A strong response to selection on mass-independent maximal metabolic rate without a correlated response in basal metabolic rate.

Metabolic rates are correlated with many aspects of ecology, but how selection on different aspects of metabolic rates affects their mutual evolution is poorly understood. Using laboratory mice, we artificially selected for high maximal mass-independent metabolic rate (MMR) without direct selection on mass-independent basal metabolic rate (BMR). Then we tested for responses to selection in MMR and correlated responses to selection in BMR. In other lines, we antagonistically selected for mice with a combination of high mass-independent MMR and low mass-independent BMR. All selection protocols and data analyses included body mass as a covariate, so effects of selection on the metabolic rates are mass adjusted (that is, independent of effects of body mass). The selection lasted eight generations. Compared with controls, MMR was significantly higher (11.2%) in lines selected for increased MMR, and BMR was slightly, but not significantly, higher (2.5%). Compared with controls, MMR was significantly higher (5.3%) in antagonistically selected lines, and BMR was slightly, but not significantly, lower (4.2%). Analysis of breeding values revealed no positive genetic trend for elevated BMR in high-MMR lines. A weak positive genetic correlation was detected between MMR and BMR. That weak positive genetic correlation supports the aerobic capacity model for the evolution of endothermy in the sense that it fails to falsify a key model assumption. Overall, the results suggest that at least in these mice there is significant capacity for independent evolution of metabolic traits. Whether that is true in the ancestral animals that evolved endothermy remains an important but unanswered question.

Multitrait fine mapping of quantitative trait loci using combined linkage disequilibria and linkage analysis.

A novel multitrait fine-mapping method is presented. The method is implemented by a model that treats QTL effects as random variables. The covariance matrix of allelic effects is proportional to the IBD matrix, where each element is the probability that a pair of alleles is identical by descent, given marker information and QTL position. These probabilities are calculated on the basis of similarities of marker haplotypes of individuals of the first generation of genotyped individuals, using "gene dropping" (linkage disequilibrium) and transmission of markers from genotyped parents to genotyped offspring (linkage). A small simulation study based on a granddaughter design was carried out to illustrate that the method provides accurate estimates of QTL position. Results from the simulation also indicate that it is possible to distinguish between a model postulating one pleiotropic QTL affecting two traits vs. one postulating two closely linked loci, each affecting one of the traits.

A computer-assisted management information system for nutrition services.

A computer-assisted management information system was created to facilitate effective management of clinical nutrition services and labor resources in a 330-bed teaching and research hospital. Standards were developed for the quality and quantity of nutrition care, time required to provide nutrition care, and utilization of dietitians' time. Computer software was developed to report the volume of services provided, the need for services, and the utilization of labor hours. Data were evaluated to determine whether services were consistent with standards and to calculate a recommended number of clinical dietitian full-time equivalents for the hospital. Furthermore, the management information system was instrumental in developing a fee-for-service structure, for evenly distributing work loads among dietitians, and for monitoring adherence to standards of care.

Analysis of selection experiments using mixed model methodology.

Use of mixed model techniques to estimate genetic variance and selection response is illustrated by simple examples. A minimum variance quadratic unbiased estimator (MIVQUE) of genetic variance using a reduced animal model is derived. Properties of the mixed model estimator of response are discussed and illustrated with results from Monte Carlo simulation. The mixed model estimator of response requires knowledge of the base population heritability. When the latter is not known, simulation results suggest that using a MIVQUE estimate obtained from the data yields estimates of response in good agreement with the true response. If a number of conditions are satisfied, the mixed model estimator of response partitions the phenotypic trend into its genetic and environmental components, without need for a control population. These conditions are unlikely to hold in long-term selection experiments. More work is needed to understand the implications of finite numbers of loci or the presence of unaccounted natural selection opposing artificial selection, for example, on the properties of the mixed model estimator of response.

Estimation of direct and correlated responses to selection using univariate animal models.

