal and total RNA was extracted to generate the labeled target for microarray experimentation. These targets were hybridized to a complementary DNA (cDNA) microarray of 9,274 probes from cattle muscle and subcutaneous fat cDNA libraries. After edits, 151,904 expression intensity levels of 4,747 genes were analyzed. Emphasis was given to the choice of power transformation of the intensity channel readings and to the consistency of readings within each diet quality group. The statistical approach to isolate differentially expressed genes was based on model-based clustering via a mixture of normal distributions estimated through maximal likelihood. The base-2 logarithm was found to be the optimal power transformation to normalize gene intensity levels. A two-sample t-statistic was defined as a measure of possible differential expression. For each of the three diet contrasts, HIGH vs. LOW, HIGH vs. MED, and MED vs. LOW, three clusters were found, two of which contained more than 94% genes with almost no altered gene expression levels, whereas the third cluster contained the remaining genes with a differential expression. Results from the HIGH vs. LOW contrast identified 27 genes with a greater than 95% posterior probability of belonging to the cluster of differentially expressed genes.

Animal Feed↗

PubMed2002

Purebred-crossbred performance and genetic evaluation of postweaning growth and carcass traits in Bos indicus x Bos taurus crosses in Australia.

Growth and carcass data on 7,154 cattle from a purebred project and 1,241 cattle from a crossbred project, comprising 916 first-crosses and 325 purebred Brahman controls, were analyzed to estimate genetic parameters, including the genetic correlations between purebred and crossbred performance (rpc). The data also allowed the estimation of sire breed means for various growth and carcass traits. Crossbred calves were produced using 9 Angus, 8 Hereford, 7 Shorthorn, 14 Belmont Red, and 8 Santa Gertrudis sires bred to Brahman dams. These same sires produced 1,568 progeny in a separate purebreeding project. Cattle in both projects were managed under two finishing regimens (pasture and feedlot) to representative market live weights of 400 (domestic), 520 (Korean), and 600 kg (Japanese). The traits studied included live weight at around 400 d of age (400W), hot carcass weight (CWT), retail beef yield percentage (RBY), intramuscular fat percentage (IMF), rump fat depth (P8), and preslaughter ultrasound scanned eye muscle area (SEMA). Estimated breeding values (EBV) of sires from their BREEDPLAN genetic evaluations were used to assess their value in predicting crossbred performance. Regressions of actual crossbred calf performance on sire EBV for each of the traits differed little from their expectation of 0.5. Angus sires produced crossbred carcasses with the highest P8 and lowest RBY but highest IMF. In contrast, crossbred progeny from Belmont Red sires had the lightest 400W and CWT, lowest P8, and highest RBY. Estimates of rpc were 0.48, 0.48, 0.83, 0.95, 1.00, and 0.78 for 400W, CWT, RBY, IMF, P8, and SEMA, respectively. Commercial breeders selecting sires for crossbreeding programs with Brahman females, based on EBV computed from purebred data, might encounter some reranking of sire's performance for weight-related traits, with little expected change in carcass traits.

Adipose Tissue↗

PubMed2001

Estimation of genetic covariances with method R.

Method R is a simple and computationally inexpensive method for estimating (co)variances. The objective of the study was to investigate properties of Method R for estimation of (co)variance components with emphasis on covariance estimation. Theoretical Method R formulas were developed for simplified single-variate and bivariate models. In single-trait models, the curve of the regression of Method R was continuous and monotonic and its slope depended on the amount of information on each animal and on the variance ratio. The curve became steeper as the number of records per animal decreased. For covariance, the curve of the regression was monotonic but not continuous. However, a regression coefficient of 1 still corresponded to the correct covariance. Similar curves were observed in analyses of simulated data sets. Because of the observed discontinuity, algorithms implementing Method R that require a continuous regression curve would not work in models with covariances. An alternative algorithm was based on a transformation matrix obtained by multiplying a matrix of numerators with the inverse of a matrix of denominators of the regression factors. Such an algorithm converged reliably for all models tested. Method R can be modified to estimate covariances in models too large for other methods.

Algorithms↗

PubMed2000

Genetic analyses of live-animal ultrasound and abattoir carcass traits in Australian Angus and Hereford cattle.