Analytic results obtained using simple models show that estimates of selection response of univariate experiments using animal models are completely dependent on the heritability used as prior when fixed effects are nested within generations, and both on the prior and on the true heritability parameter when fixed effects overlap across generations. Univariate animal model estimators of correlated changes of a trait not selected directly are usually biased. The absolute value of the estimate of the correlated response is smaller than the true value when the traits are only genetically correlated and larger than the expected value of zero when they are only environmentally correlated. The validity of the results derived from the analysis of simple models is confirmed using computer simulations, which illustrate the magnitude of the bias. It is emphasized that use of univariate animal models to estimate response in breeding programs whose breeding objectives include several correlated traits may lead to erroneous conclusions.

Effects of disruptive selection on genetic variance.

Theoretical predictions of changes in variance with disruptive selection have used models of infinitely many genes so the increase in variance was necessarily due to linkage disequilibrium. With small numbers of loci, the disequilibrium is shown still to comprise the major part of the changes in variance.In a replicated experiment with Drosophila melanogaster, disruptive selection was practised for three generations, and this was followed by 5 or 7 generations of random mating. The heritability, as estimated from regression of progeny on parent, rose from 37% to 68% on selection, and subsequently declined to 45% on random mating. Changes of variance can be interpreted invoking the build up of linkage disequilibrium during selection followed by its breakdown upon relaxation. The results agree well with those obtained from Monte Carlo simulation.

The use of the relationship matrix to account for genetic drift variance in the analysis of genetic experiments.

Selection experiments can provide information on genetic parameters such as realized heritability and response to selection. Often, due to lack of adequate replication, empirical sampling variances of estimated response cannot be computed and therefore use must be made of theoretical formulae. Most of the variance between a conceptually large number of selected lines drawn from the same base population is contributed by genetic drift, which depends on the population structure and can therefore be predicted before the experiment is carried out. The theory of variation of response to selection has been developed mainly by Hill, who produced formulae to adjust the variance of estimators to take account of genetic drift. In this paper, we draw attention to properties of the additive genetic relationship matrix that lead to well established results in population genetics theory. We show how inclusion of the additive genetic relationship matrix among the observations leads to sampling variances of estimators of genetic means that account for the variance due to genetic drift.

Effect of short term directional selection on genetic variability: experiments with Drosophila melanogaster.

Experimental checks on theoretical predictions of the build up of negative linkage disequilibrium with directional selection were made using abdominal bristle number in Drosophila melanogaster. Selection was practised for three generations before relaxation. Realized heritabilities and thus genotype variances were estimated by divergent selection. In one replicate, little change of variance occurred but in the other it increased substantially on relaxation. This result is compatible with a model of one or more genes of large effect at extreme frequencies in the base population. This is illustrated with Monte Carlo simulations. Interpretations of results was aided by considering the build up of negative disequilibrium.

Relationships between herd type and genetic and environmental variances in Holsteins.

Type classification data from the Holstein Association of Canada on 354,308 daughters of 13,694 sires were subdivided into 20 groups of approximately equal size according to herd average final score. Genetic and environmental variances were estimated for each group by Henderson's new method. Traits considered and their heritabilities averaged over groups were: final score .13; final class .11; dairy character .17; capacity .24; rump .16; feet and legs .10; mammary system .12; fore udder .12; rear udder .12; size .31. Regressions of genetic variances on group number ordered according to herd final score indicated significant trends only for rump and feet and legs for which higher genetic variances were found for higher type scores. Regressions of environmental variances on group number were significant for all traits. Higher environmental variances were found for lower final scores, final class, dairy character, and capacity, but the opposite was observed for size. The distribution of environmental variances across groups was U-shaped for rump, feet and legs, for udder, and rear udder; lowest variances were associated with intermediate scores for overall type.

Response to selection for litter size in Danish Landrace pigs: a Bayesian analysis.