In order to estimate genetic parameters, abattoir carcass data on 1,713 Angus and 1,007 Hereford steers and heifers were combined with yearling live-animal ultrasound measurements on 8,196 Angus and 3,405 Hereford individuals from seedstock herds. Abattoir measures included carcass weight (CWT), percentage of retail beefyield (RBY), near-infrared measured intramuscular fat percentage (CIMF), preslaughter scanned eye muscle area (CEMA), and subcutaneous fat depth at the 12th rib (CRIB) and at the P8 site (CP8). Ultrasound scans on yearling animals included 12th-rib fat depth (SRIB), rump fat depth at the P8 site (SP8), eye muscle area (SEMA), and percentage of intramuscular fat (SIMF). Records on CWT were adjusted to 650-d slaughter age, and the remaining abattoir traits were adjusted to 300-kg CWT. Scan data were analyzed treating records on males and females as different traits. Multivariate analyses were performed on a variety of trait combinations using animal model and REML algorithm. Heritability (h2) estimates for CWT, RBY, CIMF, CP8, CRIB, and CEMA were .31, .68, .43, .44, .28, and .26, respectively, for Angus and .54, .36, .36, .08, .27, .38, respectively, for Hereford. Pooled across sexes, h2 estimates for SIMF, SP8, SRIB, and SEMA were .33, .55, .51, and .42, respectively, for Angus and .20, .31, .18, and .38, respectively, for Hereford. Genetic correlations (r(g)) between the same pair of carcass traits measured at yearling through scanning and directly at the abattoir were moderate to strongly positive, suggesting that selection using yearling ultrasound measurements of seedstock cattle should result in predictable genetic improvement for abattoir carcass characteristics. Estimates of r(g) between the scanned fat measurements and RBY were negative, ranging from -.85 for Angus heifers to -.05 for Hereford heifers. Also, the estimates of r(g) between SEMA and the fat records measured at the abattoir were negative and ranged from -.94 in Hereford heifers to -.02 in Angus heifers.

Adipose Tissue↗

PubMed1998

A bootstrap approach to confidence regions for genetic parameters from Method R estimates.

Confidence regions (CR) for heritability (h2) and fraction of variance accounted for by permanent environmental effects (c2) from Method R estimates were obtained from simulated data using a univariate, repeated measures, full animal model, with 50% subsampling. Bootstrapping techniques were explored to assess the optimum number of subsamples needed to compute Method R estimates of h2 and c2 with properties similar to those of exact estimators. One thousand estimates of each parameter set were used to obtain 90, 95, and 99% CR in four data sets including 2,500 animals with four measurements each. Two approaches were explored to assess CR accuracy: a parametric approach assuming bivariate normality of h2 and c2 and a nonparametric approach based on the sum of squared rank deviations. Accuracy of CR was assessed by the average loss of confidence (LOSS) by number of estimates sampled (NUMEST). For NUMEST = 5, bootstrap estimates of h2 and c2 were within 10(-3) of the asymptotic ones. The same degree of convergence in the estimates of SE was achieved with NUMEST = 20. Correlation between estimates of h2 and c2 ranged from -.83 to -.98. At NUMEST < 10, the nonparametric CR were more accurate than parametric CR. However, with the parametric CR, LOSS approached zero at rate NUMEST(-1). This rate was an order of magnitude larger for the nonparametric CR. These results suggested that when the computational burden of estimating genetic parameters limits the number of Method R estimates that can be obtained to, say, 10 or 20, reliable CR can still be obtained by processing Method R estimates through bootstrapping techniques.

Animals↗

PubMed1997

Assessing the efficiency of multiplicative mixed model equations to account for heterogeneous variance across herds in carcass scan traits from beef cattle.

Data (n = 2,658) from live animal ultrasonic measures from 17 Angus herds were used to evaluate a multiplicative mixed model that incorporates scaling factors to correct for across-herd heterogeneity of variance. Traits included were ribeye muscle area (EMA), surface fat at the P8 site (P8), surface fat between the 12th and 13th ribs (RIB12), and weight at scanning (WEIGHT). Cattle ranged in age from 501 to 698 d and represented 291 contemporary groups. Data were initially analyzed using single-trait, animal model, Method R procedures to estimate variance components and heritabilities (h2). These estimates were incorporated into a multiplicative mixed model that simultaneously estimates breeding values (EBV) and heterogeneity factors. Re-estimation of h2 after scaling the data with the correction factors was explored to obtain a measure of the improvement in the genetic evaluation and to detect changes in ranking of individuals and herds. Initial h2 estimates for EMA, P8, RIB12, and WEIGHT were .36, .39, .29, and .48, respectively. Scaling factors ranged from .25 for P8 in a herd with eight records to 1.96 for RIB12 in a herd with 86 individuals. Re-estimates of h2 increased by an average of 4.2% for all the traits as a result of correcting for heterogeneity. Deviations of new scaling factors were within expectations. Correlations between EBV with and without heterogeneity correction were greater than .97 for all the traits. However, some substantial re-rankings of herds were observed for some traits in the smaller herds.

Analysis of Variance↗

PubMed1997

Approximate confidence intervals for heritability from method R estimates.

Method R estimates of heritability (h2) and associated confidence intervals (CI) were obtained from simulated data using a single trait, direct effects, full animal model, with 50% subsampling. Five hundred data sets were simulated for each of five levels of h2 (.10, .20, .30, .40, and .50) and two types of pedigree structure (random pedigree structure [N = 2,000] that varied over simulations, or the pedigree structure from a real data set [N = 2,644] that was constant for all simulations). The first 10, 20, and all 50 h2 estimates were used to obtain 80, 90, 95, and 99% CI for each data set. The variance of h2 estimates within data sets approximated the sampling variance of the h2 estimates. The Box-Cox transformation was used to normalize the distribution of estimates from each data set. Confidence intervals were computed on the transformed scale as CI = mu +/- (T x sigma), where mu and sigma = the mean and SD of the N transformed h2 estimates, respectively, and T = the critical value from the T distribution for a 1-alpha CI, with df = N-1. Upper and lower CI bounds were converted back to the original scale by reversing the transformation. The percentages of CI containing the true h2 value, pooled across all levels of h2, types of pedigree, and number of estimates used to obtain CI, for 80, 90, 95, and 99% CI were 81.14, 90.96, 95.27, and 98.76%, respectively. These results suggested that Method R h2 estimates can be used to obtain reliable CI.

Analysis of Variance↗

PubMed1997

The role of different pedigree structures on the sampling variance of heritability estimates.

A computer-intensive process was performed to simulate 12,600 data sets each with n = 5,000 individuals from distinct pedigree structures to assess the effect of pedigree information on the sampling variance of heritability (h2) estimates. Pedigree structures were determined by varying the proportion of foundation animals (PF), percentage replacement rates for males (RM) and females (RF), and ratio of females to male (F2M). A 2(3) factorial design was modeled; levels of RM and RF were 10 and 20%, and levels of F2M were 10 and 20. For each of the eight cells, 60 foundation animals were simulated, each with 10 replicates. The required mating seasons (MS) to obtain the number of individuals was simulated based on PF and F2M. A REML algorithm was used to estimate h2 and its associated SE. The effect of all factors was analyzed in a regression model with linear and quadratic components for PF. An alternative model with MS replacing PF was also investigated. There was a non-monotonic association (P < .01) between PF and h2 SE. The minimum h2 SE occurred when PF ranged from 20 to 40%. Here, the proportion of first-generation progeny was near its maximum with rapid increases in the proportion of subsequent descendants. Among the class effects, F2M yielded the highest mean square (P < .001). When considering more than one MS, h2 SE was positively associated (P < .01) with RF and F2M and negatively associated with RM. Results suggest that h2 is most accurately estimated when there is performance information on many animals closely related to foundation animals.

Algorithms↗

PubMed1995

Technical note: assessing the consistency of measurement procedures in animal energetics and nutrition.

Our objective was to assess the consistency of representative digestion and energetics determinations used in animal nutrition. We used distribution theory of quadratic forms that allow for the attainment of width of confidence intervals (WI) for intraclass correlations. Three models commonly used in animal nutrition were analyzed, and their respective programs were coded to obtain the required confidence limits. Data sets were obtained from previous research published by our laboratory. Urinary, CH4, and ME were analyzed assuming a two-factor nested balanced variance component model. Rate of ruminal NDF disappearance (kd) and DM digestibility by an 8-d conventional collection trial were fitted to a two-factor crossed variance component model without interaction with a single observation per cell. Empty BW (EBW), carcass energy, and EBW energy were fitted to a two-factor crossed variance component model with interaction. Widths of confidence intervals varied with the example data set and variable tested. The narrowest WI was that of DM digestibility, less than .07 at a 95% confidence level for all the intraclass correlations, which shows the high consistency of the DM digestibility measurement in the specific study. Medium to large WI were found for kd and EBW; WI estimates were less than .70 at a 95% confidence level. Large WI, from .8 to 1.0 at a 95% confidence level, were found for the remaining variables, indicating the greater variability of these measurements. This methodology allows the assessment of the consistency of a measurement process and provides a method to monitor it each time a determination is made.