A replicated selection experiment aimed at increasing litter size (total number of pigs born per litter) in Danish Landrace pigs was conducted from 1984 to 1991. The experiment included two selection and two control lines. In each generation, 30 and 14 first litters were produced in selection and control lines, respectively, and dams produced two litters. Each replicate, consisting of one selection and one control line, was founded from 60 families chosen randomly from the population at large. Family selection was practiced, and the criterion was the predicted breeding value for litter size computed using a repeatability animal model, and taking into account all available information. The data consisted of 947 records from 523 dams (424 dams had two litters) representing five cycles of selection of increased litter size. Data were analyzed from a Bayesian perspective, based on marginal posterior distributions of genetic parameters of interest. Marginalization was achieved using Gibbs sampling, with a single chain length of 1 205 000. After discarding the first 5 000 iterations, a sample was drawn every ten iterations, so 120 000 samples in total were saved. Densities were estimated and plotted, and summary statistics were computed from the estimated densities. The posterior means (± standard error) of heritability and repeatability were 0.22 ± 0.06 and 0.32 ± 0.05, respectively. These point estimates of genetic parameters were within the range of literature values, although on the high side. The posterior mean (± standard error) of genetic response to selection, defined as the difference between the mean breeding values of the selected lines and that of the base population, was 1.37 ± 0.43 pigs after five cycles of selection. The regression (through the origin) of breeding values in the selected lines on generation was 0.25 ± 0.08 pigs. Several informative priors constructed from information obtained with field data in this population were used to examine their influence on inferences. The priors were influential because of the relatively small scale of the experiment. An analysis excluding data from one of the control lines gave smaller genetic variance and heritability, and a smaller response to selection. However, it appears that selection for litter size is effective, but that the true rate of response is probably smaller than data from this experiment suggest.

Genetic parameters for milk production and persistency for Danish Holsteins estimated in random regression models using REML.

(Co)variance components for milk, fat, and protein yield of 8075 first-parity Danish Holsteins (DH) were estimated in random regression models by REML. For all analyses, the fixed part of the model was held constant, whereas four different functions were applied to model the additive genetic effect and the permanent environment effect. Homogeneous residual variance was assumed throughout lactation. Univariate models were compared using a minimum of -2 ln(restricted likelihood) as the criterion for best fit. Heritabilities as a function of time were calculated from the estimated curve parameters from univariate analyses. Independent of the function applied and the trait in question, heritabilities were lowest in the beginning of the lactation. Heritabilities for persistency of fat yield were slightly higher than heritabilities for persistency of milk and protein yield. Genetic correlations between persistency and 305-d production were higher for protein and milk yield than for fat yield. Bivariate analyses between the production traits were carried out in sire models using the models with the best 3-parameter curve fit in the univariate analyses. Correlations between traits were calculated from covariance components for curve parameters estimated in bivariate analyses. Genetic correlations between milk and protein yield were higher than between milk and fat yield.

Bayesian estimates of covariance components between lactation curve parameters and disease liability in Danish Holstein cows.

In the present work, covariance components for milk yield and disease liability were estimated with bivariate random regression test-day sire models using a Bayesian approach and implemented via the Gibbs sampler. The data consist of 8075 first-parity Danish Holstein (DH) cows, from 1259 sires, performing in 57 herds from 1992 to 1997. Treatments associated with five different type of diseases were pooled into a single general disease liability for each cow. Two models were fitted to the data. First, using a bivariate model, milk yield is modeled via a random regression, and disease liability via a repeatablility model. Second, using a bivariate model, both milk yield and disease liability are modeled using random regressions. A comparison based on a Bayes factor provides very strong support for the bivariate random regression model. Posterior means of heritabilities for each of the traits were estimated for five different points in time throughout lactation. Across models, heritabilities for milk yield are lowest in the beginning of the lactation (0.19) and highest at the end of the lactation (0.35). Posterior means of heritabilities of disease liability range from 0.04 to 0.10 for test days, and is equal to 0.20 for the whole lactation. Heritability of persistency measures estimated from the two models are 0.20 and 0.21. Estimates of posterior means of genetic correlations between single test-day milk yield and single test-day disease liability are in the range of 0.31 to 0.57. The estimates of posterior mean and of the 95% posterior interval of the genetic correlation between persistency and (total) disease liability using the model with the highest posterior probability are -0.12 and (-0.44; 0.20), respectively. Even though the largest proportion of the posterior probability mass is spread along negative values of the correlation (indicating that individuals with a flatter lactation curve tend to have lower disease liability), a value of zero of the genetic correlation falls comfortably within the 95% posterior interval. Thus the prospects of reducing incidence of disease by manipulating persistency as defined in this work remain inconclusive.