Analysis of Variance↗

PubMed1995

Technical note: changes in genetic predictions between subsequent evaluations.

A procedure was developed to compute the proportion (P) of future genetic predictions that would be within 1 SE of previous predictions. The procedure is based on the Central Limit Theorem. Whatever the distribution function, provided only that it has a finite variance, the sample mean will have approximately the normal distribution for large samples. The proportion of new individual genetic predictions being within 1 SE of their previous evaluation is expressed as a function of the change in accuracy (ACC) between the previous and subsequent evaluations. If little additional information is made available since the previous evaluation, the increase in ACC will be almost negligible. As anticipated the vast majority of genetic predictions will be within 1 SE of their previous evaluation. The proportion determined from the results of the analysis can be compared to P. An additional appealing feature of the procedure presented is the ease of implementation with most computer softwares. Finally, application to both simulated and field data is presented.

Analysis of Variance↗

PubMed1994

Technical note: detection of bias in genetic predictions.

The theoretical development of a procedure to detect bias in genetic predictions is presented. The procedure is based on the expectation of three statistics. These statistics detect bias by identifying systematic, unexpected change in subsequent analyses. Expectations of the following statistics were obtained: linear correlation coefficient between subsequent predictions, linear regression of recent (more accurate) on previous (less accurate) genetic prediction, and variance of the genetic prediction difference (recent minus previous genetic prediction). Deviations from these expectations can be used to indicate bias. The covariance between subsequent BLUP of genetic value is shown to equal the variance of the early estimate, implying that the expected value of the regression of recent on previous genetic prediction equals 1 regardless of the distribution of the observations and predictions. Also, the expected value of the linear correlation coefficient between subsequent genetic predictions equals the square root of the ratio of the means of the square of accuracy values. The expected value of the variance of the genetic prediction difference was shown to be equal to the difference between prediction error variances.

Analysis of Variance↗

PubMed1994

Method R variance components procedure: application on the simple breeding value model.

An algorithm for estimating variance components (Method R) based on the linear regression coefficient (R) of recent (more accurate) on previous (less accurate) individual genetic predictions is presented. The previous prediction is obtained by analyzing a subsample of the whole data set. First raw moment of R equals 1 regardless of the distribution of observations and predictions. A condition such as the use of inappropriate variance components ratio (VC) can cause this regression to deviate from its expectation. If the computed R (Rc) is greater than 1, then VC ratio has been underestimated, and if Rc is less than 1, then VC ratio has been overestimated. Several iterations are performed, changing the VC ratio at each iteration, until Rc approximately equal to 1. When an Rc is obtained that is acceptably close to 1 (precision is reached), then the appropriate VC has been used. Method R does not require computation of the inverse of the coefficient matrix and has desirable properties of convergence, precision, and computing feasibility. Additional sampling variance in the estimate of VC is expected due to the requirement of taking a subsample of the entire data set to obtain the lower accuracy predictions. This sampling variance is shown to be small for simulated datasets of size n = 10,000 with no selection.

Algorithms↗

PubMed1992

Effects of growth curve parameters on cow efficiency.

Weight-age data from 50 Retinta beef cows from 8 to 97 mo of age located in southwestern Spain were fitted to von Bertalanffy, Brody, and Richards functions to determine the relationship between growth curve parameters and cow efficiency. Only cows having at least 31 weights were included in the analysis. Von Bertalanffy, Brody, and Richards functions were fitted to weights of each cow. Relevant parameters of the three functions are A and K, associated with the asymptotic mature weight and rate of maturing, respectively. Criteria for comparisons among the three functions were computing difficulty, goodness of fit, and lack of bias of A. Productivity indicators were number of calves weaned during the first five calving seasons (NC), average birth weight (BWT), average weaning weight (WW), and average weaning weight per cow per year (WWY). The von Bertalanffy function was selected as the most appropriate. Least squares means for A and K were 650 +/- 8.17 kg and .038 +/- .001 mo-1, respectively. The values of NC, BWT, WW, and WWY were 4.0 +/- .11 calves, 38.2 +/- .4 kg, 218 +/- 5 kg, and 172 +/- 5 kg, respectively. Regression analysis for A indicated a decrease in NC when mature weight increased (P less than .05). There was a nonsignificant trend for heavier cows (higher A) to have calves with heavier BWT or WW. The value of WWY increased (P less than .05) with increased maturing rate (K) of cows. No significant associations were found between K and BWT or WW.(ABSTRACT TRUNCATED AT 250 WORDS)

Animals↗